People tend to think of AI as a plug-in, you buy a software cog, install it, train up a couple of teams, and expect transformation to occur. The reality is that transformation seldom materializes in this way.
When used properly, AI alters decision-making processes and how companies manage their operations. This is why an organization’s operating model is critical when deploying AI; it’s not a question of tools, but one of structure.
In retail, for instance, price adjustments are typically made by a pricing committee that meets weekly, with AI the ability to make those adjustments can occur almost instantly, based on signals from demand, competitors, and inventory. But the question is, who makes that decision… the algorithm? the manager? both?
Organizations typically procrastinate making changes to their operating model because they implement AI technology, but keep their existing decision-making structures intact. Consequently, they will experience slower decision-making as opposed to faster decision-making.
Check out: Multi-Agent Orchestration
However, establishing a new operating model by changing the amount of trust leaders place in decision-making is difficult. Leaders must have confidence in their ability to delegate particular decisions to the system while retaining oversight over the outcomes. This is a heavy lift on the part of most organizations.
Accountability is another area that changes when implementing AI. If an AI-assisted decision were to result in an unfavorable outcome, the level of responsibility does not disappear; the level of responsibility is simply shifted — often, uncomfortably, to another team or person.
Thus, AI-based operating models are primarily about redesigning decision rights, workflows, and feedback loops, not automating them. Without thoughtful redesigning of these components, organizations that implement AI can only expect an assistant, not a backbone.
