Short answer: because AI transformation is still transformation. The same forces that stall big change stall strategic AI initiatives. In this article we will explore those forces and see how to overcome them.
Where execution breaks
- Fuzzy goals. If teams don’t know what problem they’re solving or what “good enough” looks like they optimize for activity and features, not outcomes.
- Priority overload. When everything is “now,” nothing finishes and very little value is actually realized.
- Decision latency. Unclear owners, endless rehashing, and slow approvals stall momentum.
- Capacity myths. Plans assume 100% availability and number one priority; reality never works like that.
- Hidden dependencies. Systems, data, and customer experience are more intertwined than the plan admits.
- Change fatigue. Big-bang rollouts are a huge risk, overwhelming line teams already doing their day job.
- Misaligned incentives. Local KPIs conflict with enterprise goals (e.g., “cut cost” vs “invest to modernize”).
- Data quality gaps. You can’t automate ambiguity. Garbage in—Garbage out (GiGo) still applies.
- Governance theater. Blue key status without conflict resolution, risk ownership, or roadblock removal.
- Drive-by sponsorship. Leaders cheer from the sideline instead of clearing the path.
How to make it easier (what we run in practice)
- Define outcomes first. One page, in business terms. Get crystal clear on what problem, for whom, and how we’ll know it worked.
- Reduce WIP. Start with a POC, then a controlled launch sequence. Prove value with each step, write down what worked, improve it, and expand.
- Clarify decision rights. Who decides what, by when, using which inputs. Create a clear escalation path.
- Plan to capacity. Load only the effort you truly have; adjust scope or dates early (not after missed commitments).
- Make work visible. Lightweight build sheets (what connects to what) and cut sheets (how we’ll change it safely).
- Mobilize in small batches. Small repeatable changes flowing within the rhythm of the business→ build trust, understanding, and value. Avoid big-bang changes.
- Instrument the work. Ruthless transparency: outcomes, risks, blockers, burn-down, and “what’s needed next” on one dashboard.
- Set guardrails. Sponsor-approved guiding principles (e.g., like-for-like; first do no harm, no scope sneaks without a trade-off).
- Hold short huddles. Daily: raise risks, roadblocks, and resource conflicts. Raised in the meeting, resolved in the hallway.
- Fund what you’ll finish. Tie budget and people to shorter achievable outcomes; line up the next one after that, right now.
Bottom line: AI Transformations aren’t hard because people don’t care; they’re hard because systems and people are complex. In order to achieve your AI transformation goals, put in the right structure to reduce the complexity. Make the system visible, right-size the load, speed decisions and execution will flow.
Not sure where your strategy execution is braking down? Check if your PMO is holding your strategy execution back.
