The GenAI Paradox: Adoption Is Up, Impact Is Down
Everyone’s talking about generative AI.
Companies are pouring billions into it. Adoption is surging—more than 78% of organizations report using GenAI in at least one function. But here’s the twist: most of them are seeing no measurable impact on the bottom line.
Welcome to the GenAI paradox.
We’re living through an extraordinary moment in enterprise tech. Generative AI has gone mainstream. It’s accessible, it’s impressive, and it promises massive value. And yet… that value isn’t showing up where it counts—on income statements or balance sheets.
So, what’s really going on?
The Disconnect Between AI Investment and Results
Before GenAI, traditional AI already held huge promise—up to $18 trillion in potential value. Then GenAI added another $2.6 to $4.4 trillion to that total.
And unlike traditional AI, GenAI made playing with machine intelligence as easy as opening a browser tab.
That accessibility fueled unprecedented adoption. But despite this explosion in use, the return on investment is murky at best.
The issue? A fundamental mismatch between horizontal and vertical AI use cases.
Horizontal vs. Vertical Use Cases
Horizontal use cases (like Microsoft 365 Copilot or general-purpose chatbots) are easy to deploy and benefit individual productivity—but their gains are scattered and hard to measure.
Vertical use cases (AI built for finance, logistics, manufacturing, etc.) are much more specific and have real potential for direct impact—but they rarely scale.
In fact, less than 10% of vertical AI projects ever make it out of pilot phase. Even when they do, they often support only a tiny slice of a larger business process.
Why? Six big reasons:
Fragmented initiatives – lots of bottom-up pilots with little CEO sponsorship.
Lack of packaged solutions – vertical use cases need bespoke development.
LLM limitations – hallucinations, lack of memory, and passivity make them hard to trust.
Siloed AI teams – disconnected from IT, data, and business operations.
Poor data quality – especially unstructured data, which AI can’t easily use.
Organizational resistance – fear of disruption, job loss, and inertia.
These are big hurdles. But they’re not fatal. Because all this experimentation has laid the groundwork for what’s coming next.
The Shift: From Tools to Agents
We’re now entering the era of AI agents.
Unlike LLMs that wait for a prompt, agents act. They combine language models with memory, planning, orchestration, and system integration to execute real tasks autonomously.
Think of them as digital teammates—able to understand goals, break them down into steps, take action, learn, and adapt. In real time.
They’re not just helping humans work faster. They’re starting to do the work themselves.
Five Ways AI Agents Transform Operations
Faster execution – by eliminating handoffs and working in parallel.
Real-time adaptability – dynamically responding to changes or anomalies.
Personalization at scale – decisions and actions tailored to each customer.
Elastic capacity – scale operations up or down instantly.
Resilience – detect issues, reroute workflows, escalate only when necessary.
This isn’t hypothetical. Let’s look at some real-world examples.
Real Examples, Real Impact
A global bank modernizing its core system used human-AI “digital factories,” cutting delivery time by over 50%.
A market research firm automated data anomaly detection, showing over 60% productivity gains and $3 million in projected savings.
A retail bank used agents to help relationship managers draft credit memos—resulting in up to 60% productivity increases and 30% faster loan decisions.
These aren’t tweaks. They’re reinventions.
Optimization vs. Reinvention
Let’s make a critical distinction here.
Optimization: GenAI speeds up parts of a process—maybe a 5–10% boost.
Reinvention: Agentic AI restructures the entire workflow. It reallocates tasks, changes sequence logic, removes steps entirely. That’s where the real transformation happens.
Reinvention can lead to:
60–90% reductions in process time.
80% autonomous resolution rates.
Entirely new workflows—and new business models.
But to achieve this, we need a new kind of architecture.
The Agentic AI Mesh: A New Operating System
The Agentic AI Mesh is a next-gen enterprise architecture designed specifically for agents.
It’s:
Composable – plug-and-play across tools and vendors.
Distributed – complex tasks handled by swarms of cooperating agents.
Modular – logic, memory, and interfaces separated.
Vendor-neutral – open standards prevent lock-in.
Governed – with embedded controls and permissions.
It’s the infrastructure for an agent-first future. But here’s the surprising part:
The Hardest Problems Aren’t Technical—They’re Human
The most common failure points? They’re organizational:
Human-agent cohabitation – How do people trust, collaborate with, and oversee agents?
Autonomy control – How do we give agents freedom without chaos?
Sprawl containment – Without guardrails, you risk shadow AI and fragmented solutions.
Which leads us to the final, crucial piece: leadership.
The CEO Mandate: Four Imperatives for Transformation
This isn’t an IT issue. This is a C-suite imperative. And CEOs must lead across four dimensions:
Strategy – Move from scattered pilots to focused reinvention of high-impact domains.
Processes – Rethink end-to-end workflows—not just automate steps.
Delivery – Break down silos. Build cross-functional teams from day one.
Implementation – Design for scale. GenAI’s run costs can eclipse build costs.
And to enable all this, you need:
A workforce trained in human-agent collaboration.
Strong AI governance and risk frameworks.
Agentic-ready architecture.
Clean, productized, accessible enterprise data.
It’s Go Time: The Three Next Moves
So what should leaders do right now?
Close the experimentation phase. Capture lessons. Retire low-impact pilots.
Redesign your AI governance model. Create a cross-functional AI council.
Launch a lighthouse transformation. Choose a high-impact area. Build. Scale. Prove the model.
Some companies are already moving. Moderna, for example, merged HR and IT leadership to signal AI’s enterprise-wide impact.
Final Thought
AI agents aren’t some distant vision. They’re here—and ready. This moment represents a real strategic inflection point.
Done right, agentic AI could redefine how your business competes and creates value. Done wrong—or ignored—it could accelerate your decline.
So here’s your challenge:
What would your business look like if agents ran 60% of a core process? What would need to change?
It’s not just about working faster. It’s about working differently. And the time to begin… is now.
This commentary is based on the McKinsey paper, “Seizing the agentic AI advantage A CEO playbook to solve the gen AI paradox and unlock scalable impact with AI agents”
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