I count myself to be very fortunate to be part of the AI revolution professionally and personally as this is once in a generational step change that is beyond hype. Over the past two years what I have learnt are the following:
(1) Customer centricity: The best defense against revolutionary change is embrace it, by fostering innovation from within, but link it directly to business problems that need to be solved, which generally has associated funding assigned, ROI and customer benefit.
(2) Partnership: Be clear who are the 'small set' of strategic partners with the right risk appetite, culture and values to build the capability for the future at Tcale. They could be hyper-scalars like Google or NVIDIA or start-ups where you could co-invest and growth together.
(3) Talent: Hire a diverse group of complementary skilled resources with learning agility. At the end of the day, you need business translators to communicate and crystalize the business requirements; data scientists to undertake proof of concepts; data engineers to truly leverage big data and productionise the data products at scale.
(4) Leadership buy-in: Work with the coalition of the willing or the data literate leaders and gamify the strategy
(5) Architecture: This is one that is commonly missed or glossed over. To build AI data products at scale the foundational data architecture is absolute critical - getting the basics right such as metadata, data cataloguing, data quality, data mesh, MCP, API connectors, AI evals are just as important as selection of the AI model and toolkit. They impact the reusability, scalability, resilience and cost of run and change in the future.