The Human Layer

I spent time at Data Decoded in London speaking with people working across data, technology and artificial intelligence. As with many industry events, there was plenty of discussion about platforms, emerging capabilities and the pace at which organisations are expected to adapt. What stayed with me afterwards, however, was how often those conversations returned to people.

That may sound obvious. Technology has always been developed, implemented and used by people. Yet the excitement around AI can sometimes make organisational capability seem like something that will follow automatically once the right tools have been purchased.

A conversation about leadership and capability

One discussion that particularly interested me was an Eden Smith roundtable on The Talent and Culture Challenge: Building Leadership Capability for the AI Era. A recurring theme was the difficulty organisations face in deciding what they actually expect from AI investment.

There is considerable pressure on leadership teams to demonstrate progress. Nobody wants to be seen as ignoring a technology that may change how work is done. But agreeing to invest in AI is not the same as agreeing what problem it should solve, how the result will be assessed or what people will need to do differently once it is introduced.

I don’t think this is simply a failure of leadership. The technology is developing quickly, and the implications are not always clear. Nevertheless, it makes the conversations about purpose and organisational readiness more important. Without them, a programme can appear successful because a tool has been deployed, even when its contribution to the organisation remains difficult to explain.

The people who make the technology useful

One of the things I find most valuable in data work is bringing different perspectives together. Technical specialists can explain how information is processed and where its limitations lie. Operational colleagues understand the circumstances that produce the records. Leaders can connect those observations to wider priorities and the decisions that need to be made.

When those perspectives remain separate, even capable systems can reinforce misunderstandings. When they are brought together, technology has a much better chance of supporting decisions that matter.

That is why I see AI adoption as a question of capability rather than procurement alone. People need enough understanding to question an output, recognise when information is incomplete and know who can resolve a problem. Teams need opportunities to learn and experiment, but they also need clear responsibilities and the confidence to challenge assumptions.

The return on investment question follows naturally from this. What becomes easier, more reliable or more useful after implementation? What can people now do that they couldn’t do before? Have we improved the quality of decisions, or simply introduced a faster way to produce information?

I left Data Decoded thinking that the organisations best placed to benefit from AI may not necessarily be those adopting the greatest number of tools. They may be the ones willing to invest just as seriously in the understanding, relationships and judgement needed to use those tools well. For MycoFlow Systems, that remains an important part of what it means to build connected, intelligent systems.