AI Adoption 'Incredibly Shallow,' Says Economist

Dr. Rebecca Homkes, an economist and lecturer at the London Business School, has stated that while AI adoption is widespread, it is often "incredibly shallow." In a recent discussion, Homkes highlighted that many organizations are not yet seeing significant growth or value creation from their AI initiatives, with only an estimated 10-15% of businesses truly integrating AI effectively into their operations.

The Shallow Reality of AI Adoption

Homkes observed that while the buzz around AI is undeniable, the practical application is lagging. Many companies are experimenting with AI for basic tasks like productivity improvements or cost reductions, but the deeper, transformative applications that could drive significant revenue or competitive advantage are not yet widespread. She noted that only about 10% to 15% of businesses are actively using AI in ways that yield substantial gains, indicating a significant gap between adoption rates and impactful implementation.

From Dabbling to Deliberate Integration

The economist traced the evolution of AI adoption in organizations from a phase of cautious experimentation to a more deliberate approach. Initially, companies were hesitant, testing AI without a clear strategy. This was followed by a period of "dabbling," where AI tools were adopted without a deep understanding of their potential or limitations. Homkes emphasized that the current phase is about moving towards a more considered, deliberate integration of AI, where the focus is on redesigning workflows and ensuring ethical deployment. This shift is crucial for organizations to realize the full potential of AI.

The full discussion can be found on Bloomberg Podcast's YouTube channel.

Measuring AI's True Impact

A key challenge identified by Homkes is the difficulty in measuring the true return on investment for AI. While companies can track metrics like compute cost and licensing fees, they often fail to measure the impact on outcomes and value creation. Homkes pointed out that many organizations excel at tracking the output of AI systems but struggle to link these outputs to tangible business results. This lack of clear, outcome-oriented measurement hinders progress and makes it difficult to justify further investment.

The Importance of Strategy and Governance

Homkes stressed that successful AI integration requires more than just implementing new technologies. It necessitates a clear strategy, robust governance, and a focus on organizational change. She highlighted that companies with clear boundaries and well-defined governance structures are more likely to see faster and more impactful results from their AI efforts. This includes upskilling teams, ensuring ethical considerations are addressed, and aligning AI initiatives with broader business objectives.

The economist concluded that organizations that are making the most progress with AI are those that are not only focused on the technology itself but also on the human and organizational aspects of its implementation. This involves understanding how AI can reshape jobs, how to foster ethical AI practices, and how to measure success beyond mere output metrics.