# AI Transformation — Scenaryo > Successful generative AI rarely fails on technology — it fails when strategy, tools and people are not thought through together. Scenaryo runs a multi-directional AI transformation: top-down executive vision combined with bottom-up momentum from the teams. Strategy, use cases, Responsible AI and a solid data foundation. - **URL**: https://ai.scenaryo.de/ai-transformation - **Provider**: Scenaryo GmbH, Unertlstrasse 36, 80803 München, Germany - **Languages**: English (default, unprefixed), German (under /de/) - **Service area**: DE, AT, CH, EU - **Contact**: info@scenaryo.de --- ## Why most AI initiatives stall A pure top-down mandate creates pilots without buy-in. Pure bottom-up experiments create island solutions without scale. The value emerges in between — in a multi-directional strategy. According to MIT, around 95% of gen AI pilots fail: not on the technology, but on the gap between vision and everyday work. Reference figures: - 65% of companies regularly use gen AI — nearly doubled within 10 months (McKinsey, State of AI 2024). - ~95% of gen AI pilots show no measurable P&L impact (MIT NANDA, 2025). - 74% see ROI within the first year when implemented correctly (Google Cloud / National Research Group, 2024). - $2.6–4.4 trillion annual value potential of gen AI (McKinsey, 2023). --- ## The two forces of an AI strategy - **Executives and high-level management:** executive sponsorship, a clear vision, alignment of AI initiatives with business goals, adequate resourcing, organisation-wide support — while empowering teams to experiment. - **Mid-level managers and individual contributors (ICs):** proximity to daily operations and end-users to identify high-impact, feasible solutions; driving adoption; feedback loops that integrate tools into existing processes. --- ## Six dimensions of every initiative For each initiative — top-down or bottom-up — Scenaryo works through six dimensions, clarifying the role of leadership and of teams: 1. **Strategic focus** — vision and guardrails (leadership) vs. prioritising real pain points, human-centered and low-risk (teams). 2. **Exploration** — space and budget to experiment vs. hands-on testing and surfacing results. 3. **Responsible AI** — binding standards and governance vs. adherence, risk anticipation, bias/safety/ethics testing. 4. **Resourcing** — platform, data access, skills, budget vs. leveraging existing assets and making the ROI case. 5. **Impact** — KPIs tied to business goals vs. quantifying effects and tracking KPIs. 6. **Continuous improvement** — institutionalised learning loops vs. feedback and iteration. --- ## Finding use cases: the creative matrix A grid maps business goals (improve customer experience, increase efficiency, drive innovation, raise employee productivity) against AI capabilities, producing concrete ideas in each cell. Ideas are then prioritised by impact × feasibility, starting where high value meets low risk. Every idea is validated before implementation. --- ## Augmentation before automation - **Augment** (humans get stronger): critical thinking and problem solving, creativity and innovation, relationships and collaboration, strategy and vision. - **Automate** (humans get relieved): repetitive, rule-based tasks; time- and resource-intensive tasks. - **Human-in-the-loop** remains indispensable even when automating: data selection and preparation, prompt design and refinement, output review and correction, continuous monitoring and feedback. --- ## The foundation: data - **Data quality:** accuracy, completeness, representativeness, consistency, relevance. - **Data accessibility:** availability, cost, format. - **Structured vs. unstructured** data require different approaches. Clarifying type, quality and structure is the first step of every implementation. --- ## Maturity and approach Maturity levels: tactical → strategic → transformational, aligned to the themes Learn, Lead, Access, Secure, Scale. Five-step approach: (1) Assessment of maturity, data and pain points; (2) Prioritisation via creative matrix; (3) Pilot with a low-risk use case, human-in-the-loop and clear KPIs; (4) Responsible-AI check (bias, safety, compliance, values); (5) Scale and learning loop. --- ## FAQ **Top-down or bottom-up — which first?** Both in parallel. Vision without practice fizzles out, practice without vision doesn't scale. **We don't have a clean data foundation yet. Too early for AI?** No — start with low-risk use cases and build data readiness in parallel. **What if the AI makes mistakes?** Human-in-the-loop is mandatory, not optional — in data selection, prompting, output review and monitoring. **Will AI replace our people?** The focus is augmentation: people are freed up for strategy, judgment and creativity, while the repetitive work is automated. **Do we have to commit to a specific AI platform?** No. The approach is tool-agnostic — the methodology (creative matrix, maturity levels, Responsible AI) works independently of platform and product. --- ## Sources Methodological framework based on the Google Cloud AI Adoption Framework and the Responsible AI Principles. Figures: McKinsey (State of AI 2024; Economic Potential of Generative AI 2023), MIT NANDA (State of AI in Business 2025), Google Cloud / National Research Group (ROI of Gen AI 2024). Figures as of the respective publication.