AI Strategy: Optimizing Today and Reinvent Tomorrow

For executives, the current AI landscape feels like a frontier. The parallels to the early days of the PC, web, and mobile eras are clear: a surge of frontier spirit, a dizzying array of real possibilities, and a cacophony of vendors hawking solutions, often to problems we don't yet have. This creates two distinct challenges for any leader: the paradox of choice and the overwhelming marketing noise. Amidst this, the crucial question gets lost. We are not just asking what to do with AI, but why. Are we seeking incremental improvements, or are we pursuing a riskier, revolutionary path? What problem are we truly trying to solve?

The stakes of this decision are higher than simply allocating budget. Choosing the wrong path, or no path at all, leads to predictable failures. One is "AI Theatre"—high-cost, high-visibility projects that use impressive technology but generate no meaningful business value, ultimately eroding organizational confidence. The other, more silent threat is strategic obsolescence, where a competitor, by correctly applying AI to a key constraint or an overlooked customer job, fundamentally reshapes the market and renders your business model uncompetitive. The challenge, therefore, is not just to act, but to act with intention.

The answer requires two different mindsets and two distinct strategic frameworks.

The Evolutionary Path: A Framework for Optimization

The most common starting point is optimization: applying AI to achieve measurable gains in existing processes. The strategic tool for this is the Theory of Constraints. This framework dictates that you identify the single biggest bottleneck limiting your organization's output and apply focused effort there. This approach avoids random acts of AI. Instead, it targets the one area where improvement will have the greatest systemic impact.

• Instead of just faster customer service, it's using AI to analyze service transcripts to find the bottleneck in your product's user experience, feeding intelligence back to development.
• Instead of just generating more content, it's using AI to optimize conversion rates on your most constrained marketing channel.
• In technology, instead of merely generating reams of new code (that creates a review backlog), it’s applying AI to the bottleneck of refactoring legacy codebases—reducing the technical debt that slows all future development.

However, the evolutionary path has a bump in the road: becoming trapped at a "local maximum." You can become so proficient at optimizing a specific process that you fail to recognize when that process itself should be eliminated. A crucial, often overlooked, benefit of these evolutionary AI projects is the high-quality, structured data they produce as a byproduct. This data about your core business processes is the essential fuel needed to train the next generation of models that can power a truly revolutionary shift.

This path is analogous to the "brochureware" phase of the early web. It's a necessary first step—optimizing an existing system. It delivers tangible value and builds foundational capabilities without betting the company.

Crucially, the optimization phase serves a dual purpose that extends beyond immediate efficiency gains; it is a period of strategic exploration. When teams apply new AI tools to well-understood bottlenecks, they are not just solving a problem, but are actively charting a new and unknown landscape. This hands-on application is where true learning occurs, building an organisational intuition for a tool's latent capabilities, its limitations, and its unexpected strengths. This is not knowledge that can be gleaned from vendor demos or whitepapers. The "Aha!" moments that spark genuine revolution are rarely conceived in a vacuum; they are forged in the practical, where hands-on experience with new tools illuminates the path from incremental improvement to fundamental reinvention.

The Path of Reinvention: A Framework for Creating New Value

A fundamental shift requires a different framework that encourages you to redesign the business model itself. This is a synthesis of Jobs-to-be-Done (JTBD) theory and Blue Ocean Strategy. The goal is not to optimize the current system, but to make it irrelevant. This framework starts by ignoring your internal processes and asking: What is the fundamental "job" our customer is trying to accomplish?

A historical example of this is the Nintendo Wii. In the mid-2000s, the console market was a "Red Ocean" where Sony and Microsoft were in a technology arms race for the "hardcore gamer."

1. Identify the "Job-to-be-Done": Nintendo looked past this market and saw a much larger group of "non-customers." Their job wasn't "to experience photorealistic graphics." For families and social groups, the job was "to find a fun, simple, and inclusive social activity for the living room."
2. Redesign the Value Proposition: Using a Blue Ocean approach, Nintendo engineered the Wii to serve this job. They eliminated cutting-edge graphics, reduced the price and controller complexity, raised accessibility, and created a new market of "non-gamers."

Pursuing such a revolutionary path has profound organizational implications. It cannot be a side project managed by the existing business unit, which is incentivized to protect the status quo. It requires explicit executive sponsorship, a ring-fenced budget, and a dedicated, cross-functional team that operates with a high degree of autonomy. The funding model must also be different, managed more like a venture capital portfolio where the goal is not immediate ROI, but validated learning through rapid experimentation. Failure of individual experiments is not just tolerated; it is expected as a necessary part of navigating the unknown.

The greatest risk of the revolutionary approach is building a "technology in search of a problem." Many ambitious projects fail because they are born from a fascination with what AI can do, rather than a deep, obsessive understanding of a customer's JTBD. This is why grounding the effort in the JTBD framework is non-negotiable. Without it, your "blue ocean" is likely to be a mirage, leading to a product no one wants to hire. A deep understanding of the customer's struggle is the only true anchor in the turbulent waters of radical innovation.

Nintendo's competition wasn't Sony or Microsoft; their competition was board games and family inactivity. They didn't win the arms race; they created a new market where it didn't matter. A revolutionary AI strategy applies the same thinking.

Unified Strategy: From Optimization to Reinvention

The choice between optimization and reinvention is not a binary decision; it's a virtuous cycle.

1. Start with Optimization: Use the Theory of Constraints to apply AI to your most significant bottleneck. This delivers immediate, measurable value, reduces risk, and builds internal AI capability.
2. Fund the Reinvention: The data, efficiencies, and capital gained from your evolutionary wins provide the critical insights and resources needed to identify and pursue a revolutionary opportunity. By optimizing your current business, you learn what your customers truly value and where the compromises are in the current model.

This dual approach is ultimately a test of leadership. The executive's role is to manage this portfolio: demanding rigorous ROI and efficiency metrics from the evolutionary projects that optimize the core business, while simultaneously protecting and guiding the revolutionary bets with a different set of metrics focused on learning velocity and hypothesis validation. It is a dynamic balance between running a highly efficient operation today and inventing the business that will replace it tomorrow.

This transforms AI adoption from a high-risk gamble into a strategic, self-funding engine for growth. It is about making the right technical choices to solve today's business needs while simultaneously architecting the future.