Beyond Dive In: What kind of workplace do we want to create with AI?

After the final Dive In Festival, Alice Scott, Asta’s Head of Culture & Talent, reflects on the relationship between AI and inclusion, what it means for Asta and why the insurance industry needs to think carefully about the workforce it is creating for the future.

One of the things that struck me most at this year’s Dive In Festival was how much the conversation around AI has moved on.

We are spending less time asking what AI is, or simply how it can make us more productive, and more time thinking about what it means for how we work.

That brings the discussion much closer to the themes Dive In has explored over the past 12 years. Inclusion and AI may initially appear to be very different subjects, but both raise questions about access and opportunity, and about who might be left behind if we don’t pay attention.

As someone who spends most of my time thinking about culture, talent and leadership, that is where I think some of the most important conversations now sit. AI is not simply a technology transformation. It is a people transformation too.

Keeping people up to speed

One of the risks is not simply that AI develops quickly, but that organisations move ahead while their people are still trying to understand where they fit.

There is an understandable tension here. AI brings enormous possibilities and there is plenty to be excited about, but that sits alongside real uncertainty about where the technology is taking us and what it could mean for individual roles.

We are already seeing a degree of FOBO – Fear of Becoming Obsolete – which can affect how people engage with AI. Some are curious, some sceptical and some genuinely concerned about whether sharing their knowledge with these tools could ultimately make their own expertise less valuable.

Those concerns do not disappear because an organisation announces an AI strategy. If we want people to embrace new ways of working, they need the confidence to experiment, ask questions, make mistakes and admit when they do not know something. That requires clear guidance and training, but it also requires trust and psychological safety.

At Asta, we have tried to approach AI with optimism and pragmatism, helping colleagues build confidence rather than positioning it as something only technical specialists need to understand.

Our Digital Innovator network is important here because some of the best learning happens when colleagues share what they have tried, what didn’t work and what they learnt from it.

AI literacy is becoming a workplace skill in its own right. But being AI literate does not just mean knowing how to write a prompt or get an answer from a tool. It means knowing when to trust an output, when to challenge it and when human judgement should take priority.

The best users will not necessarily be those who get an answer fastest. They will be the people who know when, how and why to question it.

Faster recruitment isn’t the goal

I took part in a Dive In session looking specifically at AI and recruitment, which provided a useful example of some of these wider questions.

There are clear opportunities to remove or improve repetitive administration and make the recruitment process work better for candidates. AI could also help us search for skills differently, identify people we might otherwise miss and look beyond traditional career paths. For a growing business such as Asta, that could include helping us reach talent outside insurance as well as broadening the pool within it.

But the starting point should always be the problem we are trying to solve. Too often, conversations about technology become conversations about speed: can we hire faster, screen faster or reduce effort?

Of course we can. But becoming faster and becoming better are not the same thing.

We have become slightly obsessed with measures such as time to hire, perhaps because they are easy to quantify. Reducing a six-week recruitment process to three weeks – or even two - sounds impressive, but it only matters if we are still finding the right people, giving candidates a good experience and creating the conditions for those people to succeed when they join.

Quality of hire and time to effectiveness are harder to measure, but ultimately much more valuable.

There is also the question of bias. We should start by assuming it exists. AI did not create bias, but nor does using technology make it disappear. Bias can already be present in data, historical decisions, hiring patterns and the systems from which AI learns.

We therefore need to pay attention to outcomes. If a system is consistently favours particular backgrounds, experiences, educational routes or career histories, we need to understand why and be prepared to change course.

Used well, AI should create more space for good human judgement, not less.  The objective is not to remove people from decision-making, but to help them make better decisions.

Don’t remove the first rung of the ladder

One of the conversations that stayed with me most throughout Dive In was what happens to early careers as AI becomes capable of undertaking more routine work?

There is an understandable attraction in automating repetitive tasks. But asking whether we still need the same entry-level roles may be the wrong question. We should also be asking how people will develop expertise if we remove some of the opportunities through which previous generations learnt.

Today’s entry-level employee is tomorrow’s specialist, underwriter, manager or leader. If we remove the first rung of the ladder, we need to think about how people are going to reach the second, third and fourth.

Entry-level work has never been only about completing relatively straightforward tasks. It is also where people observe experienced colleagues, put knowledge into practice, make mistakes, receive feedback and gradually develop judgement. Capability is built through experience as well as education.

If AI removes some of those traditional learning opportunities, we need to create new ones deliberately. Early-career colleagues will still need exposure, context, practice and feedback.  if they are going to develop the knowledge and judgement the business will need from them later. Judgement does not suddenly appear after ten years in a role; it develops over time.

Otherwise, we could solve an immediate productivity problem while creating a capability problem several years down the line.

There is an inclusion issue here too. Entry-level positions have traditionally provided an important route into insurance for people who do not already have industry connections, family networks or previous exposure to the profession. They have helped us broaden access and diversify our talent pools.

If those opportunities disappear without credible alternatives, we risk narrowing access at exactly the point when technology has the potential to help us widen it.

Human judgement still matters

Judgement was one of the words I heard repeatedly during Dive In and I think there is a good reason for that. AI is becoming increasingly good at producing answers. The challenge is knowing whether those answers are any good.

If technology can analyse information and generate recommendations in seconds, value increasingly lies with people who understand the context, can spot when something does not look right, ask the right questions, challenge assumptions and take accountability for the eventual decision.

Insurance will continue to need people who can do those things, as well as understand risk and build relationships. In many respects, human judgement therefore becomes more important not less.

That is why I prefer to talk about augmentation rather than replacement. At Asta, that is certainly how we are approaching recruitment: using technology where it can help while retaining structured, skills-based assessment, clear evidence and human accountability. 

The same principle applies more broadly. As technology develops, we need to make sure we continue to develop the human capabilities that sit alongside it – curiosity, critical thinking, decision-making, relationship building and the confidence to challenge.

What comes after Dive In?

This year also carried particular significance because it was the final Dive In Festival in its current form. Over 12 years, Dive In created space for conversations that our industry was not always having elsewhere. It helped move inclusion from the margins towards mainstream business discussion and connected it with wider questions about leadership, culture, talent and the future of work.

Asta has been proud to play an active role in that, whether through contributing to sessions, supporting events or helping to shape the discussions.

For me, though, Dive In’s legacy is not the number of events that took place. It is what happened because of them: the conversations that continued afterwards, the assumptions that were challenged and the occasions when someone learnt something, changed their perspective or did something differently as a result.

The end of the festival does not mean the end of that work. Inclusion cannot be something we concentrate on for one week a year. It needs to be reflected in how we recruit, develop talent, design jobs and make decisions every day.

Expanding opportunity

Looking back on the week, I kept returning to the connection between AI and inclusion. Both are ultimately conversations about opportunity: who gets the chance to contribute, learn and progress, and who could be excluded if we are not paying attention.

AI has enormous potential. It can help us work differently, make better use of information and create capacity for higher-value work. But success should not be measured simply by the number of hours we save.

A better measure will be whether we use technology to create better organisations: organisations that make better decisions, develop stronger talent, build more inclusive routes into our industry and create more opportunities than they remove.

We already know AI will change how we work. The more important question is whether we are being deliberate about the workforce we create as a result.

The future of work is not simply something happening to us. We have a part in shaping it, and now is the time to think carefully about what we want it to look like.