The Reformist CTO's Guide to Impact Intelligence

Impact Intelligence is a disciplined habit of constantly asking how every technical or business initiative moves the needle on real business metrics. It replaces vague output measures-lines of code, sprint velocity, or story points-with a visual impact network that links low-level outcomes to high-level revenue, cost, or risk signals. The network shows both direct (proximate) effects and downstream consequences, making hidden dependencies visible.

The article argues that most tech organizations are trapped in a productivity-obsessed cycle. Leaders are pressured to prove higher output, while executives demand clear business impact. Without an impact feedback loop, teams launch features in a spray-and-pray fashion, inflating the tech estate and burdening maintenance budgets. The result is a vicious loop where budget requests are answered with more productivity hacks instead of evidence of value.

Concrete examples illustrate the gap. An AI chatbot may boost session counts, but the real business question is how many call-center contacts it saves and how that translates to cost reduction. A regulatory-compliance assistant shows higher usage metrics, yet the downstream metric that matters is reduced time-to-decision for critical cases. An email-marketing SaaS adds open-rate tweaks, but the true impact lies in additional revenue generated from converted leads. In each case, proximate metrics are easy to measure; downstream impact is the decisive signal for investment decisions.

To break the cycle, leaders should institutionalize strong demand management and use the impact network as a common language between engineering and the C-suite. Documenting the business case for each idea, tying it to measurable outcomes, and iterating on impact measurements creates accountability. Presenting this framework to the COO, CFO, or CEO-backed by the book's recommendations-gives tech CXOs a practical roadmap to shift conversations from raw productivity to real business impact.