In this interview, Allen Roh, Senior Marketing Manager, Growth and Activation at Autodesk, explains how his team adopted AI agents across Autodesk’s go-to-market engine. His experience offers a practical look at how large, complex organisations can begin using AI agents in a thoughtful, measurable, and scalable way.
This guide summarises Allen’s most useful frameworks and lessons — designed to help other teams understand where to start, how to assess value, and what structures are needed to adopt AI agents effectively.
Initial mindset: speed vs. quality
Allen’s initial reaction to AI agents was enthusiasm for their speed, paired with caution around quality and brand consistency.
Starting small: experimentation over transformation
Autodesk didn’t aim for broad adoption at the start. Instead, the team used experimentation to learn quickly and identify where agents could have the strongest impact.
Defining success: one clear metric
A precise, actionable metric ensured the team remained focused. Instead of broad goals like “increase pipeline,” Allen chose specific conversion moments tied to revenue.
This clarity made it easier to measure lift and evaluate whether the agent was succeeding.
Training agents like employees
Allen emphasises that agents must be trained, managed, and evaluated much like human employees.
Knowledge architecture: building reusable building blocks
To ensure agents operate consistently, Autodesk broke its knowledge base into reusable components.
Data-first decision-making
Allen repeatedly highlights the principle: “data first, confidence second.”
If data showed lift, they scaled. If not, they iterated.
Treating agents as evolving systems
There is no final version of an AI agent. Prompts age, markets change, and models need ongoing refinement.
Cross-functional collaboration
Autodesk’s success depended heavily on collaboration across the organisation.
Shared goals and shared analytics made it easier for teams to support and trust the AI program.
Horizontal expansion: scaling what works
Once a single agent delivered measurable value, Autodesk expanded horizontally.
Advice for teams getting started
Allen’s guidance for organisations just getting started:
Autodesk’s experience shows that effective AI adoption comes from clear goals, disciplined experimentation, cross-functional alignment, and a commitment to ongoing refinement. With a focused starting point and a data-driven approach, any organisation can begin building an AI workforce that delivers meaningful impact.