Guest: Glenn Russell (LinkedIn)
Company: Egen
Show Name: An Eye on AI
Topic: AI Infrastructure

When AI delivers real, measurable results, it stops being an experiment and becomes transformation. In this conversation from An Eye on AI, Glenn Russell, Global AI Practice Lead at Egen, shares a compelling real-world case study where artificial intelligence moved beyond theory to tangible business impact.

Russell describes how a large financial organization partnered with Egen to enhance its customer outreach process. The company’s sales team, each responsible for an entire region, could typically manage around 20 calls per day. Each interaction was repetitive — checking on renewals, add-ons, or follow-ups. To improve efficiency without sacrificing customer experience, Egen deployed a custom-built AI agent powered by Google’s Gemini models.

This agent could initiate calls, confirm whether customers were available to talk, collect key information, and record next steps. It acted as a “digital assistant” for human sales reps, allowing them to focus on complex interactions rather than routine qualification tasks. “Those same people who previously could only make 20 calls a day could go to 200, or even 2000,” Russell explains.

The real challenge, however, wasn’t just building the AI — it was integrating it seamlessly into existing workflows. Russell emphasizes that Egen’s success came from working closely with the client to understand their sales process and customer expectations. “If you have a poor experience with an AI agent, you’re not only annoyed at the agent, you’re annoyed at the company,” he says.

Egen’s method involved starting small, testing the AI system with a limited group of users, and measuring clear KPIs: could a salesperson successfully qualify 200 leads instead of 20? Once validated, the system was scaled up to full production, resulting in significant improvements in efficiency and throughput.

Russell deliberately downplays the role of technology in the story. “You’ll notice I didn’t talk about technology basically at all — and that’s deliberate,” he tells TFiR. “AI is an enabler. If you don’t understand the problem you’re trying to solve, all the AI in the world won’t make you money.”

This case captures a shift in how AI is being adopted across industries. For Russell and Egen, success isn’t measured by how advanced the model is but by how effectively it integrates into human workflows and business outcomes. Their approach reflects a growing recognition that enterprise AI maturity depends more on process design, governance, and incremental validation than on any single breakthrough model.

The result: a scalable, human-centric AI system that delivers measurable ROI. For organizations still navigating the early stages of AI adoption, Egen’s approach offers a clear roadmap — start small, validate outcomes, and scale with purpose.