10-step AI enablement model

Jumping into full-scale AI implementation without testing is high-risk and costly, and with the number of abandoned AI initiatives doubling in 2025 (up from 25% to a staggering 42%), experimenting is absolutely critical to ensure potential can be translated into value.

 

Running proof of concepts (POCs) and then small-scale AI pilots allows businesses to test, measure, and refine AI solutions before widespread adoption, reducing risks and ensuring AI is fit for purpose through real-world results.

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The key to any successful tech experiment is in the metrics - anecdotal reports of time saved and ease of use will be great, but to secure implementation funding you are going to need hard facts.

 

And lets not forget the PR campaign that needs to accompany your trial - a successful pilot can generate excitement and a sense of 'demand' from your teams, all of which will support a successful business case to scale. Don't be scared to report a failed pilot either - being honest about a product that doesn't deliver value will engender trust in your overall AI innovation programme and dispel any fears that your organisation is just 'jumping on the AI bandwagon'