Know Who Is Going To Ghost Before It’s Too Late

PLUS: 26′ Predictions and a thank you from the aibl Team

From the aibl team

As the year winds down, I wanted to say a quick thank-you for reading.

If there’s one thing that’s clear about 2025, it’s that AI in the mid-market is shifting beyond hype into the “so what do we actually do with it?” phase. Less breathless promise and more organisational reality.

Across the UK mid-market, we’ve seen teams wrestling with the similar questions about who owns AI, where value really shows up, how risk is understood (or misunderstood) and how culture often matters more than tooling.

In the new year, we’ll continue focusing on what actually works — practical use cases, honest barriers, and the decisions leaders are making when there’s no perfect playbook to follow.

We’ll also be sharing new research, more case studies and interviews with people making AI a reality, including the speakers you can look forward to meeting at aiblLIVE.

Thank you for your attention, your curiosity and the thoughtful replies. It’s all genuinely appreciated.

Next year will fast paced for us all, so we with you a calm and rejuvenating end to 2025.

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Playbook of the week

The Churn Hunter

Spotting Customers Who Are Leaving Before They Ghost

A Christmas Surprise

During a December retention review, a mid-market IT provider found a problem hiding in plain sight. Nearly four in ten of their customers were dormant, showing no meaningful engagement in over 60 days. Revenue was still coming in, but the relationships were already gone. The company had no system to tell which customers were salvageable and which were truly lost.

Where the logic failed

The team initially built an agent to look for “quiet” accounts. It flagged 47 “risky” customers. The Head of Customer Success called ten of them and found that most were perfectly happy.

The issue was that quiet periods are common in managed services, especially when things are working and don’t reliably indicate churn. The team had confused low usage with churn risk.

The Solution: Detector vs. Diagnoser

To add useful nuance, they built a two-stage system and you can too.

Continue reading.

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NEWS

We’re still collecting stories from the field, what worked, what didn’t, and what surprised you. If you’ve got a case study that belongs on the aiblLIVE stage, get in touch with via John@aiblmedia.com.

1. 2026 Predictions: The Autonomous Business, Dec 2025 Talent retention becomes the competitive edge
   Vertical AI solved customer-facing problems in 2025. In 2026, the same tools move inside the organisation. Systems once used to personalise customer journeys are now used to tailor training, support development, and flag retention risk. For mid-market firms under constant hiring pressure, this is a practical advantage, not a cultural experiment. The monolithic suite keeps losing ground
   By 2026, mid-market firms stop buying full suites and start buying specific functions. Accounts payable. Tier-one support. Each delivered as a service and integrated into existing systems. What once required an enterprise platform is now viable in smaller, focused components. The physical world comes into focus
   AI first reshaped knowledge work. The next shift is into physical operations. In manufacturing, logistics, and retail, AI now works alongside sensors, cameras, and machines to manage activity on the floor. Issues are flagged earlier. Work is rerouted in real time. The value isn’t novelty. It’s tighter operational control.
2. The AI Layoff Myth: Why Speed Without Readiness Is a Liability, Dec 2025 If the predictions sound bold, execution risk is higher still. Large enterprises are cutting middle layers and claiming AI will fill the gap. For mid-market CEOs, the question is simpler: can AI do the work reliably and legally in your context? Most mid-market firms still lack clean data, consistent knowledge management, and basic governance. Speed without readiness is a liability. Where readiness is high and results are verified, moving faster makes sense. The article lays out a practical frame:Run AI-readiness audits before cuts. Map use cases, data requirements, governance, and payback. If ROI is modelled but unproven, hold staffing steady until pilots deliver validated results over two quarters. Redeploy managers into process redesign and data stewardship. Time-box pilots to 90 days. Protect the leadership pipeline. Reset spans of control using benchmarks. Communicate like an owner, not a trend follower.

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PRODUCT SPOTLIGHT OF THE WEEK

This week we’ve been tracking InvGate, a platform gaining traction with mid-market CIOs who find competitors too costly and complex to run day to day. InvGate combines IT service management and asset management in a single platform, keeping tickets, devices, and infrastructure context in one view rather than spread across stitched-together tools.

It stands out for how it tackles a familiar mid-market constraint: high ticket volumes with lean support teams. Through its AI Hub, InvGate deploys a Virtual Service Agent focused on deflection first. It surfaces relevant knowledge inside chat tools like Microsoft Teams or the self-service portal before a ticket is logged. 

Once tickets are live, it supports agents with summarisation, solution suggestions, and early signals when SLAs are at risk.

It’s a clear example of pragmatic AI for the mid-market. InvGate deploys quickly, often in days, with transparent per-agent pricing for service management and per-node pricing for assets. Teams can start small and add AI as volumes grow.