Building the agentic enterprise

Most of the conversation about AI agents right now is about speed: resolving the support ticket or closing the books faster. Those gains are real, and they undersell what's actually happening.

The more interesting question is what becomes possible once agents are built into how a business runs. I spend most of my time on real implementations with customers, and one pattern is consistent: the companies pulling ahead are redesigning their operations and offerings so that agents can discover, reason about, and act on them.

What an agentic enterprise actually is

When someone asks me to define the agentic enterprise, I describe a company doing a few deliberate things at once.

The agentic enterprise simplifies the tools its own people use to build, test, and ship agents safely and quickly. It makes its products and services easy for external agents to discover and act on, the way companies once made their pages easy for search engines to find. Beyond its own walls, an agentic enterprise works with regulators and compliance teams to shape the legal and policy rules that will let agents transact, and it joins the standards groups working out how agents from different companies will find one another and do business. Underneath all of it, leaders set a clear direction and align incentives so their people and the market trust where the company is heading.

This goes beyond efficiency

Two themes from the field show what an agentic enterprise can do that a traditional one can't.

The first is volume: handling a level of inbound demand that a conventional operation can't keep up with. One bank I work with serves 120 million monthly active digital customers. Today most of those relationships move through a manual, human-curated path across the web and the branch, and going from first contact to a completed transaction can take days to weeks.

We ran the numbers on a different approach. If the bank offered even a fraction of its products in formats built for inbound agents, the time to close could drop from weeks to minutes. The market it can reach also grows well beyond its current customers, because agents are out there scanning an entire sector for the best deal on their owner's behalf. Customer acquisition costs fall, the room for revenue grows quickly, and every interaction feeds a loop of data and insight that gets better the more it runs.

Learning faster

The second theme I see in agentic business is process reinvention: turning a rule-based operation into one that learns and improves with every decision. I can share an example from a very common back-office process, invoice-to-pay. Much of it is still done by hand, and earlier attempts to automate it ran into a wall, because the work depends on a mix of well-documented steps and tacit knowledge that lives in people's heads.

One customer cracked it with an approach we call the Agent Gym. The agent handles the parts of the process it can navigate well, where the guardrails are clear and the reasoning is sound. When it reaches a gray area, such as an unclear condition or a judgment call, it pauses and asks a subject matter expert to weigh in. Here's the part that matters: the expert documents the reasoning behind each decision, whether an approval, a rejection, or a change, and that explanation feeds back into the system for every transaction that follows. The agent goes to the gym, so to speak, and gets a little stronger with each rep. The company starts to multiply the returns from its tacit knowledge across the entirety of its operations. 

The result is a modern division of labor. The agent does the bounded reasoning. The company's existing policies and regulatory responsibilities act as the guardrails. The expert spends their time on genuinely hard cases and on making the whole organization smarter. The repetitive, low-value work is off their plate. Once one team sees that working, others want it too. That's the moment learning across the company starts to accelerate sharply.

Where to start, and in what order

CIOs often ask me where to begin, and the order matters.

Start with why. Before reaching for any technology, get specific about the goal: revenue, cost, reaching new markets, a better customer experience, whichever business drivers actually matter to you. The most common mistake I see is doing agents for the sake of doing agents. Plenty of areas benefit from this work, and not everything needs an agent. Every worthwhile agentic project hits a hard stretch where progress stalls. If the underlying problem doesn’t really matter, it’s easy to give up. 

Next, look hard at your data. Agents need good data, and they need it accessible and streaming. A lot of today's systems still operate in silos and batch mode. That won't keep up. The constant, recursive way agents work makes this critical.

While you map your business processes and open up high-quality data, start sandboxing a few projects to build real skill and an agentic instinct. Building for agents is different from building for people, and the shift is genuinely disruptive. You can't develop a feel for it by hearing about it at a conference. We've been building agentic workflows inside Google for a few years, and only now is that institutional instinct starting to sharpen. 

Finally, most enterprises run on systems and software built for the way a human-centric, pre-agent business was organized. Within a few years, agents may become the main way people get work done across all of it: someone says what they want in one place, and agents translate that into action across the systems underneath. We are building toward that future with Gemini Enterprise, giving teams one place to create and manage agents across their data and systems. This is Google’s own front door to the agentic enterprise. We take what we learn and build that into the platform for all of our customers. 

The work starts now. Make your existing systems and data discoverable and usable by agents. Agents need context, tools, and data; teams need a consistent place to build, deploy, and improve what they create. No one, human or agent, should have to learn ten systems to get one job done. The advantage goes to companies that stop letting their tools dictate how work happens and start making those tools serve the way work should happen. 

What changes for the people

Managing a team of people and agents together is mostly a design job, and it's where a leader's role changes the most. Part of that design is composing agents that play different roles: one routes the work to the right place, another assembles the pieces, another synthesizes a result a person can act on. The day-to-day approving and processing moves to the agents. What lands on the manager is the harder question of where the boundaries belong: what an agent can decide on its own, and the point where a person needs to step in. A team once judged by how much work it pushed through is now judged by how well it sharpens the system for what comes next. That shift is worth planning for from the start.

How to tell it's working

If you start now, here's roughly what the first year looks like.

At three months, with an ambitious goal set, solid data in place, and a team building fluency with agentic design patterns, you should see early, measurable gains against the goal you set.

At six months, most projects will hit a wall. You'll run into incomplete data, assumptions that turn out wrong, and procedures more brittle than anyone realized. This is normal, and it's the moment to press on. 

Some of the most valuable projects look like failures first. Our own invoice-to-pay effort looked dead on arrival, until we found a way to capture the tacit knowledge it depended on. We now execute this complex business process at a quality level higher than humans alone could achieve and in one-tenth of the time. 

By the one-year mark, the real payoff reaches past the productivity number everyone quotes. The capability compounds and spreads. A method that cracked one process becomes the starting point the next team builds on, and the organization gets better at the work itself.

What the winners will share

I've been at this long enough to avoid making definitive predictions. However, based on what’s happening in our own four walls at Google, and across the hundreds of customers my team works with around the world, I'm confident about the shape of the transformation possibilities and what that means for competitive advantage. The durable edge will go to the companies that learn the fastest, the ones where curious, humble, and action-oriented people hand the routine work to agents so they can spend their own time on the problems that matter most. What they share is a focus on concrete outcomes for customers, real gains in how the work gets done, and the capability they build along the way.   

Pick a goal that means something, get your data ready, and put small teams to work building, failing, learning quickly and progressing. Let them build the instinct the rest of the company will need to succeed in the age of agentic AI.

The companies that build relentlessly, putting their technology, data, human expertise, and market knowledge to work, will move up the learning curve fastest and prove hard to catch.