Agents are here—is your company prepared?

But not every company is on the same clock. To understand what separates frontrunners from the rest of the field, Microsoft surveyed 500 enterprise decision-makers across 13 countries and 16 industries, spanning companies from $1 billion to over $50 billion in revenue. The survey looked at how well organizations are prepared to design, deploy, and operationalize agents, and tied those readiness levels directly to real-world speed of deployment.

We grouped companies by how ready they are to use agents: “Achievers” score high on both strategy and execution and scale fastest; “Visionaries” have big ideas but weak execution; “Operators” are strong executors but lack a clear strategy; and “Discoverers” score low on both, which is why their pilots take longest to turn into real results.¹

The organizations that say they’re truly ready for agentic AI—Achievers, who will win the race to become Frontier Firms—expect to scale roughly 2.5 times faster than the organizations still ramping up with their AI strategy and execution (Discoverers). And the gap keeps widening.

What separates these companies isn’t AI budget or technical firepower. It’s preparation.

That readiness gap matters more than ever because agents are fundamentally different from anything that came before. They do more than check boxes and run scripts. They keep the workflow moving while you handle the big decisions. Under human direction, agents sort through leads, process exceptions, reconcile data across platforms, route approvals, and flag what needs escalation. They shoulder the slog that keeps your best people from doing their best work, giving teams back time and energy for strategy, creativity, and the judgment calls that truly move business forward.

And unlike previous waves of automation, agents compound. They start small, but once they’re embedded throughout an organization, the acceleration becomes exponential. It’s like going from powering a single bulb to running an entire city. That’s why many CEOs see agents as the defining shift of the decade.

But here’s the catch: That compounding only happens if the foundation is right. The organizations racing ahead map workflows before dispatching agents to run them. They unify data before trying to act on it. They put governance in place before they need it.

So how prepared is your organization? The survey results show what it takes to be agent-ready.

More than 80% of leaders expect agents to be moderately or extensively integrated into their AI strategy within the next 12–18 months, according to the 2025 Work Trend Index report. And in our survey, about 4 in 5 organizations report being in or past the pilot stage—32% preparing to scale and 15% revisiting their strategy after early tests.

That’s the story companies tell themselves. The truth is messier.

Reported execution across functions is slightly uneven, as reflected in the survey’s data on current and planned AI-adoption across teams. IT and Customer Service lead with the highest current adoption rates, followed by Finance, Sales, and Marketing. Procurement, HR, and Supply Chain trail behind but show promise, with bold planned adoption increases of 20–23%.

Then there are issues related to information and culture. Nearly 80% of organizations say they can’t share data across teams in ways that make agentic AI work. Two-thirds lack executive champions willing to clear the path.

Without those foundations, AI strategy stalls before it starts. You’re asking agents to work in the dark.

The distance between fast movers and everyone else comes down to five core capabilities. These aren’t abstract principles. When it comes to agent adoption, they’re the tactical differentiators that separate months from years.

1. Wire agents directly into business outcomes

Why it matters:
Vision without execution is just aspiration.

Companies with grand strategies but weak operations (Visionaries) say they average at least nine months to deploy. Top performers (Achievers) report under six.

The difference reveals the chasm between the strategy deck and the unglamorous daily work that makes agents succeed.

The gap shows up in every metric: Compared to those organizations still nascent in their AI strategy (Discoverers), top firms (Achievers) are 3-5x more likely to “strongly agree” to emphasizing enterprise-wide deployment (59% vs. 15%), investing in AI to achieve long-term goals (61% vs. 27%), and committing to establishing clear KPIs (62% vs. 12%).

What this looks like in practice:

Eaton, the global intelligent power-management firm, has embedded agents across product design, manufacturing, and customer support. Agents provide transformative technical opportunities that advance Eaton’s focus on energy resilience and efficiency, and free its teams to focus on higher-value, strategic work so they can better serve their customers. One example: 10,000 SOPs documented in 10 minutes instead of an hour.

Other success stories: DuPont has prioritized measurable business outcomes with agentic AI, redesigning end-to-end processes to unlock efficiency and innovation at scale. And Levi Strauss & Co. aligned its agentic strategy with its shift to operating and executing as a best-in-class, direct-to-consumer-first retailer.

The pattern? These companies set metrics before starting. They tracked leading indicators (agent accuracy, adoption) and lagging indicators (cost savings, revenue lift, satisfaction). To return to our race-car metaphor: They tuned the car before sending it out on the track.

Bottom line: Don’t leave performance to chance. Know what “good” looks like and adjust fast to hit your targets.

2. Map the work before automating it

Why it matters:

Based on 500 respondents, an average of only 22% “strongly agree” that their organization has documented key processes and data dependencies; the remaining 78% selected another response.

Top firms (Achievers) are about 7 times more likely than those getting started with AI (Discoverers) to report having documented their processes—and it shows. They deploy faster because they know exactly where agents fit, what success looks like, and which systems need to talk to each other.

Skip this step and agents fly blind. They make mistakes, create logjams, or optimize the wrong goals entirely.

Mapping points the way forward.

What this looks like in practice:

Ramp, a leading financial-operations platform, mapped every handoff across finance workflows and exposed every hidden slowdown. That clarity let them deploy agents that can process five million receipts a month, saving 30,000 hours and closing the books dramatically faster.

Nearly half of leading firms (Achievers) set hard targets upfront—cycle time, error reduction, cost savings. Draw a line in the sand (“shave 30 minutes off each support case”), and you can actually measure whether agents are earning their keep.

Bottom line: Speed comes from clarity. On average, just 31% of companies surveyed “strongly agree” they’ve identified and tracked the technologies, tools, and applications used across their workflows. Leading firms map it all. Map the work, name the goal, understand the tools—then test and learn quickly, and roll out with confidence instead of hope.

3. Treat data like infrastructure, not leftovers

Why it matters:

Agents are only as smart as the information they can access. This requires having your data house in order—and most companies aren’t ready. 80% of leaders we surveyed say data isn’t accessible across teams. On average, just one in four organizations “strongly agrees” to having clearly defined owners responsible for keeping their knowledge sources current and reliable—a basic prerequisite for deploying AI.

If your data is a mess, your AI will be too.

Clean data powers your agents. Assign clear owners, break silos, track data health, and make development routine.

What this looks like in practice:

The companies getting lift from agents fix the basics first, with data that’s clean, owned, and easy to move. Everything downstream gets faster. LinkedIn, for example, organized product development around “full-stack pods”—small teams where product, design, and engineering sit side-by-side from day one. Insights travel instantly. Decisions land faster. No one waits for the next handoff.

Bottom line: Data quality decisions made today determine how fast you can scale tomorrow. If a customer name is spelled 5 different ways across systems, don’t blame the agents for slowdowns.

4. Redesign work, not just workflow

Why it matters:

Here’s where too many companies stumble: They automate the work but ignore the workers.

An average of just 17% of companies insist they have a clear talent strategy that spells out the future jobs, roles, and skills needed to support an AI‑driven business. On average, 26% agreed strongly to embracing a culture of innovation by building with AI-powered tools. Among leading firms (Achievers) surveyed, 50% are already reimagining roles and career paths for an AI-first business. Among slower adopters (Discoverers), it’s essentially zero.

The real differentiator is change management. 56% of leaders surveyed in these top firms strongly agree to having solid plans to help employees adapt to new ways of working, compared to 4% of slower adopters.

What this looks like in practice:

Clarify which work stays human and which new roles emerge as routine tasks shift to agents. Picture inventory agents flagging low stock automatically while store associates pivot from scanning shelves to advising customers and resetting the floor.

Be transparent with teams about impact prior to deployment. Before rolling out an invoice-matching agent, for example, the CFO sits down with teams to show which parts of their workflow the agent will handle and which parts they’ll still own. No one wonders if their job is on the line.

Barclays uses AI in contact centers to summarize customer calls and find answers—but only to guide human agents in real time on the next best action. At Lumen, employees have completed 35,000+ AI trainings since the company introduced Copilot across the business in 2023 (61% of top firms train employees to thrive in augmented workflows).

Bottom line: Make talent strategy a business priority. Otherwise, agents remain unused or workarounds become the norm.

5. Make compliance and control non-negotiable

Why it matters:

If no one owns AI, no one fixes AI. Only about 1 in 3 companies we surveyed say they’ve designated an executive sponsor accountable for the success of their AI initiatives. Among leading firms (Achievers), that number jumps to 61%.

Safeguards are just as scarce: An average of just 29% of organizations we surveyed “strongly agree” to having real protections in place for safe AI use. Only 26% on average agree strongly to actively monitor AI to ensure compliance.

When someone’s in charge of security and governance, things don’t fall through the cracks. Those leaders set the guardrails, make fast decisions, and keep AI running safely and consistently as it scales.

What this looks like in practice:

At Clifford Chance, the CTO led the global law firm’s effort to ensure AI was deployed responsibly, working closely with the Chief Risk and Compliance Officer to build AI principles and policies before deployment—specifically to maintain high ethical standards and ensure compliance amid fast-evolving regulations. That same team designed a regulatory compliance agent to automatically track changing laws and generate tailored impact assessments.

Leading firms test agents before launch, set up human review for high-stakes decisions, monitor for bias, and create escalation paths.

Bottom line: The leaders who define ownership, run tests, and extend governance across the business end up with agents that earn trust every time they run.

The window is closing. In about 18 months, the market will split between organizations that scaled agents across core workflows and those still stuck at the starting line.²

Organizations that act now will move faster toward capturing value at scale. Those that wait will likely spend years chasing the frontier they could have helped shape.

The race has already started. The only choice now: Will your company be in it?