CEOs worldwide face a perplexing challenge: Data has become ubiquitous, but monetizing it remains elusive. Their companies have invested heavily to unlock data’s latent value. They have built warehouses, created dashboards, embedded analytics into their operations, and more recently, built data product businesses. These investments have delivered significant value—but far more value remains untapped.
Our research shows that a third of global executives believe their companies’ data assets have unrealized potential (Exhibit 1). They want to extract full value from their data but are unsure how to wring out those last drops. Gen AI could be the answer.
Generating value from data with gen AI
Gen AI is reshaping the conversation about how to best generate value from large data sets. It’s no longer about monetizing data itself, or even mining insights from that data through analytics. With gen AI, companies can generate intelligence at scale. Gen AI’s ability to reason and make autonomous decisions means it can extract insights from data and then turn those insights into action directly within business workflows—enabling the data-driven enterprise of the future. Companies that craft strong data-driven operating models and technical road maps have the best chance of tapping the full value of gen AI.
In this article, we will provide concrete tactics CEOs can use to incorporate gen AI into their companies’ data monetization strategies. We define data monetization broadly as using data to generate quantifiable economic benefit by selling it to third parties, deploying it internally to improve business outcomes, or creating new data-driven products and services.
The stakes have never been higher to generate value from data. Top-performing organizations attribute 11 percent of their revenue to data monetization—over five times more than their lower-performing peers.1 Companies that want to extract more value from their data are already making the leap from creating static data products to launching AI-powered intelligence. And this shift delivers clear commercial upside.
Consider Scintilla, Walmart’s platform to help its suppliers uncover insights into their customers. Developed by Walmart Data Ventures, a business Walmart created to commercialize data products and build a scalable intelligence enterprise, Scintilla is powered by Walmart’s expansive shopper behavior data. The product, first called Luminate, was launched in October 2021. One year later, Walmart announced that revenue from the product had grown 80 percent quarter over quarter during its first year.2 In October 2024, Walmart announced a 173 percent year-on-year growth in customers and a 100 percent renewal rate for Scintilla, with all customers signing on for at least another three years.3 Walmart also recently launched Scintilla Insights Activation, an AI-powered platform that turns data insights into automated, real-time recommendations for audience targeting and advertising campaigns.4
How gen AI moves companies up the data pyramid
The data, information, knowledge, and wisdom (DIKW) pyramid has long served as a conceptual map for extracting value from data (see sidebar, “What is the DIKW pyramid?”). Gen AI accelerates movement up the pyramid, turning raw data not just into insight but into intelligence that can serve as the foundation for building entirely new businesses (Exhibit 2).
We see gen AI driving two transformative shifts in data monetization. First, gen AI is unlocking unstructured data, making it possible for companies to clean, connect, and extract value from formats that were previously difficult to leverage. Second, gen AI is turning raw data into actionable insights embedded directly into workflows.
Unlocking unstructured data
More than 90 percent of organizational data is unstructured, comprising documents, images, social media posts, and voice recordings. For years, unstructured data has remained out of reach for effective monetization.5 Gen AI changes that. New gen AI tools can clean, analyze, and “productize” unstructured data quickly. Critically, gen AI doesn’t just make unstructured data usable—it makes it connectable—turning isolated signals into strategic intelligence. Advances in natural language processing allow organizations to extract structured meaning from raw text, converting narratives, messages, or transcripts into standardized, analyzable formats.
At the core of this transformation is a semantic layer powered by ontologies and knowledge graphs, which map relationships across disparate data types to create a unified, contextualized view. These frameworks serve as a connective tissue, bridging unstructured inputs and structured systems by aligning data to shared meaning, and thus, intelligence.
An example of this is how a call center might connect data from a customer complaint to better solve issues. A voice call transcript could be linked to the customer’s transaction history, product SKUs, and service tickets, as well as their geographical location, phone number, and purchase preferences. This connectivity could enable a call center operator to send a digital coupon to the customer’s phone, redeemable in the local store closest to their home. Gen AI not only understands unstructured data sets but also creates connections among them for richer pattern recognition. This can result in next-generation data products that make more accurate predictions and deliver deeper monetization potential.
Turning raw data into actionable insights
Companies are increasingly using gen AI to build data products that package, prepare, and transform raw data into actionable insights. Product users are increasingly demanding this capability. Take the example of a data tool used for compensation benchmarking. Imagine an HR hiring manager for a hospital system in Chicago wants to set the salary of a new pediatrician. They can use a data tool to find a compensation benchmark, but how do they know it’s an apples-to-apples comparison for the specific role they are hiring for? How should they adjust the salary based on the experience of the new hire, the specifics of the role, or the location? Is the data behind the benchmark recent enough to reflect the high rate of inflation? This example shows how a data benchmark is not useful without tailored insights. The user needs an actionable recommendation that accounts for specific context. Gen AI is starting to enable data products to deliver this level of decision-ready intelligence.
Modern data monetization models
For years, many organizations in sectors such as finance, healthcare, and advertising have built data monetization businesses by aggregating and reselling raw or anonymized data sets. Today, the landscape has fundamentally changed, shifting from data resale to intelligence-driven business building. On one side, increasing regulatory scrutiny and rising enforcement are making it more costly and complex to use personal data, particularly when clear up-front consent is needed. On the other side, AI models now require massive volumes of training data, and real-world data sets alone are no longer enough to meet these requirements. By 2026, three-quarters of businesses will use gen AI to create synthetic customer data, up from less than 5 percent in 2023.6
As a result, the data brokerage model is facing a multidirectional squeeze: Prices are falling as raw data becomes a commodity; access is tightening under regulatory pressure; and differentiation is eroding since synthetic data can deliver comparable performance at lower cost and risk.
To move up the DIKW pyramid, some companies offer analytics as a service—layering interpretation on top of their data. These offerings help clients make sense of complex data sets to support better-informed decisions and are often packaged as “insight platforms.” They curate and contextualize data to provide predictive recommendations. But insight is not intelligence. Insights organize and explain data, anchoring the knowledge layer of the DIKW pyramid. Intelligence goes further, using that data to recommend, predict, and increasingly, act—bringing organizations closer to the wisdom layer of the DIKW pyramid. The shift from interpretation to action in data monetization is where gen AI plays the biggest role.
We see gen AI helping companies create intelligent data products in two main ways: by delivering personalized content and by enabling real-time decision-making through agentic AI.
Personalized content
Gen AI enables data providers to act like always-on analysts, automatically generating personalized reports, commentary, and recommendations tailored to each customer.
For example, a leading automotive manufacturer built an AI-powered data analytics stack to strengthen its after-sales performance of parts and services. The solution integrates data from enterprise resource planning, customer relationship management, and external platforms into a “digitized installed base,” which is a digital record of all the components or equipment the company has sold, including which exact customers purchased them, in which demographic segments, and how they are being used. Based on this foundation, the company developed a lead engine to generate personalized product and service recommendations tailored to each customer’s life cycle stage. Gen AI was used to qualify leads and craft personalized outreach, which was then seamlessly integrated into the CRM system to enable sales teams to engage with potential customers supported by full context and suggested offers. This end-to-end system streamlined the automotive manufacturer’s sales motion and significantly improved commercial performance—driving a 15 to 25 percent increase in qualified leads and a 25 to 30 percent uplift in sales for parts and services.
Real-time decision-making powered by agentic AI
Organizations are increasingly embedding gen AI into live workflows to automate decisions and deliver intelligence in real time.
For example, financial-services companies are designing gen-AI-enabled data systems to support real-time decision-making. One example involves collections optimization. A bank created a gen AI tool to analyze data to identify the right customers to contact, when to reach out to them, and through which channels. By making these decisions instantly, the data tool boosts the chances of reaching the right person and securing a promise to pay. It’s a clear example of how gen AI can drive faster actions to improve real-time decision-making.
The next step will see gen-AI-powered data tools act on the insights they surface. And it’s already happening. Powered by agentic AI, data systems are evolving from real-time decision support tools into fully autonomous actors capable of coordinating tasks across complex environments. Instead of extracting data from one platform to pass to another, AI agents can collaborate in real time to orchestrate actions. Importantly, humans remain in the loop, providing oversight, monitoring, and feedback to ensure alignment with business goals and ethical considerations.
The ability to monetize data through intelligent AI agents is emerging as a competitive advantage. By acting autonomously, these agents can amplify existing revenue streams and unlock entirely new business models. In e-commerce, agents embedded into digital storefronts could analyze real-time user behavior and context to trigger intelligent upselling or cross-selling. Service providers could encapsulate domain expertise such as legal, tax, or procurement into autonomous agents and then commercialize them as white-labeled APIs, delivering “intelligence-as-a-product.” These types of capabilities are turning agentic AI into a critical foundation for business building.
Gen AI will impact the full data value chain
Organizations that treat gen AI as an enterprise-wide capability, not merely a bolt-on technology, can create monetization opportunities at every step of the data value chain, from data acquisition and preparation to model training, productization, and delivery. They can use gen AI to orchestrate more seamless data flows, make more accurate predictions, and do more targeted interventions. As these capabilities mature, they create a virtuous cycle of innovation, expanding the frontier of what is possible with data and intelligence to fully rewire their companies with gen AI.
Gen AI is reshaping how companies monetize their data all along the value chain.
| Stage | Example gen AI implementation | ||
| Data acquisition Identifying, collecting, and sourcing raw data from internal and external sources, including systems, sensors, and 3rd-party providers |
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| Data preparation and cleaning Processing, cleaning, and normalizing raw data to remove errors, ensure quality, and enrich context for subsequent analysis |
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| Model training and development Designing, developing, and training analytical or predictive models using processed data to uncover patterns and insights |
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| Productization and integration Transforming, embedding, and integrating models and insights into scalable products or applications for end users |
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| Delivery and action Delivering, deploying, and operationalizing insights to enable real-time decisions and embed intelligence into business workflows |
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While gen AI holds huge potential to help companies monetize their data, that doesn’t mean every organization has started the process. We see a typical three-stage maturity journey that organizations follow as they build AI-enabled data businesses.
- Internal optimization: Organizations focus on leveraging gen AI internally to improve decision-making, automate processes, and enhance operational efficiency. These efforts typically unlock significant cost and productivity gains.
- Opportunistic monetization: Having seen initial value internally, organizations begin to selectively monetize insights externally, often through tailored analytics products or by offering data-driven services to partners and customers.
- Full marketplace monetization: Leading companies build entire data businesses, packaging their intelligence as products and embedding them directly into external marketplaces. At this stage, data monetization becomes a stand-alone revenue stream, supported by robust go-to-market and commercial models.
Evolving through this three-phase monetization road map takes time, but CEOs can start by defining a strategy that assigns measurable goals to each step. Based on our experience building data businesses across sectors, we have identified six foundational elements that organizations can master to scale AI-enabled data businesses. These dimensions form the blueprint for transforming gen AI from a capability into a monetizable growth engine for data.
1. Strategy and product
The most successful data monetization strategies start with a sharp understanding of a company’s proprietary advantage. Whether this advantage is privileged access to high-quality data, deep customer knowledge, or domain-specific infrastructure, data product strategies should be grounded in what makes the organization uniquely positioned to win. Take the earlier example of Walmart’s Scintilla data tool. As the world’s largest retailer, Walmart has a scale of proprietary shopper data that few competitors can match.7
Companies that design a clear road map to identify where and how their data can drive the most impact are also well positioned to succeed. The most successful data monetization efforts treat it not as a side project but as a vehicle for business building. Data can be the foundation for scalable, intelligence-driven services that align with the company’s broader growth goals. This involves understanding the unique value of an organization’s data, prioritizing high-potential use cases, and aligning efforts with broader business objectives. Without this clarity, even the most promising data initiatives risk becoming misaligned with strategic goals. Leading organizations take a disciplined approach, ensuring that their data monetization efforts not only have an impact in the short term but are also sustainable over the long term.
2. Go-to-market strategy and growth
Commercializing AI-generated intelligence requires a go-to-market model that clearly articulates how value will be realized. Traditional software pricing models, such as flat-rate subscriptions, often fall short. When data products deliver continuously tailored intelligence—adapting in real time to user behavior, business context, and market shifts—rigid models feel increasingly out of step.
In this context, companies can offer more flexible pricing models for their AI-driven data products. Usage-based or outcome-based contracts, modular add-ons, and adaptive tiers that evolve alongside customer maturity levels are some sales models that work well. However, the biggest evolution lies beyond the sale itself. Post-sale engagement and customer success are becoming critical levers for growth. These functions help companies move beyond simply expanding user counts: They enable the next set of high-value use cases for their customers.
Customer success teams are shifting from support roles to impact-oriented partnerships, helping clients identify new applications, unlock additional value, and integrate gen AI into evolving data workflows. When it comes to developing data products to meet customer needs, continuous iteration through feedback loops, model retraining, and business co-innovation is becoming the norm. Meanwhile, companies are distributing their gen-AI-enabled data products through partner ecosystems, embedding them directly into third-party platforms to accelerate trust and adoption.
3. Technology and data
The next generation of data monetization demands that companies create performant data products built on robust, scalable, and cloud-native infrastructure. To do this, they can use modular architectures, automated data pipelines, and integration into existing products to enable real-time, decision-grade intelligence. Leading organizations are building agile data products capable of handling both structured and unstructured data, with the flexibility to adapt as new use cases emerge. In this context, gen AI agents are increasingly playing a pivotal role. Multiagent architectures further enable scalability, with agents working collaboratively to automate data preparation, streamline workflows, and support continuous learning and model retraining.
In one example, Bloomberg’s BloombergGPT tool is a 50-billion-parameter domain-specific large language model (LLM) trained on 40 years of the company’s financial data from news archives, filings, and pricing models. This AI platform is cloud native and designed to handle both the dense structured data sets and the vast unstructured textual information that Bloomberg collects.8
A scalable AI tech stack, paired with a fit-for-purpose ecosystem of partners, tools, and platforms, is essential for transforming data into monetizable intelligence. At the same time, robust data governance frameworks and responsible AI practices are critical to ensuring compliance, transparency, and trust—foundations for sustainable value creation. Without these guardrails, even the most advanced AI systems risk undermining stakeholder confidence and exposing the organization to reputational or regulatory risks.
4. People
Data monetization requires a team that blends technical expertise, commercial thinking, and product ownership. These teams, often spanning engineering, design, and go-to-market strategies, not only build the data product but also own its adoption, evolution, and impact. Unfortunately, the reality is stark: This type of talent is in short supply. The blend of skills needed to develop and commercialize gen-AI-enabled data products is rare, and many organizations lack the in-house capabilities to build and scale these products effectively. Organizations that succeed invest in both attracting external talent and unlocking the potential of internal teams by upskilling, delineating clear career paths, and providing outcome-based incentives.
For example, one leading financial institution built a deep bench of AI and data talent, forming a dedicated data chapter with over 700 data professionals. To ensure alignment and momentum, the bank also established a task force with senior executives to oversee gen AI adoption and drive accountability.
5. Operations and management
An experimental data product must at some point scale into a bona fide business. That means building the operations needed to get from point A to point B. From customer support and compliance to legal, finance, and performance tracking, companies can tailor each function to the specific demands of delivering real-time intelligence. For all gen-AI-driven data products, companies will need to include mechanisms to manage LLM governance, versioning, observability, and regulatory compliance. Organizations that prioritize operational design early on have a better chance at creating durable data products that can sustain rapid growth without sacrificing quality.
However, scaling a data product without addressing foundational questions of ownership and governance poses significant risks. As gen AI systems generate insights and intelligence, questions about data rights and intellectual property become critical. If a client feeds its proprietary data into a model, do they retain rights to the output? Can they reuse or resell that output? What if the model was trained on third-party content or built on external platforms? Without clear governance frameworks, companies risk monetizing data assets they cannot fully control—or worse, exposing themselves to legal disputes or reputational damage. Leading organizations mitigate these risks by designing ownership frameworks in tandem with product strategy.
6. Capital
AI-powered data businesses are capital intensive by nature. Beyond initial R&D, the true costs lie in training large models, maintaining performance in production, and delivering intelligence in real time. Infrastructure requirements are nontrivial, as well. Compute costs can spike rapidly as more data flows through the platform, particularly for models handling unstructured data at scale. Meanwhile low-latency delivery expectations will require demand-resilient, high-performance architectures. Gen AI models also degrade over time, requiring ongoing retraining and tuning to remain accurate and effective. Without disciplined capital allocation, costs can quickly erode monetization margins. Leading organizations thus approach funding as both a growth enabler and a margin safeguard—aligning capital deployment with product maturity, customer adoption, and technical scalability.
As organizations look to build and scale AI-powered data businesses, they must also address the associated risks, such as data privacy, intellectual property, algorithmic bias, and broader ethical considerations. To do so, companies can create new legal and risk roles specific to AI, as well as embed responsible AI frameworks into their organizational structures to ensure inclusive, human-centric AI development and transparent data usage. These frameworks define continual monitoring of AI tools and usage, guide data integration across platforms, govern personally identifiable information, and embed technical controls for high-risk use cases.
Owning the most data is no longer the ultimate competitive advantage. Data alone is increasingly seen as a commodity. Intelligence, on the other hand, is emerging as the new currency. Companies that continue to rely solely on selling raw data or building static analytics products risk being outpaced by AI-native challengers that are building data businesses designed from the ground up for intelligence delivery. To remain competitive, businesses must rethink how gen AI will impact data productization, working toward a future where AI agents do not just deliver data-driven insights but act on them autonomously.
Gen AI’s impact on data monetization could extend far beyond today’s models. We can imagine a future where self-learning data assets continuously adapt to market needs, gen AI agents orchestrate cross-industry data syndication, and marketplaces exist where AI agents negotiate, price, and deliver data to customers. These platforms will move beyond traditional volume-based pricing to embrace value-based data trading—where data is valued for the outcomes it drives, not just its size or scope. Furthermore, we see the emergence of synthetic data exchanges, where gen AI agents create and refine synthetic data sets that companies can use to reduce regulatory risk. Together, these shifts will transform data monetization from a static business into a dynamic, self-optimizing ecosystem.
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