The Framework for AI-Augmented Engineering: RACER

Key research insights

AI adoption does not equal AI impact

High usage of coding assistants rarely translates directly into faster delivery or better quality without changes to workflows, measurement, and constraints.

AI exposes bottlenecks rather than removing them

Teams see productivity gains in coding and testing, but overall performance is limited by constraints like unclear requirements, slow reviews, brittle environments, and weak documentation.

Measurement gaps stall progress

Most organizations track AI usage, but few connect it to core engineering metrics such as cycle time, predictability, throughput, and quality across the full SDLC.

Operating models must evolve, not just tools

The highest-performing teams rethink planning, roles, processes, and rituals to fully capitalize on AI and prepare for more agentic workflows.

What is the RACER Framework?

RACER is a five-part model designed to help engineering leaders systematically move from AI rollout to measurable results:

Rollout

Ensure teams have access to AI tools and are using them consistently, frequently, and with confidence.

Approach

Match the right AI usage pattern to the task, from assisted coding through to supervised and autonomous agents.

Constraints

Identify, categorize, and remove SDLC bottlenecks that limit the effectiveness of AI tools.

Engineering impact

Measure changes in flow, speed, quality, and predictability across the full delivery lifecycle.

Results (ROI)

Translate engineering improvements into business outcomes such as faster time to market, cost reduction, and increased capacity for innovation.

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Why RACER matters now

AI-augmented engineering has moved from experimentation to accountability. Leadership teams are being asked to justify investment, quantify ROI, and demonstrate impact.

RACER provides a repeatable, metrics-driven way to:

Track real AI adoption across teams and workflows
Surface the constraints holding back performance
Measure engineering improvements without sacrificing quality
Connect delivery metrics to business outcomes

What You’ll Learn

Why traditional engineering models break down in the AI era
How to measure AI impact beyond vanity metrics
The constraints most organizations overlook
How leading teams operationalize AI at scale
A practical roadmap from rollout to results

How Plandek supports RACER

Plandek is built for engineering leaders navigating AI-augmented delivery. The platform brings together data across your DevOps toolchain to support every stage of the RACER Framework, from adoption and constraints through to engineering impact and ROI.

With end-to-end visibility and Dekka, Plandek’s AI copilot for engineering intelligence, teams can move from intuition to evidence and from dashboards to action.

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