Scaling AI in the enterprise: Leading with agentic AI

Meet your newest team member

Agentic AI puts agents to work that act on their own, learn from every deliverable and coordinate across areas. They understand your business context, execute where it matters and improve with use.

Gartner estimates that by 2028 a third of enterprise software will include agentic AI. Today it is less than 1%. The question for your company is no longer whether you will have agents: it is whether you will be able to operate them — knowing what data each one touches, who approved what, what it cost and what it contributed.

That is what this guide is about. How to take agents from pilot to production, scale with confidence and measure impact without your operation turning into a black box.

What you'll find in this guide

• The types of agentic AI and what changes when an agent acts on its own. What autonomy really means inside a workflow, where it fits and where it doesn't, and what has to be in place before moving from pilot to production.
• The technical, operational and control decisions that make an agent reliable. How human review is designed, what safeguards each level of autonomy needs and how to avoid the failures behind Gartner's projection that more than 40% of agent projects will be cancelled by 2027.
• How to measure what is already running. What data each agent consumes and produces, who approved each result, what it cost and what it contributed per deliverable — and how that evidence accumulates so your operation improves with every cycle instead of restarting with every project.

Who it's for

• Leadership. You need to see the full operation and decide on evidence, not demos.
• Business and operations. You already identified processes you want to delegate and need to know whether your area is ready before deploying.
• IT and data. You have agents showing up across the company and you need each one to have visible inputs, outputs and permissions.