Why agentic AI became a key enterprise agenda item in 2025 and 2026
Anil Yarimca
TL;DR
Agentic AI refers to AI systems that can plan and execute multi step work across tools, with defined permissions, monitoring, and human oversight. In enterprise settings, the differentiator is not the model’s raw intelligence, it is the operating model: control, observability, governance, and repeatable outcomes.
From GenAI wow factor to operational pressure
In 2025, the enterprise conversation shifted from copilots that assist individuals to agents that change workflow throughput, which made governance and risk controls part of the buying criteria.
In 2023 and 2024, many enterprise GenAI wins were productivity wins. Faster drafting, quicker summaries, better search, nicer copilots. Useful, but often hard to tie directly to balance sheet outcomes.
By 2025, leadership teams started asking a sharper question: where is the measurable business value. That pressure pushed conversations away from “assist the employee” and toward “change the workflow.”
This is the simplest reason the rise of agentic AI in enterprise became real. Agents promise to move beyond content and into execution, where value shows up as cycle time reduction, fewer handoffs, lower error rates, and better throughput.
McKinsey framed this as a shift from reactive GenAI to goal driven agents that can plan and execute across workflows, and they positioned agentic AI as a lever for deeper process transformation.
What changed in 2025: enterprise buyers got specific
Quick answer: why did agentic AI become an agenda item
Because enterprises needed systems that can execute across real tools and still be controlled like production software. Once agents can take actions, exceptions, monitoring, auditability, and rollback become executive level requirements.
In 2025, buyers started asking questions like:
- Can it run the workflow end to end, not just recommend steps
- Can it handle exceptions, not just happy paths
- Can it be monitored like production software
- Can it be controlled when it is wrong
- Can we connect it to real tools without turning security into a nightmare
That is why you saw agentic AI become an agenda item. It was no longer a research topic. It became an operating model topic.
A very practical data point: McKinsey’s 2025 State of AI report notes meaningful experimentation with AI agents, and a smaller but real portion of organizations reporting that they are already scaling agentic systems in parts of the enterprise.
Why agents, not just copilots: the workflow argument
The strongest enterprise case for agents is not “better answers.” It is “better outcomes.”
Copilots tend to sit inside a single interface. Email, docs, tickets, IDE. That helps individual productivity.
Agents are designed to move across systems. They read a request, decide what to do, call tools, update records, trigger downstream steps, and report status. That design matches how enterprises actually operate, as connected workflows that cross CRM, ERP, ticketing, data warehouses, portals, and internal services.
McKinsey explicitly highlights the potential for agents to automate complex business processes by combining autonomy, planning, and integration, which is exactly the business argument behind the rise of agentic AI in enterprise.
The enterprise pain that made agentic AI feel urgent
The automation gap
Agentic AI entered the agenda because it can add reasoning and flexibility in the messy parts of the workflow, then hand off to deterministic automation for execution.
The integration sprawl problem
Enterprises have too many systems and too many handoffs. Every new system adds more coordination work. Agentic AI pitches a way to reduce manual coordination by letting an agent orchestrate the steps across tools.
The customer expectation problem
Customers now expect faster resolution, more personalization, and 24 7 responsiveness. Agents promise to make operations more responsive without adding headcount.
These pressures did not start in 2025, but 2025 is when leaders started looking for a new lever that was not “hire more people” or “do another ERP project.”
Why 2025-2026 specifically: the infrastructure started to look usable
A big reason agentic AI became boardroom friendly is that the ecosystem started to provide better building blocks for connecting models to tools in a more standardized, safer way.
For example, you are seeing industry moves toward reusable “skills” and standardized ways to connect agents to external services and data. This is part of what makes 2026 feel like the year enterprises stop experimenting and start standardizing agent operations.
At the same time, the market also created noise. Many vendors used the word “agentic” for products that were closer to chat plus scripts. That forced enterprises to develop stricter evaluation criteria, which is another reason the topic became a senior agenda item.
The risk story: autonomy raised the governance bar
The rise of agentic AI in enterprise brought a new category of risk into the mainstream. When systems can take actions, mistakes scale.
This is why in late 2025 and heading into 2026, many enterprise discussions shifted from “can we build an agent” to “can we control an agent.”
CIO coverage in December 2025 captures this shift, focusing on governance and trust issues as key prerequisites for successful agentic deployments in 2026.
At the framework level, NIST’s Generative AI Profile (a companion to the AI Risk Management Framework) provides practical risk categories and emphasizes lifecycle management, measurement, and human AI configuration risks such as over reliance, goal misspecification, and misuse, all of which become more serious in agentic systems.
If you want the short version: agents moved the conversation from “model quality” to “operational control.”
The real reasons enterprises put agentic AI on the 2026 roadmap
Here are the motivations that show up repeatedly in enterprise planning cycles.
1) ROI moved from individual productivity to process throughput
Agentic AI is pitched as a path to measurable metrics: time to resolve, cost per case, order cycle time, invoice processing time, onboarding time. That is easier to justify than “employees write faster.”
2) Leaders wanted automation that adapts to exceptions
Agents are positioned as the layer that can interpret messy inputs, select the correct workflow path, and handle exceptions more gracefully.
3) The “agentic enterprise” narrative became a competitive frame
Large enterprise events and industry commentary in 2025 increasingly used language like “agentic enterprise,” which influenced planning cycles and budget conversations.
4) Security teams demanded new controls
As autonomy increases, controls must increase. That tension created a wave of interest in agent governance, observability, evaluation, and policy enforcement. You can see vendors and platforms responding directly to that demand.
Practical playbook: how enterprises establish “control” before scaling agents
If you want to ride the rise of agentic AI in enterprise without chaos, the winning pattern is “start narrow, prove control, then expand.”
Step 1: Tier use cases by impact
Low impact agents can be deployed faster, high impact agents need approvals and stronger monitoring. This reduces risk and speeds learning.
Step 2: Limit actions before you optimize intelligence
Give agents an allow list of tools and actions. Start with read only where possible, then expand to bounded writes with caps and approvals.
Step 3: Validate with scenario suites, not demos
Enterprises that scale agents treat them like production systems. They test normal cases and failure cases, and they keep regression suites as models and prompts evolve.
Step 4: Make observability non negotiable
Require run traces, tool call logs, decision summaries, and ownership for alerts. Without this, agents become a black box operations risk.
Step 5: Control change like software releases
Version prompts, policies, tool schemas, and model configurations. Promote changes through environments. Keep rollback paths.
Example workflow: why the “agent” approach won in enterprise ops
Use case: invoice intake to ERP draft creation
Before agents
A human reads an email, downloads an invoice, extracts fields, checks vendor rules, enters data into ERP, resolves exceptions, asks approvals, and logs the outcome.
With an agentic design
An intake step collects inputs, an extraction step structures fields, a validation step checks policy and thresholds, an execution step creates a draft record, then an approval gate controls posting.
Why this became a 2025-2026 agenda item
This workflow is measurable. It has clear cycle time. It has clear error costs. It has lots of unstructured inputs. It crosses systems. It matches the enterprise definition of “worth automating,” and it illustrates why the rise of agentic AI in enterprise is tied to operations, not novelty.
The honest bottom line for 2026 planning
Agentic AI became a key agenda item in 2025 because enterprises moved from curiosity to accountability. Leaders wanted systems that change throughput, not just writing speed.
In 2026, the differentiator will not be who has an agent demo. It will be who has an agent operating model, with evaluation, observability, permissions, and governance built in.