Research Catalogue
Intelligence Reports
Strategic intelligence for business — every claim traced to its source
Brief: Enterprise AI vendor Advisory
This brief is about the evidence underneath enterprise AI investment cases: how the adoption, revenue and reliability numbers in front of a CIO are produced, and what they hold up to once you know. I should be clear about who I'm picturing, because it shapes the whole thing. You're a CIO past the "should we try this" stage — several agent pilots run, some already in production and wired into live workflows. The board pack in front of you quotes figures on how many peers run agents, how much revenue vendors book from them, how much they save. This brief is about whether those figures mean what the slide implies. Three claims follow, each resting on the last. That the headline adoption and revenue figures are definitional artefacts, not measurements: change the population or the survey question and the same data moves by a factor of three. That a separate research literature, one your vendors don't cite, says the automation business case was wrong on the day it was approved, because the arithmetic assumed a reliability the technology doesn't deliver. And that both conclusions change the terms of the contract you're about to sign, in a direction nobody is currently negotiating. Every figure carries three things: where it came from, how much weight it bears, and who benefits if you believe it. Where a widely-quoted number couldn't be traced to a source, it's flagged as unverified rather than used. There's a fourth finding, and it surprised me enough to earn its own section: why the fix for the first problem quietly becomes the largest switching cost in your stack
The Home became the Ward
The NHS has already run the experiment private healthcare is still debating. Over 12,700 virtual ward beds across every English region have settled the clinical question — acute care at home is safe, and often better than the ward. The unsolved problems are operational and economic: carer burden, patient communication, and the data continuity that ultimately decides whether a home programme grows a bed estate or quietly hollows it out.
Time Bought, Not Time Won
Your Healthcare AI Portfolio Just Got a New Compliance Baseline The EU AI Act Omnibus deal landed on 7 May 2026 — and every compliance roadmap built against the original deadlines needs updating. For healthcare AI investors, the structural uncertainty that made planning impossible has narrowed dramatically. What you get: a planning baseline, a due diligence checklist update, and a cost framework — not a regulatory summary.
Clinical AI Governance Without National Standards
UK clinical AI governance is operating in a documented vacuum. The MHRA National Commission — tasked with producing a unified national framework — has no confirmed publication date for its recommendations, with Q3/Q4 2026 the earliest plausible window. Clinical AI adoption, particularly documentation AI, has already outpaced that timeline. In the absence of national standards, two reference points now define the operative framework. The GMC has confirmed that existing professional standards apply to AI use — clinicians remain responsible for decisions informed by AI, and deployments must preserve the conditions for those standards to be met: judgement, consent, safety reporting, and escalation. NHS England has published updated governance guidance for ambient scribing (v2, April 2026) — the only use-case-specific NHS guidance currently available — which sets out a concrete procurement and deployment checklist and explicitly acknowledges that liability in NHS AI settings remains complex and uncharted, with Trust exposure a live risk where supplier liability cannot be established. Tort law analysis confirms primary care as structurally most exposed: GP practices lack the procurement and governance infrastructure to self-assess AI risk, making ICB and federation-level intervention a necessity rather than an option. The recommended action is "Triage": an immediate internal assessment of all AI tools in deployment or procurement against the GMC standards checklist and the NHS England ambient scribing governance framework. Organisations that wait for MHRA recommendations are accumulating unreviewed exposure month by month. Any governance framework implemented now should have a built-in refresh cycle for when national standards arrive.