The AI Agent Trap: Why Most SaaS Businesses Are Automating the Wrong Things
Many SaaS businesses are deploying AI agents to automate support, onboarding and renewals, and the cost saving looks good in the board pack. This post explains the automation trap: customers who are technically served but emotionally disengaged, who rarely expand and will not give a reference. It draws on Gartner's consumer survey of 5,728 customers, Gartner's forecast that half of organisations planning AI-led service cuts will abandon them by 2027, Klarna's public reversal on AI customer service, an NBER study of 5,179 support agents showing AI as an amplifier of human judgement, and Gainsight's CS Index. It sets out three questions to ask before any customer experience automation project and a diagnostic: compare NRR for AI-managed and human-managed customer cohorts. Written by James Lawson of River Consultancy Group for PE-backed SaaS leaders who want AI ROI that holds up at exit.
Your Exit Starts 3 Years Out, Not 12 Months: Building the SaaS Exit Readiness Narrative Buyers Will Pay a Premium For
Most PE-backed SaaS exits start too late: EBITDA focus at eighteen months, a numbers clean-up at twelve, pipeline theatre at six and an Information Memorandum written three months out. This guide argues that SaaS exit readiness is an operating discipline that should begin two to three years before a process, and sets out the evidence a buyer will test: net revenue retention, a scalable commercial model, operational maturity, a proven land-and-expand motion and AI capability with EBITDA proof. It draws on the EY 2026 Global PE Exit Readiness Study, KPMG's 2026 dealmaker survey, SaaS Capital and Software Equity Group valuation data, and sets out the three questions every exit IM must answer under buyer diligence. Written by James Lawson of River Consultancy Group for founders, CEOs and PE operating partners planning a SaaS exit.
Your Business Isn't 'AI-Powered.' It's AI-Adjacent, and AI Due Diligence Will Expose It
Many European SaaS businesses call themselves AI-powered, but few can show the EBITDA impact of their AI investment or explain why their capability is defensible. This post explains why AI due diligence and technology stack diligence will become a standard exit risk for PE-backed software companies, what buyers such as strategic acquirers and growth equity firms are already asking, and what a genuine AI readiness assessment covers: data infrastructure, operating processes, change management and the commercial model. It draws on Grant Thornton, Mayer Brown, Gartner, McKinsey and MIT NANDA research, sets out five AI use cases tied to net revenue retention and cost lines, and explains how the EU AI Act timeline and AI governance affect deal readiness. Written for founders, PE operating partners and growth leaders preparing for exit.