How to communicate the reality of AI-assisted engineering in today’s hype cycle

AI-assisted engineering is everywhere in today’s conversations—but much of the messaging is overblown. Claims of “10x productivity” or AI fully replacing developers are easy for executives to encounter and hard for them to evaluate. This guide helps engineering leaders communicate the reality of AI adoption to their stakeholders, separating hype from the real opportunities to improve developer productivity.

How to communicate AI’s real business value

For executives and non-technical leaders, understanding AI’s role in software engineering requires context. This guide shows how to:

• Anchor conversations in real business problems before discussing tools
• Reassure stakeholders with clear discovery and experimentation strategies
• Explain the enablement and training required for successful AI adoption
• Share metrics that tie AI usage directly to team productivity and engineering outcomes

By framing AI around solving friction in the development process, engineering leaders can help their organizations invest wisely and avoid being swayed by inflated promises.

What it takes to make AI adoption succeed

AI code assistants and GenAI tools don’t generate value on their own. Success requires developer training, executive sponsorship, and strong enablement programs. Without these, adoption lags, and productivity benefits remain untapped. Using proven frameworks like the DX Core 4 and the DX AI Measurement Framework, leaders can capture the full picture of AI’s impact, from usage and adoption to real cost savings and team efficiency.

A playbook for engineering leaders

This guide equips platform leaders, DevEx champions, and engineering executives with practical strategies for communicating AI’s impact in ways that resonate with the business. By combining data, storytelling, and clear benchmarks, leaders can move beyond hype and position their organizations to realize the true productivity gains of AI-assisted engineering.