AI Is No Longer a Differentiator: 3 Takeaways from Quirk’s London

Back from Quirks London and if I only had £1 for every time someone said “AI” I could have enough to rival Sam Altman.

Because it’s everywhere. Everyone’s using it. Everyone’s selling it.

And that’s the shift – in the space of a year, AI has gone from “differentiator” to “default.” It’s now the baseline, not the brag.

So if you’re a marketer or in house insights lead, here are 3 things that actually matter now – and more importantly, what to do about them:

1. AI isn’t the story – how you apply it is

A lot of what’s being labelled as “AI” is essentially smarter automation or enhanced tooling. Useful? Absolutely. Transformational on its own? Not really.

The real question moving forward is:

• Where is AI genuinely improving speed, efficiency, or scale?
• And where do you still need human judgement to make sense of it?

Because while AI can process, structure and summarize faster than ever, it doesn’t replace:

• context
• commercial understanding
• or the ability to challenge what the data is actually saying

Therefore if you’re commissioning research (or building it internally), push for clarity:

• Ask your teams or partners to map exactly where AI is used in the process
• Challenge whether it’s improving the decision, not just the output speed
• Invest just as much in interpretation as you do in tooling

The future isn’t AI vs humans – it’s AI for efficiency + humans for meaning.

2. “Speed” is getting louder – but quality still decides outcomes

There’s a clear push across the industry towards faster, lighter research. And in some cases, that’s exactly what’s needed – quick pulses, directional reads, early-stage thinking.

But what’s often missing from the conversation is:

• how robust the sample is
• how well it’s been validated
• and whether the output can actually support a strategic decision

Especially in B2B, where audiences are harder to reach and stakes are higher.

So rather than trying to make everything faster, the smarter play is to separate your approach:

• Directional / tactical work – Faster, lighter, clearly framed for early decisions
• Strategic / high rigor work – More time, more validation, built for confidence and impact

And then look for speed in the right places:

• onboarding
• contracting
• internal processes
• automation of repetitive tasks

That’s where AI can genuinely unlock value – without compromising what really matters.

3. Trust, data ownership and reassurance are becoming critical

One of the more interesting (and slightly uncomfortable) themes was around data ownership – particularly the idea that some platforms may be able to learn from studies run on them.

At the same time, clients are becoming more aware – and more cautious – about:

• where their data sits
• who has access to it
• and whether their competitors could indirectly benefit from it

This isn’t just a legal or procurement discussion anymore. It’s a trust conversation.

So treat data governance as part of your insight quality, not an afterthought:

• Build a simple internal checklist:
• Where is the data stored?
• Who can access or learn from it?
• What is retained vs deleted?
• Be proactive with stakeholders:
• Don’t wait to be asked – lead the conversation on reassurance
• Position data protection as a strength, not just compliance

In a world where tools are becoming more similar, trust becomes a differentiator.

Final thought

The industry is getting faster, noisier and more automated.

But that doesn’t necessarily mean it’s getting clearer.

The teams that will stand out aren’t the ones doing the most – they’re the ones who are most deliberate in how they do it.

• Clear on where AI adds value
• Clear on when speed is appropriate
• Clear on how they protect what matters

To discuss how our tailored insights programs can help solve your specific business challenges, get in touch and one of the team will be happy to help.