AI deployment in the corporate world is at a critical inflexion point, often stuck at the proof of concept (POC) stage. The need for translation between data science teams and business stakeholders means that corporates should consider appointing a Chief AI Officer (CAIO) to set strategy, support technology choices, and drive roll-out. As machine learning becomes business as usual, the ambition should be to make them redundant. But until you are there, a CAIO may be just the shock to the system that your business needs.
The Chief Executive has attended the conference, her senior team have had the briefings. The Board have asked the questions. Multiple startups have been invited in for coffee. The pilot projects are fascinating – if inconclusive. Chatbots have proliferated. Every team has some version of ‘How AI will transform the business’ written up for sales or strategy purposes. But somehow, projects remain stuck at the POC stage. Mobilising the IT and operational resources to deliver on wildly promising but slightly woolly business cases is proving complex. The ROI on the expensive data scientists and strategising workshops remains limited. Sound familiar?Getting AI live in the organisation is hard work. The challenge is two-fold. Firstly, expectations about the technology probably need resetting. That self driving car is still half a decade away and chatbots remain frustratingly easy to ‘break’. There is a hype blip to be lived through – but the opportunity is real. AtBest Practice AIwe have captured over 600 use cases for AI, many of which are implementable tomorrow, and over 1,200 case studies where this has happened in reality.The second critical challenge is that getting machine learning (the main driver of current AI) into operational production is hard. Getting to proof of concept is complex and costly: accessing the right combination of data science and domain expertise skills, identifying and optimising appropriate data sets and then managing the compute costs and technology is not a simple task. But it is often simpler and cheaper than the next stage of embedding the machine learning output in the business and wider organisation.
Overcoming these hurdles will get easier over time — each iteration reduces the challenges for the next one, and scaled roll-out will ultimately make this simply one tool amongst many. But the situation at the moment is analogous to the early days of the world wide web. To make change happen, many organisations turned to a Chief Digital Officer. Over time this became redundant as digital became something that every process had baked in. But, initially, they had a critical role: part prophet, part teacher, part strategist, part operator and part venture capitalist. To make AI work your organisation may need the modern equivalent: the Chief AI Officer. Broadly, CAIOs will be responsible for five things.1. Toset a visionof where the organisation should be going.
