I have spent the past two decades building and scaling operations-intensive businesses, including founding freshlyAcquired by Nestlé in a deal valued at approximately US$1.5 billion, currently in a leading position pet persona rapidly growing veterinary clinic platform with over $150 million in capital backing. Amid these experiences, one lesson becomes increasingly clear: When a new technology meaningfully changes what is possible, organizations must rethink the way they operate to capture its full value.
Executives and boards across industries have expressed similar concerns. After investing billions in AI initiatives, many organizations report little measurable return. This frustration is real and well documented. According to PwC’s Global CEO Survey, Which wealth Already reported before56% of companies said AI has yet to deliver cost savings or increase revenue, with only about 12% reporting gains in both areas.
talk with wealth In Davos, Mohamed Kande, global chairman of PwC, argued that the gap lies not in AI capabilities but in execution, noting that many companies have “forgot the basics” including clean data, rigorous processes and governance.
Many leaders have concluded that AI is failing to live up to expectations.
This conclusion is wrong.
The problem is not technology. This is how leaders frame opportunities and measure success.
Most companies deploy AI from an efficiency perspective. They ask where it can reduce labor, automate workflows, or provide quick returns within existing organizational structures. They then evaluate those efforts using traditional return-on-investment metrics designed for software tools or layoffs.
This approach misunderstands what AI actually changes.
Artificial intelligence is not just a better way to do the same job. This is a new economic input that reduces the marginal cost of high-quality analytical and intellectual labor. Most organizations are only beginning to understand the consequences of this shift.
Integrating human intelligence into time as a new unit of work
Every major business transformation of the past century has followed the same basic pattern. Basic inputs become cheaper and usage increases exponentially. During the Industrial Revolution, falling energy costs transformed mechanical power into efficiently cheap machine hours, allowing machines to increase manual labor on an unprecedented scale. Recently, cloud computing has reduced computing costs, storage has become virtually unlimited, and digital distribution has gone global overnight.
Artificial intelligence now represents the next turn in the economic wheel. It is driving the marginal cost of high-quality thinking toward zero.
To clearly describe this transformation, it helps to name it. I call this “Synthetic Human Intelligence Time” (SHIH).
Synthetic human intelligence time is high-quality analytical and intellectual work generated by artificial intelligence at near zero marginal cost and deployable at scale. They are not artificial beings. They are synthetic intelligence capabilities. A new unit of productive effort.
Once you look at AI from this perspective, the confusion around adoption starts to make sense. Organizations are trying to impose a technology capable of creating “synthetic human intelligence time” onto systems designed for scarce human attention.
This mismatch is clearly visible in the data. MIT research report based on its 2025 State of Artificial Intelligence Business Study, Which wealth Also coveredfound that only about 5% of comprehensive AI pilot projects provided measurable value, while about 95% showed no clear financial impact. Researchers describe this gap as the “GenAI gap.”
The report further explains that most failures stem not from the models themselves, but from poor integration with actual workflows, over-reliance on common tools, and the tendency of companies to treat AI as stand-alone experiments rather than embedding it into core operations. Findings are based on interviews, employee surveys, and analysis of real enterprise deployments.
This statistic is often cited as evidence that artificial intelligence doesn’t work. A more accurate explanation is that leaders are measuring the wrong things. They are using efficiency-based metrics to evaluate investments in capacity expansion.
This was a leadership failure, not a technical failure.
What is it really like inside a company?
At Petfolk, we currently operate in 36 veterinary clinics and are scaling to hundreds more as part of our $150 million-plus effort to fundamentally disrupt veterinary medicine. Our Territory Managers are responsible for nearly all aspects of store-level performance within their territory: revenue, workforce, scheduling, inventory, purchasing, care quality, compliance, patient outcomes, pricing, customer experience, team development, retention, training and culture.
Each regional manager is responsible for literally thousands of micro-decisions each week and is informed by hundreds of reports, dashboards, audits, reviews and operational signals. All of this ultimately depends on the performance of individual clinics.
Today, a good district manager may spend forty to fifty hours a week reviewing reports, identifying problems, and providing support to clinic leadership. Even with good analysts, work is limited by time. Tradeoffs are inevitable. You sample the data instead of checking everything. You go deep in some areas and deep in others.
Our goal next year is to fundamentally break this limitation.
We are building artificial intelligence agents to generate synthetic human intelligence time alongside our regional managers. Our goals are simple yet radical. We want to transform a 40 to 50 hour work week for humans into the equivalent of 500 hours per week for analytics without requiring humans to work anymore.
The regional manager still works forty hours. The remaining 460 hours are SHIH.
These agents will review every invoice, every schedule, and every inventory decision. They will analyze each NPS score, eNPS score, Google Reviews, performance indicators, etc. Not only do they compare results week-by-week, but they also compare results across time frames, groups and locations. They will develop customized development plans for individual team members through our entire learning and development library.
All this intelligence is synthesized and delivered to the regional manager. Human beings decide what is important. Humanity comes first. Humans communicate and lead.
Functionally, the role has changed. Territory managers no longer rely on the analytical skills of one person to operate. They are operating with people who previously would have required an entire team of analysts.
We would never have tried to do this in the past. Not because it’s not valuable, but because it’s financially impossible. The cost of manual analysis makes it unscalable.
Artificial intelligence changes the equation.
Why ROI missed the point early on
One reason many leaders are frustrated with AI is that such changes are not clearly or immediately reflected in financial results.
Turning on artificial intelligence time does not immediately reduce costs. It doesn’t automatically increase revenue the week it’s deployed. In the early stages, the benefits are subtle. Decision-making gets slightly better. Catch patterns earlier. The team keeps improving. Waste is reduced quietly, not dramatically.
This is not a flaw. This is the nature of composite systems.
The rewards of expanded intelligence capabilities accrue over time. Like any compound effect, they appear small at first and almost invisible in isolation. But in the long run, they dominate outcomes.
Organizations that evaluate AI based solely on short-term efficiency metrics will miss this point entirely. Organizations that view SHIH as a composite advantage will design for durability rather than direct optics.
This disconnect helps explain why PwC also found that CEO confidence in revenue growth is at a five-year low. Weak returns from AI are fueling broader strategic uncertainty, not because these tools lack power but because organizations have yet to be redesigned around them.
The benefits manifest not as a single project but as better decisions repeated thousands of times.
Important questions now
As the marginal cost of thinking falls, the scope of what organizations can analyze expands dramatically. The competitive gap will not be between companies that automate faster and those that automate less.
The competition will be between those companies that continue to think about efficiency and those that redesign around capacity and compounding advantages.
Artificial intelligence will not replace humans. It will redefine what small, focused teams can accomplish.
The question leaders should be asking now is not where they can cut costs.
Here it is: If high-quality thinking was virtually free, how many hours of synthetic human intelligence would you deploy, and what problems would you end up solving?
The views expressed in Fortune opinion pieces are solely those of the author and do not necessarily reflect the views and beliefs of: wealth.