Adopting Agentic AI in 2026: 5 Things You Can Do Right Now

What Is Agentic AI — And Why It Matters in 2026

Before diving into action steps, it’s useful to clarify what makes agentic AI different from traditional forms of artificial intelligence.

Traditional AI — including many generative models — is reactive: it responds to questions, generates content, or makes predictions. Agentic AI, by contrast, is proactive and autonomous: it plans multi-step actions, interacts with systems, learns from outcomes, and executes tasks with minimal human direction. These agents can coordinate work across systems, interpret data in context, and complete tasks that previously required many manual steps.

In business terms, this means shifting from AI as an assistant to AI as a digital teammate — one that can improve processes, reduce friction, and help teams achieve outcomes rather than just outputs.

According to data from industry research, while many organizations are already experimenting with agentic systems, only a minority have scaled their efforts in a way that delivers sustained value. That’s an opportunity: early adopters — and those who prepare now — stand to leap ahead.

Break Open Unstructured Data with Intelligent Document Processing

Every business process — from procurement to HR onboarding to customer service — depends on information locked inside documents, emails, forms, contracts, and other unstructured sources. Yet that data is often inaccessible to AI systems because it isn’t organized in a way machines can easily interpret.

This is where intelligent document processing (IDP) becomes essential. IDP uses a mix of optical character recognition (OCR), classification, extraction, and AI to turn unstructured text into structured, actionable data. This investment enables agentic AI by giving it clean, reliable inputs, which in turn improves accuracy, speed, and confidence in decision‑making.

By investing in IDP early, organizations gain two major advantages:

Better foundation for autonomous action: When data is structured and contextualized, agents can interpret it correctly and take meaningful steps across workflows.

Immediate operational gains: Processes that used to require manual document handling are automated, freeing your teams to focus on higher‑value work.

In short: you can’t have effective agentic AI without great data. Start improving your data readiness today.

Experiment with AI Agents Across Use Cases

Once your data foundation is stronger, the next step is to start exploring the possibilities — not just thinking about them.

Many enterprise teams already have automation through RPA (robotic process automation), business rules engines, or scripted workflows. Agentic AI takes this a step further. Agents can interpret data, make context‑aware decisions, reason across steps, and orchestrate actions that would otherwise require human intervention.

But jumping straight to large, enterprise‑wide deployments is risky and expensive. Instead, take a phased experimentation approach:

Start small: Identify specific business problems where autonomy could add value — for example, collections follow‑up, procurement approvals, customer onboarding, or cybersecurity triage.

Use low‑code tools: Empower business technologists and automation developers to build and test agentic prototypes without heavy engineering overhead.

Involve specialists when needed: Engineering teams should handle more complex integrations or advanced reasoning capabilities using SDKs and platform extensions.

Encourage a culture where teams ask questions like:

What could an AI agent do in customer support?

How could an agent improve procurement cycle times?

What would it look like if an agent handled incident response?

The early experiments you run now won’t just produce ideas — they’ll give you real prototypes, learnings, and a roadmap for where to scale next.

Rethink Processes With Agents in Mind

Most enterprise processes today were designed for human execution — linear, sequential, and dependent on manual decisions. Agentic AI doesn’t fit neatly into this model.

To unlock the full potential of agentic AI, organizations must redesign processes to leverage autonomy and contextual decision‑making. Simply embedding an agent in a single step won’t deliver transformational value; you need to reimagine how the whole process flows when decisions can be automated and orchestrated end to end.

Here’s what this redesign involves:

Process mapping with agentic checkpoints: Identify where human judgment is essential, where agents can take over deterministic work, and where hybrid collaboration makes sense.

Optimizing handoffs: Ensure transitions between human and agent tasks are smooth, transparent, and auditable.

Anticipating exceptions: Embed guardrails so agents know where to escalate and when to seek human input.

This is not a short exercise, but the payoff is huge. With well‑designed processes, agents don’t just automate — they enhance performance by reducing error, speeding work, and enabling real‑time orchestration.

A unified model of process + agentic autonomy is what distinguishes pilot projects from enterprise‑level deployment.

Use Process Intelligence to Choose the Right Opportunities

Before you assume where agentic AI will add value, you need to understand where your processes actually struggle today. This is where process intelligence plays a critical role.

Process intelligence tools analyze real workflow execution — from start to finish — and uncover:

• Inefficiencies, delays, and bottlenecks

• Rework loops and compliance risks

• Variation in how work gets done

• The true duration and cost of a process

With these insights, you can prioritize where agents will create the most impact. For example:

If a key process has high cycle time and low error tolerance, an agent could reduce delays and improve quality.

If compliance reporting is slow and manual, an agent could automate data aggregation and ensure consistency.

Rather than deploying agents based on intuition or enthusiasm, process intelligence provides data‑driven guidance on where autonomy will yield measurable outcomes.

This grows your confidence that each agent you deploy is a strategic choice, not an experiment in search of a problem.

Establish Governance Before You Scale

Innovation without governance is like a racecar without brakes: exciting until something goes wrong. As agentic AI systems begin interacting with sensitive data, making autonomous decisions, and performing actions with real business consequences, governance becomes essential.

Strong governance does not slow innovation — it enables responsible scaling. Governance frameworks should include:

Visibility into usage: Know which agents are deployed, where they run, and what decisions they make.

Data access controls: Clearly define what data agents can interact with and under what conditions.

Auditability: Maintain logs and explainability so you can understand how decisions were reached.

Policy and compliance standards: Set rules for approvals, performance metrics, risk boundaries, and ethical guidelines.

User training and accountability: Teach teams how to use agents responsibly, when human oversight is needed, and how to interpret results.

With governance in place, organizations gain the confidence to scale agentic AI across departments — without losing control or exposing the business to unnecessary risk.

Frequently Asked Questions

What is agentic AI?

Agentic AI refers to artificial intelligence systems that can act autonomously, make decisions, plan multi-step actions, and learn from outcomes. Unlike traditional AI, which responds passively to inputs, agentic AI functions more like a digital teammate capable of executing complex workflows.

How is agentic AI different from regular AI or RPA?

Traditional AI mainly analyzes or predicts, while RPA (robotic process automation) follows predefined rules. Agentic AI combines these capabilities, enabling the system to interpret data, make context-aware decisions, and act across multiple systems without constant human supervision.

Which business processes benefit most from agentic AI?

Agentic AI works best in processes that are data-heavy, repetitive, and multi-step, including:

• Procurement and invoice processing

• Customer onboarding and support

• Compliance reporting

• IT or security incident triage

• Supply chain orchestration

How do I start adopting agentic AI in my organization

Start small and strategic:

• Improve data readiness with intelligent document processing.

• Experiment with pilot AI agents in specific processes.

• Redesign workflows for autonomy.

• Use process intelligence to identify high-impact opportunities.

• Implement governance frameworks to manage risk and compliance.

Do I need technical experts to implement agentic AI?

While technical expertise helps for complex integrations, low-code and no-code platforms now allow business teams to prototype and deploy AI agents. Collaboration between business users and technical teams produces the best results.