Developing enterprise AI strategy
Five lessons learned
Going beyond the AI hype
Artificial intelligence (AI) promises to transform industries, create competitive advantages, and unlock new opportunities to create and capture value. However, developing a successful AI strategy is fraught with challenges. Many organizations rush into AI initiatives without a deep understanding of how AI fits into their existing structures and processes, leading to expensive missteps, wasted efforts, and ultimately lost opportunities to develop true value. Drawing from our experience working with companies on integrating AI into their enterprise strategies, we've identified five key lessons that can help organizations avoid common pitfalls and design AI strategies that deliver real, sustainable value.
Five principles: Aligning AI implementation with strategy
Lesson 1: Industry Structures and Value Chains Shift Slowly Over Time
While AI has the potential to upend traditional business models, industry structures and value chains evolve slowly. This is especially true in industries with high regulation, long capital cycles, or large install bases. To succeed, AI-enabled business models need to initially align with the existing realities of the industry. Companies that try to leap too far ahead often struggle because they are working against entrenched dynamics rather than leveraging them. Instead, consider how AI can augment or enhance existing processes before attempting more radical shifts.
Lesson 2: Understand Where Value Accrues in the Value Chain
Successfully using AI to create a competitive advantage requires a clear understanding of the value chain and where value is generated. Not all parts of the value chain offer equal opportunities for disruption or leverage. Identifying the nodes where AI can create the most impact—whether through cost reduction, enhanced decision-making, or automation—is critical. Would-be disruptors, especially industry outsiders, fail to appreciate the hard problems in delivering value. Without that understanding, organizations apply AI in areas of limited value and impact.
Lesson 3: Patiently Test and Evolve New Business Models
AI enables new types of business models, but these innovations take time to mature. Supply of solutions and demand from customers must move together in lock step - if you just build it, they probably won't come. Developing and scaling new models requires a willingness to experiment and iterate while maintaining a long-term perspective. Disruptive growth tends to come very slowly and then all at once. Successful AI strategies embrace the learning process and remain adaptable, being less patient for profits but more patient for growth.
Lesson 4: Develop a deliberate and strategic approach to data
Data is crucial for AI, yet many organizations underestimate the importance of a strategic approach to data. Good AI strategy requires a deep focus on data—understanding what data is available, how it can be collected and curated, and whether it is fit for purpose. Organizations frequently misjudge the value of their data, either hoarding useless data or giving away highly valuable assets. Additionally, organizations must invest in data infrastructure and governance before AI can deliver value, a step that can be expensive and time consuming to fix in retrospect.
Lesson 5: Build Feedback Loops to Continuously Improve AI Models and Applications
AI initiatives succeed when they create continuous learning opportunities that refine model performance. Establishing feedback loops allows organizations to gather real-world insights, identify areas for improvement, and adapt AI models effectively. These loops help create a data flywheel, improving models as more data is collected. By integrating user feedback, monitoring performance, and making iterative updates, organizations can ensure their AI models continuously improve and remain effective.
From hype to value-centered growth
Developing an AI strategy that enhances value creation and creates competitive advantage requires critical thinking, deep industry knowledge, and a disciplined approach. The lessons learned from our experience—recognizing the slow evolution of value chains, identifying leverage points, being patient with new business models, putting data first, and creating feedback loops—are essential components of a successful AI journey. Organizations that take the time to align their AI initiatives with industry realities and human needs are more likely to realize the full potential of AI and build sustainable competitive advantages than those simply trying to surf the hype.