2026: The Year the Agentic Enterprise Takes Flight(?)

One constant in the business world is that things change fast. Costs are still rising, supply chains are still fragile, and markets remain unpredictable.

The equally constant and inescapable business topic of the last few years is Enterprise AI, specifically how it can help enterprises overcome some of these challenges.

Enterprise AI is about more than just problem solving, though. It’s a practice – the strategic shift required for infusing AI into your operations.

The leaders who have begun to consider how Enterprise AI as a practice will reinvent operations are also beginning to reimagine the way work gets done (beyond just solving problems). What does that look like? Using AI agents to automate tasks, make decisions, and adapt with minimal human input.

As we asked 1,649 senior business leaders about their AI ambitions in 2026, new questions emerged:

• What will it take to make AI work in the enterprise?
• Are we on the precipice of the agentic enterprise?
• And if so, are enterprise operations currently good enough to layer AI on top of them?

Our third annual Process Optimization Report explores how organizations are using AI to transform operations and the obstacles they face along the way.

The findings confirm that hopes for Enterprise AI are high, despite 60% of businesses struggling to adapt their operations quickly enough to get ROI from AI investments.

It’s no longer a question of whether enterprises are willing to adopt AI. They are. Now, leaders want to know what it will take to be ready to do it successfully.

AI ambitions meet reality

Heading into 2026, the AI landscape has shifted dramatically. AI ambitions among enterprise leaders are high. Leaders and employees alike are more open to AI usage at work than ever before. And many leaders are seeing the importance of having multi-agent systems be able to tackle complex problems.

As we began to understand where leaders and enterprises are heading in 2026, an interesting story took shape. Leaders appear to have big ideas for AI adoption, but operationally, the enterprise just isn’t there (yet):

• Big Agentic Goals: A significant 85% of businesses aim to become an 'agentic enterprise' within the next two to three years. Autonomous agents, capable of dynamic reasoning and decision making, are at the center of this ambition.
• Current AI Use: While 83% of businesses are using Generative AI (GenAI) tools to aid daily work, and 61% are deploying AI chatbots or copilots, the use of more sophisticated tools is lower. Only 27% are building specialist AI assistants, and 23% are developing sophisticated AI agents.
• Multi-Agent Systems: Nine-in-ten businesses are already using or exploring multi-agent systems. Currently, 19% of enterprises are using these systems, with 71% exploring them, suggesting increased adoption is likely in 2026.

Readiness, not resistance is now the biggest obstacle to Enterprise AI success

Resistance to AI adoption is fading in large numbers. This year, just 6% of business leaders cite resistance to change as a major hurdle to scaling AI.

Instead, operational readiness is the obstacle.

Leaders spoke of a lack of internal AI expertise as a significant hurdle (47%). Additionally, 45% of respondents cited a lack of alignment between IT and business stakeholders.

Some 82% of leaders agree that AI can only deliver ROI if it understands how the business runs. And yet, 45% note difficulty in getting AI to understand the necessary business context, such as rules, KPIs, and enterprise architecture.

The desire to reshape the enterprise via AI is stronger than ever. But leaders are realizing they may have more foundational issues to deal with.

Unsteady foundations point to sub-optimal processes

AI needs business context to be effective. Without context, AI contains loads of potential for transformation, but none of the facts required to make it happen.

That’s why processes, the most accurate representations of how a business runs, are the underlying foundation needed to make AI work. Yet, over three-quarters (76%) of businesses report they’re only "getting by" with sub-optimal processes.

Leaders know they need to improve the enterprise at a foundational level if they hope to achieve the agentic ambitions noted earlier. Some 67% say they have concerns about the data they’re working with to improve processes.

That’s why we asked them to identify some of the biggest red flags that let them know their processes aren’t working. Difficulty adopting new technologies (e.g., AI, automation) topped the list at 20%. Frequent process delays or bottlenecks accounted for another 19% while another 12% cite rising operational costs.

While red flags can alert a leader to a potential problem, leaders said the top obstacles actually stopping processes from working are complex, outdated, or disconnected systems (45%), lack of coordination between departments (44%), and poor availability, visibility, or quality of process data (43%).

Taking action on operational improvement

The good news is that enterprises largely understand the need for process improvement: 60% view process improvement as a critical, continuous business-wide initiative.

They’re also more likely than ever to take action with a variety of tools at their disposal. Traditional Business Intelligence (BI) tools (74%) still top the list with 74% saying it’s their preferred method of improvement. However, AI-powered tools (66%) have caught up with process mapping workshops (66%), while 38% of businesses are currently using a digital process twin.

The landscape is shifting. Leaders are getting a clearer picture of what it’s going to take to realize their agentic ambitions. Now it’s time to take Enterprise AI to the next level.

While you’re here, take 2 minutes to see how your approach to and use of AI compares to those in our survey. Click here to take our interactive quiz and see how you’re doing.

Also, don’t forget to download a copy of the full 2026 Process Optimization Report to learn how your enterprise can master its processes, build AI-ready operations, and provide the business context needed for AI success.