#10 The Agentic AI reality check: 5 problems nobody wants to talk about

Also, where $2.5 trillion is really flowing, why 74% of early movers hit ROI while everyone else is stuck, and the workforce prediction keeping execs up at night.

AuthorAuthor

Ryan Alexiev & Ram Menon
February 02, 2026

đź“ť EDITOR'S NOTE

Welcome to Issue #10. This week's theme: the great AI reckoning.

$2.5 trillion is about to flood the market and most of it will be wasted. We dug into the research to find out why pilots keep dying in production, what the companies achieving ROI actually did differently, and why KPMG thinks your hiring strategy is already obsolete.

The gap between AI winners and losers is widening fast. This issue is your field guide to landing on the right side.

đź’° THE BIG STORY

$2.5 trillion is flooding into AI. Almost none of it is going where you think.

Gartner just dropped a bombshell: global AI spending will hit $2.52 trillion in 2026, up 44% from last year. The size of that number matters, but where the money is going matters more.

Here's what nobody's talking about: almost none of it is going to the flashy demos and chatbots dominating LinkedIn. The bulk of spend is flowing into infrastructure. AI-optimized servers are expected to grow 49% and account for 17% of total AI spending. The physical buildout alone adds $401 billion in 2026.

This is the paradox of what Gartner calls the "trough of disillusionment." Money is pouring in higher than ever, but so is suspicion. The market is done paying for science projects. It wants systems that integrate, govern, and scale.

As one Gartner analyst put it: "The improved predictability of ROI must occur before AI can truly be scaled up." Translation? Show me the business value or show yourself out.

What this means for enterprise buyers: AI is increasingly being sold by your existing software vendors—not bought as part of moonshot experiments. Your CRM, ERP, and productivity suite are all getting AI features whether you asked for them or not. That creates opportunity (lower integration friction) and risk (vendor lock-in, varying quality). Choose wisely.

🔍 ENTERPRISE DIVE

The 5 structural failures killing agentic AI in the enterprise

The gap between a successful demo and a successful deployment has always been enterprise technology's cruelest teacher. It's one thing to show an AI agent booking appointments in a conference room and quite another to have that same agent handle millions of interactions under HIPAA compliance while avoiding liability.

Enterprise reality has a way of grounding even the most promising pilots

Avaamo's Robert Smith breaks down the five structural challenges killing agentic AI in the enterprise:

  • Governance & Safety: An agent that can't operate within regulatory constraints is useless—or worse, a liability

  • Data & Access Control: Enterprise data is fragmented by design. An agent that can't navigate authentication across EHRs, HRIS, and CRMs can't deliver value

  • Reliability & Observability: Demos showcase best-case scenarios. Production requires worst-case resilience

  • Workflow Integration: Answering questions is table stakes. Agents must do work—and that requires deep system integration

  • Economics: If deployment requires hiring an AI team and six months of custom dev, the unit economics don't work

The bottom line: The companies that win won't have the most sophisticated models. They'll be the ones that make deployment predictable, safe, and economically viable. There's no partial credit for getting four out of five right.

Read the full breakdown: Navigating the Agentic AI Maze →

đź’ˇ WHAT ACTUALLY WORKS

74% Hit ROI in Year One. Here's What They Did Differently.

While most companies are stuck in pilot purgatory, Google Cloud's 2025 ROI of AI Report reveals what separates the winners: 74% of executives report achieving ROI within the first year, and 39% have already deployed more than 10 agents across their enterprise. These aren't experiments. They're operating at scale.

The productivity gains are real. Telus has 57,000+ team members using AI, saving 40 minutes per interaction. Danfoss automated 80% of transactional decisions, cutting customer response time from 42 hours to near real-time.

The conversation has shifted from "can AI do this?" to "how do we govern, secure, and scale it?" The agentic era isn't coming—it's here. The question is whether your organization will lead or follow.

đź“Š ENTERPRISE TRENDS

KPMG: 64% of companies just rewrote their hiring playbook. Did yours?

The KPMG Q4 AI Pulse Survey dropped this week, and the workforce implications are staggering. AI isn't just changing jobs, it's rewriting how companies think about talent entirely.

Start with the money: 67% of leaders say they'll maintain AI spending even if a recession hits in the next 12 months. The projected average deployment? $124 million over the coming year. AI has become recession-proof infrastructure.

But the workforce numbers are what should keep you up at night. 64% of organizations have already altered their approach to entry-level hiring due to AI agents—up from just 18% last quarter. That's not a trend; that's a phase change. Meanwhile, 44% of leaders expect AI agents to take lead roles in managing specific projects within 2-3 years. And 76% are willing to pay a 10% premium for candidates with strong AI skills.

The security elephant in the room: 80% of leaders now say cybersecurity is the single greatest barrier to achieving AI strategy goals. Half plan to allocate $10-50 million just to secure their agentic architectures in the coming year.

As KPMG's Steve Chase put it: "AI isn't just an investment, it's becoming the backbone of enterprise strategy. What the numbers don't show is the growing divide: while some organizations stall after early deployments, the leaders are scaling fast and pulling ahead."

That’s it for now, talk soon —Avaamo Team