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Show what your AI investment is actually returning

Engineering Leaders

Show what your AI investment is actually returning

AI is now a real line in your engineering budget, and finance wants proof, not sentiment. LinearB helps you explore your data, measure AI's impact on delivery and act on what you find, across AI Leverage, Predictability, Flow Efficiency and Developer Experience.

Trusted by the teams transforming software delivery

  • SurveyMonkey
  • Peloton
  • Equinix
  • Norwegian Cruise Line
  • Vizio
  • Gainwell
  • BigID
  • Expedia
  • Syngenta
  • Ingka
  • Silicon Labs
  • Astronomer
  • Mitratech
  • Solace

The AI gap between teams is widening, and finance has noticed

AI has moved from a side experiment to a real share of engineering spend. Some teams are turning it into delivered work while others stall. Finance now wants proof of value, so the budget starts to behave like a portfolio: fund what's proven.
8.1M+
pull requests in LinearB's benchmark, from 4,800+ teams in 42 countries
LinearB 2026 Software Engineering Benchmarks Report

Prove AI ROI to your executive team

The AI ROI dashboard turns engineering cost and delivery data into one number executives can act on: the total value your AI investment returned over a period.

Total value gain

The drop in cost per effective PR between two periods, multiplied across the PRs you delivered.

Cost per effective PR

Total engineering cost, people and AI together, divided by merged PRs net of rework.

Rework rate guardrail

Confirms falling cost isn't coming at the expense of quality.
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Prove AI ROI to your executive team

Balance speed with delivery confidence

AI payback follows a J-curve: verification and instability costs show up before the gains do. LinearB maps APEX to the DORA AI value chain, so you can set expectations and see when throughput gains start to hold.

Throughput and stability together

Track cycle time alongside change failure rate and rework, so faster delivery doesn't hide rising instability.

Set expectations early

Show leadership why payback dips before it climbs, using the DORA AI value chain.
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Balance speed with delivery confidence

Align resources to business priorities

Resource allocation and investment profiles show where engineering capacity actually goes, across features, bugs, tech debt and infrastructure, so you can rebalance before planning season.

Investment profile

See the mix of new features, maintenance and unplanned work by team and initiative.

FTE-based allocation

Effort is calculated from Git and project activity, with no timesheets.
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Align resources to business priorities

Syngenta cut cycle time by 81%

Results like this are benchmarked against 8.1M+ pull requests, so you can see where your teams stand as well as how far they've come.
81%
reduction in cycle time at Syngenta

What you'll report on

1.

AI Leverage: Total value gain

The return on your AI investment as one number, built from cost per effective PR.
2.

Predictability: Planning accuracy

How much of what teams commit to each sprint actually ships.
3.

Flow Efficiency: Cycle time

How long work takes from first commit to production, broken down by stage.
4.

Developer Experience: Survey scores

How developers rate their tools, process and workload, next to their Git data.

I use LinearB data and charts directly in my board decks, because my board is specifically asking questions about our use of AI and what our ROI is.

Ken Ringdahl
Engineering Chief of Staff, Emburse
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Related use cases

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AI ROI

Turn AI spend into a number your executive team can trust
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Engineering Spend

Justify every engineering dollar, including AI
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Delivery Predictability

More output shouldn't mean less predictability
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More resources

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See what your AI investment is returning, on your own data.
Your total value gain from AI
Planning accuracy and cycle time by team
Where engineering capacity is going