The end of experimentation with AI agents

For the last several years, enterprises have treated AI as something to test. A pilot here, a proof of concept there. That era is ending. According to new global DeepL research, a survey of 5,000 global executives on the impact of AI agents reshaping business, 69% expect AI agents to fundamentally change how their companies operate in 2026. Nearly half anticipate major transformation, while another quarter say that change is already underway.

This moment didn’t arrive overnight. While 2025 was the year agentic AI moved from theory to application, enterprises are making the shift structural this year. Leaders are no longer asking whether AI works but rather deciding where AI agents belong inside their operating model. As tools mature and agentic systems become capable of coordinating work across functions, AI agents are unlocking new opportunities—not just automating tasks. By eliminating manual coordination, AI agents enable organizations to move faster and smarter, enabling the creativity, problem-solving depth, and judgment that turn velocity into measurable value.

Yet, when it comes to scaling agents and validating their investment, most organizations remain stuck in pilot mode. McKinsey reports that while 62% of companies are experimenting with agents, only 10% are scaling them across a single function, and just 32% of leaders report an impact on EBIT at the enterprise level. The gap between early adopters and those who hold out will widen in 2026—not because of trial and error, but execution.

Three shifts will define this gap—how enterprises automate core operations, deploy AI for growth, and build the communication infrastructure agents require.

AUTOMATE THE CORE

AI agents are no longer confined to experimentation or pilots. Enterprises are deploying them into operational workflows like processing returns in customer service, investigating customer complaints, automating approvals and ticketing, supporting prospect and competitor research in sales and marketing, and optimizing working capital in finance. What’s changing is continuity. Instead of accelerating individual tasks, organizations are increasingly making agents responsible for managing handoffs between them—reducing friction.

Looking at AI agent adoption more broadly, DeepL’s research shows global executives cite proven ROI and efficiency (22%), workforce adaptability (18%), and enterprise readiness (18%) as the primary reasons they feel confident expanding agent deployment. Results, not optimism, are driving this shift.

At the same time, known barriers are beginning to soften. Cost (16%), workforce preparedness (13%), and technology maturity (12%) remain challenges, but enterprises are actively addressing them as they gain experience operating agents in production environments.

The real risk now is inaction. Organizations that fail to identify which workflows should be automated first keep valuable talent focused on low-leverage work—while competitors redesign operations around intelligent systems. Customer service offers a clear example. Companies like Perk are deploying AI agents to take on routine operational work in customer support, while human agents focus on complex, relationship-driven scenarios.

As Tom Davis, senior director of operational excellence at Perk, notes: “When we’ve got travelers stuck in airports, we want our humans focused on those moments—and in the background, a machine of AI handling the grunt work.” That division allows human agents to focus on high-stakes relationship work while AI agents manage operational tasks at scale.

AI AS A GROWTH ENGINE

AI is no longer confined to cost reduction. It’s becoming a driver of growth.

The broader AI landscape shows strong momentum: 67% of executives reported measurable impact from AI initiatives in 2025, and 52% expect AI to contribute more to company growth than any other technology in 2026.

Enterprises seeing the strongest returns are applying AI across revenue-generating functions—customer service, marketing, sales, finance, legal, HR, and IT support—rather than limiting it to back-office automation. The competitive advantage comes from scale and integration, not just isolated use cases.

But the real gain isn’t just efficiency—it’s faster, higher-quality decision making. As b2ventures noted in their work with AI agents, the technology helps them make higher-quality investment decisions faster because agents excel at evaluating companies and surfacing insights that inform critical choices.

According to DeepL’s research, leaders in the UK (80%), Germany (78%), and the U.S. (71%) are seeing measurable performance gains from AI initiatives. This underscores that execution and organizational readiness are just as important as access to technology when it comes to turning AI into a strategic advantage.

Ignoring AI in growth-critical areas is no longer conservative. It’s a strategic risk, particularly in sectors where margins and customer expectations are shifting fast.

LANGUAGE AND VOICE AI

As AI agents move deeper into enterprise workflows, they’re changing how people interact with software itself. Instead of clicking through dashboards or submitting forms, employees increasingly instruct systems through natural language. In an agent-driven operating model, language becomes the primary user interface. This is the mechanism through which work gets done.

That shift raises the stakes for fluency and accuracy. When language is the interface, a mistranslated prompt or misunderstood instruction doesn’t just slow down communication; it can derail an entire workflow. For enterprises scaling agents across teams and regions, language precision becomes a requirement, not a nice-to-have.

This is reflected in enterprise priorities where 64% of companies plan to increase investment in language AI in 2026, while organizations expect adoption of real-time voice translation to rise to 54%. These investments aren’t standalone initiatives. They are foundational to making AI agents reliable, scalable, and effective.

EXECUTION, NOT EXPERIMENTATION

This year, the organizations experiencing the biggest impacts will stop experimenting with AI and start embedding AI agents into core operations and applying them across growth-critical functions. By turning AI into a strategic advantage, these companies will streamline operations, make better decisions, and unlock measurable business value. Those who delay will watch the gap grow as early adopters accelerate ahead.

Jarek Kutylowski is the CEO and founder of DeepL.

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