This episode features Daniel Hulme, founder of Satalia and chief AI officer at WPP. What is AI good at today? And how can organisations increase the likelihood of deploying AI successfully?02.55 What is AI good at today?03.25 Deep learning isn’t yet being widely used in companies. Executives are wary of self-adapting systems04.15 Six categories of AI deployment today04.20 1. Automation. Using “if … then …” statements04.50 2. Generative AI, like Dall-E05.15 3. Humanisation, like DeepFake technology and natural language models05.40 4. Machine learning to extract insights from data – finding correlations that humans could not06.05 5. Complex decision making, aka operations research, or optimisation. “Companies don’t have ML problems, they have decision problems”06.25 6. Augmenting humans physically or cognitively06.50 Aren’t the tech giants using true AI systems in their operations?07.15 A/B testing is a simple form of adaptation. Google A/B tested the colours of their logo08 .00 Complex adaptive systems with many moving parts are much riskier. If they go wrong, huge damage can occur08.30 CTOs demand consistency from operational systems, and can’t tolerate the mistakes that are essential to learning09.25 Can’t the mistakes be made in simulated environments?10.20 Elon Musk says simulating the world is not how to develop self-driving cars10.45 Companies undergoing digital transformations are building ERPs, which are “glorified databases”11.20 The idea is to develop digital twins, which enable them to ask “what if…” questions11.30 The coming confluence of three digital twins: workflow, workforce, and administrative processes12.18 Why don’t supermarkets offer digital twins to their customers? They’re coming14.55 People often think that creating a data lake and adding a system like Tableau on top is deploying AI15.15 Even if you give humans better insights they often don’t make better decisions15.20 Data scientists are not equipped to address opportunities in all 6 of the categories listed earlier15.40 Companies should start by identifying and then prioritising the frictions in their organisations16.10 Some companies are taking on “tech debt” which they will have to unwind in five years16.25 Why aren’t large process industry companies boasting about massive revenue improvements or cost savings?17.00 To make those decisions you need the right data, and top optimisation skills. That’s unusual17.55 Companies ask for “quick wins” but that is an oxymoron18.10 We do see project ROIs of 200%, but most projects fail due to under-investment, or mis-understandings19.00 Don’t start by just collecting data. The example of a low-cost airline which collected data about everything except rivals’ pricing20.15 Humans usually do know where the signals are22.25 Some of Daniel’s favourite AI projects23.00 Tesco’s last-mile delivery system, which saves 20m delivery miles a year24.00 Solving PwC’s consultant allocation problem radically improved many lives25.10 In the next decade there will be a move away from pure ML towards ML+ optimisation26.35 How these systems have been applied to Satalia28.10 Daniel has thought a lot about how AI can enable companies to be very adaptable, and allocate decisions well29.00 Satalia staff used to make recommendations for their own salaries, and their colleagues would make AI-weighted votes29.30 The goal is to scale this approach not just across WPP, but across the planet30.35 Heads of HR in WPP operating companies love the ideaDaniel's entry on Wikipedia: https://en.wikipedia.org/wiki/Daniel_J._HulmeAudio engineering by Alexander Chace.Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration