#223: Thinking about AI and strategy in 2023 Getting the tactics right and driving value across the organisation

In this latest episode, Felipe takes an in-depth look at the strategy and tactics behind AI and modelling, and looks at where organisations might be driving through the year. One of the first things to understand is the difference between tactics and strategy. Strategy is the broad view – the understanding of where the organisation wants to be, while tactics form the pathway on how to get there. Too often organisations mix tactics and strategy up, and allow a narrow focus to dominate their approach to data, models and AI. By looking at the big picture, 2023 will see an explosion in the number of models that are created, and the proliferation of AI and machine learning across the enterprise. Currently, the focus is on a small team of data scientists creating models of high value, but the future will see the number of models being created balloon out to thousands, driven by AI across the organisation. There will also be an ongoing trend that more people across the organisation develop a basic understanding of models and AI, so they can deploy and monitor these models within their own teams. The question then becomes what does this mean to the data scientists? As we discuss, the role of data scientists will remain as critical as ever in creating those big-value, transformative models and managing the change management across the organisation. Indeed, as teams are increasingly able to create the smaller models for themselves, the role of the data science team as disruptive innovators is only going to become greater. Tune into the podcast for these insights, and many more. Thank you to our sponsor, Talent Insights Group! Join us in Sydney for OpsWorld: https://www.datafuturology.com/opsworld Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What We Discussed: 00:00 Welcome to Data Futurology 02:34 The one-line summary of what a data analytics and AI strategy is. 05:19 Thoughts on the growth of data models. Currently, organisations have a small number of models in production, but the future will see organisations running with thousands of models in production. 7:51 The way that I like to apply automated machine learning solutions – as a first pass of models and a first benchmark before deploying something at scale. 9:26 The two “extreme” approaches to creating AI in organisations and kickstarting the journey towards deriving value from them.  Quotes: Strategy defines where you are, as an organisation, looks at where you want to be, and then fills in the path in-between, which is where the tactics come in. We’re going to go from 10, to 20, to 1000, and then 10,000 plus models in production. This is exciting – a little scary, but it’s definitely the case that we will want to have AI embedded throughout the organisation, supporting every decision in every process. We can have a workforce that can create machine learning models, and help themselves and their teams on the daily tasks… we’re moving towards a world where more people in the organisation have a little knowledge of machine learning and AI. We will always need that team of specialists to be working on the high value items, and to improve the models that have been created by people in the business.