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COOKIE POLICY
Everyone’s talking about AI, but how do you actually move from buzz to business impact? We sat down with UDig CTO Josh Bartels to break down what it really takes to move beyond experimentation and build meaningful, scalable adoption across the enterprise.
A: There’s a lot of excitement around AI right now. Everywhere you turn, there are bold ideas, breakthrough demos, and stories of transformative results. But the big question is: how do you take that inspiration and actually drive impact inside your organization?
The key is moving from exploration to action. Start by identifying where AI can make the biggest difference, then narrow your focus. Instead of chasing broad, undefined initiatives, pick one specific workflow. Utilize AI to enhance that workflow in a manner that delivers clear, measurable value. That success becomes your foundation for scaling.
In our own work, we’ve seen how a learn-by-doing approach accelerates adoption. It helps teams quickly understand what works, what doesn’t, and how to build the confidence and momentum to go further.
A: AI use is uneven today. Many individuals are experimenting with generative tools, but turning that enthusiasm into an enterprise-wide program is a different challenge. Adoption at scale requires more than access. It’s about systems, coordination, and culture.
AI often feels like a “magic wand,” exciting but unpredictable. Enterprises, however, need consistency and trust. Teams must know outputs are reliable, data is safeguarded, and guardrails are clear. Without that confidence, adoption struggles.
The answer is balancing risk with hands-on experience. Guardrails alone won’t drive adoption; people need to build with AI to understand both its power and its limits. That’s why we created a program where every employee built something real while still managing their day jobs. Through structured, time-boxed experiments, people saw quick wins, friction points, and gained the fluency needed to guide scaling decisions.
The lesson: start small, design use cases that are intuitive to adopt, and create opportunities for shared learning. When teams have direct experience supported by strong governance, organizations reduce risk and build confidence to scale.
A: Many enterprises are still focused on one-off, human-in-the-loop solutions—an LLM that drafts text, a chatbot that answers questions, or an assistant that supports a single team. Useful, but not transformative.
True enterprise value comes when AI use cases are linked into trusted agents that work together. For example, instead of separate tools for contract review, compliance, and approval routing, imagine an integrated system where:
This interconnected approach turns isolated capabilities into coordinated intelligence that reasons across business processes. Building that intelligence requires a human team that can verify each agent within the team. Humans provide feedback and guidance to the different agents that make up the system.
The result isn’t just incremental efficiency, it’s a new way of operating, where work flows intelligently across the enterprise.
A: We asked every employee to build something real, while still doing their day jobs. It was ambitious, but the payoff was huge.
People quickly moved beyond the “magic wand” mindset. They saw where AI excels, such as summarization, pattern recognition, and augmentation, and where it requires structure or traditional software.
Our teams learned that simplicity wins. Ambitious ideas make progress when broken into achievable steps. Big ideas also sparked new ones; one project often inspired three more. Adoption became less about access and more about experience; solutions had to feel intuitive to stick.
Prompting mattered, vectorized data unlocked smarter search, and constraints sharpened focus. People also realized different models are better suited for different tasks.
The result was AI fluency: quick wins that built confidence to scale adoption with clarity and discipline.
A: Success with AI isn’t about finding the perfect model; it’s about building organizational muscle to adapt as the technology evolves. A few lessons stand out:
The companies that succeed will be the ones that learn faster than their competitors. That’s the spirit of our program. Prove what works, uncover what doesn’t, and build the confidence to scale responsibly.
With over 15 years at the forefront of technology innovation, I've dedicated my career to delivering strategic solutions that drive business growth. As the Chief Technology Officer of UDig, I lead our technology vision, architecting solutions that transform how organizations leverage technology to generate impact.
