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VAKE

AI Consulting

Most companies use AI. Far fewer get results from it. We help you close that gap, from a readiness check to measured value.

1 · Where adoption stands

AI use is spreading fast. Value is not following.

Surveys of executives say AI use is close to universal. Counts of ordinary companies are lower but rising fast, and in Germany most companies that use AI say they exploit little or none of its potential.

The figures describe the market and come from published studies, not from VAKE projects. Each one links to its source. The studies ask different groups and measure different things, so they should not be added up or compared directly.

2 · What stands in the way

Why adoption stalls, and what we propose for each obstacle

Six obstacles come up again and again in the research. In front of each one is what we propose.

  1. The challenge

    Pilots stay pilots

    25%of AI initiatives have delivered the ROI CEOs expected, and only 16% have scaled across the enterprise.

    Proofs of concept are easy to start and hard to finish. Without an owner, a clear measure of success and a route into everyday work, they stall.

    IBM Institute for Business Value, CEO Study (May 2025) (opens the source in a new tab)

    What we propose

    Prove it in a pilot built to scale

    We choose one use case, agree the success measures before anything is built and plan the route to production from the start, so a good pilot can become part of daily work.

  2. The challenge

    The payback takes longer than expected

    2–4 yearsis how long most executives in Europe and the Middle East say a typical AI use case takes to pay back. For other technology they expect 7 to 12 months.

    Benefits such as time saved or better decisions are hard to put a number on, data sits in silos and the measures keep changing. The result is a business case nobody can check.

    Deloitte, AI ROI: the paradox of rising investment and elusive returns (October 2025) (opens the source in a new tab)

    What we propose

    Measure from the first day

    We agree what success means before the pilot starts — hours saved, errors avoided, cost or revenue — and report against it, so the value is visible and a project that is not paying off can be stopped early.

  3. The challenge

    The skills and know-how are missing

    70.9%of EU enterprises that had considered AI but did not use it name a lack of relevant expertise as a reason.

    The shortage is not only data scientists. HR teams, managers and IT all need to know what AI can and cannot do in their own work.

    Eurostat, Use of artificial intelligence in enterprises (2025) (opens the source in a new tab)

    What we propose

    Build skills where the work happens

    We train HR, managers and IT for the AI they will actually use, with your data and your processes, and leave your own people able to run it.

  4. The challenge

    Data and systems are not ready

    50%of CEOs say the pace of recent AI investment has left their organization with disconnected, piecemeal technology.

    AI that cannot reach clean, current HR data, or does not connect to SuccessFactors, payroll and the systems around them, stays a demo.

    IBM Institute for Business Value, CEO Study (May 2025) (opens the source in a new tab)

    What we propose

    Start from your data and your SAP landscape

    We check data quality, access and integration first, then connect AI to SuccessFactors and your other systems with SAP Integration Suite, cloud apps and secure APIs.

  5. The challenge

    Nobody owns the result

    46%of organizations do not involve the CHRO, the chief HR officer, when AI strategy is set.

    When AI is treated as an IT project, HR, legal and the business find out late, and nobody is accountable for the outcome.

    IBM Institute for Business Value, 2026 CHRO Study (opens the source in a new tab)

    What we propose

    Put HR, IT and the business around one table

    We set ownership and decision rights at the start: who chooses the use cases, who owns the data, who answers for the result and who signs off the risks.

  6. The challenge

    People already use AI, outside the rules

    48%of employees who use AI at work say they have put company information into public AI tools.

    Where there is no clear guidance and no approved tool, people use what they find. In the study, bans and policies alone did not stop it; clear guidance and training are needed.

    University of Melbourne and KPMG, Trust, attitudes and use of AI: a global study (2025) (opens the source in a new tab)

    What we propose

    Give people a safe way to use AI

    We write simple rules with you, set up approved tools with the right access controls and train people on them, so the safe route is also the easy one.

For HR leaders in Europe

What the EU AI Act means for AI in HR

AI that recruits, evaluates or manages people is one of the AI Act's high-risk uses. The dates moved in 2026, so here is where things stand.

  1. 2 Feb 2025

    Bans and AI literacy

    Banned practices, including inferring the emotions of people at work, and the AI literacy duty began to apply.

  2. 2 Aug 2026

    General application

    The AI Act applies in general and enforcement starts, with transparency duties for certain AI systems.

  3. 2 Dec 2027

    High-risk rules for HR

    AI used to recruit or select people, to decide on promotion, termination or the terms of work, to allocate tasks or to monitor performance is high-risk. Employers that use such systems must meet the high-risk requirements, including human oversight and informing workers. Moved from 2 August 2026 by the Digital Omnibus.

Dates as set by the AI Act (Regulation (EU) 2024/1689) as amended by Regulation (EU) 2026/1744, the Digital Omnibus, in force since 27 July 2026. This summary is for orientation and is not legal advice.

European Commission, AI Act: application timeline (opens the source in a new tab)

3 · What we propose

Five steps from first idea to measured results

We take an AI initiative from first assessment to measured results, with governance built in from day one.

  1. 1

    Assess your readiness

    We review your data, processes, SAP landscape, security requirements and team skills, so we start from where you really are.

  2. 2

    Set strategy and priorities

    We turn business goals into an AI roadmap and rank use cases by value, effort and risk, so the first projects are the ones most likely to pay off.

  3. 3

    Prove it in a pilot

    We build a small, time-boxed pilot for the top use case, with success measures agreed up front, before you commit to a larger investment.

  4. 4

    Scale with guardrails

    We take what worked into production on your SAP platform, with SAP Joule and Microsoft Copilot where they fit, under a responsible AI framework for security, privacy and oversight.

  5. 5

    Drive adoption and measure results

    We train your teams, support the change and track results against the measures set at the start, so the value stays visible and keeps growing.

Our AI services

What we do, from first assessment to daily use

Eight service lines cover the path described above. You can start with one and add the others as you go.

The result

What you get

  • A ranked list of AI use cases, by value, effort and risk
  • Pilots with success measures agreed before they start
  • A clear view of what the EU AI Act means for your HR use cases
  • Guardrails for security, privacy and human oversight
  • Teams that know how to use AI in their own work
  • Results you can show, tracked against the measures set at the start

Where we help

AI products we have built

Two assistants that answer questions about your SAP SuccessFactors data, built by VAKE.

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