Beyond the GenAI Hype: Finding Your True Competitive Edge

Like most organizations, our Outthinker Networks community of chief strategy officers has been exploring the strategic implications of generative AI (GenAI) throughout the year. This month, an insight from our roundtable with Michael Jacobides of London Business School brought remarkable clarity to our discussions.

While many organizations are rushing to implement AI everywhere possible and then piecing together use cases, Jacobides’ research finds that the key to creating lasting competitive advantage lies in a more focused approach.

Generative AI’s Strategic Imperative

During the roundtable, one of our CSO members commented: “If you’re not planning for an AI transformation, you’re already falling behind. This isn’t just digital transformation anymore; it’s AI changing everything from customer insights to competitive strategy.”

But as we’ve discovered through discussions with members, who are all senior strategy executives in large, complex organizations, the path to effective AI transformation isn’t about implementing every possible use case. It’s about identifying where AI can create genuine competitive differentiation.

Pattern Recognition + Proprietary Data = Differentiation + Disruption

While GenAI is commonly used for cost reduction, according to Jacobides, companies are beginning to realize the highest value comes from generating new revenue streams and enhancing product/service engagement. His research found that it offers the most differentiation between companies and disruption across industries when firms:

1. Leverage proprietary data sets
2. Apply GenAI’s superior pattern recognition capabilities to those data sets

An obvious example is Amazon’s product recommendation engine. It combines sophisticated pattern recognition (analyzing customer browsing patterns, purchase history, and product relationships) with proprietary data (customer behavior data, purchase history, and product interaction data that only Amazon possesses). This creates a competitive advantage that’s difficult – if not impossible – for competitors to replicate, even if they use similar AI technology.

Many companies are still in the process of determining the most valuable use cases for GenAI. Deloitte advises organizations to focus on human-centric integration, digital transformation, data management, industry innovation, and next-generation managed services. McKinsey recommends leveraging AI for organizational transformation, strategy adaptation, employee engagement and upskilling, and risk management and governance. While these are all correct paths, Jacobides’ insights pinpoint specific directions to identify use cases: look for solutions that leverage both proprietary data and pattern recognition.

Breaking Down Barriers

In a recent Outthinkers Podcast interview with David Edelman, author of Personalized: Customer Strategy in the Age of AI, he took the discussion one step further by sharing how generative AI is revolutionizing the way we do business. We’re seeing AI break down walls between databases by automatically writing code to connect previously siloed data, both within and between companies. In my Harvard Business Review article published earlier this year, I discuss how this enables companies with complementary products to more efficiently coordinate themselves to help customers achieve the outcomes they are after.

When you consider Jacobides’ lens of proprietary data and pattern recognition, this opens up fascinating possibilities for both business use cases and strategic partnerships, such as:

• Smart mobility solutions where gas stations collaborate with car manufacturers, restaurants, and experiential organizations to optimize charging/refueling networks, food and beverage, and activity recommendations.
• Retail innovation where food producers partner with grocery chains, chefs, and media companies to develop data-driven menu suggestions and inventory management.
• Healthcare ecosystems where providers and insurers share data to improve patient outcomes while reducing overhead.

Next Steps

As you think about how to apply these insights to your organization's AI strategy, here are six essential steps to get started:

1. Data audit: Start by assessing your position. What unique data do you have that competitors can’t easily replicate?
2. Pattern recognition opportunities: Where could AI identify valuable patterns within your proprietary data?
3. Competitive analysis: In which areas could the combination of pattern recognition and proprietary data create sustainable advantages?
4. Prioritize use cases: Focus on applications where you have both strong proprietary data and clear pattern recognition opportunities. These use cases should also align with your overall organizational strategy.
5. Build data moats: Further develop systems to capture and maintain unique data sets that strengthen your competitive position.
6. Create feedback loops: The advantages of applying generative AI to your unique data sets have the potential to compound. As you step into the future, could you design implementations that continue to enrich your data through use of the solution?

While other organizations are still focusing on basic automation and cost reduction, forward-looking leaders are already growing proprietary data sets and pattern recognition capabilities that will define tomorrow’s market leaders. The real challenge isn’t about keeping pace with today’s plentiful AI implementations, but about creating the foundational advantages that will compound over time.