In Conversation with Justin Swansburg: Lessons from Real-World AI Deployments and The Next Wave of Enterprise AI

In this episode of The Tale of Two AIs, Razi Raziuddin sits down with Justin Swansburg, Founder of ES Consulting and former VP of Applied AI at DataRobot, to discuss how enterprises can cut through AI hype and focus on building solutions that deliver lasting business value. With years of experience advising organizations across industries, Justin offers a grounded perspective on the future of data science, the rise of AI agents, and what it means to create strategies that actually work in practice.

Context Is King—and Agents Are Poised to Unlock It

Justin emphasizes the importance of context—predictive models provide it, generative models leverage it, and agents weave it all together into workflows that empower users. The rise of agents means:

• Teams across functions can access insight tools easily 
• Predictive models can become centralized services, reusable by multiple agents across marketing, finance, customer success, and more
• Accuracy matters more than ever as use cases proliferate, making robust model evaluation critical

According to Justin, agents are only as good as the tools they can trigger. Building that tooling layer is where enterprise value truly accrues.

Lessons from Real-World AI Deployments

Justin’s career is full of stories that illustrate both the promise and pitfalls of AI adoption. From predicting chicken survival rates in agriculture to advising Fortune 500 companies on customer retention, he’s seen how the same principles apply across industries: success requires clarity of purpose, thoughtful experimentation, and collaboration across technical and business teams.

The lesson is that impact comes from operationalizing insights, not just generating them, and that simplicity, usability, and context are the difference between a promising proof of concept and a transformative deployment

The Enduring Value of Data Scientists

As AI capabilities advance, some argue that the role of the data scientist is fading. Justin disagrees. While generative tools can speed up coding and model building, data scientists provide the critical thinking, domain knowledge, and statistical grounding that ensure AI solutions actually solve the right problems. His advice to data scientists: 

• Get hands-on. Run your own small agent experiments today—playbooks, prototypes, and even YouTube are the best teachers.
• Centralize predictive intelligence. Build reusable model services that agents across your enterprise can call on.
• Focus on tooling. Treat tools like model services, API layers, and external integrations as core assets, not afterthoughts. Even as the agent landscape evolves, this “scaffolding” will allow your team to scale.

Looking Ahead: AI Agents and Enterprise Innovation

When asked about the future, Justin pointed to the growing importance of AI agents in enterprise contexts. However, he emphasized that agents will only succeed when paired with the right infrastructure: robust APIs, underlying predictive models for business context, strong data governance, and human oversight.

He also predicted that the next wave of enterprise AI will prioritize integration and usability—tools that slot seamlessly into daily workflows rather than demanding disruptive overhauls. 

Justin’s perspective is a reminder that AI success isn’t about chasing hype—it’s about aligning technology with business needs, experimenting pragmatically, and integrating solutions responsibly. Data science, generative AI, and AI agents each have a role to play, but the real challenge lies in combining them thoughtfully to drive measurable outcomes.

 Listen to the full episode of The Tale of Two AIs to hear more about building AI strategies that deliver real business value: