Just Because AI is Implemented...

Just because we adopt AI doesn't mean we are done. In this article, I want to discuss the critical need for operating model transformation. This is part of my series of articles focused on why some AI implementations can fail.

We are probably observing a fascinating phenomenon that has become quite common across industries. Companies are proudly announcing their major AI implementation, but when it comes to capturing the promised value, they are lost.

Despite having cutting-edge AI capabilities, they are unable to translate insights into action fast enough. Why?

This is because their operating model, starting from decision-making processes to organizational structures are still caught up in a world of weekly planning cycles and hierarchical approvals.

This scenario can be quite common across sectors. Take for example, a retail company that can predict demand shifts in real-time, but their store operations still require multi-level approvals for basic inventory adjustments. Or consider manufacturing, where AI can now predict equipment failures hours in advance, yet maintenance schedules remain locked in rigid weekly planning cycles. I am sure you can apply similar scenarios across other industries and business processes where traditional processes prevent quick decision making resulting in poor outcomes.

Just because you have adopted AI doesn't mean your transformation is complete. In fact, it might be just beginning.

Outdated Business Models Impeding Value

Let us review some common scenarios across other industries, where companies are struggling when it comes to generating value because their fundamental operating models cannot leverage these AI capabilities effectively.

- Financial services firms have AI that can detect market opportunities in milliseconds but require committee approval to act on them

- Healthcare providers possess predictive analytics to predict patient flow but cannot adjust staffing in real-time due to their rigid scheduling systems

- Manufacturing plants have AI-powered quality control but cannot adjust their production parameters without going through traditional change management procedures

- Professional services firms have AI tools for resource optimization but still allocate talent through quarterly planning cycles

As you can see, this is not just about outdated technology, but is also about outdated thinking. These business models have been built for old technologies and need to change for AI technologies. They were built around predictable patterns such as annual planning cycles, fixed organizational hierarchies, and standard operating procedures.

Consider how decisions flow through your organization. If your AI can identify an opportunity or threat in minutes, but your approval process takes days, you've created a system designed to fail.

It's like installing a Formula 1 engine in a car with bicycle wheels, meaning the power is there, but the supporting structure cannot handle it.

Path Forward

The organizations that are succeeding with AI are not just adding new capabilities to old models. They are fundamentally rewiring how they operate.

To truly capitalize on AI speed, we need -

1. Dynamic Decision Frameworks

- Replacing rigid hierarchies with empowered decision-making at the edge

- Creating clear guardrails instead of approval chains

- Enabling real-time responses within defined parameters

2. Continuous Operating Rhythms

- Moving from periodic to continuous planning cycles

- Implementing rolling forecasts and adjustments

- Building feedback loops that operate in real-time

3. Dynamic Organizational Structures

- Breaking down traditional functional silos

- Creating cross-functional teams that can act autonomously

- Developing adaptive organizational boundaries

4. Adaptable Capabilities

- Building systems that can evolve as technology advances

- Creating flexible processes that adapt to new insights

- Developing skills for continuous learning and adaptation

The gap between what AI makes possible and what traditional operating models can support is growing wider every day.

The urgency of this change cannot be overstated. Every week organizations that are stuck with outdated operating models are leaving value on the table and risking their very competitive position. Just because you have implemented AI does not mean you are done. In many ways, that is just the beginning of your real transformation journey.

The key question is not whether you have AI but whether your organization can act at the speed of AI        

How is your organization dealing with the growing gap between AI's possibilities and traditional operating models?

#DigitalTransformation #AI #OperatingModel #ceo @cto OrganizationalChange #Leadership #Strategy

All opinions are my own and not those of my employer.