Evolve or Exit: Gen AI, Agents, and the New Business Natural Selection

Since my first job building PCs in the early 90s, I've witnessed numerous radical changes: from client/server to virtualization, from cloud computing to containers. But the rapid rise of generative AI and autonomous agents? This just feels different. We're not just enhancing efficiency, we're fundamentally redefining how businesses evolve and compete across their entire lifecycle. Let's explore how AI is revolutionizing each phase of Geoffrey Moore's Category Maturity Life Cycle, reshaping both core competencies and contextual activities along the way.

Dealing with Darwin Revistied

Geoffrey Moore's seminal work "Dealing with Darwin" presents a powerful framework for understanding business evolution and innovation. At its heart, Moore argues that businesses must continually innovate to survive and thrive, much like species in nature. He introduces the concept of "core" versus "context" - core being the activities that differentiate a company and drive its competitive advantage, while context encompasses necessary but non-differentiating work.

Moore argues that as markets evolve through a lifecycle from growth to maturity to decline, companies must shift their innovation strategies accordingly. In early stages, innovation focuses on product leadership and capturing market share. As markets mature, the focus shifts to operational excellence and customer intimacy. Throughout this lifecycle, Moore emphasizes the critical need for businesses to reallocate resources from context to core activities, ensuring they maintain their competitive edge. This dynamic approach to innovation and resource allocation, Moore argues, is essential for companies to navigate the ever-changing business landscape and avoid becoming obsolete.

In this article, I describe how AI is critical for each each phase and regardless of where you are in the company's lifecycle there is an opportunity to increase your competitiveness and win more customers.

Phase A: Technology Adoption Life Cycle

In this early stage, AI is turbocharging innovation and market entry:

• Idea Generation: AI analyzes vast datasets of market trends, scientific research, and consumer behavior to spark novel product concepts.
• Rapid Prototyping: AI-powered design tools can generate and simulate thousands of product variations, dramatically accelerating the development cycle.
• Market Fit Prediction: Machine learning models can predict market responses to new products with unprecedented accuracy.

Example: Adobe's Sensei AI technology is revolutionizing creative software, enabling features like generating images from text descriptions. This positions Adobe at the cutting edge of the creative tech adoption curve.

Phase B: Early Main Street

As products gain traction, AI helps scale operations and refine offerings:

• Dynamic Pricing: AI algorithms optimize pricing in real-time based on demand, competition, and market conditions.
• Personalized Marketing: AI-driven marketing platforms deliver hyper-targeted campaigns, improving conversion rates and customer acquisition.
• Supply Chain Optimization: Predictive AI models manage inventory and logistics, ensuring smooth scaling as demand grows.

Example: L'Oréal's AI-powered "Shade Finder" tool exemplifies how AI can enhance product customization and customer experience in the early growth phase.

Phase C: Mature Main Street

In mature markets, AI drives efficiency and customer intimacy:

• Predictive Maintenance: AI analyzes equipment data to forecast failures, minimizing downtime and maintenance costs.
• Customer Insights: Advanced analytics and natural language processing extract deep insights from customer interactions, informing product improvements and service strategies.
• Process Automation: AI-powered robotic process automation (RPA) streamlines operations, reducing costs and improving accuracy.

Example: Siemens uses AI for predictive maintenance in industrial settings, analyzing sensor data to forecast equipment failures and optimize operations.

Phase D: Declining Main Street

As markets saturate, AI helps identify new opportunities and optimize resources:

• Market Trend Prediction: AI analyzes global trends to identify emerging markets and opportunities for category expansion.
• Resource Reallocation: Machine learning models optimize resource allocation, shifting investments from declining areas to growth opportunities.
• Product Line Optimization: AI helps determine which products to sunset and which to double down on based on profitability and market potential.

Example: JPMorgan Chase's "COiN" AI system, which analyzes legal documents, showcases how AI can find new efficiencies and applications in mature industries.

Phase E: End of Life

Even in declining markets, AI can extract value and facilitate transitions:

• Asset Optimization: AI helps maximize the value of existing assets and intellectual property.
• Transition Planning: Predictive models assist in timing and executing market exits or pivots.
• Knowledge Transfer: AI-powered knowledge management systems capture and transfer critical insights as markets wind down.

Redefining Core and Context with AI

As AI permeates each phase of the business lifecycle, it's blurring the lines between what we traditionally considered core and context:

• Core Competencies Evolve: AI integration itself is becoming a core competency. The ability to effectively leverage AI across all business functions is now a key differentiator.
• Context Becomes Strategic: Previously contextual activities, when enhanced by AI, can become sources of competitive advantage. Data analysis, once a support function, is now often central to business strategy.
• Fluid Boundaries: The distinction between core and context becomes more dynamic. AI allows companies to rapidly pivot, turning contextual capabilities into core offerings as market conditions change.

The Path Forward: Embracing AI-Driven Evolution

As we navigate this AI-enhanced business landscape, the key to success lies in embracing AI not just as a tool, but as a fundamental driver of business evolution. As you begin your work week, here are a few ideas to think about and consider where you are in your learning journey.

1. Audit Your Current Position: Determine where you are in the Category Maturity Life Cycle and assess your current AI capabilities. Have conversations with your peers and get real - Ray Dalio style.
2. Identify AI Opportunities: For each phase of your business, pinpoint where AI can drive the most significant improvements or innovations.
3. Invest in AI Literacy: Ensure your entire organization, from the C-suite to the front lines, understands AI's potential and limitations. There is no knowledge without action.
4. Start Small, Scale Fast: Begin with pilot projects that demonstrate quick wins, then scale successful initiatives.
5. Stay Adaptable: Remember, the power of AI lies not just in optimizing current processes, but in enabling rapid pivots as markets evolve.

The businesses that thrive in this new era will be those that harness AI to continuously redefine their core competencies and nimbly navigate each phase of the Category Maturity Life Cycle. The future isn't just AI-enhanced; it's AI-defined.