We have officially passed the “shiny object” phase of Artificial Intelligence. For the last two years, boardrooms have been flooded with pilot programs and experiments. The verdict is now in, and the data reveals a stark performance gap that demands immediate attention from founders and executives.
While general AI projects—mostly generative tools that summarize text or draft emails—saw a respectable return on investment (ROI) of 67% last year, Agentic AI delivered an average ROI of 80%.
The distinction is critical: Generative AI talks about work; Agentic AI does the work.
The Pivot from Insight to Action
The patience for unstructured experimentation is evaporating. CEOs and investors are no longer interested in AI for AI’s sake. They want hard returns. This is where Agentic AI separates itself. Unlike models that require human interpretation to be useful, autonomous agents execute workflows within strict rules. They close the gap between insight and action.
Consider the difference:
- Generative AI: “Here is a summary of the invoice discrepancies.” (Requires a human to read and act).
- Agentic AI: “I have identified, verified, and corrected the invoice discrepancy based on the approved vendor list.” (Done without human intervention).
The Proving Ground: Why Finance Leads the Way
Finance departments are currently the most effective launchpad for this technology. Why? Because Agentic AI thrives on structure, and few environments are as structured as Accounts Payable (AP).
72% of finance leaders view AP as the obvious starting point. The data is clean, the rules are rigid, and the inputs (invoices) are standardized. Agents here aren’t just drafting emails; they are automating invoice capture, detecting fraud, and reducing overpayments with high autonomy.
This transforms the finance function from a back-office utility into a strategic lever. When you automate the manual cleaning of data, you don’t just save time—you create operating leverage. Teams stop chasing paper and start managing liquidity.
The “Build vs. Buy” Decision Matrix
For business owners deciding how to procure these capabilities, the path forward is actually quite pragmatic. The divergence in adoption suggests a simple rule of thumb:
- BUY if the process is standard: If the AI improves a process shared across many organizations (like Accounts Payable), embed it via a vendor solution. 32% of leaders are already doing this. There is no competitive advantage in reinventing how an invoice is processed.
- BUILD if it differentiates you: If the AI creates a unique competitive advantage (like proprietary Financial Planning & Analysis), build it in-house.
Governance is an Accelerator, Not a Brake
The biggest barrier to adoption isn’t technology; it’s trust. Nearly half of finance leaders won’t deploy an agent without clear governance. This caution is rational, but the most successful organizations view governance differently.
Instead of using governance to stop deployment, they use it to scale. They treat AI agents like junior colleagues. You wouldn’t let a new intern sign off on a million-dollar transfer on day one. You give them small tasks, review their work, and gradually increase their autonomy as they prove competence.
The leaders who apply this “trust but verify” mindset are seeing the highest returns, moving their teams from experimental pilots to production powerhouses. The era of playing with AI is over; the era of putting it to work has begun.
