This report is a collaborative effort by Alexander Sukharevsky, Dave Kerr, Klemens Hjartar, Lari Hämäläinen, Stéphane Bout, and Vito Di Leo, with Guillaume Dagorret, representing views from QuantumBlack, AI by McKinsey and McKinsey Technology.
by Arthur Mensch, CEO of Mistral AI
We’re at a moment when gen AI has entered every boardroom, but for many enterprises, it still lingers at the edges of actual impact. Many CEOs have greenlit experiments, spun up copilots, and created promising prototypes, but only a handful have seen the needle move on revenue or impact. This report gets to the heart of that paradox: broad adoption with limited return.
The current diagnosis is this: Today, AI is bolted on. But to deliver real impact, it must be integrated into core processes, becoming a catalyst for business transformation rather than a sidecar tool. Most deployments today use AI in a shallow way—as an assistant that sits alongside existing workflows and processes—rather than as a deeply integrated, engaged, and powerful agent of transformation.
Agentic AI is the catalyst that can make this transition possible, but doing so requires a strategy and a plan to successfully power that transformation. Agents are not simply magical plug-n-play pieces. They must work across systems, reason through ambiguity, and interact with people—not just as tools, but as collaborators. That means CEOs must ask different questions: not “How do we add AI?” but “How do we want decisions to be made, work to flow, and humans to engage in an environment where software can act?”
Redefining how decisions are made, how work is done, and how humans engage with technology requires alignment across goals, tools, and people. That alignment can only happen when openness, transparency, and control are central to your technology and implementation—when builders have an open, extensible, and observable infrastructure and users can easily craft and use agents with the confidence that the work of agents is safe, reliable, and under their control. That alignment creates the trust and effectiveness that is the currency of scalable transformation that delivers results rather than regrets.
The technology to build powerful agents is already here. The opportunity now is to deploy agents in ways that are deeply tied to how value is created and how people work. That requires an architecture that is modular and resilient and, more importantly, an operating model that centers on humans—not just as users but as co-architects of the systems they will be living and working with.
This report lays out the playbook not for tinkering but for reinvention. ROI comes from strong intent: define the outcomes, embed agents deep in core workflows, and redesign operating models around them. Organizations that win will pair a clear strategy with tight feedback loops and disciplined governance, using agents to rethink how decisions are made and how work gets done—and turning novelty into measurable value.
Tuesday, July 29th
10:30 – 11:00 a.m. EDT / 4:30 – 5:00 p.m. CEST
Join McKinsey’s Michael Chui, Roger Roberts, and Lareina Yee as they share our latest research on how leaders can capture value from the 13 technology trends that are potentially reshaping industries and creating new growth opportunities. They’ll explore how AI is powering innovation across industries, how technologies like agentic AI and autonomous systems are gaining momentum, and what leaders can do to stay ahead.
Tuesday, July 29th
10:30 – 11:00 a.m. EDT / 4:30 – 5:00 p.m. CEST
Join McKinsey’s Michael Chui, Roger Roberts, and Lareina Yee as they share our latest research on how leaders can capture value from the 13 technology trends that are potentially reshaping industries and creating new growth opportunities. They’ll explore how AI is powering innovation across industries, how technologies like agentic AI and autonomous systems are gaining momentum, and what leaders can do to stay ahead.
QuantumBlack, McKinsey’s AI arm, has been helping businesses create value from AI since 2009, expanding on McKinsey’s technology work over the past 30 years. QuantumBlack combines an industry-leading tech stack with the strength of McKinsey’s 7,000 technologists, designers, and product managers serving clients in more than 50 countries. With innovations fueled by QuantumBlack Labs—its center for R&D and software development—QuantumBlack delivers the organizational rewiring that businesses need to build, adopt, and scale AI capabilities.
Even before the advent of gen AI, artificial intelligence had already carved out a key place in the enterprise, powering advanced prediction, classification, and optimization capabilities. And the technology’s estimated value potential was already immense—between $11 trillion and $18 trillion globally—mainly in the fields of marketing (powering capabilities such as personalized email targeting and customer segmentation), sales (lead scoring), and supply chain (inventory optimization and demand forecasting). Yet AI was largely the domain of experts. As a result, adoption across the rank and file tended to be slow. From 2018 to 2022, for example, AI adoption remained relatively stagnant, with about 50 percent of companies deploying the technology in just one business function, according to McKinsey research (Exhibit 1).
Gen AI has extended the reach of traditional AI in three breakthrough areas: information synthesis, content generation, and communication in human language. McKinsey estimates that the technology has the potential to unlock $2.6 trillion to $4.4 trillion in additional value on top of the value potential of traditional analytical AI.
Two and a half years after the launch of ChatGPT, gen AI has reshaped how enterprises engage with AI. Its potentially transformative power lies not only in the new capabilities gen AI introduces but also in its ability to democratize access to advanced AI technologies across organizations. This democratization has led to widespread growth in awareness of, and experimentation with, AI: According to McKinsey’s most recent Global Survey on AI, more than 78 percent of companies are now using gen AI in at least one business function (up from 55 percent a year earlier).
However, this enthusiasm has yet to translate into tangible economic results. More than 80 percent of companies still report no material contribution to earnings from their gen AI initiatives. What’s more, only 1 percent of enterprises we surveyed view their gen AI strategies as mature. Call it the “gen AI paradox”: For all the energy, investment, and potential surrounding the technology, at-scale impact has yet to materialize for most organizations.
Many organizations have deployed horizontal use cases, such as enterprise-wide copilots and chatbots; nearly 70 percent of Fortune 500 companies, for example, use Microsoft 365 Copilot. These tools are widely seen as levers to enhance individual productivity by helping employees save time on routine tasks and access and synthesize information more efficiently. But these improvements, while real, tend to be spread thinly across employees. As a result, they are not easily visible in terms of top- or bottom-line results.
By contrast, vertical use cases—those embedded into specific business functions and processes—have seen limited scaling in most companies despite their higher potential for direct economic impact (Exhibit 2). Fewer than 10 percent of use cases deployed ever make it past the pilot stage, according to McKinsey research. Even when they have been fully deployed, these use cases typically have supported only isolated steps of a business process and operated in a reactive mode when prompted by a human, rather than functioning proactively or autonomously. As a result, their impact on business performance also has been limited.
What accounts for this imbalance? For one thing, horizontally deployed copilots such as Microsoft Copilot or Google AI Workspace are accessible, off-the-shelf solutions that are relatively easy to implement. (In many cases, enabling Microsoft Copilot is as simple as activating an extension to an existing Office 365 contract, requiring no redesign of workflows or major change management efforts.) Rapid deployment of enterprise chatbots also has been driven by risk mitigation concerns. As employees began experimenting with external large language models (LLMs) such as ChatGPT, many organizations implemented internal, secure alternatives to limit data leakage and ensure compliance with corporate security policies.
The limited deployment and narrow scope of vertical use cases can in turn be attributed to six primary factors:
Despite its limited bottom-line impact so far, the first wave of gen AI has been far from wasted. It has enriched employee capabilities, enabled broad experimentation, accelerated AI familiarity across functions, and helped organizations build essential capabilities in prompt engineering, model evaluation, and governance. All of this has laid the groundwork for a more integrated and transformative second phase—the emerging age of AI agents.
LLMs have revolutionized how organizations interact with data—enabling information synthesis, content generation, and natural language interaction. But despite their power, LLMs have been fundamentally reactive and isolated from enterprise systems, largely unable to retain memory of past interactions or context across sessions or queries. Their role has been largely limited to enhancing individual productivity through isolated tasks. AI agents mark a major evolution in enterprise AI—extending gen AI from reactive content generation to autonomous, goal-driven execution. Agents can understand goals, break them into subtasks, interact with both humans and systems, execute actions, and adapt in real time—all with minimal human intervention. They do so by combining LLMs with additional technology components providing memory, planning, orchestration, and integration capabilities.
With these new capabilities, AI agents expand the potential of horizontal solutions, upgrading general-purpose copilots from passive tools into proactive teammates that don’t just respond to prompts but also monitor dashboards, trigger workflows, follow up on open actions, and deliver relevant insights in real time. But the real breakthrough comes in the vertical realm, where agentic AI enables the automation of complex business workflows involving multiple steps, actors, and systems—processes that were previously beyond the capabilities of first-generation gen AI tools.
On the operations side, agents take on routine, data-heavy tasks so humans can focus on higher-value work. But they go further, transforming processes in five ways:
In a complex supply chain environment, for example, an AI agent could act as an autonomous orchestration layer across sourcing, warehousing, and distribution operations. Connected to internal systems (such as the supply chain planning system or the warehouse management system) and external data sources (such as weather forecasts, supplier feeds, and demand signals), the agent could continuously forecast demand. It could then identify risks, such as delays or disruptions, and dynamically replan transport and inventory flows. Selecting the optimal transport mode based on cost, lead time, and environmental impact, the agent could reallocate stock across warehouses, negotiate directly with external systems, and escalate decisions requiring strategic input. The result: improved service levels, reduced logistics costs, and lower emissions.
Agents can also help spur top-line growth by amplifying existing revenue streams and unlocking entirely new ones:
In short, agentic AI doesn’t just automate. It redefines how organizations operate, adapt, and create value.
The following case studies demonstrate how QuantumBlack helps organizations build agent workforces—with outcomes that extend far beyond efficiency gains.
The problem: A large bank needed to modernize its legacy core system, which consisted of 400 pieces of software—a massive undertaking budgeted at more than $600 million. Large teams of coders tackled the project using manual, repetitive tasks, which resulted in difficulty coordinating across silos. They also relied on often slow, error-prone documentation and coding. While first-generation gen AI tools helped accelerate individual tasks, progress remained slow and laborious.
The agentic approach: Human workers were elevated to supervisory roles, overseeing squads of AI agents, each contributing to a shared objective in a defined sequence (Exhibit 3). These squads retroactively document the legacy application, write new code, review the code of other agents, and integrate code into features that are later tested by other agents prior to delivery of the end product. Freed from repetitive, manual tasks, human supervisors guide each stage of the process, enhancing the quality of deliverables and reducing the number of sprints required to implement new features.
Impact: More than 50 percent reduction in time and effort in the early adopter teams
The problem: A market research and intelligence firm was devoting substantial resources to ensure data quality, relying on a team of more than 500 people whose responsibilities included gathering data, structuring and codifying it, and generating tailored insights for clients. The process, conducted manually, was prone to error, with a staggering 80 percent of mistakes identified by the clients themselves.
The agentic approach: A multiagent solution autonomously identifies data anomalies and explains shifts in sales or market share. It analyzes internal signals, such as changes in product taxonomy, and external events identified via web searches, including product recalls or severe weather. The most influential drivers are synthesized, ranked, and prepared for decision-makers. With advanced search and contextual reasoning, the agents often surface insights that would be difficult for human analysts to uncover manually. While not yet in production, the system is fully functional and has demonstrated strong potential to free up analysts for more strategic work.
Impact: More than 60 percent potential productivity gain and expected savings of more than $3 million annually.
The problem: Relationship managers (RMs) at a retail bank were spending weeks writing and iterating credit-risk memos to help make credit decisions and fulfill regulatory requirements (Exhibit 4). This process required RMs to manually review and extract information from at least ten different data sources and develop complex nuanced reasoning across interdependent sections—for instance, loan, revenue, and cash joint evolution.
The agentic approach: In close collaboration with the bank’s credit-risk experts and RMs, a proof of concept was developed to transform the credit memo workflow using AI agents. The agents assist RMs by extracting data, drafting memo sections, generating confidence scores to prioritize review, and suggesting relevant follow-up questions. In this model, the analyst’s role shifts from manual drafting to strategic oversight and exception handling.
Impact: A potential 20 to 60 percent increase in productivity, including a 30 percent improvement in credit turnaround
Realizing AI’s full potential in the vertical realm requires more than simply inserting agents into legacy workflows. It instead calls for a shift in design mindset—from automating tasks within an existing process to reinventing the entire process with human and agentic coworkers. That’s because when agents are embedded into a legacy process without redesign, they typically serve as faster assistants—generating content, retrieving data, or executing predefined steps. But the process itself remains sequential, rule bound, and shaped by human constraints.
Reinventing a process around agents means more than layering automation on top of existing workflows—it involves rearchitecting the entire task flow from the ground up. That includes reordering steps, reallocating responsibilities between humans and agents, and designing the process to fully exploit the strengths of agentic AI: parallel execution that collapses cycle time, real-time adaptability that reacts to changing conditions, deep personalization at scale, and elastic capacity that flexes instantly with demand.
Consider a hypothetical customer call center. Before introducing AI agents, the facility was using gen AI tools to assist human support staff by retrieving articles from knowledge bases, summarizing ticket histories, and helping draft responses. While this assistance improved speed and reduced cognitive load, the process itself remained entirely manual and reactive, with human agents still managing every step of diagnosis, coordination, and resolution. The productivity improvement potential was modest, typically boosting resolution time and productivity between 5 and 10 percent.
Now imagine that the call center introduces AI agents but largely preserves the existing workflow—agents are added to assist at specific steps without reconfiguring how work is routed, tracked, or resolved end-to-end. Agents can classify tickets, suggest likely root causes, propose resolution paths, and even autonomously resolve frequent, low-complexity issues (such as password resets). While the impact here can be increased—an estimated 20 to 40 percent savings in time and a 30 to 50 percent reduction in backlog—coordination friction and limited adaptability prevent true breakthrough gains.
But the real shift occurs at the third level, when the call center’s process is reimagined around agent autonomy. In this model, AI agents don’t just respond—they proactively detect common customer issues (such as delayed shipments, failed payments, or service outages) by monitoring patterns across channels, anticipate likely needs, initiate resolution steps automatically (such as issuing refunds, reordering items, or updating account details), and communicate directly with customers via chat or email. Human agents are repositioned as escalation managers and service quality overseers, who are brought in only when agents detect uncertainty or exceptions to typical patterns. Impact at this level is transformative. This could allow a radical improvement of customer service desk productivity. Up to 80 percent of common incidents could be resolved autonomously, with a reduction in time to resolution of 60 to 90 percent (Exhibit 5).
Of course, not every business process requires full reinvention. Simple task automation is sufficient for highly standard, repetitive workflows with limited variability—such as payroll processing, travel expense approvals, or password resets—where gains come primarily from reducing manual effort. In contrast, processes that are complex, cross-functional, prone to exceptions, or tightly linked to business performance often warrant full redesign. Key indicators that call for reinvention include high coordination overhead, rigid sequences that delay responsiveness, frequent human intervention for decisions that could be data driven, and opportunities for dynamic adaptation or personalization. In these cases, redesigning the process around the agent’s ability to orchestrate, adapt, and learn delivers far greater value than simply speeding up existing workflows.
To scale agents, companies will need to overcome a threefold challenge: handling the newfound risks that AI agents bring, blending custom and off-the-shelf agentic systems, and staying agile amid fast-evolving tech (while avoiding lock-ins).
These challenges cannot be addressed by merely bolting new components, such as memory stores or orchestration engines, on top of existing gen AI stacks. While such capabilities are necessary, they are not sufficient. What’s needed is a fundamental architectural shift: from static, LLM-centric infrastructure to a dynamic, modular, and governed environment built specifically for agent-based intelligence—the agentic AI mesh.
The agentic AI mesh is a composable, distributed, and vendor-agnostic architectural paradigm that enables multiple agents to reason, collaborate, and act autonomously across a wide array of systems, tools, and language models—securely, at scale, and built to evolve with the technology. At the heart of this paradigm are five mutually reinforcing design principles:
The emerging architecture for agentic AI relies on seven interconnected capabilities:
The agentic AI mesh acts as the connective and orchestration layer that enables large-scale, intelligent agent ecosystems to operate safely and efficiently, and continuously evolve. It allows companies to coordinate custom-built and off-the-shelf agents within a unified framework, support multiagent collaboration by allowing agents to share context and delegate tasks, and mitigate key risks such as agent sprawl, autonomy drift, and lack of observability—all while preserving the agility required for a rapid technology evolution (see sidebar “Seven interconnected capabilities of the AI agentic mesh”).
A few characteristics are key for LLM providers to take into account in the agentic era:
Beyond this architectural evolution, organizations will also have to revisit their LLM strategies. At the core of every custom agent lies a foundation model—the reasoning engine that powers perception, decision-making, and interaction. In the agentic era, the requirements placed on LLMs evolve significantly. Agents are not passive copilots—they are autonomous, persistent, embedded systems. This creates five critical categories of LLM requirements, each aligned with specific deployment contexts, for which different kinds of models will be relevant (see sidebar “Foundational models for agents: Five requirements”).
Finally, to truly scale agent deployment across the enterprise, the enterprise systems themselves must also evolve.
In the short term, APIs—protocols that allow different software applications to communicate and exchange data—will remain the primary interface for agents to interact with enterprise systems. But in the long term, APIs alone will not suffice. Organizations must begin reimagining their IT architectures around an agent-first model—one in which user interfaces, logic, and data access layers are natively designed for machine interaction rather than human navigation. In such a model, systems are no longer organized around screens and forms but around machine-readable interfaces, autonomous workflows, and agent-led decision flows.
This shift is already underway. Microsoft is embedding agents into the core of Dynamics 365 and Microsoft 365 via Copilot Studio; Salesforce is expanding Agentforce into a multiagent orchestration layer; SAP is rearchitecting its Business Technology Platform (BTP) to support agent integration through Joule. These changes signal a broader transition: The future of enterprise software is not just AI-augmented—it is agent-native.
As agents evolve from passive copilots to proactive actors—and scale across the enterprise—the complexity they introduce will be not only technical but mostly organizational. The real challenge lies in coordination, judgment, and trust. This organizational complexity will play out most visibly across three dimensions: how humans and agents cohabit day-to-day workflows; how organizations establish governance over systems that can act autonomously; and how they prevent unchecked sprawl as agent creation becomes increasingly democratized.
Agents unlock the full potential of vertical use cases, offering companies a path to generate value well beyond efficiency gains. But realizing that potential requires a reimagined approach to AI transformation—one tailored to the unique nature of agents and capable of addressing the lingering limitations they alone cannot resolve. This approach is the subject of our next chapter.
Unlike gen AI tools that could be easily plugged into existing workflows, AI agents demand a more foundational shift, one that requires rethinking business processes and enabling deep integration with enterprise systems. McKinsey has a proven Rewired playbook for AI-driven transformations. To capitalize on the agentic opportunity, organizations must build on that, fundamentally reshaping their AI transformation approach across four dimensions:
Redesigning the approach to AI transformation is an important step, but it is not enough. To unlock their full potential at scale, organizations must also activate a robust set of enablers that support the structural, cultural, and technical shifts required to integrate agents into day-to-day operations. These enablers span four dimensions—people, governance, technology architecture, and data—each of which is a foundation for scalable, secure, and high-impact deployment of agents across the enterprise.
The rise of AI agents is more than just a technological shift. Agents represent a strategic inflection point that will redefine how companies operate, compete, and create value. To navigate this transition successfully, organizations must move beyond experimentation and pilot programs and enter a new phase of scaled, enterprise-wide transformation.
This pivot cannot be delegated—it must be initiated and led by the CEO. It will rely on three key actions:
Like any truly disruptive technology, AI agents have the power to reshuffle the deck. Done right, they offer laggards a leapfrog opportunity to rewire their competitiveness. Done wrong—or not at all—they risk accelerating the decline of today’s market leaders. This is a moment of strategic divergence.
While the technology will continue to evolve, it is already mature enough to drive real, transformative change across industries. But to realize the full promise of agentic AI, CEOs must rethink their approach to AI transformation—not as a series of scattered pilots but as focused, end-to-end reinvention efforts. That means identifying a few business domains with the highest potential and pulling every lever: from reimagining workflows to redistributing tasks between humans and machines to rewiring the organization based on new operating models.
Some leaders are already moving—not just by deploying fleets of agents but by rewiring their organizations to harness their full disruptive potential. (Moderna, for example, merged its HR and IT leadership—signaling that AI is not just a technical tool but a workforce-shaping force.) This is a structural move toward a new kind of enterprise. Agentic AI is not an incremental step—it is the foundation of the next-generation operating model. CEOs who act now won’t just gain a performance edge. They will redefine how their organizations think, decide, and execute.
The time for exploration is ending. The time for transformation is now.
Alexander Sukharevsky is a senior partner in McKinsey’s London office, where Dave Kerr is a partner; Klemens Hjartar is a senior partner in the Copenhagen office; Lari Hämäläinen is a senior partner in the Seattle office; Stéphane Bout is a senior partner in the Lyon office; Vito Di Leo is a partner in the Zurich office; and Guillaume Dagorret is a senior fellow with the McKinsey Global Institute and is based in the Paris office.
The authors wish to thank Alena Fedorenko, Annie David, Clarisse Magnin, Lareina Yee, Larry Kanter, Michael Chui, Roger Roberts, Sarah Mulligan, Thomas Vlot, and Timo Mauerhoefer for their contributions to this report.
This article was edited by Larry Kanter, a senior editor in the New York office.
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How beauty players can scale gen AI in 2025 – McKinsey
/in website SEO, Website Traffic/by Team ZYTBeauty is no longer in the eye of the beholder; it’s at the fingertips of the generative AI (gen AI) prompter. Gen AI could add $9 billion to $10 billion to the global economy based on its impact on the beauty industry alone,1“The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023. The beauty market is defined as skin care, fragrance, makeup, and hair care. Figures assume a relatively high range of gen AI impact on consumer packaged goods (1.4 to 2.3 percent of total industry revenue) because of the high impact of marketing and sales strategies in beauty. and early movers have already begun testing the technology. But scaling these experiments will be challenging, given the velocity of gen AI innovation.
The gap between the laggards and leaders in the beauty industry will only grow once leaders successfully deploy gen AI at scale. The fast will become faster, more responsive, and better equipped to anticipate and deliver what consumers want, while those left behind may find it harder to hold on to slivers of market share.
Beauty players who focus on priority use cases and customizing gen AI to meet their needs can help realize the technology’s full potential. This article outlines four gen AI use cases that beauty players can prioritize, explains how to bring gen AI to the organization, and lays out a set of imperatives to support the use of gen AI in beauty over the long term.
More than a dozen gen AI use cases that apply to the broader consumer sector also apply to beauty. These use cases span the organization from front to back, including functions from user experience to customer support (table).
To prioritize the use cases, we consider that the beauty sector relies on speed in bringing products to market and responding to consumer feedback. On that basis, four gen AI use cases are likely to have the greatest impact: hyperpersonalized targeting, experiential product discovery, rapid packaging-concept development, and innovative product development. These employ gen AI tools at various stages of adoption. Some (for example, gen AI customer chatbots) are already in fairly wide use among beauty players, whereas others are nascent but promising.
One of the most important moves a beauty brand can make to survive in the competitive beauty sector is to develop a unique value proposition. But beauty players must also ensure that the products they have thoughtfully positioned reach the consumers who will be most receptive to them.
Today, most beauty companies can afford to target only a handful of consumer segments because they have limited capabilities to personalize messages on a bigger scale. This broad approach to consumer segmentation leaves much of the market untapped. But with gen AI, beauty brands can create hyperpersonalized marketing messages, which could improve conversion rates by up to 40 percent, based on our observations.
AI can analyze large consumer data sets, detect patterns, and create microsegments based on pattern recognition algorithms. From there, a beauty brand can train its gen AI platform using a variety of inputs, including customer data, inputs that describe the brand voice, and product information. When entering new markets, beauty brands can train gen AI models on internal product data as well as external market research, such as customer surveys. Gen AI can then create and test variations of text and images to see what resonates best with each consumer segment.
Consider the hypothetical automated texts that might be delivered to an imaginary customer named Camille. The beauty brand knows that Camille lives in France, has a low annual spend, and recently purchased a face sunscreen. Camille has responded positively to promotions in the past. Before gen AI, an automated text to Camille might say, “Exciting news! New products are here. Take up to 20 percent off when you shop sale.” After gen AI, the automated text might say, “Bonjour, Camille! Did you know that our special cleansing foam for face sunscreen removal is now 20 percent off? It will pair perfectly with your recent face sunscreen purchase.”
Marketing specialists should review AI-generated messages before they are sent to ensure they reflect the brand’s ethos and value proposition while avoiding plagiarism or potentially harmful connotations. Some messages that seem innocuous can be detrimental to a brand’s image. In the previous example, the gen-AI-created greeting might have said, “Good evening, lovely lady,” instead of “Bonjour.” A customer may find the tone of this message offensive or inappropriate, or the message might be at odds with the brand’s overall ethos. The marketing team should deliver feedback to the gen AI model—perhaps rating its outputs with a thumbs-up or thumbs-down mechanism and entering detailed comments in free-text fields. The gen AI platform can then process the feedback and convert it into new training data.
Beauty brands will also need to integrate their gen AI models with assets from their digital-asset-management (DAM) systems, which serve as the repository for all the digital creative assets a brand uses, as well as integrate the models with the brand’s campaign management tools. Gen AI can categorize the creative assets in the DAM system—a task that would otherwise have to be done manually. This automation frees up time for the marketing team to focus on higher-value tasks.
Even as they continue to work with marketing agencies to develop their brand strategy and deliver specialized campaigns, large beauty enterprises might consider investing in in-house hyperpersonalization capabilities. This would offer two main advantages: companies can use their own consumer data to train gen AI models, and they can create and test personalized communications with greater speed and agility.
Despite the tech-powered innovations in consumer product discovery over the past few years, there is ample room for improvement. The first generation of consumer chatbots, for instance, provide relatively rigid answers and can be frustrating for consumers to use. When a consumer asks for a recommendation for a new blush for a darker complexion, for example, a chatbot might give a generic list of products, rather than personalizing the conversation for a specific shopper and engaging them in deeper conversation. Virtual try-ons are helpful but can be glitchy or fail to accurately reflect what a product would look like on a consumer. In these cases, online purchases often can lead to costly returns, since returned beauty products generally cannot be resold.
Gen-AI-powered chatbots can help improve the shopping journey and decrease the likelihood of returns. These large language model (LLM) chatbots, which are trained on product data and consumer preferences, can respond to a wider variety of questions and offer more personalized recommendations, both of which can improve conversion rates. One global lifestyle player developed a gen-AI-powered shopping assistant and saw its conversion rates increase by as much as 20 percent.
The virtual try-on experience—which has already proven successful in other consumer categories, such as accessories and eyeglasses—might also be enhanced with gen AI. Using the same technology that powers image-generating gen AI tools, consumers can see the look of different products on their skin in different settings or see the potential benefits a product could have to their appearance over time. An online shopper who wishes to lighten dark spots, for example, could virtually try on a brand’s spot-lightening serum by uploading a photo on a beauty player’s website and running a simulation of the serum’s possible effect on their skin over several months.
Gen AI could also enhance experiential product discovery in physical stores. Today, interactive touchscreen monitors in stores can show products available both in-store and online, allowing customers to browse through SKUs, select items they want to see in person, or scan QR codes for exclusive offers. Even with their limited functionality, these screens have been shown to improve the in-store shopping experience and conversion rates.2 “Digital screens and kiosks are driving shopper engagement,” AV Magazine, December 18, 2023. Gen AI can boost the effectiveness of these screens. For example, when a shopper who has location services enabled on a beauty player’s app walks into the company’s store, gen AI could generate content personalized to that consumer based on customer profile and purchase history. Given what we know about the effectiveness of personalized content, these principles could translate to the store setting, though large-scale implementation hasn’t happened yet.
When evaluating a beauty product, consumers consider both the product itself and its branding and packaging. Beauty brands typically spend months developing new branding and packaging concepts—a process that typically requires designers, copy editors, strategists, and packaging experts to iterate on ideas.
Gen AI wouldn’t necessarily eliminate this process, but it could dramatically accelerate it. Here’s how it could work. A packaging designer asks a gen AI platform the following prompt: “Show me five packaging options for a nighttime moisturizer, emphasizing skin care benefits and sustainable packaging materials.” The designer then modifies the gen AI platform’s output based on information about customer preferences, which could come from focus groups and customer surveys. Next, an advertising designer uses mockups of the new packaging in digital advertisements to test whether the images appeal to consumers, based on online engagement with the new ads. That data is then used to further refine gen-AI-powered concept creation and prototyping. With this basic approach, one beverage company reduced its concept development time by 60 percent.
Creating new beauty product formulas is a multiyear process. It requires beauty players to partner with laboratories to research ingredients and experiment with formulas to determine the safety, stability, and efficacy of a new product.
Gen AI can speed up this process. A gen AI model—once it has been trained on a beauty product’s bill of materials, raw material usage, process parameters, internal research data, and other data (such as product patents or previous product trials)—can identify the ingredients that may be best suited for a new product, predict the product’s benefits, and recommend formula recipes.
Returning to the example of a nighttime moisturizer, assume that a formulation scientist could prompt the gen AI tool to create a new formula that emphasizes neuropeptides, a popular skin care ingredient, and prioritizes anti-aging benefits while also reducing formulation costs. Once the tool creates a potential recipe, the scientist would run lab tests to assess the compatibility and stability of ingredients in the formulation, as well as additional safety and consumer testing and clinical trials, if applicable. Formula iteration would continue based on consumer feedback.
While the physical testing process will still take time, McKinsey analysis has found that gen AI tools can reduce the time it takes to research new products from weeks to days. This can help save up to 5 percent on raw materials when developing those products.
The market for gen AI enterprise platforms is growing. But which approach—if any—is best suited for beauty players?
Organizations can bring in gen AI tools in any of three ways—what we call the taker, shaper, and maker approaches. Most beauty players likely won’t take the maker approach, where companies build their own LLM models from scratch. That would require capital expenditures and talent investments greater than most beauty companies can justify; it could also unhelpfully dilute a beauty player’s focus on its core competencies. However, beauty players can still get value out of the two other approaches:
Taker approach. The taker approach entails integrating off-the-shelf third-party gen AI solutions into a business’s workflows, with little to no customization. This is the least costly and resource-intensive of the three approaches, so it is an attractive option for beauty brands that rely on retailers for distribution (and therefore have less consumer data with which to customize models), have less tech talent, or have less cash for investments.
In evaluating a gen AI tool or platform, beauty players should ask questions such as the following: What are the data privacy and encryption protocols in place at the vendor? Will the vendor use the brands’ data to train third-party or first-party proprietary models? Who owns the copyrights to the outputs? How easy is the integration with the beauty player’s internal systems? (For example, does the vendor have an Application Programming Interface? Are they integrated with players like Google Analytics to enable broader use cases?)
Piloting the tool is crucial, of course. Most reputable gen AI vendors offer a low-cost pilot for a limited time—usually around one month.
Shaper approach. Being a shaper means training third-party gen AI models on the company’s own data and insights related to specific geographic, sector, organization, and business case needs. For example, for hyperpersonalized targeting, the data may include information about a brand’s voice, customer demographics and preferences, or successful campaigns. For innovative product development, raw data from clinical test results could help train models.
Larger beauty brands or retailers with a wealth of consumer data may choose the shaper approach. They will need a bench of tech talent that can add new components to the gen AI tool, integrate it into existing workflows, and deploy it across the organization.
Beauty players can use a mix of the taker and shaper approaches to gen AI, depending on their specific needs and use cases. Speed—in getting to market and responding to consumer demand—is particularly important for beauty players. For this reason, beauty organizations should consider modular gen AI components, which make switching between LLM providers easier to do, so scaling is easier. Gen AI may enable streamlining and automation in beauty, but the industry is as much science as it is art; it will be critical to keep a human in the loop to check for risks and inject uniquely human creativity into, say, marketing and packaging design.
To outcompete in digital and AI, consumer-packaged-goods players should consider critical questions such as “Where is the value?” and “Are leaders from the business side actively part of the transformation?” In addition, beauty players can take four steps to truly integrate gen AI into the business:
While much of the beauty industry’s products are cosmetic, gen AI applications in beauty are more than skin-deep. Integrating the technology alongside other digital and AI tools and boosting organizational capabilities can differentiate leaders in beauty for years to come.
Anna Checa is a partner in McKinsey’s Miami office; Kristi Weaver is a senior partner in the Chicago office; Megan Pacchia is a partner in the New Jersey office; Sara Hudson is a partner in the London office; Wei Wei Liu is a partner in the Bay Area office, where Ana Bujosa is a consultant; and Alexis Wolfer is an associate partner in the Southern California office.
This article was edited by Alexandra Mondalek, an editor in the New York office.
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Generative Use Cases, Examples, & Applications – IBM
/in website SEO, Website Traffic/by Team ZYTGenerative Use Cases, Examples, & Applications IBM
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Amazon sellers can now automatically improve product listings with our new Gen AI tool – About Amazon
/in website SEO, Website Traffic/by Team ZYTLeveraging Gen AI learnings, Amazon is empowering sellers with new Enhance My Listing tool to make listing optimization effortless and effective.
Written by Mary Beth Westmoreland, Vice President, Worldwide Selling Partner Experience
Last updated: May 08, 2025
7 min read
Amazon is continuing to innovate on how it helps sellers succeed by giving them the option to provide a URL to their own website and leverage a new generative AI capability to easily create high-quality product detail pages in Amazon’s store.
Our fifth annual report highlights the continued success of our strategy to drive counterfeits to zero.
AI feature works on your behalf to constantly watch out for new products in Amazon’s store that match your interests.
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Seizing the agentic AI advantage – McKinsey
/in website SEO, Website Traffic/by Team ZYTThis report is a collaborative effort by Alexander Sukharevsky, Dave Kerr, Klemens Hjartar, Lari Hämäläinen, Stéphane Bout, and Vito Di Leo, with Guillaume Dagorret, representing views from QuantumBlack, AI by McKinsey and McKinsey Technology.
by Arthur Mensch, CEO of Mistral AI
We’re at a moment when gen AI has entered every boardroom, but for many enterprises, it still lingers at the edges of actual impact. Many CEOs have greenlit experiments, spun up copilots, and created promising prototypes, but only a handful have seen the needle move on revenue or impact. This report gets to the heart of that paradox: broad adoption with limited return.
The current diagnosis is this: Today, AI is bolted on. But to deliver real impact, it must be integrated into core processes, becoming a catalyst for business transformation rather than a sidecar tool. Most deployments today use AI in a shallow way—as an assistant that sits alongside existing workflows and processes—rather than as a deeply integrated, engaged, and powerful agent of transformation.
Agentic AI is the catalyst that can make this transition possible, but doing so requires a strategy and a plan to successfully power that transformation. Agents are not simply magical plug-n-play pieces. They must work across systems, reason through ambiguity, and interact with people—not just as tools, but as collaborators. That means CEOs must ask different questions: not “How do we add AI?” but “How do we want decisions to be made, work to flow, and humans to engage in an environment where software can act?”
Redefining how decisions are made, how work is done, and how humans engage with technology requires alignment across goals, tools, and people. That alignment can only happen when openness, transparency, and control are central to your technology and implementation—when builders have an open, extensible, and observable infrastructure and users can easily craft and use agents with the confidence that the work of agents is safe, reliable, and under their control. That alignment creates the trust and effectiveness that is the currency of scalable transformation that delivers results rather than regrets.
The technology to build powerful agents is already here. The opportunity now is to deploy agents in ways that are deeply tied to how value is created and how people work. That requires an architecture that is modular and resilient and, more importantly, an operating model that centers on humans—not just as users but as co-architects of the systems they will be living and working with.
This report lays out the playbook not for tinkering but for reinvention. ROI comes from strong intent: define the outcomes, embed agents deep in core workflows, and redesign operating models around them. Organizations that win will pair a clear strategy with tight feedback loops and disciplined governance, using agents to rethink how decisions are made and how work gets done—and turning novelty into measurable value.
Tuesday, July 29th
10:30 – 11:00 a.m. EDT / 4:30 – 5:00 p.m. CEST
Join McKinsey’s Michael Chui, Roger Roberts, and Lareina Yee as they share our latest research on how leaders can capture value from the 13 technology trends that are potentially reshaping industries and creating new growth opportunities. They’ll explore how AI is powering innovation across industries, how technologies like agentic AI and autonomous systems are gaining momentum, and what leaders can do to stay ahead.
Tuesday, July 29th
10:30 – 11:00 a.m. EDT / 4:30 – 5:00 p.m. CEST
Join McKinsey’s Michael Chui, Roger Roberts, and Lareina Yee as they share our latest research on how leaders can capture value from the 13 technology trends that are potentially reshaping industries and creating new growth opportunities. They’ll explore how AI is powering innovation across industries, how technologies like agentic AI and autonomous systems are gaining momentum, and what leaders can do to stay ahead.
QuantumBlack, McKinsey’s AI arm, has been helping businesses create value from AI since 2009, expanding on McKinsey’s technology work over the past 30 years. QuantumBlack combines an industry-leading tech stack with the strength of McKinsey’s 7,000 technologists, designers, and product managers serving clients in more than 50 countries. With innovations fueled by QuantumBlack Labs—its center for R&D and software development—QuantumBlack delivers the organizational rewiring that businesses need to build, adopt, and scale AI capabilities.
Even before the advent of gen AI, artificial intelligence had already carved out a key place in the enterprise, powering advanced prediction, classification, and optimization capabilities. And the technology’s estimated value potential was already immense—between $11 trillion and $18 trillion globally2“The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.—mainly in the fields of marketing (powering capabilities such as personalized email targeting and customer segmentation), sales (lead scoring), and supply chain (inventory optimization and demand forecasting). Yet AI was largely the domain of experts. As a result, adoption across the rank and file tended to be slow. From 2018 to 2022, for example, AI adoption remained relatively stagnant, with about 50 percent of companies deploying the technology in just one business function, according to McKinsey research (Exhibit 1).
Gen AI has extended the reach of traditional AI in three breakthrough areas: information synthesis, content generation, and communication in human language. McKinsey estimates that the technology has the potential to unlock $2.6 trillion to $4.4 trillion in additional value on top of the value potential of traditional analytical AI.3“The economic potential of generative AI: The next productivity frontier,” McKinsey, June 14, 2023.
Two and a half years after the launch of ChatGPT, gen AI has reshaped how enterprises engage with AI. Its potentially transformative power lies not only in the new capabilities gen AI introduces but also in its ability to democratize access to advanced AI technologies across organizations. This democratization has led to widespread growth in awareness of, and experimentation with, AI: According to McKinsey’s most recent Global Survey on AI,4“The state of AI: How organizations are rewiring to capture value,” McKinsey, March 12, 2025. more than 78 percent of companies are now using gen AI in at least one business function (up from 55 percent a year earlier).
However, this enthusiasm has yet to translate into tangible economic results. More than 80 percent of companies still report no material contribution to earnings from their gen AI initiatives.5“The state of AI: How organizations are rewiring to capture value,” McKinsey, March 12, 2025. What’s more, only 1 percent of enterprises we surveyed view their gen AI strategies as mature.6Hannah Mayer, Lareina Yee, Michael Chui, and Roger Roberts, “Superagency in the workplace: Empowering people to unlock AI’s full potential,” McKinsey, January 28, 2025. Call it the “gen AI paradox”: For all the energy, investment, and potential surrounding the technology, at-scale impact has yet to materialize for most organizations.
Many organizations have deployed horizontal use cases, such as enterprise-wide copilots and chatbots; nearly 70 percent of Fortune 500 companies, for example, use Microsoft 365 Copilot.7Satya Nadella, “Microsoft Fiscal Year 2025 First Quarter Earnings Conference Call,” Microsoft, October 30, 2024. These tools are widely seen as levers to enhance individual productivity by helping employees save time on routine tasks and access and synthesize information more efficiently. But these improvements, while real, tend to be spread thinly across employees. As a result, they are not easily visible in terms of top- or bottom-line results.
By contrast, vertical use cases—those embedded into specific business functions and processes—have seen limited scaling in most companies despite their higher potential for direct economic impact (Exhibit 2). Fewer than 10 percent of use cases deployed ever make it past the pilot stage, according to McKinsey research.8New at McKinsey Blog, “McKinsey’s ecosystem of strategic alliances brings the power of generative AI to clients,” April 2, 2024. Even when they have been fully deployed, these use cases typically have supported only isolated steps of a business process and operated in a reactive mode when prompted by a human, rather than functioning proactively or autonomously. As a result, their impact on business performance also has been limited.
What accounts for this imbalance? For one thing, horizontally deployed copilots such as Microsoft Copilot or Google AI Workspace are accessible, off-the-shelf solutions that are relatively easy to implement. (In many cases, enabling Microsoft Copilot is as simple as activating an extension to an existing Office 365 contract, requiring no redesign of workflows or major change management efforts.) Rapid deployment of enterprise chatbots also has been driven by risk mitigation concerns. As employees began experimenting with external large language models (LLMs) such as ChatGPT, many organizations implemented internal, secure alternatives to limit data leakage and ensure compliance with corporate security policies.
The limited deployment and narrow scope of vertical use cases can in turn be attributed to six primary factors:
Despite its limited bottom-line impact so far, the first wave of gen AI has been far from wasted. It has enriched employee capabilities, enabled broad experimentation, accelerated AI familiarity across functions, and helped organizations build essential capabilities in prompt engineering, model evaluation, and governance. All of this has laid the groundwork for a more integrated and transformative second phase—the emerging age of AI agents.10Lareina Yee, Michael Chui, Roger Roberts, and Stephen Xu, “Why agents are the next frontier of generative AI,” McKinsey Quarterly, July 24, 2024.
LLMs have revolutionized how organizations interact with data—enabling information synthesis, content generation, and natural language interaction. But despite their power, LLMs have been fundamentally reactive and isolated from enterprise systems, largely unable to retain memory of past interactions or context across sessions or queries. Their role has been largely limited to enhancing individual productivity through isolated tasks. AI agents mark a major evolution in enterprise AI—extending gen AI from reactive content generation to autonomous, goal-driven execution. Agents can understand goals, break them into subtasks, interact with both humans and systems, execute actions, and adapt in real time—all with minimal human intervention. They do so by combining LLMs with additional technology components providing memory, planning, orchestration, and integration capabilities.
With these new capabilities, AI agents expand the potential of horizontal solutions, upgrading general-purpose copilots from passive tools into proactive teammates that don’t just respond to prompts but also monitor dashboards, trigger workflows, follow up on open actions, and deliver relevant insights in real time. But the real breakthrough comes in the vertical realm, where agentic AI enables the automation of complex business workflows involving multiple steps, actors, and systems—processes that were previously beyond the capabilities of first-generation gen AI tools.
On the operations side, agents take on routine, data-heavy tasks so humans can focus on higher-value work. But they go further, transforming processes in five ways:
In a complex supply chain environment, for example, an AI agent could act as an autonomous orchestration layer across sourcing, warehousing, and distribution operations. Connected to internal systems (such as the supply chain planning system or the warehouse management system) and external data sources (such as weather forecasts, supplier feeds, and demand signals), the agent could continuously forecast demand. It could then identify risks, such as delays or disruptions, and dynamically replan transport and inventory flows. Selecting the optimal transport mode based on cost, lead time, and environmental impact, the agent could reallocate stock across warehouses, negotiate directly with external systems, and escalate decisions requiring strategic input. The result: improved service levels, reduced logistics costs, and lower emissions.
Agents can also help spur top-line growth by amplifying existing revenue streams and unlocking entirely new ones:
In short, agentic AI doesn’t just automate. It redefines how organizations operate, adapt, and create value.
The following case studies demonstrate how QuantumBlack helps organizations build agent workforces—with outcomes that extend far beyond efficiency gains.
The problem: A large bank needed to modernize its legacy core system, which consisted of 400 pieces of software—a massive undertaking budgeted at more than $600 million. Large teams of coders tackled the project using manual, repetitive tasks, which resulted in difficulty coordinating across silos. They also relied on often slow, error-prone documentation and coding. While first-generation gen AI tools helped accelerate individual tasks, progress remained slow and laborious.
The agentic approach: Human workers were elevated to supervisory roles, overseeing squads of AI agents, each contributing to a shared objective in a defined sequence (Exhibit 3). These squads retroactively document the legacy application, write new code, review the code of other agents, and integrate code into features that are later tested by other agents prior to delivery of the end product. Freed from repetitive, manual tasks, human supervisors guide each stage of the process, enhancing the quality of deliverables and reducing the number of sprints required to implement new features.
Impact: More than 50 percent reduction in time and effort in the early adopter teams
The problem: A market research and intelligence firm was devoting substantial resources to ensure data quality, relying on a team of more than 500 people whose responsibilities included gathering data, structuring and codifying it, and generating tailored insights for clients. The process, conducted manually, was prone to error, with a staggering 80 percent of mistakes identified by the clients themselves.
The agentic approach: A multiagent solution autonomously identifies data anomalies and explains shifts in sales or market share. It analyzes internal signals, such as changes in product taxonomy, and external events identified via web searches, including product recalls or severe weather. The most influential drivers are synthesized, ranked, and prepared for decision-makers. With advanced search and contextual reasoning, the agents often surface insights that would be difficult for human analysts to uncover manually. While not yet in production, the system is fully functional and has demonstrated strong potential to free up analysts for more strategic work.
Impact: More than 60 percent potential productivity gain and expected savings of more than $3 million annually.
The problem: Relationship managers (RMs) at a retail bank were spending weeks writing and iterating credit-risk memos to help make credit decisions and fulfill regulatory requirements (Exhibit 4). This process required RMs to manually review and extract information from at least ten different data sources and develop complex nuanced reasoning across interdependent sections—for instance, loan, revenue, and cash joint evolution.
The agentic approach: In close collaboration with the bank’s credit-risk experts and RMs, a proof of concept was developed to transform the credit memo workflow using AI agents. The agents assist RMs by extracting data, drafting memo sections, generating confidence scores to prioritize review, and suggesting relevant follow-up questions. In this model, the analyst’s role shifts from manual drafting to strategic oversight and exception handling.
Impact: A potential 20 to 60 percent increase in productivity, including a 30 percent improvement in credit turnaround
Realizing AI’s full potential in the vertical realm requires more than simply inserting agents into legacy workflows. It instead calls for a shift in design mindset—from automating tasks within an existing process to reinventing the entire process with human and agentic coworkers. That’s because when agents are embedded into a legacy process without redesign, they typically serve as faster assistants—generating content, retrieving data, or executing predefined steps. But the process itself remains sequential, rule bound, and shaped by human constraints.
Reinventing a process around agents means more than layering automation on top of existing workflows—it involves rearchitecting the entire task flow from the ground up. That includes reordering steps, reallocating responsibilities between humans and agents, and designing the process to fully exploit the strengths of agentic AI: parallel execution that collapses cycle time, real-time adaptability that reacts to changing conditions, deep personalization at scale, and elastic capacity that flexes instantly with demand.
Consider a hypothetical customer call center. Before introducing AI agents, the facility was using gen AI tools to assist human support staff by retrieving articles from knowledge bases, summarizing ticket histories, and helping draft responses. While this assistance improved speed and reduced cognitive load, the process itself remained entirely manual and reactive, with human agents still managing every step of diagnosis, coordination, and resolution. The productivity improvement potential was modest, typically boosting resolution time and productivity between 5 and 10 percent.
Now imagine that the call center introduces AI agents but largely preserves the existing workflow—agents are added to assist at specific steps without reconfiguring how work is routed, tracked, or resolved end-to-end. Agents can classify tickets, suggest likely root causes, propose resolution paths, and even autonomously resolve frequent, low-complexity issues (such as password resets). While the impact here can be increased—an estimated 20 to 40 percent savings in time and a 30 to 50 percent reduction in backlog—coordination friction and limited adaptability prevent true breakthrough gains.
But the real shift occurs at the third level, when the call center’s process is reimagined around agent autonomy. In this model, AI agents don’t just respond—they proactively detect common customer issues (such as delayed shipments, failed payments, or service outages) by monitoring patterns across channels, anticipate likely needs, initiate resolution steps automatically (such as issuing refunds, reordering items, or updating account details), and communicate directly with customers via chat or email. Human agents are repositioned as escalation managers and service quality overseers, who are brought in only when agents detect uncertainty or exceptions to typical patterns. Impact at this level is transformative. This could allow a radical improvement of customer service desk productivity. Up to 80 percent of common incidents could be resolved autonomously, with a reduction in time to resolution of 60 to 90 percent (Exhibit 5).
Of course, not every business process requires full reinvention. Simple task automation is sufficient for highly standard, repetitive workflows with limited variability—such as payroll processing, travel expense approvals, or password resets—where gains come primarily from reducing manual effort. In contrast, processes that are complex, cross-functional, prone to exceptions, or tightly linked to business performance often warrant full redesign. Key indicators that call for reinvention include high coordination overhead, rigid sequences that delay responsiveness, frequent human intervention for decisions that could be data driven, and opportunities for dynamic adaptation or personalization. In these cases, redesigning the process around the agent’s ability to orchestrate, adapt, and learn delivers far greater value than simply speeding up existing workflows.
To scale agents, companies will need to overcome a threefold challenge: handling the newfound risks that AI agents bring, blending custom and off-the-shelf agentic systems, and staying agile amid fast-evolving tech (while avoiding lock-ins).
These challenges cannot be addressed by merely bolting new components, such as memory stores or orchestration engines, on top of existing gen AI stacks. While such capabilities are necessary, they are not sufficient. What’s needed is a fundamental architectural shift: from static, LLM-centric infrastructure to a dynamic, modular, and governed environment built specifically for agent-based intelligence—the agentic AI mesh.
The agentic AI mesh is a composable, distributed, and vendor-agnostic architectural paradigm that enables multiple agents to reason, collaborate, and act autonomously across a wide array of systems, tools, and language models—securely, at scale, and built to evolve with the technology. At the heart of this paradigm are five mutually reinforcing design principles:
The emerging architecture for agentic AI relies on seven interconnected capabilities:
The agentic AI mesh acts as the connective and orchestration layer that enables large-scale, intelligent agent ecosystems to operate safely and efficiently, and continuously evolve. It allows companies to coordinate custom-built and off-the-shelf agents within a unified framework, support multiagent collaboration by allowing agents to share context and delegate tasks, and mitigate key risks such as agent sprawl, autonomy drift, and lack of observability—all while preserving the agility required for a rapid technology evolution (see sidebar “Seven interconnected capabilities of the AI agentic mesh”).
A few characteristics are key for LLM providers to take into account in the agentic era:
Beyond this architectural evolution, organizations will also have to revisit their LLM strategies. At the core of every custom agent lies a foundation model—the reasoning engine that powers perception, decision-making, and interaction. In the agentic era, the requirements placed on LLMs evolve significantly. Agents are not passive copilots—they are autonomous, persistent, embedded systems. This creates five critical categories of LLM requirements, each aligned with specific deployment contexts, for which different kinds of models will be relevant (see sidebar “Foundational models for agents: Five requirements”).
Finally, to truly scale agent deployment across the enterprise, the enterprise systems themselves must also evolve.
In the short term, APIs—protocols that allow different software applications to communicate and exchange data—will remain the primary interface for agents to interact with enterprise systems. But in the long term, APIs alone will not suffice. Organizations must begin reimagining their IT architectures around an agent-first model—one in which user interfaces, logic, and data access layers are natively designed for machine interaction rather than human navigation. In such a model, systems are no longer organized around screens and forms but around machine-readable interfaces, autonomous workflows, and agent-led decision flows.
This shift is already underway. Microsoft is embedding agents into the core of Dynamics 365 and Microsoft 365 via Copilot Studio; Salesforce is expanding Agentforce into a multiagent orchestration layer; SAP is rearchitecting its Business Technology Platform (BTP) to support agent integration through Joule. These changes signal a broader transition: The future of enterprise software is not just AI-augmented—it is agent-native.
As agents evolve from passive copilots to proactive actors—and scale across the enterprise—the complexity they introduce will be not only technical but mostly organizational. The real challenge lies in coordination, judgment, and trust. This organizational complexity will play out most visibly across three dimensions: how humans and agents cohabit day-to-day workflows; how organizations establish governance over systems that can act autonomously; and how they prevent unchecked sprawl as agent creation becomes increasingly democratized.
Agents unlock the full potential of vertical use cases, offering companies a path to generate value well beyond efficiency gains. But realizing that potential requires a reimagined approach to AI transformation—one tailored to the unique nature of agents and capable of addressing the lingering limitations they alone cannot resolve. This approach is the subject of our next chapter.
Unlike gen AI tools that could be easily plugged into existing workflows, AI agents demand a more foundational shift, one that requires rethinking business processes and enabling deep integration with enterprise systems. McKinsey has a proven Rewired playbook for AI-driven transformations.11Eric Lamarre, Kate Smaje, and Rodney Zemmel, “Rewired to outcompete,” McKinsey Quarterly, June 20, 2023. To capitalize on the agentic opportunity, organizations must build on that, fundamentally reshaping their AI transformation approach across four dimensions:
Redesigning the approach to AI transformation is an important step, but it is not enough. To unlock their full potential at scale, organizations must also activate a robust set of enablers that support the structural, cultural, and technical shifts required to integrate agents into day-to-day operations. These enablers span four dimensions—people, governance, technology architecture, and data—each of which is a foundation for scalable, secure, and high-impact deployment of agents across the enterprise.
The rise of AI agents is more than just a technological shift. Agents represent a strategic inflection point that will redefine how companies operate, compete, and create value. To navigate this transition successfully, organizations must move beyond experimentation and pilot programs and enter a new phase of scaled, enterprise-wide transformation.
This pivot cannot be delegated—it must be initiated and led by the CEO. It will rely on three key actions:
Like any truly disruptive technology, AI agents have the power to reshuffle the deck. Done right, they offer laggards a leapfrog opportunity to rewire their competitiveness. Done wrong—or not at all—they risk accelerating the decline of today’s market leaders. This is a moment of strategic divergence.
While the technology will continue to evolve, it is already mature enough to drive real, transformative change across industries. But to realize the full promise of agentic AI, CEOs must rethink their approach to AI transformation—not as a series of scattered pilots but as focused, end-to-end reinvention efforts. That means identifying a few business domains with the highest potential and pulling every lever: from reimagining workflows to redistributing tasks between humans and machines to rewiring the organization based on new operating models.
Some leaders are already moving—not just by deploying fleets of agents but by rewiring their organizations to harness their full disruptive potential. (Moderna, for example, merged its HR and IT leadership13Julien Dupont-Calbo, “L’IA n’est plus un outil, c’est un collègue”: Moderna fusionne sa DRH et sa DSI, [“AI is no longer a tool, it’s a colleague”: Moderna merges its HR and IT departments], Les Echos, May 15, 2025.—signaling that AI is not just a technical tool but a workforce-shaping force.) This is a structural move toward a new kind of enterprise. Agentic AI is not an incremental step—it is the foundation of the next-generation operating model. CEOs who act now won’t just gain a performance edge. They will redefine how their organizations think, decide, and execute.
The time for exploration is ending. The time for transformation is now.
Alexander Sukharevsky is a senior partner in McKinsey’s London office, where Dave Kerr is a partner; Klemens Hjartar is a senior partner in the Copenhagen office; Lari Hämäläinen is a senior partner in the Seattle office; Stéphane Bout is a senior partner in the Lyon office; Vito Di Leo is a partner in the Zurich office; and Guillaume Dagorret is a senior fellow with the McKinsey Global Institute and is based in the Paris office.
The authors wish to thank Alena Fedorenko, Annie David, Clarisse Magnin, Lareina Yee, Larry Kanter, Michael Chui, Roger Roberts, Sarah Mulligan, Thomas Vlot, and Timo Mauerhoefer for their contributions to this report.
This article was edited by Larry Kanter, a senior editor in the New York office.
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Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI – Amazon Web Services
/in website SEO, Website Traffic/by Team ZYTAWS Blogs
Generative AI revolutionizes business operations through various applications, including conversational assistants such as Amazon’s Rufus and Amazon Seller Assistant. Additionally, some of the most impactful generative AI applications operate autonomously behind the scenes, an essential capability that empowers enterprises to transform their operations, data processing, and content creation at scale. These non-conversational implementations, often in the form of agentic workflows powered by large language models (LLMs), execute specific business objectives across industries without direct user interaction.
Non-conversational applications offer unique advantages such as higher latency tolerance, batch processing, and caching, but their autonomous nature requires stronger guardrails and exhaustive quality assurance compared to conversational applications, which benefit from real-time user feedback and supervision.
This post examines four diverse Amazon.com examples of such generative AI applications:
Each case study reveals different aspects of implementing non-conversational generative AI applications, from technical architecture to operational considerations. Throughout these examples, you will learn how the comprehensive suite of AWS services, including Amazon Bedrock and Amazon SageMaker, are the key to success. Finally, we list key learnings commonly shared across these use cases.
Creating high-quality product listings with comprehensive details helps customers make informed purchase decisions. Traditionally, selling partners manually entered dozens of attributes per product. The new generative AI solution, launched in 2024, transforms this process by proactively acquiring product information from brand websites and other sources to improve the customer experience across numerous product categories.
Generative AI simplifies the selling partner experience by enabling information input in various formats such as URLs, product images, or spreadsheets and automatically translating this into the required structure and format. Over 900,000 selling partners have used it, with nearly 80% of generated listing drafts accepted with minimal edits. AI-generated content provides comprehensive product details that help with clarity and accuracy, which can contribute to product discoverability in customer searches.
For new listings, the workflow begins with selling partners providing initial information. The system then generates comprehensive listings using multiple information sources, including titles, descriptions, and detailed attributes. Generated listings are shared with selling partners for approval or editing.
For existing listings, the system identifies products that can be enriched with additional data.
The Amazon team built robust connectors for internal and external sources with LLM-friendly APIs using Amazon Bedrock and other AWS services to seamlessly integrate into Amazon.com backend systems.
A key challenge is synthesizing diverse data into cohesive listings across more than 50 attributes, both textual and numerical. LLMs require specific control mechanisms and instructions to accurately interpret ecommerce concepts because they might not perform optimally with such complex, varied data. For example, LLMs might misinterpret “capacity” in a knife block as dimensions rather than number of slots, or mistake “Fit Wear” as a style description instead of a brand name. Prompt engineering and fine-tuning were extensively used to address these cases.
The generated product listings should be complete and correct. To help this, the solution implements a multistep workflow using LLMs for both generation and validation of attributes. This dual-LLM approach helps prevent hallucinations, which is critical when dealing with safety hazards or technical specifications. The team developed advanced self-reflection techniques to make sure the generation and validation processes complement each other effectively.
The following figure illustrates the generation process with validation both performed by LLMs.
Figure 1. Product Listing creation workflow
Human feedback is central to the solution’s quality assurance. The process includes Amazon.com experts for initial evaluation and selling partner input for acceptance or edits. This provides high-quality output and enables ongoing enhancement of AI models.
The quality assurance process includes automated testing methods combining ML-, algorithm-, or LLM-based evaluations. Failed listings undergo regeneration, and successful listings proceed to further testing. Using causal inference models, we identify underlying features affecting listing performance and opportunities for enrichment. Ultimately, listings that pass quality checks and receive selling partner acceptance are published, making sure customers receive accurate and comprehensive product information.
The following figure illustrates the workflow of going to production with testing, evaluation, and monitoring of product listing generation.
Figure 2. Product Listing testing and human in the loop workflow
Given the high standards for accuracy and completeness, the team adopted a comprehensive experimentation approach with an automated optimization system. This system explores various combinations of LLMs, prompts, playbooks, workflows, and AI tools to iterate for higher business metrics, including cost. Through continuous evaluation and automated testing, the product listing generator effectively balances performance, cost, and efficiency while staying adaptable to new AI developments. This approach means customers benefit from high-quality product information, and selling partners have access to cutting-edge tools for creating listings efficiently.
Building upon the human-AI hybrid workflows previously discussed in the seller listing example, Amazon Pharmacy demonstrates how these principles can be applied in a Health Insurance Portability and Accountability Act (HIPAA)-regulated industry. Having shared a conversational assistant for patient care specialists in the post Learn how Amazon Pharmacy created their LLM-based chat-bot using Amazon SageMaker, we now focus on automated prescription processing, which you can read about in The life of a prescription at Amazon Pharmacy and the following research paper in Nature Magazine.
At Amazon Pharmacy, we developed an AI system built on Amazon Bedrock and SageMaker to help pharmacy technicians process medication directions more accurately and efficiently. This solution integrates human experts with LLMs in creation and validation roles to enhance precision in medication instructions for our patients.
The prescription processing system combines human expertise (data entry technicians and pharmacists) with AI support for direction suggestions and feedback. The workflow, shown in the following diagram, begins with a pharmacy knowledge-based preprocessor standardizing raw prescription text in Amazon DynamoDB, followed by fine-tuned small language models (SLMs) on SageMaker identifying critical components (dosage, frequency).
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Burak Gozluklu is a Principal AI/ML Specialist Solutions Architect and lead GenAI Scientist Architect for Amazon.com on AWS, based in Boston, MA. He helps strategic customers adopt AWS technologies and specifically Generative AI solutions to achieve their business objectives. Burak has a PhD in Aerospace Engineering from METU, an MS in Systems Engineering, and a post-doc in system dynamics from MIT in Cambridge, MA. He maintains his connection to academia as a research affiliate at MIT. Outside of work, Burak is an enthusiast of yoga.
Emilio Maldonado is a Senior leader at Amazon responsible for Product Knowledge, oriented at building systems to scale the e-commerce Catalog metadata, organize all product attributes, and leverage GenAI to infer precise information that guides Sellers and Shoppers to interact with products. He’s passionate about developing dynamic teams and forming partnerships. He holds a Bachelor of Science in C.S. from Tecnologico de Monterrey (ITESM) and an MBA from Wharton, University of Pennsylvania.
Wenchao Tong is a Sr. Principal Technologist at Amazon Ads in Palo Alto, CA, where he spearheads the development of GenAI applications for creative building and performance optimization. His work empowers customers to enhance product and brand awareness and drive sales by leveraging innovative AI technologies to improve creative performance and quality. Wenchao holds a Master’s degree in Computer Science from Tongji University. Outside of work, he enjoys hiking, board games, and spending time with his family.
Alexandre Alves is a Sr. Principal Engineer at Amazon Health Services, specializing in ML, optimization, and distributed systems. He helps deliver wellness-forward health experiences.
Puneet Sahni is Sr. Principal Engineer in Amazon. He works on improving the data quality of all products available in Amazon catalog. He is passionate about leveraging product data to improve our customer experiences. He has a Master’s degree in Electrical engineering from Indian Institute of Technology (IIT) Bombay. Outside of work he enjoying spending time with his young kids and travelling.
Vaughn Schermerhorn is a Director at Amazon, where he leads Shopping Discovery and Evaluation—spanning Customer Reviews, content moderation, and site navigation across Amazon’s global marketplaces. He manages a multidisciplinary organization of applied scientists, engineers, and product leaders focused on surfacing trustworthy customer insights through scalable ML models, multimodal information retrieval, and real-time system architecture. His team develops and operates large-scale distributed systems that power billions of shopping decisions daily. Vaughn holds degrees from Georgetown University and San Diego State University and has lived and worked in the U.S., Germany, and Argentina. Outside of work, he enjoys reading, travel, and time with his family.
Tarik Arici is a Principal Applied Scientist at Amazon Selection and Catalog Systems (ASCS), working on Catalog Quality Enhancement using GenAI workflows. He has a PhD in Electrical and Computer Engineering from Georgia Tech. Outside of work, Tarik enjoys swimming and biking.
Figure 3. (a) Data entry technician and pharmacist workflow with two GenAI modules, (b) Suggestion module workflow and (c) Flagging module workflow
The system seamlessly integrates experts such as data entry technicians and pharmacists, where generative AI complements the overall workflow towards agility and accuracy to better serve our patients. A direction assembly system with safety guardrails then generates instructions for data entry technicians to create their typed directions through the suggestion module. The flagging module flags or corrects errors and enforces further safety measures as feedback provided to the data entry technician. The technician finalizes highly accurate, safe-typed directions for pharmacists who can either provide feedback or execute the directions to the downstream service.
One highlight from the solution is the use of task decomposition, which empowers engineers and scientists to break the overall process into a multitude of steps with individual modules made of substeps. The team extensively used fine-tuned SLMs. In addition, the process employs traditional ML procedures such as named entity recognition (NER) or estimation of final confidence with regression models. Using SLMs and traditional ML in such contained, well-defined procedures significantly improved processing speed while maintaining rigorous safety standards due to incorporation of appropriate guardrails on specific steps.
The system comprises multiple well-defined substeps, with each subprocess operating as a specialized component working semi-autonomously yet collaboratively within the workflow toward the overall objective. This decomposed approach, with specific validations at each stage, proved more effective than end-to-end solutions while enabling the use of fine-tuned SLMs. The team used AWS Fargate to orchestrate the workflow given its current integration into existing backend systems.
In their product development journey, the team turned to Amazon Bedrock, which provided high-performing LLMs with ease-of-use features tailored to generative AI applications. SageMaker enabled further LLM selections, deeper customizability, and traditional ML methods. To learn more about this technique, see How task decomposition and smaller LLMs can make AI more affordable and read about the Amazon Pharmacy business case study.
To comply with HIPAA standards and provide patient privacy, we implemented strict data governance practices alongside a hybrid approach that combines fine-tuned LLMs using Amazon Bedrock APIs with Retrieval Augmented Generation (RAG) using Amazon OpenSearch Service. This combination enables efficient knowledge retrieval while maintaining high accuracy for specific subtasks.
Managing LLM hallucinations—which is critical in healthcare—required more than just fine-tuning on large datasets. Our solution implements domain-specific guardrails built on Amazon Bedrock Guardrails, complemented by human-in-the-loop (HITL) oversight to promote system reliability.
The Amazon Pharmacy team continues to enhance this system through real-time pharmacist feedback and expanded prescription format capabilities. This balanced approach of innovation, domain expertise, advanced AI services, and human oversight not only improves operational efficiency, but means that the AI system properly augments healthcare professionals in delivering optimal patient care.
Whereas our previous example showcased how Amazon Pharmacy integrates LLMs into real-time workflows for prescription processing, this next use case demonstrates how similar techniques—SLMs, traditional ML, and thoughtful workflow design—can be applied to offline batch inferencing at massive scale.
Amazon has introduced AI-generated customer review highlights to process over 200 million annual product reviews and ratings. This feature distills shared customer opinions into concise paragraphs highlighting positive, neutral, and negative feedback about products and their features. Shoppers can quickly grasp consensus while maintaining transparency by providing access to related customer reviews and keeping original reviews available.
The system enhances shopping decisions through an interface where customers can explore review highlights by selecting specific features (such as picture quality, remote functionality, or ease of installation for a Fire TV). Features are visually coded with green check marks for positive sentiment, orange minus signs for negative, and gray for neutral—which means shoppers can quickly identify product strengths and weaknesses based on verified purchase reviews. The following screenshot shows review highlights regarding noise level for a product.
Figure 4. An example product review highlights for a product.
The team developed a cost-effective hybrid architecture combining traditional ML methods with specialized SLMs. This approach assigns sentiment analysis and keyword extraction to traditional ML while using optimized SLMs for complex text generation tasks, improving both accuracy and processing efficiency. The following diagram shows ttraditional ML and LLMs working to provide the overall workflow.
Figure 5. Use of traditional ML and LLMs in a workflow.
The feature employs SageMaker batch transform for asynchronous processing, significantly reducing costs compared to real-time endpoints. To deliver a near zero-latency experience, the solution caches extracted insights alongside existing reviews, reducing wait times and enabling simultaneous access by multiple customers without additional computation. The system processes new reviews incrementally, updating insights without reprocessing the complete dataset. For optimal performance and cost-effectiveness, the feature uses Amazon Elastic Compute Cloud (Amazon EC2) Inf2 instances for batch transform jobs, providing up to 40% better price-performance to alternatives.
By following this comprehensive approach, the team effectively managed costs while handling the massive scale of reviews and products so that the solution remained both efficient and scalable.
Having explored mostly text-centric generative AI applications in previous examples, we now turn to multimodal generative AI with Amazon Ads creative content generation for sponsored ads. The solution has capabilities for image and video generation, the details of which we share in this section. In common, this solution uses Amazon Nova creative content generation models at its core.
Working backward from customer need, a March 2023 Amazon survey revealed that nearly 75% of advertisers struggling with campaign success cited creative content generation as their primary challenge. Many advertisers—particularly those without in-house capabilities or agency support—face significant barriers due to the expertise and costs of producing quality visuals. The Amazon Ads solution democratizes visual content creation, making it accessible and efficient for advertisers of different sizes. The impact has been substantial: advertisers using AI-generated images in Sponsored Brands campaigns saw nearly 8% click-through rates (CTR) and submitted 88% more campaigns than non-users.
Last year, the AWS Machine Learning Blog published a post detailing the image generation solution. Since then, Amazon has adopted Amazon Nova Canvas as its foundation for creative image generation, creating professional-grade images from text or image prompts with features for text-based editing and controls for color scheme and layout adjustments.
In September 2024, the Amazon Ads team included the creation of short-form video ads from product images. This feature uses foundation models available on Amazon Bedrock to give customers control over visual style, pacing, camera motion, rotation, and zooming through natural language, using an agentic workflow to first describe video storyboards and then generate the content for the story. The following screenshot shows an example of creative image generation for product backgrounds on Amazon Ads.
Figure 6. Ads image generation example for a product.
As discussed in the original post, responsible AI is at the center of the solution, and Amazon Nova creative models come with built-in controls to support safety and responsible AI use, including watermarking and content moderation.
The solution uses AWS Step Functions with AWS Lambda functions to orchestrate serverless orchestration of both image and video generation processes. Generated content is stored in Amazon Simple Storage Service (Amazon S3) with metadata in DynamoDB, and Amazon API Gateway provides customer access to the generation capabilities. The solution now employs Amazon Bedrock Guardrails in addition to maintaining Amazon Rekognition and Amazon Comprehend integration at various steps for additional safety checks. The following screenshot shows creative AI-generated videos on Amazon Ads campaign builder.
Figure 7. Ads video generation for a product
Creating high-quality ad creatives at scale presented complex challenges. The generative AI model needed to produce appealing, brand-appropriate images across diverse product categories and advertising contexts while remaining accessible to advertisers regardless of technical expertise. Quality assurance and improvement are fundamental to both image and video generation capabilities. The system undergoes continual enhancement through extensive HITL processes enabled by Amazon SageMaker Ground Truth. This implementation delivers a powerful tool that transforms advertisers’ creative process, making high-quality visual content creation more accessible across diverse product categories and contexts.
This is just the beginning of Amazon Ads using generative AI to empower advertising customers to create the content they need to drive their advertising objectives. The solution demonstrates how reducing creative barriers directly increases advertising activity while maintaining high standards for responsible AI use.
Non-conversational applications benefit from higher latency tolerance, enabling batch processing and caching, but require robust validation mechanisms and stronger guardrails due to their autonomous nature. These insights apply to both non-conversational and conversational AI implementations:
These patterns enable scalable, reliable, and cost-effective generative AI solutions while maintaining quality and responsibility standards. The implementations demonstrate that effective solutions require not just sophisticated models, but careful attention to architecture, operations, and governance, supported by AWS services and established practices.
The examples from Amazon.com shared in this post illustrate how generative AI can create value beyond traditional conversational assistants. We invite you to follow these examples or create your own solution to discover how generative AI can reinvent your business or even your industry. You can visit the AWS generative AI use cases page to start the ideation process.
These examples showed that effective generative AI implementations often benefit from combining different types of models and workflows. To learn what FMs are supported by AWS services, refer to Supported foundation models in Amazon Bedrock and Amazon SageMaker JumpStart Foundation Models. We also suggest you explore Amazon Bedrock Flows, which can ease the path towards building workflows. Additionally, we remind you that Trainium and Inferentia accelerators provide important cost savings in these applications.
Agentic workflows, as illustrated in our examples, have proven particularly valuable. We recommend exploring Amazon Bedrock Agents for quickly building agentic workflows.
Successful generative AI implementation extends beyond model selection—it represents a comprehensive software development process from experimentation to application monitoring. To begin building your foundation across these essential services, we invite you to explore Amazon QuickStart.
These examples demonstrate how generative AI extends beyond conversational assistants to drive innovation and efficiency across industries. Success comes from combining AWS services with strong engineering practices and business understanding. Ultimately, effective generative AI solutions focus on solving real business problems while maintaining high standards of quality and responsibility.
To learn more about how Amazon uses AI, refer to Artificial Intelligence in Amazon News.
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Supercharging Ad Creative with Amazon Bedrock and Amazon Nova: How AI is Revolutionizing Content Generation for Advertising & Marketing Use-Cases – Amazon Web Services
/in website SEO, Website Traffic/by Team ZYTAWS Blogs
The advertising industry is experiencing an exciting transformation thanks to generative AI (GenAI) tools that are revolutionizing campaign creation, digital ad generation, and marketing asset localization. While these innovations present incredible opportunities to streamline workflows and boost creativity, many organizations are navigating questions about which tools to implement, how to effectively integrate them into existing processes, and where to begin their Artificial Intelligence (AI) journey. The traditional process of developing display and video advertising assets, which typically requires weeks or months to produce multiple variations for different formats, placements, and sizes, can now be dramatically accelerated – but organizations need clear strategies to harness these new capabilities. This delay in asset creation slows down campaign launches and reduces market responsiveness.
Generative AI has emerged as a transformative solution augmenting existing human-driven processes, allowing for accelerated production at scale as well as the ability for understanding foundation models (FMs) to learn from large amounts of existing multimodal unstructured data (videos, images, brand books, previous campaign metrics and results) to then incorporate those learnings in new content generation requests.
In this blog post, we will explore the Amazon Nova family of understanding and content generation models, how the models are transforming content creation within Advertising and Marketing, Amazon Nova use-cases, as well as additional resources to get started.
AWS announced Amazon Nova at re:Invent 2024 introducing a new generation of state-of-the-art foundation models (FMs) that deliver advanced and industry-leading price-performance, available exclusively within Amazon Bedrock. The Nova family includes four understanding models (Nova Micro, Nova Lite, Nova Pro, and Amazon Nova Premier) and two creative content generation models (Amazon Nova Canvas and Amazon Nova Reel). Amazon Nova helps agencies, brands, media companies, and advertising and marketing technology organizations create high impact, on brand content at unprecedented speed and scale.
Amazon Nova Micro is a new text-only model that delivers the lowest latency responses within the Nova family of models at a very low cost. Amazon Nova Lite is a new low-cost multimodal model capable of processing image, video, and text inputs to generate text output. Amazon Nova Pro is a new highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide variety of tasks. The Amazon Nova family offers three powerful content generation models, each optimized for different needs. Nova Premier excels at generating complex, multimodal content by processing text, images, and videos with deep contextual understanding. Its 1M token capacity enables sophisticated content creation across multiple formats and extended narratives. Nova Pro and Nova Lite provide targeted content generation capabilities, with Pro offering enhanced capabilities for mid-complexity tasks and Lite optimized for straightforward content creation. All three models seamlessly integrate content generation across 200+ languages, featuring built-in connectors that make it simple to generate content using your existing systems and data sources. This flexibility allows teams to generate everything from social media posts to full marketing campaigns while maintaining brand consistency. Amazon Nova Micro, Amazon Nova Lite, and Amazon Nova Pro are at least 75 percent less expensive than the best performing models in their respective intelligence classes in Amazon Bedrock and are also the fastest models in their respective intelligence classes in Amazon Bedrock. The models also support fine-tuning, allowing advertising and marketing agencies to point the models to examples of their own data (including text, images, and videos) allowing for enhanced brand alignment, improved performance efficiency, and industry-specific expertise.
Amazon Nova Canvas is a new state-of-the-art image generation model that creates professional grade images from text or images provided in prompts. Nova Canvas also provides features that make it easy to edit images using text inputs and provides controls for adjusting color scheme and layout. Nova Canvas allows users to rapidly produce dozens of variations for every marketing material, ad format, placement, and size —reducing time to market from weeks to hours, delivering speed and scalability. The model also comes with built-in controls to support safe and responsible AI use. These include features like watermarking, which allows the source of an image to always be traced, and content moderation, which limits the generation of potentially harmful content. Amazon Nova Canvas performs better than image generators such as OpenAI DALL-E 3 and Stable Diffusion in side-by-side human evaluations conducted by a third party, and on key automated metrics.
Amazon Nova Reel is a new state-of-the-art video generation model that allows brands and agencies to easily create high-quality video from text and images, complete with natural language prompts to control visual style and pacing, including camera motion, rotation, and zooming. Nova Reel speeds up storyboarding, previsualization (previs), advertisements, trailers, or highlight reels, increasing time-to-market by accelerating overall video output and localization. Nova Reel outperforms comparable models in quality and consistency, according to side-by-side human evaluations conducted by a third party that preferred Amazon Nova Reel-generated videos over those generated by Runway’s Gen-3 Alpha. Like Amazon Nova Canvas, Amazon Nova Reel comes with built-in controls to support safe and responsible AI use, including watermarking and content moderation. Amazon Nova Reel 1.1 was also recently unveiled, upgrading video generation with better quality and reduced latency, now supporting both single-prompt and custom-prompt 2-minute videos composed of consistent-style 6-second shots through Amazon Bedrock.
Amazon Nova models revolutionize content generation, starting with powerful image creation capabilities. Nova Canvas excels at generating entirely new images and creating variations of existing ones, dramatically reducing production time and costs. It offers fine-tuning through Amazon Bedrock, allowing teams to train the model on proprietary data for customized image generation that precisely matches their brand style and characteristics. Organizations can then test and deploy these fine-tuned models using provisioned throughput for reliable performance at scale. Nova Reel extends these capabilities to video, enabling rapid creation of new video content and variations of existing footage. The Amazon Nova family of understanding models (Nova Micro, Nova Lite, Nova Pro, and Nova Premier) as well as the content generation models (Nova Canvas and Nova Reel) are all generally available.
Advertising agency creative departments want to utilize generative AI to enhance and accelerate the creative process. They need models that help them rapidly develop high-quality advertising campaign briefs, concepts, and creative assets leveraging both natural language inputs as well as different types of files including images, video, audio, structured data, and results and insights from previous campaigns. This is hard to accomplish because existing models are limited in terms of the flexibility (fine-tuning and customization) and quality (photorealistic outputs) that they deliver. Video generation is in its early days and customers are still seeking viable options that can be used for creative production needs. These use cases are also sensitive to copyright violation risk and safety considerations for generated content.
With Amazon Nova Pro, agency creative departments can rapidly retrieve insights from a company’s brand book, products descriptions, previous advertising campaigns, and existing marketing materials to generate campaign brief development in minutes instead of weeks. Its multimodal capabilities, which combine text, images, and video inputs to text output, help teams create better brand campaigns faster using AI-driven analysis to process various types of inputs such as past creative, videos, images, keywords, campaign results, and brand attributes. The personalized content creation process is more secure and customized, bringing advertising and campaigns closer to the clients.
Amazon Nova Pro, Amazon Nova Canvas, and Amazon Nova Reel empower creative teams to focus on strategy while automating content creation. Nova Pro streamlines workflows, Nova Canvas enables brand-specific image generation through Bedrock fine-tuning, and Nova Reel 1.1 produces high-quality 2-minute videos with flexible prompt controls – delivering precise, on-brand content at scale.
Creative departments can now leverage generative to accelerate their processes, though finding AI models that effectively handle multiple input types while meeting quality standards remains challenging. Amazon Bedrock addresses this by offering a broad selection of foundation models for multimodal reasoning and content generation, helping agencies enhance creativity and reduce manual tasks. Through Amazon Q or managed agents in Bedrock, agencies can automate complex workflows by integrating various data sources – from brand books to campaign results – enabling AI assistants to develop comprehensive campaign briefs in minutes rather than weeks.
Creating display and video ad campaigns traditionally requires extensive and expensive resources, with companies needing to produce dozens of assets across various formats, placements, and sizes – a process that typically takes weeks and delays market entry. While advertising platforms and media publishers seek generative AI solutions to accelerate this process, finding models that can consistently produce high-fidelity, customizable content while offering optimal price-performance has been challenging. Amazon Nova’s creative content generation models address this challenge by enabling rapid generation of high-quality copy, images, and videos using natural language prompts. This transformation reduces campaign activation time from months to hours while maintaining brand consistency and improving conversion rates, offering publishers and media companies a cost-effective solution for delivering engaging advertising content efficiently.
Using Amazon Nova Canvas and Reel, publishers and advertising technology companies like Amazon Ads can enable their advertising customers to create high-quality images and videos for their display and video campaigns, reducing time-to-activation from weeks to hours, and allowing them to achieve the best price-performance for these workloads. With Amazon Nova Reel, advertisers can use text and natural language inputs to produce high-quality short-form videos, with state-of-the-art visual and temporal consistency. With Amazon Nova Canvas, advertisers can generate highly accurate images leveraging their brand style, product imagery, and natural language inputs. Nova Reel and Nova Canvas help customers activate effective campaigns faster and increase scalability, resulting in increased advertiser satisfaction, lower costs, and faster business growth. Nova Canvas supports fine-tuning in Amazon Bedrock, allowing users to train models on proprietary data to generate brand-matched images with reliable deployment performance. Nova Reel 1.1 now delivers faster, higher-quality 2-minute videos composed of consistent-style 6-second shots, with both single and custom prompt controls through Amazon Bedrock.
Marketing and media technology companies are leveraging generative AI to revolutionize content creation, but face challenges in producing high-quality, culturally relevant content at scale. AWS addresses this through Amazon Bedrock’s comprehensive generative AI capabilities, integrating powerful models like Nova Canvas, Nova Reel, Stability AI, and Luma AI. Combined with agentic AI through Amazon Q and managed agents, teams can now automate and streamline the creation of diverse marketing assets – from copy to videos – while ensuring cultural relevance, brand safety, and proper localization. This integrated approach transforms traditional resource-intensive processes, enabling companies to simultaneously handle multiple campaign components and achieve higher productivity while maintaining quality and brand consistency across global markets.
Using Nova Pro, Nova Canvas, and Nova Reel, media companies like Hearst, Mar-Tech companies such as Shutterstock and 123RF, and their customers, can leverage Amazon Nova’s multi-modal input and content creation models for high-fidelity asset creation and localization. They can more easily and effectively create and localize media and marketing assets at scale, including copy, images, and videos, with superior language reasoning, translation into over 200 languages, and high-quality global scene and image modifications. Amazon Nova Reel and Canvas enable global-scale marketing content creation with local relevance. Nova Canvas supports fine-tuning in Amazon Bedrock on proprietary data for brand-matched images, while Nova Reel 1.1 generates high-quality 2-minute videos with flexible prompt controls, allowing companies to create customized, culturally-relevant marketing materials worldwide.
Agentic AI is reshaping advertising and marketing through intelligent automation, empowering teams to work smarter and scale efficiently. AI agents are transforming three critical areas of marketing and advertising. First, they streamline creative campaign development by analyzing vast data sets to generate comprehensive briefs in minutes instead of weeks. Second, they revolutionize media planning by automating campaign execution, providing real-time insights, and optimizing channel performance through advanced attribution modeling. Finally, AI agents enable sophisticated audience segmentation by processing real-time customer data to deliver personalized content that adapts to evolving preferences. This AI-driven approach converts traditionally lengthy, manual processes into efficient, data-informed strategies that scale effectively.
Amazon Nova Micro, Lite, and Pro are available in US East (N. Virginia), Asia-Pacific (Tokyo), and AWS GovCloud (US-West). Amazon Nova Canvas and Reel are both available in US East (N. Virginia), Europe (Ireland), Asia Pacific (Tokyo), and AWS GovCloud (US-West). Amazon Nova Premier is available in US East (N. Virginia) with support for additional regions coming soon. For pricing information, please see Amazon Bedrock pricing. In addition, AWS offers uncapped intellectual property (IP) indemnity coverage for outputs of generally available Amazon Nova models, reducing copyright and IP violation risk for customers. For more information, see AWS AI Service Cards for Amazon Nova Micro, Lite, Pro and Premier, Amazon Nova Canvas and Amazon Nova Reel.
Generative AI has emerged as a transformative force in content generation, driving significant improvements in productivity, time-to-market, and innovation acceleration. Advertising agencies and large brands with in-house creative teams can leverage GenAI to streamline campaign and content creation, enhance the briefing process, augment creativity, and maximize team productivity. AWS generative AI and machine learning (ML) services help organizations reimagine creativity and accelerate innovation so they can generate and test large volumes of creative variations much more quickly and comprehensively. This approach transforms advertising campaign development from inception to completion, providing a more efficient and dynamic creative process.
Begin your creative journey now – explore Amazon Nova through Nova.Amazon.com, Amazon Bedrock Chat and Text Playgrounds, or API integration.
Amazon Ads, Amazon’s advertising platform providing advertising tools and services to brands and advertisers, launched new AI-powered creative tools for generating images, videos, and audio content, making it easier for brands of all sizes to produce high-quality advertising materials. Amazon Ads uses Amazon Nova Canvas to generate high-quality ad creatives, and Amazon Nova Reel for video ad production and creation of varied ad formats across different placements.
Dentsu Digital Inc., a digital marketing company, is integrating Amazon Nova Reel into its creative process, enabling its team to improve and accelerate the development of its campaigns – from briefing, to concept development, to creative video content generation. Amazon Nova Reel reduces the overall time it takes to generate new assets from weeks to days.
Shutterstock is a leading creative platform offering full-service solutions, high-quality content, and tools for transformative brands, digital media, and marketing businesses. Based on the high image quality outputs of Amazon Nova Canvas, the team at Shutterstock is excited to include the model in the Shutterstock AI Image Generator, giving users an intuitive, easy-to-use offering.
Musixmatch is the world’s largest lyrics platform with over 80 million users and a database of more than 11 million unique lyrics. Musixmatch is including Amazon Nova Reel in Musixmatch Pro, which helps creators distribute lyrics across all the major digital streaming services and social networks. Emerging artists can use Amazon Nova Reel to produce high-quality music videos using their songs’ context as inputs, and customize them with natural language prompts.
For further reading, please refer to the following:
Shivam Patel is a Sr. Solutions Architect at AWS helping enterprises architect and implement cloud-native solutions. His core areas of focus are Migration & Modernization, Generative AI, and Cloud Operations/Cloud Governance. Outside of work, Shivam, is an avid food connoisseur, New York Yankees & New York Knicks fanatic, and globetrotter.
Satish is a Senior Technical Account Manager at AWS. He is passionate about customer success and technology. He loves working backwards by quickly understanding strategic customer objectives, aligning them to software capabilities and effectively driving customer success.
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Can AEO/GEO Startups Beat Established SEO Tool Companies? – Search Engine Journal
/in website SEO, Website Traffic/by Team ZYTDownload your cheat sheet and checklist to start building content that works harder.
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CEO of Conductor says established SEO platforms will beat AEO/GEO startups, predicting 95% will flatline into the SaaS abyss.
The CEO of Conductor started a LinkedIn discussion about the future of AI SEO platforms, suggesting that the established companies will dominate and that 95 percent of the startups will disappear. Others argued that smaller companies will find their niche and that startups may be better positioned to serve user needs.
Besmertnik published his thoughts on why top platforms like Conductor, Semrush, and Ahrefs are better positioned to provide the tools users will need for AI chatbot and search visibility. He argued that the established companies have over a decade of experience crawling the web and scaling data pipelines, with which smaller organizations cannot compete.
Conductor’s CEO wrote:
“Over 30 new companies offering AI tracking solutions have popped up in the last few months. A few have raised some capital to get going. Here’s my take: The incumbents will win. 95% of these startups will flatline into the SaaS abyss.
…We work with 700+ enterprise brands and have 100+ engineers, PMs, and designers. They are all 100% focused on an AI search only future. …Collectively, our companies have hundreds of millions of ARR and maybe 1000x more engineering horsepower than all these companies combined.
Sure we have some tech debt and legacy. But our strengths crush these disadvantages…
…Most of the AEO/GEO startups will be either out of business or 1-3mm ARR lifestyle businesses in ~18 months. One or two will break through and become contenders. One or two of the largest SEO ‘incumbents’ will likely fall off the map…”
Besmertnik’s remarks suggested that smaller tool companies earning one to three million dollars in annual recurring revenue, what he termed “lifestyle” businesses, would continue as viable companies but stood no chance of moving upward to become larger and more established enterprise-level platforms.
Rand Fishkin, cofounder of SparkToro, defended the smaller “lifestyle” businesses, saying that it feels like cheating at business, happiness, and life.
He wrote:
“Nothing better than a $1-3M ARR “lifestyle” business.
…Let me tell you what I’m never going to do: serve Fortune 500s (nevermind 100s). The bureaucracy, hoops, and friction of those orgs is the least enjoyable, least rewarding, most avoid-at-all-costs thing in my life.”
Not to put words into Rand’s mouth but it seems that what he’s saying is that it’s absolutely worthwhile to scale a business to a point where there’s a work-life balance that makes sense for a business owner and their “lifestyle.”
Not everyone agreed that established brands would successfully transition from SEO tools to AI search, arguing that startups are not burdened by legacy SEO ideas and infrastructure, and are better positioned to create AI-native solutions that more accurately follow how users interact with AI chatbots and search.
Daniel Rodriguez, cofounder of Beewhisper, suggested that the next generation of winners may not be “better Conductors,” but rather companies that start from a completely different paradigm based on how AI users interact with information. His point of view suggests that legacy advantages may not be foundations for building strong AI search tools, but rather are more like anchors, creating a drag on forward advancement.
He commented:
“You’re 100% right that the incumbents’ advantages in crawling, data processing, and enterprise relationships are immense.
The one question this raises for me is: Are those advantages optimized for the right problem? All those strengths are about analyzing the static web – pages, links, and keywords.
But the new user journey is happening in a dynamic, conversational layer on top of the web. It’s a fundamentally different type of data that requires a new kind of engine.
My bet is that the 1-2 startups that break through won’t be the ones trying to build a better Conductor. They’ll be the ones who were unburdened by legacy and built a native solution for understanding these new conversational journeys from day one.”
Mike Mallazzo, Ads + Agentic Commerce @ PayPal, questioned whether there’s a market to support multiple breakout startups and suggested that venture capital interest in AEO and GEO startups may not be rational. He believes that the market is there for modest, capital-efficient companies rather than fund-returning unicorns.
Mallazzo commented:
“I admire the hell out of you and SEMRush, Ahrefs, Moz, etc– but y’all are all a different breed imo– this is a space that is built for reasonably capital efficient, profitable, renegade pirate SaaS startups that don’t fit the Sand Hill hyper venture scale mold. Feels like some serious Silicon Valley naivete fueling this funding run….
Even if AI fully eats search, is the analytics layer going to be bigger than the one that formed in conventional SEO? Can more than 1-2 of these companies win big?”
Right now it feels like the industry is still figuring out what is necessary to track, what is important for AI visibility. For example, brand mentions is emerging as an important metric, but is it really? Will brand mentions put customers in the ecommerce checkout cart?
And then there’s the reality of zero click searches, the idea that AI Search significantly wipes out the consideration stage of the customer’s purchasing journey, the data is not there, it’s swallowed up in zero click searches. So if you’re going to talk about tracking user’s journey and optimizing for it, this is a piece of the data puzzle that needs to be solved.
Michael Bonfils, a 30-year search marketing veteran, raised these questions in a discussion about zero click searches and what to do to better survive it, saying:
“This is, you know, we have a funnel, we all know which is the awareness consideration phase and the whole center and then finally the purchase stage. The consideration stage is the critical side of our funnel. We’re not getting the data. How are we going to get the data?
So who who is going to provide that? Is Google going to eventually provide that? Do they? Would they provide that? How would they provide that?
But that’s very important information that I need because I need to know what that conversation is about. I need to know what two people are talking about that I’m talking about …because my entire content strategy in the center of my funnel depends on that greatly.”
There’s a real question about what type of data these companies are providing to fill the gaps. The established platforms were built for the static web, keyword data, and backlink graphs. But the emerging reality of AI search is personalized and queryless. So, as Michael Bonfils suggested, the buyer journeys may occur entirely within AI interfaces, bypassing traditional SERPs altogether, which is the bread and butter of the established SEO tool companies.
If the future of search is not about search results and the attendant search query volumes but a dynamic dialogue, the kinds of data that matter and the systems that can interpret them will change. Will startups that specialize in tracking and interpreting conversational interactions become the dominant SEO tools? Companies like Conductor have a track record of expertly pivoting in response to industry needs, so how it will all shake out remains to be seen.
Read the original post on LinkedIn by Conductor CEO, Seth Besmertnik.
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I have 25 years hands-on experience in SEO, evolving along with the search engines by keeping up with the latest …
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Flowershort Launches AI Short-Drama Co-Creation Platform for Content Creators – Macau Business
/in website SEO, Website Traffic/by Team ZYTFlowershort introduces an AI-powered platform for collaborative short-drama creation and content adaptation.
HO CHI MINH CITY, Vietnam, July 18, 2025 — Vietnamese media-tech innovator Flowershort today officially launched an AI-powered short-drama platform that enables collaborative content creation and adaptation, featuring advanced animation-to-drama tools designed to streamline the creative process.
At the core of Flowershort is a three-pillar innovation framework:
User Opportunities:
Technology Highlights:
Market Momentum:
“Flowershort turns passive viewing into ownership,” said CEO Ronen. “It’s not just entertainment — it’s a circular content economy where educators, artists, and viewers all contribute real value.”
Content Director Sam added, “With automated animation-to-drama conversion and education dramatization tools, we’re redefining what user-generated content can be.”
Supporting Materials:
Media Contact:
Business Inquiries: [email protected]
Script Submission: [email protected]
Affiliate Program: [email protected]
Website: https://flowershort.com
Contact Info:
Name: Lisa TANG
Email: Send Email
Organization: Flowershort
Website: https://flowershort.com
Release ID: 89164741
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AI Marketing Tactics That Work: How to Create, Measure, and Avoid Mistakes – MITechNews
/in website SEO, Website Traffic/by Team ZYTArtificial intelligence has moved beyond the buzz. For today’s marketers, the real conversation is about AI marketing tactics—the practical, strategic ways AI can support faster content creation, smarter decision-making, and stronger campaign performance. Businesses that once hesitated are now realizing that AI isn’t a threat to creativity or strategy—it’s a tool to amplify both. The key is using AI with intention, so your brand voice stays strong and your marketing goals stay clear.
That’s exactly what Melih Oztalay, CEO of SmartFinds Marketing, breaks down in the July 16, 2025, episode of MITechTV. Known for translating complex trends into actionable strategies, Melih shares the latest AI marketing tactics that mid-sized businesses are using to produce high-quality content, monitor results in real time, and avoid common mistakes that waste time or damage trust.
This episode covers three essential topics: how to create content at scale using AI tools, how to measure success across engagement, conversion, and SEO metrics, and what mistakes to avoid when relying on automation. Whether you’re new to AI or looking to sharpen your strategy, you’ll walk away with a clear framework to guide your marketing efforts today—and evolve them tomorrow.
The traditional content creation model—writers, designers, rounds of approvals—is slow, expensive, and hard to scale. AI flips this on its head by giving marketers the power to generate high-quality assets faster than ever.
Melih describes this shift as moving from a bottleneck to a “content assembly line.” Think ideation, drafting, visuals, and QA—all supported by AI tools that work 24/7.
Here’s how SmartFinds Marketing streamlines the process:
A B2B SaaS company needed to boost organic visibility for competitive keywords but had a small in-house marketing team. SmartFinds developed 12 unique, SEO-optimized blog posts per month using ChatGPT for first drafts, Grammarly for polishing, and Canva AI for visuals. Each blog was reviewed by editors and optimized for schema markup. Within 90 days, the client saw a 70% increase in organic traffic, several new page-one rankings, and higher engagement metrics across the board.
A global content delivery network (CDN) company faced a major challenge: despite investing over $1 million in sales hires, they were generating almost no leads. Ranked near the bottom of their competitive set in web visibility, they needed a foundational marketing strategy to fuel growth.
SmartFinds deployed a comprehensive, multi-channel program that included a redesigned website with optimized CTAs, consistent blog content, social media, press releases, and AI-enhanced tools like Looker Studio for real-time performance monitoring.
The result? Over 4,300 leads generated globally in just one year—with U.S. leads nearly doubling. SmartFinds transformed their lead generation efforts from negligible to consistent and measurable, with a 198% year-over-year increase in global lead volume. The success demonstrated how AI-supported strategies, when guided by data and expert oversight, can reshape marketing outcomes at scale.
These examples show that AI isn’t just a creative assistant—it’s a productivity engine. When integrated into a structured process, AI enables marketers to do more with less, without compromising quality or consistency. But creating great content is only half the equation. To truly unlock the value of AI marketing tactics, businesses need to know what’s working, what’s not, and how to optimize moving forward. That’s where measurement comes in.
Using AI without tracking its performance is like flying blind. It might feel efficient at first, but without data to back up your results, you’re only guessing at what’s working. Melih Oztalay emphasized in this segment that the real power of AI marketing tactics isn’t just about creating more—it’s about creating smarter. And that means marketers must adopt a measurement-first mindset. AI impacts multiple stages of the funnel, so success can—and should—be measured across engagement (are people interacting with your content?), conversion (are they taking action?), SEO visibility (can they even find you?), and CRO (are you turning interest into real leads?). If your AI content isn’t producing lift in at least one of these areas, it’s time to re-evaluate the tools and tactics behind it.
Melih emphasized that successful deployment of AI marketing tactics requires clear, consistent, and centralized reporting. Tools like Looker Studio are essential for modern marketers—they allow teams to create real-time dashboards that bring together multiple data sources across campaigns, channels, and content types. Instead of working from fragmented reports, businesses can now analyze engagement patterns, keyword lifts, conversion changes, and user behavior all in one place. This kind of visibility not only guides optimization decisions, it also helps align marketing with sales and leadership priorities. When you measure AI performance this way, you’re no longer reacting—you’re driving strategy forward with precision.
AI tools are only as powerful as the strategy behind them. With new platforms launching weekly, it’s easy for marketers to get overwhelmed by choice. Should you use ChatGPT for writing or Jasper? Is Canva enough for visuals, or should you explore Midjourney? Melih emphasized the importance of aligning tools with function and goals—not just trends. That means building a stack where each tool serves a defined purpose, complements the others, and fits seamlessly into your content workflow. The goal isn’t to chase shiny objects—it’s to build an efficient, reliable system for scaling quality marketing.
Pro Tip: Don’t get stuck on just one tool. Melih suggests experimenting across platforms to identify strengths and weaknesses in your stack. AI tools evolve fast—what works well this month might be replaced by something better next quarter. Build with flexibility in mind, and revisit your toolset often to stay competitive. The best AI marketing tactics aren’t about chasing every tool—they’re about assembling the right ones and knowing how to use them strategically.
AI can be your marketing team’s best asset—or its biggest liability. When deployed without a strategy or oversight, it’s easy to automate mistakes at scale, creating brand confusion, legal risk, or customer distrust. Melih emphasized that while AI tools are incredibly powerful, they must be managed with intent. This section outlines some of the most common mistakes companies make when jumping into AI marketing too quickly or without guardrails—and what SmartFinds Marketing does differently to avoid those pitfalls.
These mistakes are avoidable—but only if AI is treated as a team member, not a shortcut. At SmartFinds, success comes from disciplined processes: prompt testing, careful human review, and alignment with brand identity. AI marketing tactics can amplify your message and results—but only when paired with strategy, compliance, and continuous improvement. Skip the oversight, and the cost of cleanup could outweigh the benefits. Use AI smartly, and your brand will stay both efficient and credible.
If you’re feeling behind on AI adoption, you’re not alone. Many businesses are intrigued by the possibilities but don’t know how to get started—or worry they’ll do it wrong. Melih outlined a simple, actionable five-step approach to begin using AI marketing tactics without overwhelming your team or risking your brand. These steps are designed to build momentum while maintaining control, helping you ease into AI with confidence and clarity.
AI doesn’t require a massive overhaul—just a clear path forward. By starting small, choosing the right tools, and setting up guardrails, any business can begin integrating AI into their workflow. The key is to stay focused on real outcomes: better content, stronger engagement, higher conversion. And remember: the best AI marketing programs don’t start with perfection—they start with progress.
AI in marketing often raises more questions than answers, especially for teams just starting out. During the MITechTV episode, Melih addressed several of the most common questions businesses ask when trying to understand how AI fits into their strategy. From prompting to performance metrics, this FAQ provides concise answers that clarify what works, what to watch out for, and how to use AI effectively across different stages of your marketing funnel.
These frequently asked questions reflect real-world challenges and decisions that mid-sized businesses face when stepping into AI. Whether you’re wondering which tools to try, how to track results, or how to avoid mistakes, the answers above can serve as a compass. AI doesn’t have to be mysterious—just methodical. With the right approach, the learning curve flattens quickly, and the benefits compound over time.
As AI continues to reshape how businesses market, sell, and grow, one principle remains constant: strategy comes first. Melih ended the segment with a message every marketing team should remember—AI is a multiplier, not a substitute. It can accelerate what you’re already doing, but it can’t create vision, values, or leadership on its own. In other words, the real ROI comes when AI tools are matched with human insight, creativity, and accountability.
Melih closed the show with this reminder: “AI doesn’t replace strategy—it multiplies it. Use it wisely, measure everything, and keep your brand voice in the driver’s seat.”
The businesses winning today aren’t just using AI. They’re guiding it with intention, testing it with data, and integrating it with their brand strategy. That’s what separates noise from results.
Companies that treat AI as a strategic partner—not a plug-and-play solution—are already gaining a competitive edge. They’re producing more content without sacrificing quality. They’re making decisions faster because they have better data. And most importantly, they’re staying agile in a landscape that’s evolving by the week. If your organization is ready to adopt AI marketing tactics with clarity and purpose, now’s the time to act—and SmartFinds Marketing is here to help you lead that change.
SmartFinds Marketing is a digital marketing agency. SmartFinds provides full marketing strategies and solutions to businesses. The marketing process is managed by a team of contemporary marketers who manage new ideas and incorporate early adoption of new strategic technologies to achieve successful results. Helping customers understand web marketing and the web advertising world through education and consultation is part of any SmartFinds program.
“Our trusted years of experience in advertising and marketing solutions date back to 1987 and the Internet since 1994. Since the very early days of the industry, we traversed the Internet to gain the knowledge, expertise, and more importantly, the imagination to apply the Internet’s resources to your business needs”, says Melih Oztalay.
Melih Oztalay is an industry leader as a guest author on many websites like Search Engine Journal. Additionally, he is a guest speaker at many conferences and events along with being a subject matter expert called on my radio shows and podcasts.
SmartFinds Marketing….Creative strategies. Innovative ideas. Use the full power of the Internet with us!
Email: [email protected]
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ContentKeon Leads the Pack in SEO-Driven Content Writing Services in Delhi – openPR.com
/in website SEO, Website Traffic/by Team ZYTDelhi-Based Leading Content Writing Company
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