Michael Wade’s Post

I like the very comprehensive and relevant set of KPIs. However I struggle to understand how to truly assess comprehensive value created, across the 4 domains. It certainly isn't a formula that can provide it, buty would be great to have some guidance on how to approach the exercise...

Interesting read🤠

I think a good number of organisations and employees - probably in the high 80% - are not sure what they do at work, and how they provide value to the world. It's not like working in a restaurant, where you serve dishes to customers - they feel happy and leave. So you know the better the food, the better the service, the more adjusted the pricing, the more the "flywheel" keep running. AI can rapidly becomes a huge amplifier of confusion for knowledge work. If you're not sure what you build, create and why you do it - making if faster, more or bigger won't help. Having a 100 bullet point list, does not make it easier to choose an option. Up until this first clarity is not established, it will feel like the ROI is always out of reach.

My reading..

Great to see a practical focus on KPIs for AI — so often organizations invest in AI without clear measurement frameworks, making it difficult to prove ROI or optimize impact. This comprehensive, multilingual KPI map, supported by real cases, bridges the crucial gap between AI ambitions and measurable business outcomes. What’s especially exciting is that this initiative reflects some of the most interesting developments coming from one of the leading academic thinkers (Michael Wade) among top business schools alike. It shows how cutting-edge research is being translated into actionable tools that can help executives and teams make data-driven decisions and accelerate value realization from AI. Thank you for sharing this valuable resource openly — democratizing knowledge like this is essential for advancing AI adoption at scale.

Nice 🙂

KPIs are very important but equally important is: causality. An ROI is a causal metric: Return (consequence) on Investment (Cause). If you do not use the right tools (causal inference / Causal AI), you rely on correlation and mis allocate your budget. We fill this gap with Enlighten Advisory and Konstantinos Apostolatos as we wrote here: https://www.imd.org/ibyimd/artificial-intelligence/how-causal-ai-can-improve-your-decision-making/

Thanks for sharing, Michael

Thanks for sharing this Michael. That 80% failure rate on digital transformation projects due to inadequate KPIs really hits home - most organizations are flying blind on their biggest investments. The four-category framework (operational efficiency, workforce engagement, customer engagement, value creation) makes sense for creating accountability around transformation outcomes.