Building data and AI teams

How do you build data and AI teams in a modern organisation? New AI skills are sought after, but many fundamentals remain constant across three decades of technology change, which you can leverage. These are the lessons I’ll share.

You start with understanding the demand: are you turning research into products or delivering stability at scale? This helps you understand your interface to the organisation and align leading success measures. Now you can identify the skills your team needs–broadly across product, data science and software engineering–and build a capability plan. You will draw on incumbent and latent expertise, and develop a flexible and compelling proposition to recruit new talent in a competitive market.

With a team of varied specialisations, next develop your operating model to deliver success, within teams and across teams, allowing individuals to work effectively together and enjoy opportunities to grow. Establish practices to tame non-determinism while exploiting its upsides. Once effective, invest in efficiency through systems to enable your teams to scale their impact.

Then things change, as is the nature of modern organisations, and you’ll start again. But through your constant leadership investment in resilience and capability development, you’ll prepare your teams to adapt and flourish.

Three decades spans automotive computer-aided engineering in the GFC era, classical machine learning and data engineering consultancy during the high-growth 2010s, and data platform teams in our current GenAI moment. We’ll take a sneak peak at the next decade too!