AI Maturity Model and building your AI Strategy

Reimagine your data strategy and become an intelligence driven organization requires a change in mindset. There are many opportunities within many industry verticals for AI growth, a few examples below. 

• Retail - know your customer, personalisation, omnichannel optimisation, supply chain resilience and sustainability
• Manufacturing - Agile factories, remote monitoring, predictive maintenance, field logistics
• sustainability, food production and distribution, water, energy supply, transportation, ecosystems
• Health - clinical analytics, faster diagnosis, intelligent operations, patient journey
• Banking and insurance - fraud and risk, innovative insurance

AI is a disruptive technology and has various difference scopes. Using the  McKinsey’s Three Horizons Model of innovation first described in Baghai, Coley, and White in 2000, in The Alchemy of Growth it provided a useful taxonomy although it no longer applies with the advent of speed of new technological rollout. The horizon growth model is split over time 1 year, 2-3 years, 5+ years strategy. However it has some interesting things to consider 

• Operational (what must be done to stay in business) - reduce costs, optimise operations, maximize revenue streams, defend market share, improve customer satisfaction and automate processes. 
• Innovative  (what is a must do to remain current in a fast moving market) innovate within traditional industry boundaries, create new digital products and services , create new customer experiences and innovate for cloud.
• Transformational (How to define a new market) innovate across boundaries, redefine industry definition, create new customer needs  and grow through network effects.

So how do we think about measuring where we are. An AI Maturity Model model from a Microsoft presentation gives an idea on how progress is described. 

Creating patterns and knowing anti-patterns can help with building an AI strategy for customers. Considerations to also include are those for set up of an AI ethics committees. There is plenty to consider when building the disruptive agile AI approach and these are just a few areas to consider.