Adrian Carr is CEO of global master data management provider Stibo Systems, which empowers companies through trustworthy intelligence.
One of our product teams finished in an afternoon what used to take the better part of a week. Nobody announced it. It just happened, the way most real change inside a company usually does.
That is roughly where a lot of organizations sit with AI right now. It works. People use it constantly, for meetings, drafts, code, decisions that used to take longer and even for figuring out how to word a response to a difficult colleague. What is harder to find is a company that can say, with any real confidence, what all of that is actually worth.
The scale of adoption is not in question. McKinsey has estimated that generative AI could add $2.6 trillion to $4.4 trillion annually to the global economy across the use cases it analyzed. Microsoft and LinkedIn found that three-quarters of knowledge workers were already using AI at work back in 2024. Can you imagine how much higher that percentage is now? And yet, I keep running into the same paradox in conversation after conversation with customers and my own peers: the more widely AI gets used, the less certain anyone seems that the business is actually getting value from it.
I think the confusion comes from treating AI value as one thing when it’s really several.
Latte Time
The first level is the most visible and the easiest to achieve. I call it “latte time” because it captures the small pockets of time AI gives back to us (enough time for a latte, coffee or tea).
AI helps an individual finish a task faster: a summary, a first draft or a set of meeting notes. The 30-minute task now takes 10. It is a genuine improvement to our workday. It is also, in almost every organization I have seen, rarely banked as an “enterprise gain.” Few managers reduce headcounts just because emails are getting written faster. The saved time tends to disappear into everything else competing for it.
Team Lift
The team lift is where all those individual gains start to become something a function can actually measure. A field experiment involving 758 BCG consultants, run with researchers from Harvard, MIT and Wharton, found that consultants using GPT-4 completed 12.2% more tasks, 25.1% faster and with higher quality on tasks inside the tool’s capability range. Separate research on more than 5,000 customer support agents found productivity gains of roughly 14%, concentrated among less experienced staff. This is real, and it’s where AI starts to touch the economics of a business.
The Missing Part Of The AI Value Equation
However, there is a cost side to this equation that gets far less attention than the benefit side. Every prompt and agent interaction shows up on a bill. It may be a few months down the line, but it will show up. As adoption scales from a pilot of a few hundred employees to thousands of AI agents running continuously across customer service, product data and supply chain workflows, token costs become a bigger deal. A team that becomes 15% more productive is a genuine win until the AI spend that produced it grows just as quickly.
I suspect most organizations will spend the next decade learning to govern AI consumption the way we spent the last two learning to govern cloud spend and data quality.
That leaves a harder question: what does net AI value actually mean? It is not just productivity gained. It is the combination of productivity improvements, revenue growth, risk reduction and operational benefits, minus the cost of running and scaling AI. BCG’s research found that only 26% of companies had developed the capabilities to move beyond proofs of concept into anything resembling tangible value. That gap is a value-design problem.
Should our work even exist?
Process reconfiguration is where I think the greatest advantage will come from, and it is also where the hardest work begins. It requires moving beyond using AI to make existing tasks faster and asking a more uncomfortable question: Should this work exist in its current form at all? With agents, real-time data and automated decision-making becoming more capable, some processes that have existed for years may no longer need to operate the way they do today.
Customer service AI models are resolving standard cases and only handing exceptions to the humans in the loop. In supply chains, product data is being validated and enriched continuously rather than in periodic batches. In compliance workflows, regulatory changes are being interpreted and embedded automatically.
But none of this works without data the organization actually trusts. AI can and does produce confident, smart-sounding and flat-out wrong answers, particularly when it is reasoning over data that is fragmented, duplicated or out of date. Trusted data (and the trustworthy intelligence it creates) stops being a back-office discipline in this world. It becomes the foundation this entire level depends on.
The tech isn’t the hard part.
I do not think the winners of the next decade will be decided by who adopts AI first, or even who adopts the most of it. Ten years from now, I suspect executives will look back on today’s AI rollouts the way many of us now look back on early cloud migrations: less as a question of whether the technology worked, and more as a question of whether the economics did.
The technology is not really the hard part anymore. The harder questions are strategic: what should we redesign, what should we eliminate and how do we ensure the value AI creates is captured rather than absorbed into existing inefficiencies?
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