Reframing the Question: Rethinking Process in the Age of Generative AI

For years, leadership, education, and business strategy have hinged on a familiar question: Do we follow the established steps or step outside the process to creatively solve the problem? It was a practical distinction. Some situations required disciplined adherence to a proven method. Others required innovation, ambiguity tolerance, and critical thinking. However, the emergence of generative AI, natural language processing (NLP), and large language models (LLMs) has made this binary framing obsolete.

We no longer decide between following a process and thinking outside of it. We are now tasked with asking whether the process itself still makes sense.

Parallelism Over Linearity

Traditional problem-solving frameworks are rooted in a sequential model. The logic is straightforward: define the issue, analyze the variables, brainstorm solutions, test, refine, and implement. This structure emerged where human cognition, time, and information access were limiting factors. But AI does not suffer from those same constraints. Generative models can explore multiple pathways simultaneously, offer immediate synthesis of massive data sets, and iterate in real-time. That changes more than just speed. It changes the architecture of how we think about work.

We have to stop assuming a singular workflow is always the best path. Parallelism, which allows multiple threads of thought, process, or even prototype to run simultaneously, has become possible and practical. With AI, we can now develop content, analyze outcomes, test hypotheses, and refine outputs all within minutes of formulating the initial question. This isn’t about better tools for the same problems. It’s about a shift in how issues are structured, navigated, and resolved.

Cognitive Biases and the Illusion of Process Integrity

One of this transition’s most significant psychological challenges is our deep-rooted trust in established systems. Humans are naturally drawn to frameworks that offer predictability and control. The linear process satisfies our cognitive bias toward order, completion, and causality. It gives us a sense of closure and confidence, even when the outcome is suboptimal. This is known in psychology as the “effort justification” bias. We are inclined to believe that a process must be valid because we worked hard to complete it.

But AI exposes this fallacy. It can reveal that a ten-step plan may be reduced to two. That a research process spanning days could be replicated in minutes. Our verification and validation systems were not necessarily efficient; they were just familiar. And familiarity, while comforting, is not the same as value.

The Role of Critical Thinking in an AI-Augmented World

The irony is that many believe AI will reduce our need for critical thinking when it demands more of it. But the nature of that critical thinking has changed. It is no longer limited to solving problems within a known system. It now includes the meta-analysis of the system itself. Does this structure serve the current context? Are we approaching the right problem? Is our definition of success still relevant?

This is where many struggle. Psychologically, we are wired for pattern recognition and certainty. We resist ambiguity because it increases cognitive load. Generative AI introduces ambiguity not because it lacks clarity, but because it offers too many possibilities. It removes the illusion of a singular correct answer and opens the door to several valid alternatives. That kind of abundance requires discernment, not direction-following.

From Control to Design

The shift from a linear to a parallel problem-solving model means leaders, educators, and strategists are no longer just participants. They become systems designers, curators of options, and facilitators of discernment. The question is no longer “Do I want my team to follow the playbook or write a new one?” The better question is, “Should we even be using a playbook in this situation, or should we be building dynamic systems that flex, adapt, and evolve alongside the tools we now have access to?”

This is not an abstract shift. It has implications for how we hire, how we train, how we measure performance, and how we make decisions. It also requires emotional maturity and psychological resilience. Working alongside AI means accepting that we won’t always be the originators of the best idea. It requires confidence in our ability to judge, shape, and improve what is generated rather than needing to be the sole source of insight.

The Invitation to Reevaluate

The power of generative AI is not just in what it can produce. Its value lies in how it challenges the assumptions we’ve built into our workflows, hierarchies, and mental models. It’s an invitation to reevaluate everything, not out of fear but curiosity. What else have we accepted as “just the way it’s done” that could now be redesigned, redistributed, or retired?

This moment demands more than adoption. It demands reimagination. Not just of the tools we use, but of the roles we play, the structures we rely on, and the questions we ask before we begin.