Why Many AI Experiments Fail and How to Avoid Them

Read Time: 5 minutes

Generative AI has attracted massive investment, yet most organizations are struggling to see results. Studies show that half of generative AI projects fail to deliver measurable returns. The challenge lies not in technology, but in how businesses approach adoption. Many leaders risk chasing hype without a clear strategy.

The Common Pitfalls

Adopting a “10,000 flowers bloom” approach, many organizations run numerous small pilots at once, hoping a few experiments will deliver significant returns. However, without a clear link to core customer needs or business priorities, these pilots often remain scattered, isolated, and fail to scale. Generative AI should be applied to solving meaningful problems and strengthening core operations, not just explored for the sake of innovation.

Smarter Approach for AI Adoption

AI experimentation works best when it is focused and designed with scale in mind. Instead of spreading resources thin, organizations can target a small number of high-value areas where AI can deliver real value today by:

• Identifying a few well-chosen use cases that improve internal operations or enhance customer service.
• Designing pilots to be low-cost with a "learning by doing" approach, but built to scale.
• Balancing quick wins with long-term goals to secure management support.

Scaling for Lasting Impact

Pilots alone will not transform your business. Scaling is what turns your experiments into measurable change by embedding AI into existing systems, governance, and business processes so that benefits can be sustained over time. We found that companies that succeed often rely on small, specialized teams with the resources and leadership support needed to accelerate adoption.

Crowe Artificial Intelligence (AI) Center of Excellence (CoE)

The AI CoE is a strategic initiative to advance both Crowe's internal adoption of AI and our client's AI journeys in a holistic way. It comprises lab, engineering, and enablement teams that foster the development of responsible AI, from data curation and quality to AI governance, transformation, and the development of purpose-built AI solutions.