Year of AI Utility: Moving From Early Wins to Long-Term Value
AI pilot projects tied to an expanding number of use cases are proving themselves and moving into production. While challenges are ever-present, we’re no longer at the point of asking whether AI has business value. We’re at the point of asking how to build an AI strategy that maximizes that value.
Results from a recent report, “The Radical ROI of Gen AI,” which surveyed more than 3,000 organizations across nine countries, found that organizations’ AI efforts are paying off quite nicely.
In fact, of the 1,900 respondents who are building and using AI solutions today, 92% said that they’re already seeing return on investment (ROI) from their AI projects. That’s an amazing number, and if you dig into it a little more, the early adopters who are measuring ROI report that they’re seeing a 41% return on their AI investments, or $1.41 back on every dollar spent on AI.
While this widespread ROI is exciting, it’s important to note that many of these early projects are only scratching the surface of AI’s potential. As a result, organizations are investing more in AI to continue on this positive trajectory and identifying the complex use cases that will deliver even more value across their businesses.
It’s clear that AI is making teams faster, better and more cost-efficient. But this success brings its own set of strategic challenges. Namely, enterprises find themselves at a crossroads: what, exactly, to do next to build on the momentum.
The stakes are getting higher to prove that AI investments create value, and leaders face difficult decisions on where to prioritize their efforts moving forward.
Our research found that 71% of early adopters say they have more potential use cases to pursue than they can fund, 54% say it’s hard to make the right choices and 59% agree that pursuing the wrong use cases could cost them their job. In other words, the stakes are getting higher to prove that AI investments create value, and leaders face difficult decisions on where to prioritize their efforts moving forward.
It’s not even as “easy” as just choosing the right use case, either. AI projects are complex, so leaders need to evaluate them against a variety of factors like cost, staffing resources, technical constraints and more. While organizations can have limitless good ideas on where to implement AI, those ideas also need to be feasible from both a technical and resource perspective.
Nearly every one of the AI adopters in our survey — 96% — reported that at least one component of their AI initiatives cost more than anticipated last year. The trouble spots varied, but the three most common were compute cost overruns (for 64% of orgs); cost of supporting software (61%); and data collection, labeling and processing (58%). Cost overruns when implementing a new technology are not surprising, and despite such costs, nearly everyone said the overall returns were positive. Still, a key aspect of long-term strategy is making success predictable, repeatable and scalable — and avoiding budget surprises.
Building the Foundation for Enterprise AI
The data leaders I talk to — CTOs and chief data officers at the forefront of AI adoption — have seen the early wins from AI, but now they’re asking themselves a more complex question: How do we build an enterprise-wide foundation that can sustain and scale these successes?
As projects move into production, it’s no longer feasible to maintain disparate apps and tools — AI tools must be integrated seamlessly to where users are already working.
The answer lies in moving beyond isolated proofs of concept toward a comprehensive data and AI strategy that aligns with the way people actually work. As projects move into production, it’s no longer feasible to maintain disparate apps and tools — AI tools must be integrated seamlessly to where users are already working. This reality is driving organizations toward truly unified data platforms that can support AI initiatives across the enterprise and allow them to build AI tools where their data already lives.
Our research confirms this strategic shift. Among early AI adopters, 81% are increasing their investments in cloud data platforms, with an average expected increase of 24% in 2026. They’re prioritizing three critical capabilities:
- Security (84% rate it critical or important)
- Advanced AI functionality (84%)
- Integrated analytics capabilities (84%)
These priorities reflect a collective understanding that successful AI deployments require:
- A unified data foundation: When users seek information with their AI tools, they don’t distinguish between structured and unstructured data — they just want accurate answers to their questions. As a result, organizations need an AI-ready platform that breaks down traditional silos between all of their data. This isn’t just about storage — it’s about creating an environment with built-in governance, security controls and information retrieval capabilities.
- Methodical implementation: Success comes from starting with internal use cases where risks are lower and learning curves are manageable. This approach allows organizations to develop reliable systems and metrics before expanding to external applications.
- Adaptable architecture: The platform must be flexible enough to accommodate rapidly advancing AI capabilities while maintaining consistent security and governance standards.
This strategic foundation simplifies everything that follows — from protecting sensitive data to ensuring compliance and streamlining development to supporting end-to-end workflows in a secure environment. Most importantly, it creates a springboard for scaling AI initiatives throughout the enterprise, allowing organizations to tackle their backlog of high-value use cases with confidence and consistency.
Positioning for Long-Term AI Leadership
Organizations with some successful AI projects under their belts have tremendous faith in the future of AI. Particularly, many of these organizations are already starting to think about how they can harness the next wave of AI innovation: agents.
Despite how new agentic AI is for many, leaders are already evaluating which use cases could be handled by agentic AI in the near term. With longer memory, more sophisticated reasoning capabilities and the ability to take action toward a specific purpose, leaders are catching on to the massive potential of AI agents and trying to capitalize on it early. Undoubtedly, agents will create the biggest disruptions in the AI space this year.
We’re still in the very early innings of this amazing transformation. The capabilities of AI solutions are continuing to increase, and the costs are continuing to decrease. AI tools are becoming more responsive and adaptable, and at the same time, more autonomous. The opportunities to transform the way we live and work are huge.
But the industry leaders who will take us to that future are the ones today that are strategizing for the long game, building the infrastructure to support the data and models that make AI more than an occasional high point in their IT landscape.
