Using Data to Accelerate Change in Our New AI World.

[Image: AI driven decision making}

I recently attended a workshop about how to use data to support better community decision-making, which, as someone who uses data in every single project I work on, was something I was very happy to be invited to be a part of.

Amidst the various discussions with some very smart individuals, I couldn't help but wonder about the very large opportunities, wedged against the very large risks, that are rapidly approaching with the advent of AI – and what this could mean for the use of data to support social change.

As with every new human innovation, there is a lot of uncertainty around AI right now – indeed, there is also clearly a lot of fear about what the consequences could be.

I have long championed the use of data to inform the development of strategy to drive change, but, alongside the 'scientific side' of data usage, I am also a strong advocate for the 'artistic side' of generating strategy. Making use of the insights and intuition that rest with those closest to a societal problem, and blending these with what the data tells us.

Now like most (much better informed) commentators on the development of AI, I can see a range of ethical and moral challenges ahead of us when it comes to how AI is going to pervade our everyday lives in the years ahead, but what concerns me most when I think of the work I do is the potential for AI to overly focus change-makers on the scientific side of data.

Some of the transformational projects I'm most proud to have been a part of were successful only because of the flawed, intuitive decision-making processes that led to their creation. The belief of one individual that they had an idea that could make things better. The magical ideas generation process that can take place when a diverse group of 'concerned citizens' find themselves in a room together. The sudden realisation that the idea that's always been discarded, might just be right for this moment – and the circumstances that change just at that moment to make it so.

I always start a project with some kind of analysis. Whether that be just digging into the back story & history of an organisation I'm helping, or undertaking a full analysis of their recent performance and the landscape they work in. But whilst I start there, I always end up in conversation with the people most closely connected to the organisation, hearing their thoughts, ideas and desires – and often hearing the future roadmap for the organisation more clearly through those conversations, than from the more clear cut insights that the pure data can provide.

That's not to say one side is more important than the other, rather that finding the right balance between the two is really where the best solutions are to be found – and I'm not convinced AI will ever be able to reliably find that 'human balance', however many gigabytes of memory you throw at it.

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