The audio and video are in Spanish. These notes are a translation.
Today I spoke with three founders who are on the front line of the battle, taking artificial intelligence to large companies in Latin America: Matías from Vambi, Alex from Allie Systems, and Mau from Arkham. We talked about why so many AI projects fail inside corporations, how to get them from the demo to production, and how you build a startup that delivers real operational results with AI. A deep, tactical, and very honest conversation about what it really takes to achieve impact with AI.
0:00 - Why Do AI Projects Fail at Large Companies?
Most artificial intelligence projects do not achieve concrete results. Why?
- What barriers do corporate buyers run into when they try to implement AI successfully?
- How do the founders filter for the customers who are truly committed?
- How important is it to have a clear strategy and executive sponsors?
12:00 - What Does a Successful Implementation Require?
From the proof of concept all the way to the real deployment, the founders detail every step.
- How do they identify the right use case to start with?
- What role does the “forward deployment engineer” play in this process?
- How do they handle topics such as integrations, security, and the client’s internal politics?
25:00 - The Importance of Close Support and Education
Implementing AI is not only engineering. It is also the management of organizational change.
- Who leads the training and the evangelizing inside the client?
- Why does the relationship between the startup and the client resemble a couple’s relationship?
- What profiles do they hire to accompany these complex processes?
39:00 - Pricing, Business Model, and Scalability
Traditional SaaS models do not always apply.
- How do they define pricing for projects that require so much customization?
- What criteria do they use to tell a customer no?
- How do they keep the implementation from becoming endless or unprofitable?
52:00 - How Do You Scale Without Losing Quality?
Scaling complex projects with AI requires internal tools and solid processes.
- Which parts of the process have they managed to standardize in order to be able to scale?
- How do they think about the future of the “forward deployment” model?
- How far are we from a more plug & play model in AI?
Episode Mentions
You can find Matías on LinkedIn
You can find Alex on LinkedIn
You can find Mau on LinkedIn
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