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Lokomotif AI
Lokomotif AI
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We turned what the field taught us into a discipline.

For three years, across more than 100 companies, we have watched who creates value with AI and who stalls halfway. Our approach grew out of those observations.

  1. 01

    Adoption starts in the control layer, not in operations.

    CHRO
    38%
    CFO
    24%
    CEO
    22%
    CIO
    16%

    Distribution of the initiating role

    The role that initiates AI programs is usually the CHRO or the CFO; the CIO plays a supporting part. In Turkish enterprises, a significant share of the AI budget flows through HR and finance. A business-first adoption pattern, not IT-first.

  2. 02

    The clear view of ROI emerges at T₃, not at T₁.

    T₁ · end of program

    62%

    looks positive

    T₃ · +180 days

    34%

    still positive

    Pilot ROI fades over time

    A significant share of pilots that look positive in end-of-training satisfaction and T₁ measurement retreat six months later. Only T₃ validates the slope. Judging a pilot's success on the day it ends is statistically misleading.

  3. 03

    Same use case, different stakeholders, different outcome.

    Company A6 weeks
    Company B9 months

    Same use case, time to production

    A customer-service chatbot reached production in six weeks at one company and was rejected after nine months at another. Same use case; same technology; the difference lay in the clarity of the stakeholder map. The hardest variable of adoption success to learn.

  1. The five layers of RTCS-G: a prompt discipline expanding from individual talent to organizational capability.
    Method01

    RTCS-G

    We write the prompt library each role will use when working with AI, structured across the five layers of RTCS-G. The output: a company-specific, measurable, transferable decision discipline; the shift from individual talent to organizational capability.

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  2. AI Momentum: a capability slope measured at three points in time; the steepest slope highlighted.
    Capability measurement02

    AI Momentum

    We measure every participant's AI capability across four components and three points in time. The output: a map that reads as a slope, showing where everyone in the company stands; real momentum, not end-of-training satisfaction.

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  3. How we measure: baseline, T₁ and T₃ measurement points across three layers; the T₃ column highlighted.
    Measurement discipline03

    How we measure

    We take baseline + T₁ + T₃ measurements across three layers: individual capability, workflow output and business outcome. The output: data that validates the program's ROI in the field; an evidence-backed answer to whether it is still positive after 90 days.

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  4. Adoption Arc: three moves rise in stages; each move adds a new layer of capability.
    Engagement trajectory04

    Adoption Arc

    We run adoption in three moves: Adoption Sprint, Workflow Rewire and Agentic Scale. The output: a staged shift from individual usage to agent-assisted operations; a new layer of capability stacked with every move.

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  5. Manifesto: a position that writes the view of the category in nine articles; the first article highlighted.
    Category manifesto05

    Manifesto

    We write down how we see Corporate AI Adoption, where we part ways with the rest of the market, and the discipline we work with, in nine articles. The output: the operating frame underneath every program, tested in every engagement in the field.

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Fatih GünerFounder, Lokomotif AI
fatih@lokomotif.ai