Where Enterprises Should Actually Start with Agentic AI

Most enterprise teams don’t fail at agentic AI because the technology isn’t ready.

They fail because they start in the wrong place.

Their instinct is understandable: aim big. Train a custom model. Launch a super-agent. Solicit use cases from across the company. Redesign how everyone works.

That ambition often becomes the blocker.

The fastest path to real value with agentic AI isn’t to wow.
It’s to be useful.

Start With Work That Already Exists

The most successful first agentic projects share a few traits:

• the workflow is already happening
• the pain is well understood
• the outcome is uncontroversial
• and the value is immediately visible

These projects don’t require new organizational buy-in. They reduce friction people already complain about.

Simple examples include:

• asking questions about a company’s 10-K and emailing a summary
• querying internal data in plain English and generating a PDF report
• notifying a sales rep in Slack when a deal sits stalled for 30+ days

None of these are revolutionary, but that’s the point.

Small wins build confidence. Confidence compounds.

Two Common Starting Paths (That Eventually Converge)

As we’ve worked with customers, we’ve learned that enterprises tend to start agentic AI in one of two ways.

1. Self-Serve Productivity Agents

These are lightweight, internal workflows that individuals or teams can build themselves:

• status summaries
• reporting helpers
• notifications and reminders
• context gathering across documents and tools

They’re ideal for:

• proving value quickly
• building internal fluency
• letting teams experiment safely

This is where no-code and low-code tools shine. The goal isn’t scale: it’s momentum.

2. High-Impact Operational Workflows

At the same time, many enterprises see the largest immediate value in more sophisticated domains:

• technical support engineering
• deal desk, pricing, and approvals
• revenue and operations workflows

These areas are:

• context-heavy
• cross-system
• tightly governed
• and directly tied to revenue or risk

They’re not DIY projects. They require deeper system integration, guardrails, and operational design, often with vendor support.

But when they work, the impact is material.

The Important Insight: These Aren’t Competing Strategies

The mistake is treating these paths as mutually exclusive.

In practice, they reinforce each other.

Self-serve agents help organizations:

• learn how agents behave
• understand failure modes
• build trust with stakeholders

Operational agents then apply those lessons to:

• higher-stakes workflows
• broader adoption
• measurable business outcomes

What starts as a productivity tool becomes an operational system once the organization is ready.

What Not to Do First

Teams struggle when they:

• chase full autonomy too early
• start with abstract use cases
• optimize for demos instead of workflows
• or treat agents as a replacement for judgment

Agentic AI works best when it augments people, not when it tries to leapfrog them.

A Practical Way to Choose Your First Project

A simple test:

What task would someone happily stop doing tomorrow if it worked reliably in the background?

If the answer is clear, you’ve found a good starting point.

Agentic AI doesn’t need to begin with a transformation.
It needs to begin with trust.

That trust is earned, one useful workflow at a time.