Industry
AI Pilots Without Payoff
The Current State of Enterprise AI
Adoption is nearly universal. Returns are not. The 2025 surveys from RSM and McKinsey expose the same uncomfortable gap—and point to the same way across it.
The current state, in one diagram
AI is everywhere. Business impact is not.
Three years into the generative-AI era, the question is no longer whether companies use AI. It is whether AI changes how the business performs.
of organizations use AI in at least one business function
can point to any measurable impact on their bottom line
Source: McKinsey, The State of AI in 2025. Adoption has become ordinary; measurable enterprise value has not.
Adoption is no longer the story
Generative AI has stopped being an experiment. In RSM's 2025 Middle Market AI Survey, 91% of organizations across the U.S. and Canada reported using it—up from 77% a year earlier. McKinsey's global survey tells the same story: 88% now use AI in at least one business function, against 78% the year before.
The most common jobs remain grounded in what generative models do dependably: text generation and summarization, workflow development, forecasting and demand planning, and sales and marketing content.
| Use case | Organizations using it |
|---|---|
| Text generation and summarization | 49% |
| Workflow development | 45% |
| Forecasting and demand planning | 40% |
| Sales and marketing content | 40% |
AI has also moved into the core of operations: 58% use it in data analytics, 57% in IT operations, and 48% in customer service. By every surface measure, the enterprise has gone all in.
But adoption is not the same as advantage
Here the story turns. High usage has not translated into high value. In RSM's survey, 92% of companies using generative AI ran into trouble putting it to work. 53% felt only somewhat prepared, and 70% said they needed outside help to get real value.
| Stage | Share of organizations |
|---|---|
| Use AI in at least one function | 88% |
| Have begun scaling across the enterprise | ~33% |
| Report any measurable impact on profit | 39% |
| Have AI fully scaled | 7% |
| Qualify as genuine high performers | 6% |
Most companies are not failing at AI. They are stuck in the pilot—running something promising that never becomes how the business actually runs.
The scaling gap
The model is rarely the problem
When companies say AI is hard, they seldom mean the model is hard. They mean everything around it is. RSM respondents pointed inward—not at the technology, but at the organization's readiness to use it.
| Obstacle | Share citing it |
|---|---|
| Data quality issues during rollout | 41% |
| Lack of in-house expertise | 39% |
| No clear AI strategy | 34% |
| Data quality as a reason for unpreparedness | 32% |
None of those are model problems. A world-class model can still sit uselessly on top of a process no one redesigned, fed by data no one cleaned, and owned by no one in particular. AI gets added beside the work instead of built into it.
They redesign the work, not just the tool
McKinsey's small group of AI high performers—roughly 6% of companies—are about 3× more likely than everyone else to have fundamentally redesigned workflows around AI instead of bolting it onto what already existed.
| Use case | Measured outcome |
|---|---|
| Lead generation | More than $350M in leads across 45,000 customers and 8,000 untapped opportunities |
| Troubleshooting | First-contact resolution rose 50%; diagnosis fell from 30 minutes to under 1 minute |
| Scheduling | Technician capacity rose 40% while overtime fell 6% |
| Contract analysis | A contract agent saved more than €5M a year |
Each result is impressive alone. The deeper lesson appears when companies stop treating them as separate tools.
Connection is where the value compounds
The biggest gains come from linking use cases—letting one AI step hand off to the next across the service journey, so value adds up instead of sitting in silos.
One request, one AI-powered flow
Request arrives and is logged
AI scans technical manuals
The likely fault is suggested
Can it be fixed without a visit?
The right technician is dispatched
Spares are prepared in advance
The report is drafted automatically
Billing and vendor recovery close the loop
That is fundamentally different from a chatbot that answers questions. It is an operating layer for intake, diagnosis, scheduling, parts, reporting, and recovery.
Don't deploy one AI chatbot. Build an AI-powered service flow.
The workflow thesis
Where the value actually gets built
The constraint on enterprise AI is not intelligence—capable models are close to a commodity. The constraint is wiring: clean data, redesigned workflows, governance, real adoption, and people who understand both the business process and what the model can do.
That last ingredient cannot be downloaded. It is why 70% of companies say they need outside help—and why effective help looks less like a software license and more like an engineer inside the operation, mapping the workflow before writing a line of it.
That is the model Kunbyte is built around. We embed with a team, diagnose the workflow first, and build the connected flow—not a demo that dazzles in a meeting and quietly dies in production.
Five moves that separate scale from theatre
EY frames the shift from pilot to payoff as five practical disciplines.
| Move | What it means in practice |
|---|---|
| Scale through process, not tools | Redesign the whole workflow around AI; do not scatter standalone apps across an old one |
| Define value up front | Tie every build to a measurable KPI: cost, speed, accuracy, revenue, or risk reduction |
| Govern deliberately | Set enterprise-wide rules, decision rights, compliance, and real ownership |
| Make data AI-ready | Create clean inputs, traceable lineage, security, and human review where it counts |
| Run AI as a portfolio | Balance safe core bets against a few high-upside experiments |
The next winners will not have the most pilots. They will have done the unglamorous work of building AI into how they actually operate.
From pilots to payoffWorkflowDataGovernanceOwnership