There's a moment in every enterprise AI project that determines whether it moves forward or dies: the presentation to the executive committee. At that moment, narratives about digital transformation count for little. What decides the outcome is a number: the return on investment, with clear assumptions, verifiable sources, and a payback period the CFO considers reasonable.
I've taken part in dozens of these presentations over the past few years. I've watched brilliant projects stall because the team proposing them couldn't answer precisely to questions like: "In how many months do we recover the investment?" or "What baseline did you use to calculate that saving?" And I've watched mediocre projects get approved because they had a well-built financial model.
This article is a practical guide to building the business case for Aliee. It's not a theoretical exercise: it's the methodology we use at SLM Sistemas to present proposals to executives in the financial, energy, and services sectors across Latin America.
The ROI of an autonomous cognitive agent is not limited to FTE savings. That's the most common error in the financial models of AI projects: assuming the only value is headcount reduction. The reality is richer — and more persuasive — when it's broken down into four dimensions:
This is the most visible dimension and the easiest to quantify. It includes:
Reference benchmark (Forrester, 2024): Companies that deploy autonomous cognitive agents in high-volume administrative processes report an average reduction of 38% in direct operating costs in the first year of full operation, and 52% in the second year once the agent has completed its learning curve (Forrester, "Total Economic Impact of Cognitive AI Agents," 2024).
This is the least obvious impact but frequently the largest. It includes:
As I detailed in the previous article on AML/KYC, the cost of a regulatory violation frequently exceeds the total cost of implementing Aliee. This dimension includes:
IDC calculates that the total cost of regulatory compliance for a mid-sized Mexican IFPE represents between 6% and 9% of its annual operating revenue. Cognitive automation can reduce that percentage to 3% to 4% — freeing up between 2 and 5 percentage points of operating margin (IDC, "Regulatory Compliance Costs in Latin American Fintech," 2024).
This dimension is the hardest to quantify, but that's no reason to leave it out of the model. It includes the competitive position the organization gains by being an early adopter of autonomous cognitive capabilities, and the effect that has on talent retention, market perception, and the speed of new-product innovation.
To build the model, you need the following inputs from your organization:
| Variable | Description | Internal source |
|---|---|---|
| Monthly volume of transactions/case files | How many cases the area processes per month | Operations / CRM |
| FTEs dedicated to processes that are candidates for automation | Number of people, average salary + benefits | HR / Finance |
| Current error rate and rework cost | % of cases requiring correction and unit cost | Quality / Operations |
| Abandonment rate in digital processes | % of prospects who don't complete the flow | Marketing / CRM |
| Annual regulatory compliance cost | Compliance headcount + fees + historical fines | Finance / Legal |
| Average revenue per acquired customer | LTV or annual revenue per customer | Finance |
With these inputs, the 3-year ROI formula is:
ROI = (Total Benefits over 3 years − Total Investment) / Total Investment × 100
Where Total Benefits = the sum of the 4 dimensions of return over the 3-year horizon.
For a Mexican IFPE with 150 employees, 3,000 active customers, and 800 onboardings per month, the typical financial model produces the following results:
| Metric | Conservative Scenario | Base Scenario |
|---|---|---|
| Total investment (Setup + 3 years OCP) | MXN $23.4M | MXN $23.4M |
| Estimated annual benefits (Year 1) | MXN $9.8M | MXN $14.2M |
| Estimated annual benefits (Years 2-3) | MXN $13.5M/year | MXN $18.7M/year |
| 3-year NPV (WACC 12%) | MXN $11.2M | MXN $22.8M |
| Payback period | 28 months | 19 months |
| 3-year ROI | 147% | 232% |
These numbers are representative, not guaranteed. The actual model must be calibrated with your organization's specific data. But the orders of magnitude are consistent with what Forrester reports in its TEI (Total Economic Impact) studies for cognitive agents in financial services: an average 3-year ROI of 198% and an average payback period of 21 months (Forrester, "TEI of Autonomous Cognitive Agents in Financial Services," 2024).
The financial model isn't enough on its own. The presentation to the committee should include three additional elements:
1. The cost of inaction (TCO of the current situation): Project over 3 years how much it will cost to maintain the current process, assuming volume grows, labor costs rise, and regulatory demands intensify. This number is usually the most powerful argument for justifying the urgency of the investment.
2. The risk-mitigation plan: Every executive committee will ask what happens if the project doesn't deliver the projected results. Have a phased implementation plan ready that allows you to validate the ROI before committing the full budget.
3. Industry benchmarks: The Gartner, IDC, and Forrester data cited in this article is your external backing. It's not just SLM Sistemas saying Aliee works: it's the consensus of the world's leading technology analysts confirming that this category of tools delivers the projected results.
The executive committee doesn't buy technology. It buys certainty about business outcomes. Your job as the project leader is to translate Aliee's capabilities into a language the CFO, CEO, and COO can validate, challenge, and ultimately approve with confidence.
— Andrés Lozada, Executive Director | SUMāTO