Enterprises aren’t running out of ideas — they’re running out of focus. Strategies evolve, priorities shift, and decisions now depend on more information than teams can absorb in real time. Agentic AI helps break through that coordination ceiling by handling repetitive work and freeing architects to focus on higher-value tasks.
That’s why adoption is rising fast. KPMG’s September 2025 analysis found that the proportion of organizations that have deployed agentic AI has jumped from 11% to 42% in just two quarters, with annual budgets exceeding $130 million. From what we’re seeing, leaders are investing in outcomes: faster delivery, teams that can focus on strategic work, and systems that support real-time coordination across functions.
At the same time, frameworks like the EU AI Act and the UK’s AI Opportunities Action Plan demand greater transparency and control. Organizations are expected to move faster while staying fully accountable for how decisions are made.
Agentic AI supports both aims. It combines human judgment with intelligence that acts with intent. Agents collect, reason, and act within context, connecting systems and aligning data, so transformation keeps flowing.
From Isolated Automation to Intelligent Collaboration
We’ve seen most enterprise AI initiatives start small by automating reports, summarizing documents, or streamlining requests. These early wins help with efficiency, but they don’t change how teams align or make decisions. That takes something else: Intelligence that collaborates.
Getting there requires deliberate agent design. Agents perform best with a clear scope; specific goals, trusted data, and defined rules of engagement. That’s why discipline matters. As Harvard Business Review points out, Agentic AI delivers the most value when organizations resist the temptation to deploy it indiscriminately and instead approach it with clear intent and strategic focus. Much like high-performing teammates, they learn from context and improve through repetition. Individually, their roles may be narrow; together, they can take on the complexity of the enterprise.
A few agent types are proving especially useful for delivering clear, repeatable value:
- Conversational Agents that engage with people and systems to collect updates, clarify goals, and convert dialogue into data.
- Utility Agents that handle precision tasks like maintaining data quality, updating records, and ensuring consistency across tools and models.
- Insight Agents that analyze connections across portfolios, surfacing gaps, risks, and opportunities before they escalate.
- Multi-Modal Agents that combine the strengths of specialized agents, coordinating tasks end-to-end across functions and data sources to deliver complete, context-aware outcomes.
This kind of connected intelligence depends on a shared foundation. Enablers like Model Context Protocol (MCP) make it possible by connecting AI agents to enterprise repositories, tools, and APIs — ensuring their decisions and actions are grounded in live operational context. That’s the differentiator. Instead of generic AI working in isolation, these agents operate on your data, your context, and an understanding of your goals, capabilities, and dependencies.
Most Enterprise Teams Don’t Struggle with Ideas; They Struggle with Bandwidth.
When we speak with chief information officers, chief architects, and transformation leads, the same goal comes up repeatedly: Scale the team’s impact beyond its current limits. Agents help by removing the manual, time-consuming work that drains capacity, such as managing data quality, linking siloed data sets, capturing information from documents and meetings, and entering it into the EA repository.
Automating this work gives EA teams the space to focus on more strategic tasks like stakeholder engagement, communication, and supporting a greater number of initiatives. In the State of Enterprise Architecture 2025 Report, 47% of the most mature organizations identified “delivering more strategic insights to senior management” as their top priority. That’s exactly the kind of impact agents help unlock.
But strategic impact also depends on trust. Teams need to understand where an agent drew its conclusions, what data it accessed, and how it weighed different options. When people grasp the why behind AI-generated insights, they can refine them, debate them, and act faster. That combination of speed, context, and explainability is what turns automation into 10x productivity.
The Road Ahead
Agentic AI adoption is accelerating, and investment levels show strong confidence in its potential. Opportunity lies in how it’s applied, though. Organizations don’t need sweeping overhauls to see impact – they need focus. Starting with clearly defined tasks and quality data delivers faster returns and builds trust in how decisions are made.
When agents handle repetitive, manual tasks, EA teams can shift their energy to higher-value activities such as shaping business direction and increasing their strategic influence across the organization.
As this approach matures, Agentic AI can become more attuned to enterprise context and help teams uncover new business value, generate deeper insights, support more informed decision-making, and deliver outcomes that were once difficult to achieve.
In our view, the future of enterprise collaboration is pragmatic: People and agents working side by side, sharing knowledge, and turning complex change into continuous progress.
