Interviewed at innovation Day, Cambridge Consultants' Head of Artificial Intelligence, Tim Ensor, discusses how businesses can approach the challenge of deploying AI. Tim introduces the concepts of Minimum Viable Intelligence and continuous learning to improve AI performance. One area or particular interest is teaching a system to have a human-like sense of intuition, so that they perform better in the highly complex challenges that face businesses today. So today we've been talking about understanding and harnessing the potential of AI, most interested in that latter part, so where does a business begin harnessing the potential of AI? Tim: So it's an important topic and lots of people who we speak to are trying to grapple with that, and so we're proposing an approach which is very much wrapped up with this idea of Minimum Viable Intelligence. So as you can imagine from that phrase its borrowing some of the ideas from agile software development, but the fundamental idea is that you build and develop your initial version of an AI system with enough capability that you can launch something, get that into the market and deliver some value for customers. Now, in the sphere of AI we have a slightly different need for doing that than some of the kind of agile development methodologies which tend to use it, and that is that by getting something into the field, we can start collecting more data, and by collecting more data from users actually using a system, you can then feed that back into the training of your AI systems and continually improve that performance.

Artificial intelligence (AI) has been a hot topic among IT and business leaders as it promises to be the biggest driver of change in human history. The way we work, live, learn and play will never be the same once AI is infused into all of our devices, cars, appliances and everything else we interact with. CIOs are well aware of this and are looking to use AI as part of their digital transformation strategy. One of the challenges is that people often overestimate what an AI can do and they expect perfection. If there are any mistakes at all, it's back to the drawing board to refine the algorithms or spend more time in the learning phase.