Variance. Control. Adjacent Gains: AI’s Enterprise Value

Putting AI to use in an organization is as much about habit formation as it is about the technology implementation itself. Enterprise tech vendors talk about ‘change management,’ but the way ‘change’ is framed is the same way it was framed when they rolled out an ERP.

There are months of slow march towards a rollout and one dreary winter morning your organization cuts over to a new version of SAP. Change would be there, just as slippery as the black ice that you navigated to work.

With AI though, the change is different. It is gradual. It is every day. More importantly, it is a joyful kind of change. For the user, AI fills the gap, removes the grunt and makes the interface familiar, unlike software where change means learning new interfaces and new methods.

AI is intuitive in a way software never was.

AI creates value in places spreadsheets don’t naturally capture. A decision twin that reduces rework might not show up immediately in EBITDA, but it collapses hidden decision choke points. A retrieval layer that connects siloed knowledge doesn’t look like cost reduction or revenue increase. AI changes the quality of work that was sloppy to begin with.

Traditional ROI frameworks were designed for capital projects, not capability projects. AI doesn’t behave like a new machine on the plant floor. It behaves like a new way of working – one that alters throughput, reduces variance, and unlocks latent value that was previously trapped in institutional knowledge or inconsistent execution.

At Moative, we attempted to look at ROI for AI projects from a new perspective.

We come across variance reduction consistently. Variance tells you if the company’s performance is becoming more predictable, more stable, and more controllable. AI shines here. A single model that reduces decision inconsistency at a crucial point at a workflow by 30% often creates more enterprise value than a pilot project that automates scheduling candidate interviews. Pick workflows where variance reduction leads to outsized business outcomes.

Measure outcomes in terms of fewer executive hours needed for a decision or elimination of human judgement that needs middle management attention. Most work for leadership is just sloppy judgements that are pushed upwards for resolving.

The second aspect we see is control expansion. How long does it take to close the loop between signal and action? AI shrinks this everywhere: in scheduling, in forecasting, in customer support, in engineering change orders. But the real impact isn’t the hours saved – it’s the fact that faster loops allow more loops. Increased iteration is the true productivity gain. Velocity frees up time for new questions. Most departmental leadership time goes into banal work - chasing people, dashboards, and untangling mess that comes from variance at work. Remove banal work and replace it with outcome driven projects. Measure AI not by what time it saved but also by how the time is used. 

But here is the problem. Most of what I described above is viewed simply as “cycle time compression” and not “control expansion.”

Let me explain. AI that improves workflows tends to increase the surface area a team can handle without additional headcount. A planner managing 3 sites can suddenly manage 7. A support analyst who handled 20 cases a day can now oversee 50. This isn’t about replacing people. It’s about increasing the capacity of the existing system.

The other less intuitive aspect to look for to justify AI investments is adjacent gains.

Most pilots demonstrate isolated gains. Most orgs pilot AI at a function level. When they succeed they sequence the roadmap – one function after the other. We have talked about this before. Do pilots this way to demonstrate value but go enterprise-wide sooner. Unlock data across systems sooner.

Make the flywheel work. A string of adjacent gains compound value for the enterprise. One mini-rollout after the other, at one department at a time creates data and integration debt for your IT teams. They are not equipped. Besides, every new function throws new challenges that the sequence approach creates. Work is not defined by departments. Work simply flows. Its the human constraint that forced work into specialist functions. 

AI systems that conform to human-led management style and its constraints repress value – not worth building them.

To sum up,

1. Pick projects that deliver the best variance reduction at the highest leverage points in your workflows

2. Cycle time compression is great. But go beyond that and ask how the quality of your middle management has increased with fewer distractions for them

3. Allow adjacent gains to kick in fast. Don’t sequence your projects. They will never reach a steady state.

Enjoy your Sunday!

Sathish Rangarajan contributed to this article.