podcast

Unlocking engineering velocity: The eight stages of AI adoption

The way software gets built is changing fast, and engineers who were the first to embrace AI are now navigating a rapidly evolving landscape of tools, models, and workflows.

In this episode of ClearPoint Unlock, CTO Rob Cleghorn and Practice Director David Corbett walk through the eight stages of AI adoption in software engineering, from tab-completing code suggestions through to fully orchestrated multi-agent systems. They explore where most engineers sit today, what it takes to move to the next level, and share findings from a recent survey of ClearPoint's engineers on AI adoption and velocity gains across different environments.

They unpack why clinging to outdated models and opinions can hold teams back, how high-quality specifications have become the new bottleneck, and why strong engineering practices like CI/CD pipelines, observability, and decoupled architecture are what separate teams accelerating with AI from those being slowed down by it.

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While 80% of companies use AI, most see no bottom-line impact because they've focused on generic productivity tools rather than transforming core business processes. Your AI Workforce from ClearPoint breaks through this "AI paradox" by deploying intelligent agents directly into your critical business functions - where repetitive, high-volume processes drain resources and create bottlenecks. Our AI agents understand context, make decisions, and handle exceptions autonomously. By focusing on vertical, function-specific automation, we deliver concentrated value that directly impacts your P&L, not just vague productivity gains.

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