Let AI Handle the Routine. Let Humans Handle the Unexpected.
As artificial intelligence becomes more deeply embedded in our organizations, it is tempting to overstate its purpose. The promise of faster decision-making, lower costs, and greater efficiency often becomes the headline. But in reality, AI is not here to replace us. It is here to reassign us. It is here to shift our attention from the mundane to the meaningful, from routine tasks to complex problems that still require human discretion, emotional intelligence, and moral reasoning.
There is a simple but powerful classification of how work is structured into three categories: simple, complicated, and complex. Simple tasks are repeatable, predictable, and often carried out by individuals using a fixed recipe for success. Complicated tasks require coordination across a team, specialized expertise, and multiple steps that, while difficult, are still repeatable with the right experience. Complex tasks, however, are uncertain, nonlinear, and highly context-dependent. They involve variables that cannot always be anticipated and outcomes that cannot be easily predicted.
This framework offers a remarkably useful lens for thinking about how to introduce AI into an organization. It suggests that we begin not with the hard stuff, but with the easy wins. Use AI to solve the simple problems first; those that are rule-bound, repeatable, and low-risk. These are the scheduling conflicts, invoice classifications, customer FAQs, and basic forms of data extraction that consume time but offer little strategic value. Getting these right builds confidence and allows teams to see AI as a tool, not a threat.
As comfort grows, we can move into the complicated. This is where agentic AI and workflow-integrated tools begin to shine. These systems can handle cross-functional logic, navigate multi-step decision trees, and deliver insights that would otherwise take hours of analysis. When designed well, they serve as intelligent collaborators, taking on complexity without compromising control. Yet they must remain accountable. As responsibility scales, so too must the guardrails.
This is where forcing functions come into play. In engineering and software design, a forcing function is a mechanism that either requires a specific action or prevents an incorrect one. Think of a microwave that won’t start unless the door is closed, or a form that cannot be submitted without required fields. When applied to AI, forcing functions can preserve oversight, enforce compliance, and align outcomes with organizational values. They help slow the system down when precision, ethics, or human review is necessary. Far from being bureaucratic, these intentional pauses protect against harm and keep high-speed systems grounded in common sense.
But here is the paradox. As we offload the routine to machines, we are left with more uncertainty. Technology does not eliminate complexity. It often redistributes it. While AI can calculate, store, transmit, and process better than any human, it cannot navigate ambiguity, respond to the unexpected, or understand nuance with the same depth of human judgment. In trying to simplify execution, we inadvertently increase the complexity of orchestration. The work shifts from doing tasks to managing the systems that do the tasks. And those systems require humans with foresight, empathy, and systems-level thinking.
This brings us to one of the most overlooked digital transformation pitfalls: optimizing parts instead of wholes. Improving one tool, team, or one process in isolation does not guarantee organizational success. In fact, it often creates disjointed, incompatible, or redundant systems that degrade performance rather than improve it. A chatbot that works perfectly in customer service but doesn’t integrate with CRM. A predictive model that optimizes inventory without visibility into procurement constraints. Each component might look like progress, but taken together, they become a pile of highly efficient junk.
Real progress comes from optimizing within the context of the system. That means understanding the flow of work, identifying constraints, and ensuring that each AI component complements rather than competes with the others. It means introducing AI only where it improves outcomes for the whole, not just metrics for the part. It also means designing for the people inside the system, not just the processes themselves. Human adaptability, creativity, and leadership are still the binding forces behind meaningful results.
In the end, the role of AI is not to lead but to enable. It should relieve the burden of routine work, extend our capabilities in complicated environments, and support our decision-making in times of uncertainty. But it must do so as part of a system designed for balance, clarity, and resilience. We should not ask AI to do everything. We should ask it to do the right things, in the right places, for the right reasons. And then get out of the way so people can do what only people can do.
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