Rethinking the AI market intelligence stack. Keep, Cut, Rebuild

AI is changing who gets access to market and company insight. Firms outside the top 50 can now inform pitches and business planning without heavy subscription fees — and larger firms are re‑evaluating what data actually earns its place.

In this article we examine what’s changed, what still matters, and provide a Keep/Cut/Rebuild checklist to help research and marketing teams spot overlap, strengthen verification, and turn information into action.

What’s really changing inside firms

In our work supporting professional services teams, I’m seeing three moves: consolidating overlapping datasets, making source verification paramount, and prioritising sector-and-client interpretation that means that fee earners can act quickly.

To test these trends, I compared notes with my old research colleague Lucie Milosavljevich who has two decades’ experience in professional services firms.

Lucie put it simply:

“As information volumes explode, firms are grappling with familiar problems in a new form — noise, duplication, and uncertainty about what really matters commercially.”

The risk is already visible. Lucie points to Deloitte being fined by the Australian Government after using incorrect AI‑generated insight — a useful reminder that verification still matters.

How insight delivery is evolving

• Teams are consolidating overlapping datasets — and, in some cases, building their own internal sources of truth.
• Verification is becoming central: not just finding sources, but validating and sense‑checking them.
• The value remains interpretation: sector-and-client context that partners can act on, not another dashboard.

Lucie adds:

“Professional services firms are now building their own AI stacks. Some outperform legacy databases — especially when they integrate internal data like CRM or billing with external sources via tools like the Companies House API.”

This is making a usable “single client view” more achievable — at lower cost and with fewer copyright risks.

Why legacy tools are scrambling

Legacy platforms are racing to integrate AI while emphasising hallucination and accuracy risks in LLMs. The risks are real, but they don’t remove the need to rethink what you’re paying for.

Lucie notes that many providers still miss how BD and research teamsactuallyapply data: contextual intelligence that feeds live pitches, informs strategy and helps build relationships. Meanwhile, generative AI tools are giving away — or pricing down — capabilities that are still being sold as premium features by legacy platforms.

AI tools also make it practical to categorise and analyse large volumes of text and data. This is work that used to be too manual to do consistently, or required subscription platforms to do the hard work. As an example, if you need a quick view of M&A activity, LLMs plus lightweight automation can help you assemble a usable dataset for early-stage opportunity spotting.

We’re also helping firms aggregate and interpret political and legal signals faster, so they can spot emerging opportunities earlier without relying on slow, specialist analysis that historically sat in academia, think tanks, or niche research teams.

A checklist: Keep / Cut / Rebuild

Used well, AI reduces access cost, but advantage now lies in what you validate, interpret, and act on. Use the checklist below to sense-check where you’re overpaying for overlap, and where you need stronger verification and synthesis.

KEEP

• Keep partner‑ready briefs that link insight to implications and clear next steps.
• Keep being involved in reviewing LLMs, agents and subscriptions (so “useful” is agreed not assumed by IT or Compliance teams).
• Keep training fee‑earners and marketers on how to verify sources and use platforms with clear provenance.

CUT

• Cut overlapping subscriptions that duplicate coverage.
• Cut AI outputs you can’t evidence, reproduce, or explain.
• Cut dashboards that don’t change a pitch or decision within 30 days.

REBUILD

• Rebuild collaboration loops between research, marketing, and fee‑earners so intelligence lands when it counts.
• Rebuild workflows around synthesis: de‑duplicate inputs and turn data into a commercial story.
• Rebuild research roles toward strategic direction — not just information aggregation

Used well, AI doesn’t replace judgement — it raises the bar for it. As access to information becomes cheaper and faster, differentiation shifts away from what data you have and toward how confidently you can verify it, interpret it, and apply it in live commercial situations. The firms getting this right aren’t chasing every new tool; they’re making deliberate choices about where insight genuinely changes outcomes — a pitch sharpened, a sector risk spotted earlier, a client conversation made more relevant.

Rethinking the AI market intelligence stack isn’t just a technology exercise. It’s also a discipline exercise: being clear about what earns attention, what creates confidence, and what turns information into action. That’s the real test of what’s worth keeping, cutting, or rebuilding.