Turning one market study into a reusable intelligence asset

The need

Build a market position on evidence the company could own

The business wanted to become a credible source on how mid-market organizations were actually implementing AI.

There was no shortage of research about AI. But much of what the client was seeing was either broad market commentary, enterprise-focused or limited to headline measures of adoption.

That left a more useful set of questions unanswered.

Who actually owned AI inside these organizations? What happened when pilots stalled? How mature was governance? Where were employees using tools outside official processes? Were organizations able to demonstrate measurable value? And did those patterns look different across technology, operations, commercial and people functions?

The client needed an evidence base broad enough to answer the market-level questions but detailed enough to keep supporting new ones later.

The starting point

A new brand without an established body of proprietary market evidence

The company already had access to operators through its events and community.

Those conversations could show what individual organizations were experiencing, but they could not establish how widespread a pattern was across the market.

The opportunity was to create the quantitative reference point underneath those conversations.

We also wanted the fieldwork investment to do more than generate one report. That meant designing a questionnaire that could support analysis across different audiences and organizational questions rather than optimizing everything around one predetermined headline.

What we did

Built a survey around how AI was actually being implemented, then kept returning to the evidence

We designed and analyzed a study of 755 senior decision-makers at UK mid-market organizations already at least piloting or using AI.

The achieved sample covered businesses from £50 million to just under £500 million in annual revenue and included leaders from four broad functional groups: technology and data, operations and finance, sales/marketing/customer experience and people functions.

The research went substantially beyond asking whether organizations had adopted AI.

It examined deployment, ownership, paused initiatives, enabling work such as data and process change, human intervention, strategic alignment, governance, approval processes, shadow AI, AI-related failures, investment motivations and whether respondents could demonstrate measurable ROI.

Some questions applied across the whole market. Others branched into areas where particular functions could provide more useful evidence. That allowed us to retain a common market view while creating enough depth for later functional analysis.

The first analysis surfaced a series of strong relationships worth investigating further. For example, respondents describing more mature governance were substantially more likely to report measurable ROI. Greater management alignment and stronger workforce readiness also coincided with higher reported ROI.

We treated those as associations, not proof that one factor caused the other.

That distinction became increasingly important as the data began traveling into new uses.

At live events, the survey provided a market-level reference point for conversations with operators. Quantitative research could show how common something appeared to be; people in the room could explain why it happened, where the survey picture matched their experience and where reality was more complicated.

The evidence then returned to editorial work. Newsletter analysis revisited questions such as shadow AI and governance, sometimes combining a new operator interview with a fresh cut of the existing data.

The same dataset was later developed into more focused intelligence for different audiences. Instead of commissioning new fieldwork for every function, the client could examine the needs, behavior and AI experience of particular groups within the original sample and build market intelligence around them.

As generative AI became part of that activation process, our role also extended to protecting the research underneath it. AI could help surface possible questions, structure a first analysis or accelerate a draft. It could not decide that a correlation was causal, ignore a small subgroup because the result looked interesting or turn an interpretation into a measured fact.

The evidence still had to remain in control.

What they got

A proprietary evidence base that could keep producing new forms of value

The original study created a common quantitative foundation for the client's view of AI adoption in the UK mid-market.

That foundation subsequently supported market-level findings, functional analysis, event discussions, weekly editorial content and audience-specific intelligence products.

Qualitative evidence could be set against the wider market data rather than treated as representative on its own, while new editorial questions could be investigated by returning to existing responses. Different audiences could be examined without immediately commissioning another survey.

The dataset therefore became an asset in its own right rather than the raw material for one publication.

Why it mattered

One research investment gave the business something it could keep interrogating as the market changed

The client no longer had to rely solely on other organizations' studies to support its point of view.

It could start with its own evidence, add new operator experience and ask different questions of the original research as new issues emerged.

That also created a more useful role for generative AI.

The model did not need to manufacture insight. It could help people navigate and activate an existing body of evidence faster, while statistical discipline and human editorial judgment remained responsible for deciding what the research actually said.