Why most AI strategies fail before they scale
Why most AI strategies fail before they scale
Mark Simpson contributed a guest article to EnterpriseTalk on why AI strategy stalls even when organisations invest in tools.
The article covers:
Why low employee usage (including low confidence figures cited for the UK) is often treated as a skills gap when the deeper blockers are systems, processes, and ways of working
Treating AI as a business transformation programme with governance and operating models, not only a technology rollout
Fragmented adoption, siloed teams, and bolting AI onto outdated workflows that produce inconsistent data and erode trust
Reframing AI from pure automation to augmentation: human oversight, two or three high-value use cases, and working backwards on data and process needs
Structured experimentation (clear processes, human–AI collaboration, orchestration layers, and culture that embeds AI in everyday work) instead of disconnected pilots
