The 20% Capturing 74% of AI Value: What Are They Doing Differently?
Mitch Kwiatkowski, Techstra Solutions’ Head of Data & AI, has a proven track record of delivering measurable ROI from data and AI initiatives. Here is his latest in a series of articles on the topic.
In my last article, I shared reasons why most AI programs struggle to prove ROI. However, “most” is not “all”. Based on a 2026 survey by PwC, almost three-quarters of the economic value produced by AI initiatives can be found in only 20% of organizations. This is a gap that continues to grow as those same companies continuously invest in AI and scale success.
So, what are those top 20% doing differently?
They govern first and scale second.
When many people think of governance, they think of burdensome processes, slow turnaround time, and ways for someone with power to say “no” (i.e., control). It’s tempting to assume this would be the case for AI, especially during a race to innovate. However, the data seems to indicate that the small percentage of companies who say they have a comprehensive AI governance program are among many of the same companies in that top 20% of ROI generators.
Governance isn’t about saying “no” but rather how to say “yes” while ensuring things are done the right way, and for the right reasons, the first time.
They treat use cases like capital allocation.
Companies are rushing to build for flashy, high visibility use cases rather than the ones that deliver economic value by addressing high cost, repetitive, or manual processes. Top performers will certainly go after some of those exciting use cases, but that is the exception, not the norm. Robust intake and prioritization processes will scrutinize requests much like a finance team will run a capital budget. They require a business owner, a business case, and quantitative data to support the investment of time and money. If the business sponsor can’t explain why an initiative is needed or what it will bring to the organization, it’s probably not worth starting.
They have an AI operating model.
A prototype or pilot can get done with a small team and a champion. Production AI requires an operating model to support named ownership, monitoring, retraining processes for when the model drifts, support, training, and escalation paths when things don’t work as planned. Organizations that skip those things will end up with AI that worked beautifully in a demo environment but falls apart in the field. The top 20% builds these production components as part of muscle memory and patiently wait for the project to prove itself.
They measure value like other investments.
Demonstrating the value of AI requires a measurable KPI, a dollar figure, and a baseline before the build starts. The top 20% doesn’t treat ROI measurement as a special, AI-specific challenge. Instead, they use the same discipline they would apply to any capital or operating investment.
The gap is compounding rather than closing.
Every quarter that an organization defers governance, disciplined use case selection, and value measurement, the gap between it and the top 20% grows wider. Those leading organizations are compounding their learning while the rest of the pack debates the results of pilots and struggles to get them past that.
The Bottom Line
Some of the leading organizations might have more money to invest in AI, but that’s not what’s making them more successful. The difference is that they don’t treat ROI as something to prove after the fact. They start building it in from the start.
About the Author
Techstra Solutions’ Head of Data & AI, Mitch Kwiatkowski, is a recognized leader in enterprise transformation. Over the course of his 20+ year career, Mitch has led data and AI initiatives that have delivered $250 million in measurable value. His expertise includes data and AI governance programs, enterprise data strategy and architecture, data product development and commercialization, operating model and portfolio optimization, and digital ethics and risk management. Mitch is passionate about responsible AI adoption and helping organizations move beyond pilots and proofs of concept to delivering sustainable value at scale.