AI Investment Control
Too many proposals, too many pilots, too little real value. This course gives you the framework to filter AI initiatives with financial and operational criteria — and decide with evidence, not vendor enthusiasm.
The problem is not a lack of AI ideas.
It is an excess of proposals without method.
The executive committee receives AI proposals from all directions: vendors promising transformation, internal teams wanting to scale their pilot, consultancies presenting benchmarks from other industries. Every proposal sounds urgent. None of them share the same methodology for evaluation. The result is always the same: budget committed to pilots that never reach production, or — worse — systems that reach production without having demonstrated real value.
The problem is not technological. It is one of criteria. Without a common framework for evaluating AI initiatives, investment decisions are made on enthusiasm, vendor pressure, or the fear of being left behind. The cost is not only financial: it is political, operational, and credibility-related — technology's standing in front of the business suffers with every failed pilot. Each failed pilot makes the next legitimate proposal harder to approve.
This course delivers the framework Yaripo uses to evaluate AI initiatives in real organizations: how to quantify expected impact, how to design pilots that tell the truth, and how to decide with evidence when to invest, when to redesign, and when to simply stop. The Monday Outcome: the executive team leaves with their own financial and operational criteria — not borrowed from someone else's success story.
What you will build
Four modules that take you from detecting weak proposals to executive portfolio decisions, armed with your own financial and operational criteria.
Red flags in AI proposals: what to look for before committing budget. Which variables actually move EBITDA, cost, risk, or operational speed. The difference between an interesting use case and a defensible investment case in front of a finance committee.
2 hoursExpected economic impact: how to quantify it without relying on the vendor's projections. Operational feasibility and data availability. Implementation risk and technology dependency. Time-to-value and the cost of doing nothing. How to present the evaluation in a way that withstands financial scrutiny.
3 hoursCorrect hypothesis, baseline measured before the start, and a predefined success criterion. Pilot size, duration, and minimum assumptions to detect a real signal. How to avoid pilots that only prove team enthusiasm and generate no evidence for scale decisions.
3 hoursDecision committee and minimum evidence required. Signals to scale: when the pilot has said enough to commit real investment. Signals to cut without political bias or image cost. AI portfolio management versus isolated initiatives: how to prioritize the whole, not just each individual project.
2 hoursWhat you will be able to do
Framework to quantify expected impact, feasibility, and risk of any AI proposal — without relying on the vendor's projections or the internal team's enthusiasm.
Methodology to structure experiments with hypotheses, baseline, and predefined success criteria. Pilots that tell the truth, not those that justify the decision already made.
Criteria to decide when to scale, when to redesign, and when to cut without political bias. Portfolio vision that enables prioritizing the whole, not just defending each individual project in front of the committee.
Who teaches this course
Seven years leading data ecosystems at BCI, work at illumin Canadian ad tech, with stints at the IDB, PDVSA, Falabella, and Walmart across five countries. Founded Yaripo to close the gap between AI strategy and real implementation in mid-size organizations with critical operations.
Yaripo designs AI investment evaluation frameworks calibrated to the financial and operational reality of mid-size organizations with complex operations in regulated markets. Unlike generic innovation management methodologies, the Yaripo approach drills down to variables that impact EBITDA, operational cost, and technology dependency risk — with language that withstands the scrutiny of a finance committee, not just an internal technical team.
What executives ask before enrolling
The next AI pilot
could be the last one approved without criteria.
10 hours. 4 modules. An AI investment framework that withstands the scrutiny of finance, operations, and the executive committee.
Enrollment at academia.yaripo.cl · Online asynchronous format · SENCE tax credit compatible