Here’s the problem no one’s talking about.
Back in August, I wrote about J.P. Morgan’s transformation from blocking ChatGPT to deploying AI across 200,000 employees. The stats were impressive: 450+ use cases, $2B in annual value, 3× efficiency gains.
Three months later, the story’s evolved in a way that should make every enterprise leader pause.
The Scale Play
J.P. Morgan now has 250,000 employees on LLM Suite, their internal AI platform built on OpenAI and Anthropic models. They’re updating it every eight weeks, feeding it proprietary data from across the bank’s operations. Half the workforce uses it daily.
But here’s where it gets interesting: they’ve moved beyond GenAI productivity tools into agentic AI, autonomous agents handling complex, multi-step tasks across the enterprise.
Derek Waldron, their Chief Analytics Officer, showed CNBC a demo: the system generated a complete investment banking deck in 30 seconds. Waldron put it plainly: “As those agents become increasingly powerful and increasingly connected into J.P. Morgan, they can take on more and more responsibilities.”
The vision? Every employee with an AI assistant. Every process powered by AI agents. Every client experience managed by AI concierges.
The Uncomfortable Gap
Here’s what J.P. Morgan figured out early that most enterprises are missing:
You can’t scale what you can’t see.
They didn’t just deploy AI. They built the infrastructure to measure it, govern it, and optimize it. LLM Suite isn’t scattered tools. It’s a centralized platform connected to their data ecosystem, updated on a fixed cadence, with clear governance guardrails.
Most enterprises I talk to are running in the opposite direction:
- Shadow agentic workflows proliferating across teams with zero visibility
- Pilots everywhere, but no framework for measuring what’s working
- GenAI tools deployed, but no way to quantify productivity gains or ROI
- C-suite asking for proof, and teams scrambling to manufacture metrics after the fact
MIT’s study from this year said it clearly: 95% of GenAI projects show no measurable impact on P&L. J.P. Morgan is in the 5% because they solved the measurement problem first.
The Agentic Blindspot
The shift to agentic AI compounds this gap exponentially.
With GenAI tools like ChatGPT or Copilot, you at least know what employees are using. With agentic workflows, you’re deploying autonomous systems that make decisions, trigger actions, and operate across multiple applications without human intervention.
Ask yourself:
- How many agentic workflows are running in your organization right now?
- Which ones are delivering ROI vs. burning budget?
- What’s the cost per task? Success rate? Rework rate?
- Can you prove to your CFO which agents justify continued investment?
J.P. Morgan can answer these questions. They built LLM Suite as a governed, measurable platform from day one. Every agent, every workflow, every use case is connected to their intelligence layer.
Most enterprises can’t answer any of these questions because they’re building in the dark.
What This Actually Means
The gap between J.P. Morgan and everyone else isn’t just about spend. It’s about intelligence.
They’re not guessing which AI investments work. They’re measuring, optimizing, and scaling based on data. That’s why they can confidently project $2B in annual value while other banks are still debating pilot programs.
The enterprises that win in this phase will share three traits:
- Visibility across the AI stack – not just GenAI, but agentic workflows, embedded AI in SaaS tools, and every AI touchpoint in the enterprise
- Measurement tied to business outcomes – cost per task, productivity gains, success rates, ROI quantification
- Governance that enables scale -centralized intelligence platforms that let you see what’s running, what’s working, and where spend is wasted
The Real Playbook
J.P. Morgan’s blueprint isn’t complicated:
- Build or buy a centralized AI platform
- Connect it to your data and systems
- Measure everything from day one
- Govern before you scale
- Optimize based on intelligence, not intuition
The problem? Most enterprises are still treating AI like a collection of tools instead of an ecosystem that requires intelligence infrastructure.
You wouldn’t run a company without a CRM, ERP, or data warehouse. Why are you scaling AI without a system of record to measure it?
What Comes Next
Here’s the reality: your competitors are already deploying agentic AI. Some are measuring it. Most aren’t.
The ones who figure out the intelligence layer first will have a compounding advantage. They’ll know which agents to scale, which to kill, and how to prove ROI to the board. Everyone else will keep running pilots and hoping something sticks.
J.P. Morgan proved you can go from banning ChatGPT to running one of the world’s most sophisticated AI programs in under three years. But the key wasn’t the tech. It was building the infrastructure to see, measure, and govern it.
Your move.