AI Strategies for Mid-Market Companies & PE

Telecom's traditional connectivity model is hitting its limits. To capture high-margin growth in the AI era, forward-thinking operators must move beyond carrying traffic and leverage their core assets across three strategic fronts: unifying self-service, retail, and care onto a single real-time intelligence layer; monetizing distributed edge infrastructure and token economies; and bundling high-frequency connectivity with AI services through super-apps.

AI maturity assessments are now standard in PE due diligence — and buyers are pricing the gap. A company with three well-governed AI use cases embedded in the revenue model is ahead of one with a dozen disconnected pilots. The difference at exit comes down to a scored baseline, a sequenced roadmap, and two years of documented EBITDA impact. The firms that get this right start at acquisition, not in the year before the sale.

AI maturity is no longer just an operational metric — McKinsey's 2026 research of 471 PE-backed companies shows that those at the highest AI maturity level trade at a 31x revenue multiple versus 13x at the lowest. Most mid-market portfolios are running productivity AI when the multiple moves on Revenue AI: use cases scoped to business outcomes, ROI modeled at the workflow level, and a single owner accountable from roadmap through execution. Applied early in the hold period, this strategy commands the premium — not explain the discount.

An agent is only as good as the reasoning engine behind it — and most "agentic" CX rollouts skip past GenAI's real gaps in memory, factuality, and system integration. This post breaks down five structural gaps, from goldfish-memory context loss to hallucination liability, that separate a chatbot from a true resolution engine. The real ROI lives in fixing these foundations, not chasing the hype.

Most agentic AI investments underdeliver because they bolt agents onto broken workflows instead of treating the workflow itself as the real economic unit of value. Drawing on a recent McKinsey study of 50+ agentic builds, this post unpacks three hard lessons on process mapping, human oversight, and tying AI directly to EBITDA. Scaling agentic AI beyond pilots comes down to redesigning the workflow first, followed by the agent design and orchestration.

Bain research points to 20–25% EBITDA gains from AI — but that promise mostly holds for tech-forward companies with mature data and talent. For everyone else, the gap is strategic, not technical: fuzzy ROI, fragmented data, and executive hesitation get in the way. AI transformation succeeds or fails as a business translation problem, not a technology one.