Making AI Work: Start with the Operating Model
Nearly every company today is investing in artificial intelligence. From generative AI tools to automation platforms, leaders are pouring budget and enthusiasm into AI, hoping for breakthroughs in efficiency, innovation, and competitive edge. But here’s the uncomfortable truth: most of those investments fail to deliver meaningful results. Here at Active Digital AI, every week we are supporting businesses with this journey, either through AI innovation workshops or regular, fractional AI support.
Executives often blame “change fatigue” or technical complexity. But that’s not the real issue. The deeper problem? Companies are trying to deploy powerful 2025-era technology on a 2015-era operating model. That’s like installing a high-performance engine in an old chassis without updating the transmission, brakes, or fuel system, you may have great potential, but nothing works the way it should.
To truly capture value from AI, you must start by auditing your company’s operating model. Before you build dashboards, customize prompts, or stand up AI teams, you need clarity on how work actually gets done today — and how AI will reshape it tomorrow.
What Is an Operating Model (and Why It Matters for AI)
An operating model is more than a diagram or org chart. It’s the blueprint for how your company delivers value — how decisions are made, how work moves between teams, and how people interact with systems and customers. It includes six core dimensions:
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Structure and governance — Who makes decisions, how teams are organized, where authority lives.
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Processes and workflows — How work flows across functions and where handoffs occur.
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People and talent — What skills exist, where they’re located, and how capacity is allocated.
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Technology and tools — What platforms support work and how they integrate.
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Data and analytics — How information flows and how decisions are powered.
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Culture and behaviors — How employees actually work, adapt, and innovate.
Most companies invest heavily in AI tools without examining these dimensions first. But in reality, AI isn’t just another tool you plug into existing structures, because it fundamentally changes how work gets done. If your processes are siloed, your teams aren’t aligned, and your data is fragmented, then AI will simply automate broken workflows. People will revert to spreadsheets, workarounds, and old habits and your AI licenses will sit unused.
Think of an operating model audit as an “as-is” assessment before you chart a technology roadmap. It’s a systematic review of how work flows today highlighting inefficiencies, bottlenecks, skill gaps, and decision points. Only then can you identify where and how AI can actually help.
Here’s what a consultancy session from Active Digital AI can give you:
1. Clarity on Where Value Can Actually Be Captured
Without a clear picture of current workflows and outcomes, AI pilots are guesswork. An audit helps you:
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pinpoint repetitive tasks ripe for automation;
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identify decision processes that slow down operations;
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and reveal data gaps that could undermine AI insights.
2. A Roadmap Aligned to Real Business Needs
AI for the sake of AI is costly and ineffective. An operating model audit ensures your AI initiatives support your business goals. Instead of asking “What can AI do?” you ask:
What should AI do to improve our workflows, customer experience, or strategic outcomes?
That simple reframing helps prioritize high-impact use cases and avoid expensive detours.
3. Foundational Alignment on Skills and Roles
AI often creates new roles (e.g., prompt engineers, model stewards, data curators) and changes existing ones. An operating model audit helps you answer questions such as:
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What skills must we build or hire?
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Who owns AI outcomes in each business unit?
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How will decision rights shift as automation increases?
Without these answers, AI initiatives become disjointed, duplicative, or stalled.
From Audit to AI Transformation
Once your operating model is understood and aligned, your AI transformation can proceed with purpose instead of panic.
Design for work, not just technology – AI should be embedded into processes rather than bolted on.
Build cross-functional alignment – Teams that design, deploy, and use AI must share accountability.
Measure what matters – Define KPIs that reflect business outcomes, not just model performance.
In other words, adopt AI as part of your operating model, not as a separate initiative. In summary, AI success comes from understanding the context into which AI is introduced. By auditing your operating model first, you elevate your chances of turning AI investment into real business value. Start with your internal blueprint. Because unless your foundation matches your ambition, even the best AI tools won’t change your outcomes.
Partner with Active Digital AI in 2026 and let us help you transform your business.
