Sequential Thinking is Dead: Transforming Decision-Making in the AI Era
Sequential thinking is dead in the era of AI. I didn’t realize this until I worked on the first project in my MIT AI No-Code course. Forward-leaning CMOs, CEOs, and firms investing in AI must embrace parallelism, or parallel thinking, because it’s the secret sauce to successfully leveraging AI.
Many firms are implementing AI for automation, allowing teams to redirect their focus to strategic activities with greater impact. As firms get comfortable with AI, they move on to prediction. In the first project, focused on a hotel grappling with high cancellation rates, I learned that AI puts parallel decision-making on steroids.
It will include achieving stakeholder goals, building a prediction model, and critically considering the model’s long-term impacts. It also means getting comfortable with managing paradox, holding opposing truths simultaneously, as Brene Brown indicated in her interview with Amy Webb, a strategic futurist.
Shareholder Goals are a Starting Point
The hypothesis based on shareholder goals is not the only decision to be made. You’re also building a model to predict the behaviors and policies you must embrace once the solution is defined.
Policies Are Automated
As AI is integrated into the business, fact-finding will quickly become an automated policy your firm implements. The question is, what’s the impact of the automation?
Ciritical and Anticipatory Thinking Are Key
As I examined the model outputs, I was surprised by many of the insights, different from my hypothesis, on what drove the cancellation rates. However, what concerned me most was the implications of the potential policies if we automated them.
As I indicated in a previous article, AI doesn’t have feelings. Human attributes of critical thinking and subject matter expertise come into play to ask whether it’s wise to accept the model without anticipating the downstream impacts as a policy. I had more questions about the insights that would require deep thinking about what the model viewed as the root cause before accepting the model. Without validating the root cause, you could negatively impact your business.
Managing Paradox is the New Skill Set
Paradox requires us to hold opposing truths simultaneously. One truth I had was that the hotel’s challenge with its loyalty program was a major problem. The model helped me separate what’s a signal, the loyalty program, and what was a root cause to resolve the cancellation problem. Once I solved the cancellation problem, the loyalty program signal should improve.
AI is a powerful tool that companies can use to help uncover problems faster. Forward-leaning firms, CMOs, and CEOs embracing parallel thinking and paradoxes will achieve a competitive advantage.
