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Abner Ballardo | Exploring AI-Native Ventures

Former CIO, Scotiabank Peru | Enterprise technology, architecture & transformation
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Venture Notes

Short observations from exploring and building AI-native ventures—decisions, experiments, discoveries, and unresolved questions.

VN-004 — Designing Organizations for Agents

The more I think about building with agents, the more I keep coming back to context. An agent can only work with what it can access and interpret: goals, current state, constraints, permissions, history, and a clear definition of its responsibility.

Most organizational processes were built for people, not agents. People bridge missing context through memory, meetings, relationships, and experience. Agents cannot safely rely on the same informal gaps. Simply adding an agent to an existing process does not make that process agent-ready.

My working hypothesis is that an AI-native company must redesign context, handoffs, controls, and accountability from the ground up around the strengths and limits of both humans and agents. I do not yet know how much must change. But I suspect that context design—not access to the next model—will be one of the hardest parts of creating an AI-native organization.

VN-003 — What I Mean by AI-Native

In my research, I keep finding examples of companies using AI to make existing work faster or more efficient. I have not found a blueprint that answers the bigger question I care about: what changes when AI is part of the company's design from the beginning?

For now, I distinguish between AI-enabled and AI-native. An AI-enabled company improves parts of an existing business with AI. An AI-native company treats generative AI as a basic building material across the whole company: its product and customer experience, workflows, operating model, roles, context, handoffs, permissions, economics, and human accountability. It is not only an AI feature added to the business; it changes how the business is designed and run.

This is a working definition, not an industry standard. The absence of a blueprint is one of the reasons to start this journey—and to make the definition itself something I will need to test and revise.

VN-002 — Foundations Before Acceleration

Foundations have mattered in almost everything I have built. If I want to build generative AI-native startups, I need to understand what sits underneath the tools I will use.

I am not trying to become a researcher or train a foundation model from scratch. But I do want to understand how foundation models are trained, how inference works, and how context limits, retrieval, tool use, evaluation, reliability, and cost shape what can be built. Without that foundation, it would be too easy to mistake a convincing demo for sound product or company design.

The open question is how deep I need to go before building. I do not want study to become an excuse to delay. I want it to help me make better choices once I start getting my hands dirty.

VN-001 — Choosing the Venture Path

Today I decided that my next professional chapter will be building AI-native ventures.

The decision comes from experience. I have been an Agile practitioner since 2003. By the time Agile became a corporate priority, I already had years of hands-on practice behind me. That experience gave me a point of view I could not have developed by watching from the outside.

I see a similar moment with generative AI. My final day at work will be August 28, and I want to give myself time to learn by doing—to get my hands dirty building AI-native startups, not only reading or talking about them. The venture may work or it may not. Of course I want it to work, but either outcome will give me real experience. That learning is the most important part of the decision I am making now.