Why 96% AI adoption at Make didn’t start with tools or training
Watch the interview here When Sara Maldon joined Make two years ago, there was no approved AI tool. Nobody...
Read morePLUS: Stop practising on your best prospects
One of the hardest parts of building aiblLIVE has been finding operators willing to talk publicly about their AI implementations. Most case studies are vendor stories with a thin customer veneer. We wanted actual practitioners who’ve done the work and will tell it straight.
This week we confirmed five speakers for the event. What stood out to me was where they’re from.
Insurance (Vikram Lakhotia from Marshmallow), property (Ben Lee from Bidwells), and occupational health, where Jonathan Behr has been leading Medigold Health’s automation work. These aren’t the sexy, high-velocity sectors where you’d expect AI-first cultures. They’re regulated, risk-averse, and operationally complex. Exactly the environments where implementation typically bogs down.
Yet here they are, ready to walk through their implementations. Take Medigold’s AI-driven automation of occupational health reports. It’s the kind of work that doesn’t generate breathless tech coverage but transforms how much actually gets done. Measurable, repeatable, focused on solving an operational bottleneck. No one’s showing off the tech.
We’ve also locked in Alison Wright from Microsoft’s SMB team and Nisreen Ahmeen from Skills England to run workshops on adoption planning and implementation strategy. The split between case studies and hands-on sessions is holding. People learning from operator stories, then doing the work themselves.
A UK logistics firm lost a £50,000 ARR deal after a polite but unsuccessful meeting. The rep had rehearsed for an hour with his manager, covering objections, the deck, and pricing.
The problem wasn’t the prep; it was the quality of the rehearsal. The manager realized she was too close to the product to play a true skeptic. The rehearsal was polite, but the buyer wasn’t.
The Roleplay Dojo: A Three-Phase Method
The Roleplay Dojo fixes this by separating the character from the critique. First, it builds a realistic Persona of the buyer you’re actually facing. Second, it defines a ruthless Grading Rubric based on what that specific buyer cares about.
The result is a sparring partner that doesn’t just act like the buyer; it judges like them, too.
NEWS
67% of medium-sized UK firms reported a cyber breach in the past 12 months, according to the Department of Science, Information and Technology. The entry points remain basic: unpatched systems, weak authentication, exposed remote access.
Separately, fraud prevention firm Nametag warns that 2026 will be “the year of impersonation attacks,” with agentic AI “elevating the threat level.” Once an agent is hijacked, it can “initiate legitimate-looking actions, from data exports to software deployments, that bypass human oversight.” Agents expand what compromised access can do.
If mid-market deploys agents while breach rates remain this high, the security debt compounds. Agents don’t just automate workflows. They automate what attackers can do once inside. The constraint isn’t agent capability or security awareness. It’s whether the basics get fixed before agents multiply what’s at stake when they don’t.
Agentic AI tops the 2026 priority list for core mid-market firms (100-999 employees), but the rest of Techaisle’s research tells a more constrained story. Data fabric and vectorization rank #3. AI-native infrastructure is #5. Governance of shadow AI is the #2 IT challenge. These aren’t nice-to-haves. They’re prerequisites. Without them, agentic systems don’t work.
The tension is sharpest around cost and control. ROI pressure ranks #2, cloud cost management #3. Leaders are trying to move fast (#9: speed to market) while hardening governance (#7: AI TRiSM) to prevent sprawl. Skills gap ranks #4, talent augmentation top three. They’re trying to reshape work while still patching foundations.
What breaks first isn’t ambition. It’s the assumption you can skip to agentic without fixing the foundations. Treat 2026 as showcase year instead of infrastructure year and the foundations won’t hold the weight. Miss that window and you’re left with pilots that don’t scale and cost overruns that kill the business case before anything goes live.
West Monroe’s 2026 outlook finds 91% of mid-market manufacturers now use generative AI but remain stuck in pilot, unable to translate experimentation into scalable value. The constraint isn’t appetite. It’s execution.
The manufacturers breaking through share a pattern: they’re redesigning processes for human-machine collaboration instead of bolting AI onto legacy workflows. One pet-food manufacturer connected 31 machines across four departments and unlocked $13 million in incremental capacity within 16 weeks. The difference wasn’t the technology. It was treating AI as a capability that requires process redesign, not a layer that sits on top of what already exists.
High adoption rates mask low translation rates. Without that shift (embedding AI into redesigned workflows with the discipline to replicate what works) the investment compounds without the return. What breaks first is fragile data foundations, governance gaps, and the assumption that pilots scale without structural change.
Mid-market IT leaders spend year-end on roadmaps and budget reviews, but the work that matters most is the work that gets deferred: cleaning up privilege access, mapping actual environments, and building AI governance before deployment starts.
The gap is structural. Every breach has one thing in common: someone still had access they shouldn’t have. Former employees, stale service accounts, forgotten credentials. Mid-market firms are rapidly adopting AI while lacking policies or resources to enforce governance. Meanwhile, IT is planning for the environment they think they have, not mapping the one that exists. Shadow IT, sprawl, devices that don’t match inventory.
Strategic planning dominates year-end while foundational hygiene gets pushed to “later.” Yet flat or modest IT budgets for 2026 mean there’s less discretionary spending for anything that isn’t AI-related. Miss the access cleanup, the visibility audit, the governance framework now, and you’re carrying last year’s exposure into the new year. That’s the gap between intent and delivery. Not what gets planned, but what gets skipped while planning happens.
This week we’ve been looking at Prophix One, a finance platform for mid-market teams that have outgrown spreadsheets but don’t want an enterprise suite pulling IT into every change. It brings budgeting, forecasting, consolidation, close and reporting into one finance-led system.
What stands out is its shift from AI as an assistant to AI that does the work. Prophix offers autonomous agents for budgeting and reporting. They can update headcount and OPEX in plain language, then generate tables, charts and variance commentary, with oversight and a traceable audit trail.
That’s where agentic AI will stick first. Finance workflows are well-defined, repeatable, and built for speed without losing control.
Watch the interview here When Sara Maldon joined Make two years ago, there was no approved AI tool. Nobody...
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