Look for New Ways to Create Value When Deploying Gen AI

New technologies commoditize old sources of advantage. Consider the invention of the camera: Portrait painters could not adapt by simply deploying the new technology–using the camera to make their workflows faster and more efficient–because photography had commoditized their offering wholesale, reducing customers’ willingness to pay and drying up their value pool. Our research shows a similar pattern emerging with generative AI. We mapped job roles to work activities for 800 U.S. public firms and then scored each activity’s potential for augmentation or automation by gen AI, based on factors such as the need for physical presence, digital availability of inputs, and reliance on task-based logic. We find no correlation between the resulting measure of gen AI automation potential per sector and the development of sector-level profit margins since the launch of ChatGPT. In fact, sectors with the highest potential for automation—finance, tech, and media—saw margins stagnate or fall. We also found that this relationship is not mediated by differences in gen AI adoption levels across sectors. To approximate this, we used publicly available data indicating the extent to which firms are actively building or integrating gen AI capabilities–such as the share of employees with relevant skills and the number of third-party AI technology vendors firms used. In sum, even as companies pour resources into gen AI, the resulting productivity gains appear to be competed away, passed on to customers or suppliers rather than retained by firms–an early sign of commoditization. As such, the data suggests that the benefits of gen AI will not lie in using the technology simply to perform existing activities faster or more efficiently. Rather, business leaders seeking to deploy gen AI for advantage should look toward another impact of new technologies: the unlocking of new value pools. 3 Strategies for Deploying Gen AI Inside the Firm When technologies remove a bottleneck in one part of the value chain, they don’t eliminate scarcity, they relocate it. What was once valuable becomes a commodity, but at the same time, a new friction or constraint emerges, becoming a new scarcity and source of value. For example, photography made realistic representations abundant, but meaning, perspective, and emotion became new scarce differentiators. This inspired the emergence of movements like Impressionism and Cubism. New technologies don’t just shift value pools along existing chains, they also create new ones. By removing constraints, they enable business models that were previously impossible. Photography, for instance, transformed journalism, advertising, and publishing by making objective depictions of reality scalable. The shifts associated with breakthrough technologies—old value pools drying up, but new ones emerging around solving novel frictions and embracing newly possible business models—provide a useful framework for helping leaders to anticipate where future sources of value will reside in the gen AI age. The Optimizer: Enhance existing capabilities. Often, companies are deploying gen AI technology in the hope of enhancing existing capabilities—becoming faster, more productive, or more responsive. Examples include digital publishers, such as BuzzFeed, embracing LLMs to develop content, or Bloomberg integrating gen AI into its terminal service to quickly summarize earnings calls or financial reports. Such use cases can unlock new growth potential or greater efficiency, fostering a competitive advantage for the firms that are quickest to effectively deploy new technologies. However, these advantages are likely to be only temporary because they rely on activities that are increasingly commoditized by gen AI technology. In other words: Gen AI technology offers a tailwind in the short-term by making these firms more efficient. But in the longer term, as gen AI proliferates, these activities will be commoditized, executing them efficiently will become table stakes, and the underlying value pool will dry up. The Curator: Solve new frictions. Durable advantage is unlikely to come from deploying gen AI to do, faster and more efficiently, that which your firm is already doing. Rather, it will stem from shifting your focus to doing what has become harder to do with the emergence of gen AI technology. For example, advisory firms’ core offerings around research, synthesis, and the production of polished outputs are being commoditized by gen AI. However, by making it easy to create abundant content, the technology creates new frictions around verifying that content, drawing useful conclusions, and prioritizing implications. In other words, the value pools are shifting from content creation to application of judgment. Moreover, gen AI has made it feasible to produce near-infinite marketing copy, social media posts, or design ideas. The differentiating move in the face of this friction is to focus on curation—filtering and contextualizing a saturated media environment. An example of this is The Browser, a paid newsletter that selects five outstanding articles per day to share with its more than 150,000 subscribers. With no original content, its business is built entirely on using editorial taste to guide customers through the abundance of online articles. Focusing on a strategy of judgment or curation is not viable for every firm. Moreover, the underlying frictions may not persist indefinitely. Perhaps machine intelligence will, in the future, become very skilled at validating or selecting content. However, what will not change is the fact that, as technology progresses and solves existing frictions, new ones will emerge. Thus, firms should focus on identifying new frictions brought about by gen AI and positioning themselves to solve these. The Reinventor: Build new business models. Entirely new offerings are made possible because gen AI makes it possible to remove constraints around costs or operational complexity. For example, gen AI can be used to overcome the traditional trade-off between reach and personalization, unlocking the ability to serve smaller, more fragmented, or lower-margin customers with tailored experiences. This enables companies to reach segments they previously ignored because the costs of serving them were too high. For example, Spotify is using gen AI to translate and voice-clone podcasts into multiple languages, expanding the reach of its content to a broader audience. Meanwhile, Meta’s AI marketing tools generate creative assets tailored both to the product being advertised, and to the user that is being advertised to. In both examples, gen AI is used to take advantage of existing, differentiated assets (e.g., Spotify’s library of podcasts and vast user base; Meta’s central position in the advertising ecosystem). Thus, a useful way to identify potentially valuable deployments of gen AI is to evaluate how the technology might redouble existing advantages. For example, since gen AI is particularly powerful at processing knowledge and making it more accessible, it may unlock new revenue streams associated with existing unique skills and capabilities that were previously locked inside the organization. JPMorgan Chase has built IndexGPT, a gen AI tool designed to repackage its existing institutional prowess at analyzing and selecting securities into a tool available to retail customers. Similarly, scientific publisher Elsevier has unlocked a new path to monetize its vast archive of medical journals and drug databases, using it to build a gen AI-powered clinical decision support tool, which physicians can query to receive evidence-based treatment options. Finally, gen AI also enables entirely new types of delivery models and user experiences: In education, Duolingo Max uses LLMs to give every learner a personal language tutor with “role play” scenarios, featuring embedded feedback and dynamic curriculum adjustments These are strategies that are already possible with the gen AI tools available today. As the technology evolves, new capabilities will emerge and new business models will become possible and valuable, just as existing ones may again become commoditized. One implication for executives is that they should not seek differentiation by looking for activities that cannot be accomplished by gen AI yet because the value pools associated with them will shrink as gen AI evolves. Rather, the focus of the search should be on activities that will become possible as AI evolves, for example, growing or newly emerging value pools. Repositioning for a Gen AI-powered Ecosystem The above strategies focus on deploying gen AI internally to enhance products or operations, with the goal of achieving margin expansion, greater speed, or scale. But there is another path, which involves a more external perspective: recognizing the changes gen AI will drive in the broader market, leaders can reposition their firm as a valuable node in this emerging ecosystem. This does not mean developing a proprietary AI model, building a data center, or designing computing hardware. Rather, it means adjusting existing or developing new value propositions for a reality in which gen AI becomes part of all market activities. Three strategies illustrate how this development may drive shifts in value pools. The Re-packager: Rethink offerings for gen AI-powered choices. As choices are increasingly informed by —and may in the near future increasingly be made directly by—AI agents, firms must repackage their offerings for machine consumption. This means revisiting how information is provided, for instance, by offering standardized data on product attributes and compatibility, and enabling agents to interact directly with your offerings, for example, by making purchases. Firms like Peec AI and Profound are already helping brands analyze how LLMs describe and recommend their products in an emerging form of AI agent optimization that echoes search engine optimization, but for the agent-mediated economy. This new approach will complement traditional marketing strategies focused on human perception and brandingm which may lose power as agents rely on objective, verifiable inputs rather than emotional trust or recognition. Going one step further, Casas Bahia, one of Brazil’s leading retailers, has built a gen AI-powered conversational agent on WhatsApp, allowing customers to browse, compare products, and complete purchases through natural dialogue rather than a traditional website. Since launch in November 2025, this channel has attracted more than 2 million users and also influences more than 5% of the retailer’s in-store sales. Repositioning for agent-mediated choice is an important move and, as the above example illustrates, early movers are benefitting. However, in the long run, this adaptation will become table stakes, and may even drive commoditization as the competitive field flattens, with every brand optimized for agent selection. The Supervisor: Validate gen AI’s accuracy. As gen AI models and autonomous agents proliferate and start to act as economic actors in their own right, there is an increasing need for appropriate supervision to enhance credibility and ensure reliability. This is a new friction which businesses can pivot to solving, for example, by productizing capabilities they already use internally. MasterCard Decision Intelligence offers fraud-risk-scoring APIs that help payment agents decide when to approve or decline a transaction. This “compliance-as-a-service” approach converts hard-won verification and regulatory expertise into a new income stream. Similarly, NewsGuard, which rates publishers based on reliability, trustworthiness, and potential conflicts of interest, has licensed its technology to LLM providers, which use it to enable their models to select appropriate sources and to self-correct false information. More broadly, any companies with proprietary, verified data, such as clinical trial results, financial analysis, or engineering manuals, could transform that knowledge into a validation service for LLMs, ensuring that generated content is compliant and accurate. The Supplier: Provide input to gen AI. Finally, gen AI models also need input for training—an increasing need, as publicly available human-created text data is predicted to run out by 2028—as well as access to data that could be helpful in answering queries in real time. The need for curated, high-quality sources, particularly domain-specific knowledge a model developer may want their LLM to have expertise in, is unlocking another potential business model for incumbents. For example, Thomson Reuters licenses real-time news and market data into LLM pipelines, while LexisNexis supplies the structured legal ontologies and verified case law that domain-specific LLMs depend on. Emulating this approach, a global consumer goods company could monetize anonymized point-of-sale data, feeding demand signals into retail planning agents, which are used by firms willing to pay for a cleaner view of the market. . . . The six strategies above are not exhaustive, nor will they apply to every organization. Leaders can identify the most valuable strategies for their company in the gen AI age by asking themselves the following set of questions: Which of our current differentiating assets or capabilities are at risk of becoming commoditized by gen AI? Which new frictions is gen AI technology causing in our markets or for our customers? Is solving them valuable? And, if so, how can we do it? Where can we deploy gen AI to redouble existing advantages, for instance, by extracting new value from differentiated capabilities or assets? What entirely new business models become possible if we remove historical cost or complexity constraints using gen AI? Which of our assets could become inputs or infrastructure for the gen AI-driven business ecosystem? By proactively engaging with these questions, leaders can identify which strategies are at risk of commoditization—and how to unlock durable advantage.

Look for New Ways to Create Value When Deploying Gen AI

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