Channel partners pitch data repair as the answer to Britain's flat AI uptake

The people forecasting a rise in adoption are the firms that would sell the work ^[3][4]. The ITPro article says UK uptake "hasn't yet reflected these advances, and a major uptick is on the cards," and attributes that view to "many UK channel partners" ^[3]. In the same passage it describes shallow adoption as holding "opportunities to consult many new customers" ^[4].

Two years of near-flat uptake alongside better models has a plainer reading: firms tried the tools and found little worth buying ^[1][2]. The article's own research cuts against that. More than 80% of AI projects fail to operationalise into production, according to ITPro research from February ^[6], so fewer than one in five get there ^[1]. Those are firms that tried. I think that fits the article's data diagnosis better than indifference does. But the article does not break down why pilots failed, so this evidence cannot separate a data problem from a payoff problem.

The board-deck version of adoption is a software line item. The software is bought and users are set up on it, perhaps with some training ^[10]. The article's objection is that this skips the part that decides whether pilots scale. Business logic, meaning the rules, calculations and decision definitions that govern how work gets done, sits in spreadsheets, legacy systems and employees' heads ^[7]. Data across business units is fragmented and often inaccurate ^[7]. The risk of building on that compounds as uses get more sophisticated, with agents as the article's example ^[8]. "This is not a problem IT can fix in isolation," the article says ^[9].

What the article asks of partners is specific. It grants them technical and change-management skills, then adds that "a deliberate shift in partner skills and proposition framing is also required" ^[5]. It locates the answers in data and analytics advisory and says partners need to position themselves to hold them ^[16]. The article treats AI access itself as increasingly commoditised ^[7].

The remedy it proposes is a "pre-agent layer": an analytics platform beneath AI systems that holds pre-processed proprietary data, defined rules and business context ^[11]. The buyer gets something to sign off against. Managers can see what feeds an AI workflow and check its governance steps and role-based access controls before approving it ^[13]. The cost is concentration. The article says the proposition works best on platforms with visual, easy-to-build analytics workflows ^[12]. It also argues partners are well placed to build the layer because they already sit inside customer data infrastructure ^[14]. For a firm stuck at pilot stage, this quarter's decision is whether data and rules work comes before any agent ^[8][11]. Next quarter's consequence is that the partner that built the layer, on the platform it chose, holds the written-down version of how the company runs ^[12][14].