Last night, I read a thought-provoking McKinsey piece titled Seizing the Agentic AI Advantage (McKinsey & Company, 2025). It struck a chord because it captured something I’ve been observing in real time: companies everywhere are buzzing about AI, spinning up copilots, and experimenting with generative tools, yet when you look at the bottom line, the impact is often negligible.

McKinsey calls this the “gen AI paradox” – widespread deployment, minimal impact. And honestly, that paradox feels very real. I’ve sat in conversations where leaders proudly showcase copilots that can draft emails, summarize meetings, or generate reports, but months later the question surfaces: where’s the business outcome?

This tension between excitement and underwhelming returns is exactly why the shift from copilots to agents matters so much.

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Why Agents, Not Just Copilots

One of the biggest distinctions McKinsey drew was between horizontal copilots and vertical agents. Copilots are broad, off-the-shelf, and easy to roll out. They help with tasks like drafting text, answering questions, or creating summaries. They’re valuable, but their benefits are diffuse, hard to measure, and often invisible in the P&L.

Agents, on the other hand, are vertical, embedded, and transformative. They don’t just wait for a prompt. They reason, plan, and act across workflows. They can string together multiple steps, interact with humans and systems, and even take initiative to prevent problems before they escalate.

That distinction hit me hard because in my own work, I’ve seen the same thing: unless AI is deeply wired into the process itself, not just layered on top, the real gains never materialize. Copilots give you pockets of productivity. Agents, when deployed well, can rewire how work actually gets done.

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Where I See Momentum Today

Reading the McKinsey report made me reflect on industries and functions where I already see this agentic shift beginning to unfold:

• Banking: In compliance-heavy areas like KYC and sanctions screening, copilots can only go so far. What’s powerful is when agents orchestrate the full workflow, pulling data from multiple sources, cross-checking against regulations, flagging anomalies, and escalating only exceptions. That’s where compliance transforms from a bottleneck into a proactive, streamlined process.
• Legal: I’ve seen firms experiment with AI copilots for case-law research or contract drafting. Helpful, yes. But the real leap is when agents manage the entire flow, from retrieving precedents to drafting tailored clauses to tracking review cycles. Lawyers step in only for nuance, strategy, or final judgment. That’s a fundamental shift in how legal teams scale expertise.
• Enterprise Technology: Major players are already rearchitecting. Microsoft is embedding agents into Copilot Studio, Salesforce has launched Agentforce, and SAP is rolling out agent-first frameworks through Joule. These aren’t bolt-ons. They are signals that enterprise platforms are moving from AI-augmented to AI-native.

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And I’ve seen this transition firsthand too. In my own work, the gap between copilots and agents is very real. Tools like Epic AI, which acts as an enterprise knowledge copilot, show just how powerful copilots can be in helping knowledge-heavy teams such as legal, compliance, or operations surface answers instantly, grounded in facts and citations. But copilots, by design, are assistive. To actually move the needle on end-to-end workflows, I’ve seen the difference when orchestration platforms like IntellixCore come into play, enabling agents to cut across legacy systems and automate processes such as reconciliation, audit, and compliance monitoring.

Making this shift stick hasn’t just been about technology. Through our AI Center of Excellence, we’ve learned that adoption depends on governance, workforce readiness, and trust every bit as much as on models and platforms. For me, that has been the biggest reminder that agentic AI is as much an organizational transformation as it is a technical one.

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Reinvention, Not Layering

Another key learning from the McKinsey piece, and one I’ve felt personally, is that real value doesn’t come from simply inserting agents into legacy workflows. It comes from reinventing the workflow entirely.

Think about it this way: if your process is still designed for humans to handle every step sequentially, then adding agents only makes the human parts faster. But if you redesign the process around agents, parallelizing tasks, automating escalations, and rethinking decision logic, that’s when you see step-change improvements.

Take a customer service desk, for example. With copilots, you might cut ticket resolution times by 5–10%. With agents layered on top, maybe you get 30–40%. But when you redesign the entire process around agents, letting them proactively detect issues, resolve standard cases end-to-end, and escalate only exceptions, resolution times can shrink by 60–90%. That’s not optimization. That’s reinvention.

This is my biggest takeaway: adding AI helps you move faster. Redesigning for AI changes the game.

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The Human Side We Can’t Ignore

Here’s the part I don’t think gets enough attention: the hardest challenge isn’t technological. It’s human.

Agents introduce a new layer of complexity. They don’t just assist. They act. Which raises tricky questions:

• When should an agent take initiative?
• When should it defer?
• How do we keep human oversight intact without slowing everything down?
• Who decides what agents get built, and how do we prevent sprawl?

In practice, I’ve seen teams resist adopting AI not because the models were weak, but because the trust wasn’t there. People weren’t sure what the agent would do next, or whether it would align with their judgment.

That’s why we’ve emphasized governance and transparency in every LatentBridge deployment. Whether it’s Epic AI in knowledge workflows or IntellixCore in compliance orchestration, adoption has accelerated only when people feel in control, not displaced. Our AI CoE has become less about building tech and more about building trust.

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The CEO Mandate

What also resonated with me was McKinsey’s framing of the CEO mandate. This pivot, from experimentation to enterprise-scale transformation, can’t be delegated. It needs top-level ownership.

Here’s how I interpret it:

1. Conclude the experimentation phase. Not every pilot deserves to scale. Leaders need to formally close the loop on experiments and refocus on high-impact domains.
2. Redesign governance and operating models. AI can’t sit in silos anymore. You need cross-functional squads that bring together domain experts, engineers, designers, and business leaders.
3. Launch lighthouse transformations. Start with a few end-to-end processes where agents can prove transformative value, while laying the technical and cultural foundation for broader scale.

I believe this is where the winners and laggards will start to diverge. Those who continue dabbling with scattered pilots will watch their advantage erode. Those who rewire their organizations with agents at the core will leap ahead.

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My Reflection

If I had to distill my personal takeaway from reading McKinsey’s report, it would be this: the experimentation phase is over.

Leaders can’t afford to treat AI as a side project anymore. It’s time to stop asking “where can we add AI?” and start asking “what would this function look like if agents ran 60% of it?”

That’s the mindset shift. Pick a few high-impact domains. Rewire them completely. Let agents handle execution while humans focus on oversight, strategy, and exceptions. Pair knowledge copilots like Epic AI to ground decisions in the right facts, and agentic orchestration via IntellixCore to carry work across systems.

Done right, this isn’t just about efficiency. It’s about agility, resilience, and even creating new revenue streams. Done wrong, it risks wasted investments and deeper frustration.

For me, that’s the essence of the agentic advantage: not just faster tools, but a new operating model for how businesses think, decide, and execute.

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Closing Thought

Agentic AI isn’t an incremental step. It’s a foundation for the next-generation enterprise. Some companies are already moving, not just deploying fleets of agents but reshaping their entire operating models to harness them. Others are still experimenting on the edges.

At LatentBridge, I’ve been fortunate to see both sides of the journey: early copilots that showed us what’s possible (like Epic AI, our enterprise knowledge copilot) and now agent-first operations through IntellixCore that help organizations rewire how they work. What I’ve learned is simple: technology alone doesn’t create transformation. It’s when you pair it with trust, reinvention, and leadership intent that the impact becomes real.

And as I closed McKinsey’s blog last night, one thought stayed with me: the time for exploration is ending. The time for transformation is now.