Introducing AIRadar™: From token burn to business return

Rising token spend needs to translate into more than faster code creation. Now that AI tokens are forming a significant part of your engineering budget, you need to ensure that investment survives your engineering system, and makes it out the other end as customer value, business outcomes and ultimately ROI.

And today, we finally get to introduce you to our biggest product launch yet: AIRadar™.

AIRadar™ gives engineering leaders one place to manage the AI transition, from rollout to results. Built around Plandek's RACER™ Framework, it brings together leading indicators from across AI tools and the engineering system to guide the decisions that improve engineering performance and make every token count.

Follow AI from rollout to results

AIRadar™ follows this path from AI adoption through to ROI across five connected views:

Together, they show whether adoption is taking hold, how teams are using AI, what is limiting impact, how engineering performance is changing, and whether the investment is paying back.

That is the logic behind Plandek’s RACER™ framework: to trace AI spend through to engineering and business results, and make it clear where value is being created or lost.

Rollout: how is AI being adopted?

Move beyond provisioned seats and login counts. AIRadar™ shows you:

• license coverage and utilization by tool

• active users and engagement depth

• inactive licenses and wasted spend

• how users progress from active to meaningful and productive use

• AI contribution to pull requests

• token consumption and multi-tool usage

• developer trust in tooling and direction

Your Rollout dash shows you whether AI is becoming part of valuable engineering work.

Approach: are teams using AI effectively?

The next question is how teams are using AI.

AIRadar™ shows:

• the shift from inline autocomplete to chat and agentic workflows

• which types of work AI is being used for

• tool and model usage

• suggestion acceptance rates by tool

• whether agentic sessions lead to code raised, merged, and deployed

More usage is not the same as better usage. If token consumption is rising, you need to know whether those tokens are turning into useful engineering work.

Constraints: what is holding AI back?

If AI is not delivering the impact you expected, the problem may sit elsewhere in the system. AIRadar™ surfaces constraints:

• at the repository level – showing you your most constrained repositories, plus what’s causing that constraint, from missing PR templates to no AI-aware CI steps

• at the SDLC level – showing you which workflows are constrained, and why, from weak ticket linkage to high review churn

• that cause friction for your AI tools – from missing context and guardrails, to human and organizational barriers such as trust, skills, process, and governance

Your Constraints dash also gives you clear actions against the highest-priority constraints.

Engineering Impact: is AI improving the things that matter?

AIRadar™ measures engineering impact through Plandek’s 4 Pillars: Focus, Speed, Predictability, and Quality. That gives you a balanced view of whether AI is improving the engineering system overall, rather than simply making one part of it faster.

And because every result is benchmarked against Plandek’s dataset of 2,500+ engineering teams, you can see not just whether performance is changing, but whether it is actually strong.

You can see:

• Focus – whether engineering effort is being directed toward valuable work

• Speed – whether work is moving through the delivery system faster

• Predictability – whether teams are delivering more consistently

• Quality – whether increased AI contribution is holding up without creating more defects, churn, or rework

• how teams at different levels of AI maturity perform across all four pillars

• how AI-contributed pull requests compare with human-only work

• where review, approval, traceability, or governance start to weaken as AI contribution grows

This is the point of the Engineering Impact view: to show whether greater AI use is strengthening engineering performance across the system, or simply shifting the bottleneck.

Results: are the tokenomics working?

AI spend is now part of the economics of software delivery.

AIRadar™ brings token spend, seat cost and engineering cost into the same view as output, so you can see whether greater AI investment is actually improving the economics of delivery.

You can see:

• token and seat spend by tool

• idle spend and cost per AI-contributed pull request

• how AI spend is changing alongside engineering output

• cost impact against a baseline period

• employee-equivalent impact

• ROI over time

Crucially, the value of these measures isn't simply in demonstrating a return, but in deciding how to improve it.

When AI spend increases without a corresponding improvement in delivery performance, is the answer to change how teams are using AI? Invest in better context and harness engineering? Remove a constraint elsewhere in the SDLC? Or reconsider where AI budget is being allocated?

Often, the first intuition is to try to spend less or spend more on AI, but the right question is usually how to invest in the engineering system around it so that AI can deliver results. And while engineering output is not the same as business value, understanding its cost and quality is an essential step toward establishing whether AI investment is creating genuine economic leverage.

AIRadar™ gives leaders the evidence to make those decisions, rather than treating rising AI spend as an unavoidable cost of transformation.

Make every token count

You need to understand where AI is creating value, where that value is being lost, and what you can do to improve the return.

By connecting Rollout, Approach, Constraints, Engineering Impact, and Results, AIRadar™ gives you the leading indicators, benchmarks, and actionable guidance to manage the transition, and – ultimately – improve outcomes at every stage.

Ready to go from token burn to business return?

Talk to our team today.