The cultural friction of going AI-first (and why it’s worth the risk)

The cultural friction of going AI-first (and why it’s worth the risk)

Plus: The £140 stack that cut inbound admin time by 87%

From the aibl team

When do you stop protecting what works now and start building for what customers will expect next?

If you have a successful, established business with thousands of happy customers, the instinct is to bolt AI onto the edges in search of small efficiencies. It feels like an unnecessary risk to rebuild as an AI-native platform while the current business runs at full volume.

But it’s a tension we discuss constantly on the aibl Advisory Board. The longer you wait, the wider the gap grows between your product and what AI-native competitors ship.

In this week’s lead article, we look at what it takes to navigate that shift. It’s drawn from a conversation with aibl Advisory Board member and Just Move In co-founder, Ross Nichols.

Ross didn’t just add a wrapper to an existing service — he rebuilt their entire customer journey from the ground up. The piece covers the cultural friction of getting veteran teams to think AI-first, the messy reality of a staged rollout, and how Ross shipped a live feature on his own without being an engineer.

He talks openly about bugs, expensive tools nobody used, and the fact that the whole organisation still doesn’t think AI-first every day. That honesty is worth ten polished transformation stories.

No business is safe from the cycle of change. This one’s worth reading closely (or watch the full interview here).

Why Just Move In rebuilt from the ground up

This article is drawn from a recent conversation with Ross Nichols, co-founder of Just Move In.

Just Move In helps people set up the essential services they need when they move house: energy, broadband, insurance, coordinated through partnerships with estate agents and mortgage brokers. With 10 years in business, they process around 20,000 moves a month and have earned a 4.9/5 on Trust Pilot from over 3,400 reviews. By any normal measure, the product was working.

But last year, they decided to rebuild it.

The result is Jay, an AI-driven platform that handles the full customer journey from move-in through to ongoing home management. Jay is a replacement for the old product, built on new foundations, while the existing business kept running at full volume.

Last week, we looked at why so many companies believe they’re further along with AI than they really are. This week, we dig into what it took for Ross Nichols to actually close that gap.

Watch the full video interview:

AI in practice

A managed IT firm cut inbound admin time by 87% for £140 a month

For this week’s AI in practice, we spoke to the founder of a regional managed IT services provider that had grown faster than its admin processes could keep up with. Two engineering teams, 250+ support and onboarding requests a month, and a phone line that never stopped ringing.

Out-of-hours web form requests piled up overnight with no one to pick them up. Calls, emails, and portal submissions were all landing in different places with no consistent way to process them. Clients with urgent issues were often waiting hours for a response.

Coordinators were also losing around 15 hours a week to phone handling and rescheduling. Many of these needed 10–15 minutes of work before any engineering even started. Checking which engineer was free, looking up the client’s contract, confirming response commitments, then logging it all manually.

The owner wasn’t just looking to save coordinator time. He’d watched the business grow and knew managing every request by hand wasn’t going to hold. He wanted a front door that worked at any hour without adding headcount.

Product spotlight of the week

This week, we’ve been looking at Jellyfish, a platform built for a problem we keep running into. Engineering teams are buying AI coding tools fast, but many struggle to show whether they’re improving delivery or just increasing activity. Copilot licences get rolled out, usage looks healthy, and few can tie it back to cycle time or throughput.

Jellyfish pulls data from your existing dev tools — Jira, GitHub, GitLab. It layers in context from HR, finance, and calendars to show where engineering effort actually goes, useful on its own for allocation and delivery forecasting.

The timely bit is their AI Impact module. It tracks adoption and productivity across Copilot, Cursor, and Claude Code without engineers changing how they work.

Their benchmark data suggests teams ship faster without an increase in defect rates — that’s what a CTO needs when the board asks about ROI on AI tooling.

Spreadsheets and vendor dashboards can’t show you who’s using what and whether it’s moving the needle. If you’re rolling out AI dev tools without a clean way to measure impact, Jellyfish is worth a look.

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