Most businesses are using AI. Few can prove what people are doing with it.
One of the tensions facing regulated SaaS leaders today is that AI adoption is accelerating, but clarity and value creation are lagging far behind.
McKinsey reports that almost 90% of companies are experimenting with AI, but only 7% are scaling it enterprise-wide.
HBR Analytic Services has found that only 16% of organisations report a high degree of measurable value from AI.
Adoption is creating more challenges
The issue is no longer whether people are using AI - in most businesses, they already are. Teams are using new tools to research, write, code, support customers and speed up everyday work.
For leaders, that creates a tough question.
Can you see where this activity is helping the business, where it’s creating risk, and where it needs tighter control?
This matters because AI usage is spreading faster than measurement, ownership and governance. What starts as useful experimentation can quickly become fragmented, duplicated or impossible to manage.
Activity is not the same as value
High adoption does not automatically mean high impact. Gartner found that only 28% of AI use cases in infrastructure and operations fully succeed and meet ROI expectations. The same study found that 20% fail outright.
That should make leadership teams pause. AI activity can look impressive from a distance, but without baselines, success measures and commercial ownership, it becomes hard to know what’s actually working.
Teams may be saving time. They may be producing more. They may be improving specific workflows. But unless those gains are measured against business outcomes, leaders are left with anecdotes rather than evidence.
Control needs to keep pace
The risk side is just as important. Gartner has warned about AI agent sprawl, with only 13% of organisations believing they have the right AI agent governance in place.
For regulated SaaS businesses, weak visibility is not just inefficient. It can become a governance problem, a customer trust problem and a commercial problem.
As people use AI in more workflows, leaders need clearer rules, stronger ownership and better evidence of what is happening across the business.
Three questions leaders need to answer
The leadership question is not simply, “Are we using AI?” It is:
Where are people using AI to create measurable value?
Where is AI-enabled work increasing risk?
What should we stop, fix or scale?
Those questions are difficult to answer through isolated dashboards, disconnected pilots or informal feedback. They need a structured view across value, risk, adoption, ownership, delivery and measurement.
A clearer way forward
That is why Scail created the AI Risk & Value Scorecard.
The Scorecard gives regulated SaaS leaders a practical way to understand how AI is being used across the business. It looks beyond activity to show where teams are creating value, where control needs to improve, and where leadership action is needed next.
The aim is not to slow teams down. It is to help them use AI with more clarity, confidence and commercial purpose.
Most businesses are now using AI. The advantage will belong to the ones that can prove what their people are doing with it.
Read more about the AI Risk & Value Scorecard.
