Why AI Projects Stall and How to Turn Them Into Real Productivity Gains

Introduction

Artificial intelligence promises faster, smarter work, yet a growing number of companies report that their AI investments are not paying off. A recent report from the University of Manchester’s Productivity Institute highlights why the expected productivity boost often stalls and offers a roadmap for turning early‑stage efficiencies into sustained business results.

The Core Challenges

1. Up‑front Costs vs. Delayed Payback

• Organizations spend heavily on retraining staff, reorganising processes, and integrating new tools. These expenses are immediate, while the financial return may take months or years to materialise.

2. Task‑Level Gains Don't Scale Automatically

• AI can accelerate individual tasks, but those speedups rarely translate into higher output for an employee’s entire role or the wider organisation.

3. The “Ambition Gap”

• As Bart van Ark, professor at The Productivity Institute, explains: "AI's productivity potential becomes real when faster tasks lead to better performance across the business. The ambition gap lies between those individual efficiencies and sustained business results."

Closing the Gap: What Companies Must Do

| Action | Why It Matters | Practical Steps |
|--------|----------------|-----------------|
| Align AI with High‑Value Use Cases | Ensures effort is tied to strategic goals and real customer value. | Map AI projects to revenue‑generating processes, bottlenecks, or competitive differentiators. |
| Treat AI as a Workplace Redesign | Technology alone cannot change outcomes; people and processes must adapt. | Redesign workflows to embed AI outputs, create new roles (e.g., AI‑augmented analysts), and adjust KPIs. |
| Invest in Management and Skills | Skilled leaders can orchestrate the transition from isolated gains to enterprise impact. | Provide continuous learning programs, appoint AI champions, and embed cross‑functional governance. |
| Measure Beyond Task Speed | Traditional metrics miss the broader impact on quality, innovation, and customer satisfaction. | Track composite metrics such as time‑to‑value, error reduction, and customer‑experience scores. |
| Leverage Policy Support | Governments can reduce uncertainty and adoption risk. | Engage with policymakers for guidance on best‑practice AI adoption and potential incentives. |

A Quote to Remember

"The ambition gap lies between those individual efficiencies and sustained business results. Closing it requires firms to invest in management, skills and the redesign of workflows alongside the technology." – Bart van Ark, Professor, The Productivity Institute

Recommendations for Leaders

1. Start Small, Think Big – Pilot AI in high‑impact areas, then scale with a clear roadmap. 2. Create Cross‑Functional Teams – Blend data scientists, domain experts, and change managers. 3. Set Realistic Timelines – Expect a 12‑24 month horizon before measurable ROI appears. 4. Monitor and Iterate – Use continuous feedback loops to refine models and processes.

The Role of Policymakers

Policymakers can help by:

• Publishing sector‑specific AI opportunity guides.
• Offering tax incentives for AI‑driven workforce upskilling.
• Facilitating public‑private partnerships that share risk and knowledge.

Conclusion

AI projects may not deliver instant breakthroughs, but with patience, strategic focus, and a holistic redesign of work, the promised productivity gains can materialise. Companies that keep investing, align AI with core business objectives, and treat it as a catalyst for broader change are most likely to close the ambition gap and reap lasting benefits.

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Sources: The Register (30 Oct 2026) – "AI projects not paying off? Keep at it, says Uni Productivity Institute".