AI is no longer optional for enterprises—it’s becoming the connective tissue of modern workflows. But the rush to adopt often leads to wasted investment and failed deployments. Glenn Russell, Global AI Practice Lead at Egen, joined Swapnil Bhartiya to explain why companies stumble and how to approach AI adoption with clarity, discipline, and long-term vision.

The first mistake, Russell explained, is treating AI like a shortcut rather than a tool. “It’s not magic dust you can sprinkle on your business. It requires thought and planning like any other piece of technology.” Instead, leaders should define measurable outcomes—whether that’s faster ticket resolution or improved customer retention—and ensure executive buy-in from the start.

Integration, people, and skills emerged as the biggest hurdles. Russell noted that large enterprises often underestimate cultural adoption and organizational readiness. “If your digital transformation journey is still struggling with legacy systems or technical debt, you’re not ready for AI,” he said. Strong foundations in processes, change management, and data platforms are essential.

On ROI, Russell emphasized total cost of ownership. Token prices may fall, but overall platform costs can rise with scale. He advised starting with low-risk, high-impact use cases and tying outcomes directly to KPIs. A financial services client, for example, doubled throughput by augmenting workflows with human-in-the-loop AI—a clear case where investment aligned with measurable business value.

Momentum, Russell argued, depends on education and champions. Much like security adoption, teams must understand the “why” and see peers advocate for the benefits. From there, incremental wins build trust. Egen’s method is to map workflows, identify “crown jewel” processes, and improve them step by step rather than pushing AI for AI’s sake.

Looking ahead, Russell sees 2026 as the year agent-based AI scales into core enterprise workflows. Whether processing HR requests or handling customer queries, tuned agents will enhance speed and accuracy. The impact, he said, won’t be mass job losses but improved efficiency and employee satisfaction.

At the end of the day, people remain central. “A healthy dose of skepticism is critical,” Russell said. LLMs may summarize correctly 99% of the time, but that 1% failure still demands human oversight. Critical thinking and augmentation, not blind trust, define sustainable AI adoption.

For enterprise leaders, the lesson is clear: avoid hype, build on strong foundations, and measure outcomes. AI is not a revolution in itself, but a powerful evolution—one that can deliver real value if approached with clarity and patience.