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*By Alena Shurtakova, a globally recognized architect of enterprise operating models and organizational systems who designs the structural logic of how large organizations function in the age of intelligent systems.*
Implementing AI in a company is not just a technology challenge. In practice, organizations often fail not because the tools don’t work, but because their operating models and processes aren’t ready. AI exposes organizational weaknesses, accelerates workflows, and can even improve decision-making—but if deployed without a clear strategy, it can damage customer trust.
Why Most AI Implementations Fail
Tools Before Processes
Many companies start with models and technology instead of workflows and decision-making. AI is often bolted onto broken processes rather than used to improve them.
Symbolic Ownership
An “AI lead” might be appointed, but profit and loss owners remain spectators. Without real accountability, initiatives fail to scale.
Governance Bottlenecks
Either governance is absent, leading to reckless automation, or it’s overly bureaucratic, slowing progress. Too many approval layers and complex processes kill momentum.
Undefined Human Roles
Declaring “humans in the loop” isn’t enough. If human roles aren’t clearly designed, trust collapses, and adoption stalls.
Culture and Skills Gaps
AI amplifies existing capabilities. Without training and upskilling, teams revert to old habits, treating AI as optional rather than integral.
Lessons From Real-World Failures
McDonald’s ended its AI drive-thru voice ordering pilot with IBM after testing in over 100 locations. The technology worked, but the real-world environment—noise, accents, interruptions—exposed its brittleness. Without robust human fallback, customer experience suffered.
Zillow shut down Zillow Offers after AI-driven price forecasts amplified market volatility, turning small prediction errors into major financial losses.
Amazon abandoned an experimental AI recruiting tool when it was found to reproduce historical bias, scaling flawed processes rather than fixing them.
Revolut’s push for AI-driven customer support created friction for users who valued fast, competent human interaction. Automated escalation paths left customers frustrated.
These examples show that AI is not just a technology project—it’s an operating model challenge.
What Actually Works
Organizations that successfully scale AI focus on value streams, not tools. They design an operating model around where value is created, how decisions are made, and how humans and AI interact. Key principles include:
Governance as an accelerator: Low-risk use cases move fast, high-risk decisions have explicit human accountability, and exceptions are built into the system.
Human-centered design: Clearly define human roles in AI processes, ensuring trust and accountability.
Culture and capability building: Invest in role-specific training, cross-functional collaboration, and mindset shifts to embrace AI as part of everyday work.
Customer-centric approach: Keep the focus on delivering value to clients, not just efficiency.
Golden Rules for Leaders
Start with value streams, not tools.
Define decision rights before deploying AI.
Fix governance bottlenecks early.
Commit to operating model transformation, not cosmetic change.
Redefine human roles explicitly.
Measure outcomes, not activity.
Keep it relentlessly customer-centric.
AI doesn’t create capability—it amplifies it. Without the right people, clear accountability, and a tailored operating model, AI can accelerate dysfunction. With them, it becomes a force for speed, quality, and sustainable growth. Companies that win aren’t the ones with the most pilots—they are the ones that use AI to redesign how work gets done, becoming truly future-ready organizations.
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