The Deployment Gap: Why Your People Are Moving Slower Than the Tech

The Deployment Gap: Why Your People Are Moving Slower Than the Tech

Plus: How a 187-year-old Property Firm is Doubling Revenue via AI-Native Ops

From the aibl team

The technology is ready but the organisation isn’t. That’s the pattern in AI right now, and it’s showing up everywhere, from labour market data down to individual companies trying to work out what to do next.

Ben Lee at Bidwells has been living with this for a while. He runs data and AI for a 500-person property consultancy that wants to double revenue by 2030 without doubling headcount. A target like that forces you to be specific about where AI actually helps, and where it doesn’t.

His team found the usual mess. Teams solving the same problem in parallel with no clear logic for what gets built internally and what gets bought. People willing but unsure how to start without direction from the top.

So before they touched any technology, they dug into the true goals and what was getting in the way. They put governance in place before deployment, built a cross-departmental committee to cut duplication, and gave every employee an explicit expectation to find one area AI could support their work. It was important to communicate that the process wasn’t a threat, but rather a starting point (or re-starting point).

The article below walks through how Bidwells got there and what Ben learned along the way. It’s worth reading if your AI strategy still feels like a list of tools rather than a plan.

My opinion piece follows that, drawing on new Anthropic labour market data that speaks to the same broader issue from a different angle. The report shows a significant gap between what AI can really do and what’s actually being used. For many roles, adoption sits at roughly a third of what’s possible, and that’s not a tools problem. As usual, it comes down to leadership and process issues that AI is dragging into the light of day.

How a 187-year-old property firm is rebuilding around AI

Bidwells has set itself a public target: double revenue by 2030 without a proportional increase in headcount. AI is part of the plan for the 500-person property consultancy, but it’s not the plan on its own.

“AI alone won’t get us there,” says head of data and AI Ben Lee, the man tasked with understanding how AI can help Bidwells to this target. “Productivity gains are going to be incremental. But the real question is: how can we enable our people to do more so they can go out and grow the business?”

Ben started as a journalist, then spent two decades in real estate, joining Bidwells nine years ago as a comms manager. He’d been talking to the business about Generative AI for nine months before ChatGPT launched to the world. “When it did, people thought I could see the future,” he joked.

Bidwells could have hired a machine learning expert — but as he put it, “we weren’t ready for that level of sophistication.” They needed someone who understood the operations, the data, and the people.

Watch the full video interview:

NEWS

Mid-market firms are jumping to agentic AI before they’re ready

Mid-market agentic AI adoption is mostly pilots, not production. An Everest Group survey of more than 200 mid-market leaders, commissioned by R Systems, found 57% of enterprises stuck in controlled trials, with only 15% operating agents across functions. 43% report skipping earlier AI stages entirely, jumping straight to agent-based approaches without the underlying infrastructure.

The governance numbers confirm it: just 7% have agentic-specific policies in place, while around 30% are running with generic frameworks or nothing at all. For systems designed to act autonomously, that’s a problem. IT operations and software engineering are showing real gains, but none of it scales without the oversight and integration work most organisations haven’t done.

Your Senior Team Is More Exposed to AI Than Your Graduates, and Why That Matters

Anthropic just published a report measuring how AI is actually affecting the labour market, based on observed usage rather than forecasts. Most of the coverage has focused on job losses. The more useful findings sit further down, and for mid-market leaders they are the ones that matter.

I spend my days in private conversations with leaders of scale-up and mid-market companies. In public, they talk about AI with confident ambition. In private, the question is different: “How do I deal with this on top of everything else?”

It reminds me of the late 2000s, when digital marketing was moving from a niche experiment to a board-level issue. Every leader knew it was important. Most were too busy with the core business to properly engage. They delegated it, underfunded it, and hoped it was just a new channel rather than a fundamental shift in how business worked.

The Anthropic report measures what’s happening rather than what might happen. And the findings point somewhere most leaders aren’t looking.

Product spotlight of the week

This week we’ve been looking at Pistachio, a cybersecurity platform aimed at a specific mid-market gap: security awareness training that ends up under-owned because lean IT teams don’t have time to run it properly. Pistachio connects to Microsoft Entra ID, syncs your users, and runs personalised phishing simulations and training on autopilot. Setup takes under ten minutes, and there’s no ongoing content selection or maintenance from IT.

Pistachio has expanded beyond training. In late 2025 it added Presence, an insider threat detection layer that monitors cloud audit logs across M365, GitHub, HubSpot and others. It flags anomalous behaviour without the false-positive noise that makes most alerting tools useless for small security teams. It’s not a full SIEM replacement, but for firms without one, it fills a real gap.

Pricing is per-user with a 14-day full-featured trial. Worth a closer look if your security awareness programme currently lives in someone’s “I’ll get to it” pile.

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