From the aibl Team
Teams work hard to get AI budgets approved, but then nothing deploys because middle managers slow things down, which looks like resistance to leadership. Sound familiar?
At last week's aibl Advisory Board, founders and operators landed on a different read: the hesitation is rational.
Leadership approves tools but doesn't adjust what gets measured. They believe the AI can work miracles, but it can’t. They want experimentation but don't free up time for it. They want to redesign without changing what performance means. If that's the setup, caution from the middle isn't obstruction, it's responsible.
The article below walks through what our Board members are seeing and the moves that work. It covers changes to mandates, how teams are paired and what performance means when building capability. It's worth reading if this feels familiar.
How to unfreeze managers and get AI deployed
Your AI strategy has stalled - not because of the technology, but because of the people expected to make it work.
Leadership approved the budget months ago and the roadmap exists. But execution breaks down in the middle layer, where managers nod in meetings then retreat to business as usual.
At aibl we see this pattern constantly. One transformation lead put it plainly: "It's a classic case of the Emperor's new clothes. No one wants to admit they're not quite sure of the outcomes they want." Managers speak confidently about AI in meetings, then default to familiar workflows the moment they're back at their desks.
It looks like resistance to change, but really they're rational actors stepping into risk without cover.
News worth reading
The friction described above shows up in the numbers. Morgan Stanley’s latest research finds that UK companies using AI are reporting productivity gains of 11.5%, alongside net job losses of 8%. In the US, productivity is rising at a similar pace, but jobs are being added rather than cut.
When managers lack the authority to redesign how their teams work, and leaders stick with the same measures of success, AI gets used as a blunt efficiency tool. Existing tasks are automated, but the work itself doesn’t change. Output goes up, headcount goes down, and the organisation stays broadly the same shape.
The US data suggests this isn't inevitable - the underlying technology is much the same, but what differs is how it's applied. Some companies bolt AI onto existing structures. Others are prepared to loosen the middle layer and rethink what performance, roles, and growth should look like.
Most leadership teams aren't resisting AI itself - they're not ready for the hard conversation about reorganising work and being honest about which roles need to change. The Morgan Stanley figures show the outcome when that conversation never really takes place.
If this feels familiar, aiblLIVE is designed to help close that gap.
AI in Practice - Automating the First Layer of Customer Contact
A technical lead working with a specialised UK eye clinic explains how they moved from a sticky-note reception desk to a voice and text system that handles bookings and routine queries end to end.
The issue wasn't calls, it was volume. Reception spent most of the day handling the same booking and rescheduling requests. Much of it followed a script. A lot of it came in languages other than English.
When the clinic was small, the team could absorb it. As the patient base grew, reception became the bottleneck. The no-show rate started climbing because reminders were inconsistent and sometimes missed entirely.
So they built an automated receptionist using n8n, Retell, and Cal.com. Patients can now book, move, or cancel appointments by phone, SMS, email, or website chatbot. Conversations run in English and Urdu, with support for other common UK languages. Reminders go≠ out automatically by SMS and email. Certain follow-ups trigger outbound calls for post check-ups.
Product spotlight of the week
Most teams can't send behavior-triggered campaigns without engineering help. Customer.io is built for mid-market SaaS and fintech teams who need campaigns that fire when users abandon cart, hit usage thresholds, or go dormant. It handles email, SMS, push, and in-app in one place.
The AI Assistant auto-translates across 200+ languages and generates event descriptions from your data pipeline. Both features save setup time when you're building workflows, not just sending one-off campaigns.
We've tested this at aibl with mid-market teams. Event-triggered messaging only works with clean data. If your tracking is messy, Customer.io will amplify it. Pricing starts at $100/month for 5,000 profiles, then scales at $0.009 per profile.