AI Engineering ROI & Adoption

    Your engineers are using AI. Can you prove it's improving delivery?

    TeamAxiom helps software companies measure AI adoption, identify where AI is creating real engineering value, and turn fragmented experimentation into measurable workflow improvement.

    Take the Free AssessmentSee How It Works

    Free • ~3 minutes • Instant AI Engineering ROI score

    The problem

    AI adoption is moving faster than AI measurement.

    Giving developers access to Cursor, Copilot, Claude or ChatGPT is easy. Knowing whether those tools actually improve engineering performance is much harder.

    Adoption

    Who is actually using AI, where, and for which engineering workflows?

    Engineering impact

    Are cycle time, reviews, throughput, documentation or delivery actually improving?

    Quality

    Is faster code creation producing more defects, rework or review overhead?

    Cost & governance

    Are AI licences being used effectively, and are developers working within clear security and data policies?

    Free assessment

    Find out where your engineering organisation stands.

    Take the free AI Engineering ROI Assessment and get a quick maturity score across the areas that determine whether AI adoption creates measurable value.

    • AI Adoption
    • Engineering Impact
    • Measurement
    • Quality & Rework
    • Cost & Governance
    Take the Free 3-Minute Assessment

    No sales call required.

    AI Engineering ROI Score

    Example

    18 / 40

    Adopting

    AI adoption is growing, but measurement, governance and consistency have important gaps.

    ExperimentalAdoptingOperationalMeasured & Scalable
    Illustrative example only — not a customer result.

    Who this is for

    Built for engineering leaders who need more than AI usage statistics.

    Typically software, SaaS and digital product companies with around 20–200 engineers, in the UK and Europe.

    Roles we work with

    • CTOs
    • VPs of Engineering
    • Heads of Engineering
    • Technical Founders

    Most relevant when

    • Your teams already use AI coding tools such as Cursor, Copilot or Claude Code
    • AI tooling spend is growing and needs to be justified
    • Leadership or the board is asking for measurable AI ROI
    • You want to standardise how engineers use AI across teams
    • You are concerned about quality, rework, security or governance

    How It Works

    From AI experimentation to measurable engineering improvement.

    1. 01

      Assess

      Understand current AI adoption, engineering workflows, measurement maturity and governance.

    2. 02

      Measure

      Identify the metrics that matter across delivery, quality, adoption and cost — and establish a baseline.

    3. 03

      Improve

      Redesign high-value workflows, establish practical standards and enable teams where it counts.

    4. 04

      Verify

      Measure whether the changes are creating value, then keep iterating on what works.

    Services

    Start small. Expand only where the evidence supports it.

    Every engagement begins with the free assessment and a 30-minute results review. From there, work progresses only as far as it creates value.

    01 · Diagnose

    AI Engineering ROI Assessment

    A focused diagnostic engagement that establishes where AI is creating value, where measurement is missing, and where your biggest opportunities and risks are.

    • AI adoption baseline
    • Engineering workflow review
    • AI tool and spend review
    • Measurement maturity
    • Quality and rework considerations
    • Governance gaps
    • Prioritised opportunities and a 90-day action roadmap
    Start with the Free Assessment
    02 · Improve

    AI Engineering Improvement Sprint

    Turn the assessment findings into practical improvements across engineering workflows, AI usage, team practices and measurement.

    • AI-assisted development workflows
    • Code review practices
    • Testing and QA workflows
    • Documentation and onboarding
    • Internal AI usage guidelines
    • Targeted, hands-on training where needed
    • Practical automation
    03 · Sustain

    Continuous AI Performance

    Ongoing measurement and advisory support for companies that want to keep improving AI adoption, engineering impact and governance.

    • Periodic reviews and leadership reporting
    • Metrics and adoption monitoring
    • New tool evaluation
    • Workflow optimisation
    • TeamAxiom platform where relevant
    Service details, deliverables and formats

    What we measure

    Measure outcomes, not AI hype.

    The areas we evaluate with you during an engagement. Which of these can be collected automatically depends on your tooling and the scope we agree.

    Delivery

    • PR cycle time
    • Review time
    • Deployment frequency
    • Throughput

    Quality

    • Defects
    • Rework
    • Review overhead
    • Testing practices

    Adoption

    • Tool usage
    • Workflows
    • Team adoption
    • Enablement

    Economics

    • Licensing
    • AI spend
    • Utilisation
    • ROI visibility

    Governance

    • Approved tools
    • Data handling
    • Source-code policies
    • Quality controls

    TeamAxiom Platform

    Beta

    Measurement shouldn't stop when the engagement ends.

    The TeamAxiom platform is being developed to support AI adoption measurement, engineering insights and ongoing improvement. During selected engagements, it can complement our advisory work with data-driven visibility into adoption and engineering workflows.

    Learn About the Platform

    Eduard Kotanjyan

    Founder & CEO

    LinkedIn profile

    Who's behind TeamAxiom

    Built by an engineering leader, for engineering leaders.

    TeamAxiom was founded by Eduard Kotanjyan, a software engineer and technical leader who has worked as a Senior Tech Lead building scalable software systems across AdTech, SaaS and digital services.

    That background shapes how we work: AI adoption is approached from the perspective of real engineering workflows — pull requests, code review, testing, delivery and team practices — rather than generic AI training.

    About TeamAxiom

    Outcomes

    Questions we help engineering leaders answer

    • Which AI tools are actually creating value?
    • Where is AI improving delivery — and where isn't it?
    • Are we increasing output while also increasing rework?
    • Are we paying for unused AI licences?
    • Which workflows should we improve next?
    • How should we report AI ROI to leadership?
    Take the Free AssessmentBook a 30-minute review

    FAQ

    Questions engineering leaders ask us

    Find out where your AI engineering strategy stands.

    Take the free assessment and get your AI Engineering ROI maturity score in approximately three minutes.

    Take the Free AssessmentBook a 30-minute review

    Free • ~3 minutes • Instant AI Engineering ROI score