What does ‘Good Work’ look like, in the AI era?

When I asked Andy Crysell (founder of CrowdDNA) for advice he pointed out that ‘AI research’ is overly focused on technical benefits, time savings and ignoring the emotional needs of practitioners and the ‘craft’ of research. 

He had a hypothesis, based on personal experience, that AI makes work feel ‘disembodied’ and disconnects practitioners from the thinking when they outsource key parts of it to a machine.

With this in mind, we set out on a quest to speak with researchers about this topic, starting with a big broad question:

-“What does ‘Good Work’ look like, in the AI era?”

And a follow on:

How might the act of ‘thinking’ be deliberately shared between people and machines to get there?”

Andy and I spoke to some of the most interesting voices in research, which led to the Good Work Matters report (link here). In this article I’ll reflect on the main learnings and what it means for practitioners. To inform this article we conducted semi-structured interviews with 30 professionals in the US and UK spanning from independents, boutique firms to researchers at large organisations like Expedia, Universal Studio and Ipsos.

Defining Good Work
There are many colourful definitions of Good Work, but there are three interdependent qualities that are ever present:

• Good Work is actionable and integrated into decisions: might seem obvious but there is a lot of research for research’s sake out there.

• Good Work goes “viral” inside an organisation: high quality output gets passed inside the organisation and isn’t forgotten in a folder.

• Good Work is earned through a process of ‘crystallisation’: it requires clear thinking, sound judgment and the inherently human ability to sit with ambiguity.

This is not new news, but something shifted in the foundations and toolkit of how people can get there. It is now possible to get to polished, sleek, directionally correct insights skipping the last step of human crystallisation. That’s where the ‘disembodiment’ begins and the psychological ownership of the researcher slowly slips away, one prompt at a time.

What we learned is that ultimately ‘Good Work’ requires ownership, someone to be able to stand behind the thinking and justify conclusions or curveball questions with conviction. Whether we like it or not, we inevitably lose the ability to reconstruct the intricate details of the trail when we helicopter to the destination.

Beyond the Human vs AI debate

As Andy puts it: “Much of the debate around AI in qualitative research is stuck in false binaries, oscillating between outright rejection driven by fear of short-termism and flattening of the findings, versus res-tech evangelism that insists automation is the only viable future, often with vested interests on both sides.

In contrast, we found that researchers producing high-quality, strategic insights were comfortable occupying the middle ground. Those people are navigating change, but then they always have been. They’re bringing AI into their practices, but they’re just as focused on what will actually future-proof them: avoiding a race to the bottom, protecting craft, focusing on their version of the work in the AI era.” 

There’s a big theme coming out of our conversations, that ‘Good Work’ in research and insights depends on how deliberately ‘thinking’ is distributed between humans and machines. The debate is not humans vs machines but a matter of intent and knowing which forms of cognition must remain human and which can be responsibly delegated to a system without hollowing out the work.

What must remain human

Several researchers used the term ‘Alchemy of Insights’ to describe something that cannot be outsourced to AI; the process of turning hours of conversations, messy data, past decks, trends, focus groups, ethnos into, not only a coherent point of view, but a transformative story.

Discipline of alchemy of insights is made of three things:

• Deep reflection on the strategic ‘so what’

• Asking what to do with this information, why is it relevant.

• Interpretative judgement and precise writing skills 

Core principle on using AI: protect thinking time

Researchers and strategists doing ‘Good Work’ delegate the ‘what’ and ‘how much’ to AI to protect time to define the ‘so what’ for clients. They intentionally use AI towards tasks that drain cognitive energy and never towards choosing the insight or writing the narrative of a deck from scratch.

For example, here is what practitioners told us:

• Testing blind spots and challenging their own judgment after or during an analysis session.

  I tell AI what I think the main themes, tensions, and contradictions in the research are, and ask if there is more and if I missed anything.”

• Speeding up evidence finding and clustering:

  The best AI tools now can efficiently help you find all the quotes about a specific theme and visualise them in grids with links to the video.”

• Video clipping and reel creation:

  Some AI analysis tools now offer text-based video editing and reel creation functionalities. I don’t have to spend hours trimming, marking down timestamps and editing clips together.”

Ownership is your added value.

In the AI era, our added value sits in our ability to ‘own’ the work. When a curveball question lands in the room, we need to justify our point with conviction, which is only possible if we have lived and breathed the work. There is a role for AI, but it requires disciplined and intentional use, one where we’re still responsible for sense making and in control of the output.