AI initiatives aren't failing because of the technology. They’re failing because your foundation is broken.

In 2026 I'm seeing a recurring pattern across enterprise tech. Stalling initiatives. Pulled budgets. Pilots are languishing in delivery debt.

The Problem

Most organizations are running at ~50% efficiency due to structural failures. When you drop AI, a massive accelerant, onto a dysfunctional delivery machine, you don’t get innovation. You get amplified chaos.

It's like trying to win an F1 race in a car missing second and third gear. You can have the best fuel (data), the best driver (talent), but a broken transmission will stop you crossing the finish line.

The Five Gears of Delivery Recalibration

To make your AI strategy work, first recalibrate your delivery foundation. Think of it like shifting through the gears in your car. To get quicker, we need to move from one gear to the next, sequentially.

Gear 1: Map Delivery

Gain portfolio-level visibility into where work is being done and where dependencies are blocking your top initiatives.

Gear 2: Increase Strategic Throughput

The "everything is high priority" lie is killing your AI initiatives. Strategic throughput requires the "Hard No" - a Kill/Pause list that clears the tracks so valuable work can get done.

Gear 3: Streamline Delivery

Move away from shared, fragmented resources to stable, cross-functional teams that can build context, make quick decisions, and ship reliably.

Gear 4: Grow Predictable Delivery

Replace firefighting with stable long-term planning and "just-enough” governance to handle unexpected priority shifts.

Gear 5: Perfect ROI Capture

Only when you have a strong delivery engine can you safely scale winning AI experiments and pivot quickly based on real emergent value.

The Bottom Line

Before you write another AI strategy deck, before you launch another pilot, before you hire another data scientist, ask yourself: Can we actually get things done?

Because if the answer is no, your AI transformation isn't going to work. Not because AI is hard. But because your delivery practice isn't ready for it.

Fix the foundation first. Then build the future.

‍