As agentic AI moves from prototype to deployment, execution will matter more than ambition. IMD’s Salvatore Cantale explores the operational foundations that CFOs must get right to make agentic AI work.
In 2025, enterprise leaders are no longer debating the relevance of agentic AI – that’s a given. The focus has shifted to making it work.
Agentic AI systems are capable of memory-based reasoning and autonomous decision-making. Analysis of CEO earnings calls in Q4 2024 found that agentic AI was one of three themes that gained noticeable traction in that period, alongside tariffs and reshoring.
Leading firms are using agentic AI to automate complex tasks, interpret unstructured data, and manage workflows across departments. Gartner expects 15% of routine work decisions to be made autonomously by 2028, up from zero in 2024. It also forecasts that one-third of enterprise software will include agentic AI by the end of this period, up from less than 1%.
But successful deployment will require careful management. CFOs, with their broad oversight and focus on execution, are well-placed to turn experimentation into value.This article explores three key questions: what agentic AI is, how it’s evolving in the enterprise, and what CFOs must get right to make it deliver.
Goal-seeking software with autonomy. In practice, agentic AI behaves more like a goal-seeking collaborator than a traditional tool. Ask it to “plan my week” and it can scan your calendar, block time for focused work, book meetings, and adjust the schedule as conflicts arise. It doesn’t simply follow a checklist. Instead, it evaluates what needs doing, adapts to changes, and applies the right tools to deliver a result.
At its core, agentic AI works by breaking a goal into actionable steps. It reasons through options, refines its approach as it learns from past outcomes, and uses systems such as browsers, APIs, or spreadsheets to take action. This is not static automation or a reactive chatbot. It is proactive software that operates with intent.
Why should CFOs care?
Imagine assigning an agent the task “optimize our quarterly reporting workflow.” It can:
Extract and clean data from multiple systems
Spot inconsistencies
Ask clarifying questions (via email or Slack)
Submit the draft report without waiting for daily check-ins
Think of it as a digital junior controller: fast, tireless, and context aware. When applied to reporting, forecasting, or compliance, agentic AI enables a shift from task management to insight generation.
A step beyond automation and generative AI. Unlike traditional automation or even generative AI, agentic AI doesn’t wait to be told what to do. It operates with intent: pursuing goals independently, adapting as it goes, and deciding what action to take next.
Where traditional automation follows predefined scripts, agentic AI can handle ambiguity and change. And while generative AI creates outputs when prompted, agentic AI moves with purpose. It’s not answering questions; it’s solving problems.