Program Management in AI Transformations: What We Are Really Managing.

 As I looked for a topic to close out 2025, I found myself returning to program management, particularly in the context of AI and digital transformation programs, where its importance is often recognized only after momentum starts to slip.

Few initiatives expose weaknesses in program leadership as quickly as AI. These programs cut across data, technology, operations, and business teams. Expectations are high, timelines are compressed, and assumptions often go untested. Yet the fundamentals of program management in these efforts remain underfunded and undervalued.

What I have repeatedly observed is that AI programs rarely stall because of algorithms or models – they stall because of misalignment around expectations, focus, and timing. Here are some more insights.

>  Managing expectations: where most AI programs quietly go off track

Expectation management in AI initiatives begins well before the first model is built. Many programs start with ambitious goals, but imprecise definitions of success.

A request for an AI feasibility assessment or pilot often carries unspoken assumptions – production-ready solutions, measurable ROI, or a clear path to enterprise scale. In reality, those outcomes depend heavily on data readiness, integration complexity, governance, and change adoption.

Data, in particular, is frequently overestimated. Teams often discover late that critical data is fragmented, inconsistently governed, or simply not usable in its current form. What was positioned as a quick pilot, struggles because foundational assumptions were never surfaced early.

What you are really managing is alignment – between AI ambition, data reality, scope, and achievable timelines.

>  Managing focus: discipline matters more than sophistication

AI teams are naturally inclined to explore. Models can be refined, features extended, and architectures improved almost indefinitely. Without discipline, this curiosity can quietly undermine outcomes.

I have seen AI pilots expand into sophisticated technical builds while losing sight of the original business question they were meant to answer. The result is often an impressive solution in search of a decision.

In transformation programs, focus matters more than technical elegance. Clear, decision-oriented outputs, impact estimates, risks, and operational implications create momentum. Deeper technical work can and should follow once value is established.

What you are really managing is focus – ensuring effort remains anchored to both business and technical possibilities.

>  Managing timelines: protecting sponsor credibility

AI programs are especially sensitive to delays. Dependencies on data access, privacy approvals, business inputs, and validation cycles introduce uncertainty that must be actively managed.

Delays become issues when their impact is not communicated clearly and early. Sponsors are then left explaining slow progress without the context or options needed to manage expectations internally.

In AI programs, transparency around timing does more than manage delivery – it protects credibility, both for the team and for the leaders championing the initiative.

What you are really managing is trust – and the sponsor’s ability to lead the transformation with confidence.

A closing reflection

AI initiatives, more often, lose momentum quietly, through misaligned expectations, drifting focus, and unspoken delays. The programs that succeed are designed to surface reality early, force disciplined choices, and protect trust as complexity increases.

That, more than any model or platform, determines whether a program delivers value.