What problems do AI transformation projects face?

Most businesses are embarking on AI transformation projects, but the promise of an AI-enabled future doesn’t always match reality. The staggering 80% failure rate reported for AI projects suggests that it’s important to understand the systemic challenges of AI adoption and what problems these AI transformation projects will often face.

80% failure rate of AI projects

With an oft-quoted failure rate that’s twice that of standard IT projects without AI,  these transformations struggle with missed ROI, lack of proper deployment, goal misalignment, project abandonment and more. 

Let’s look into the root causes:

Challenges for AI transformation projects

• Pace of development vs innovation – There’s often a mismatch between rapid AI development cycles and slower business innovation cycles. Moving fast doesn’t always mean succeeding, and bringing the users and subject matter experts along on the journey can improve outcomes significantly.
• Technical engineering focus – When projects are led by tech for tech’s sake, you’re not aligning the AI/ML engineering to business value, and this creates failure points as well.
• Lack of domain centricity – Business context is critical to AI success, and insufficient domain knowledge within your AI teams can create breakage. Look for providers with expertise in your vertical.
• Poor guardrails & governance – Looking outside a purpose-built solution to try and mould a retail AI tool to your needs can open you up to additional vulnerabilities in the absence of responsible AI policies, bias mitigation and explainability.
• High complexity & low transparency – Technical issues can arise from the usage of black-box models, bringing interpretability issues, adding to technical debt and creating opaque workflows.
• LLM over-reliance – Some businesses are just trend-chasing with LLMs instead of finding the fit-for-purpose AI models made for their use cases. This is especially dangerous in research, biotech or controlled environments.
• Supplier ecosystems – AI transformation projects can also suffer from vendor lock-in, expensive hardware needs and a lack of modular tooling that makes scaling more costly.
• Scale infrastructure issues – There’s a belief that bigger models lead to better performance, but many project leaders are unaware of the hidden costs of scale and diminishing returns. Start small, prove your models and then grow.
• Data challenges – Poor data quality, labelling, governance, lineage and the hidden costs beyond training (data engineering, orchestration and validation) can pile up if left unchecked.

How AI transformation PMs can overcome these challenges

Work with a partner who will help you align your AI deployment with real business value, emphasise the need for cross-functional teams with domain experts and support you to invest in data curation and governance early. Look for tools like ours with explainable, modular and maintainable systems that allow you to start small, validate early and scale wisely.

A realistic, structured approach is key to AI transformation success. See how our recent work in biotech, agriculture and research is showcasing best practice from scoping to deployment.