All-In on AI: How Smart Companies Win Big with Artificial Intelligence by Thomas H. Davenport and Nitin Mittal

Transforming Business with AI: Lessons from All-In on AI

All-In on AI: How Smart Companies Win Big with Artificial Intelligence by Thomas H. Davenport and Nitin Mittal is a definitive guide for legacy organizations seeking to harness artificial intelligence (AI) to transform their operations, strategies, and business models. Published in 2023 by Harvard Business Review Press, the book profiles a rare group of “AI-fueled” companies less than 1% of large firms that have aggressively integrated AI to achieve superior performance, as seen in examples like Ping An, Airbus, and Capital One. Davenport, a distinguished professor and analytics expert, and Mittal, Deloitte’s US AI Strategic Growth Leader, draw on extensive research and case studies to provide actionable insights for business leaders. This article distills ten key teachings from the book, each supported by a quote from the authors, offering a clear and engaging roadmap for companies to go “all-in” on AI and thrive in an AI-driven future.

1. The Elite Status of AI-Fueled Organizations

The book emphasizes that AI-fueled organizations, comprising less than 1% of large companies, achieve exceptional performance by embedding AI deeply into their operations. These firms, such as Ping An and DBS Bank, outperform competitors through effective business models, superior decision-making, and enhanced customer relationships. The authors highlight that going “all-in” requires a bold commitment to AI, far beyond tentative pilots, to drive transformative value. “The AI-fueled organizations in our analysis comprise less than 1 percent of large companies. … They have effective business models, make good decisions, have close relationships with customers, offer desirable products and services, and charge profitable prices.”

2. AI as a Catalyst for Business Transformation

AI is not merely a tool but a transformative force that redefines how companies operate. The authors showcase companies like Airbus, which uses AI for autonomous navigation, and Capital One, which enhances banking with AI-driven personalization. To achieve transformation, organizations must integrate AI across strategy, processes, and products, requiring significant investment and a shift in mindset. “For many organizations, harnessing artificial intelligence’s full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. … To achieve substantial value from AI, a company should fundamentally rethink the way humans and machines interact within working environments.”

3. Broad Adoption of Multiple AI Technologies

AI-fueled companies leverage a diverse portfolio of AI technologies, including machine learning, natural language processing, and robotic process automation, to address varied business needs. For instance, Cotiviti combines rules and machine learning for insurance fraud detection. The book stresses that leaders must understand these technologies to make informed decisions, ensuring broad enterprise adoption. “Companies that are fueled by AI use it across their organizations, adopting multiple use cases or applications. AI is a general-purpose technology, and it can be used to support a wide variety of business goals and objectives.”

4. Systematic Production Deployment

A critical challenge in AI adoption is moving from pilots to production deployments. AI-fueled companies, like DBS Bank with its anti-money laundering system, prioritize full-scale deployment to realize economic value. The authors advocate planning for deployment from the start, assigning product managers, and collaborating with business stakeholders to overcome integration hurdles. “One of the challenges of AI is getting systems into production deployment. Many companies embark on pilots, proofs of concept, or prototypes, but they put few or none of them into production.”

5. Data as the Fuel for AI

Robust data management is the backbone of AI success. Companies like Scotiabank and Kroger leverage voluminous, proprietary data to train AI models, enabling personalized customer experiences and operational efficiency. The book underscores the need for modern data infrastructure and real-time analytics to maintain a competitive edge. “If AI can fuel a company, data fuels AI. Companies that are serious about AI must be serious about data collecting it, integrating it, storing it, and making it broadly accessible.”

6. Reengineering Work Processes with AI

AI enables radical improvements in business processes, reminiscent of 1990s business process reengineering. The authors highlight DBS Bank’s use of AI to streamline anti-money laundering efforts, reducing case evaluation time by a third. Companies must redesign workflows to integrate AI, using tools like process mining to identify inefficiencies and drive innovation. “More companies should address how AI can make possible dramatic improvements in business processes. To some degree this will be facilitated by a new technology that employs AI: process mining.”

7. Building AI Fluency Across the Organization

AI success hinges on human capital, particularly organization-wide fluency. Companies like Airbus and DBS Bank have trained thousands of employees in AI skills, creating “citizen data scientists.” The book emphasizes upskilling and reskilling to foster collaboration between humans and machines, reducing resistance and driving adoption. “Companies that want to use a lot of AI in their businesses need a lot of executives and employees who understand how it works.”

8. Leadership and Commitment to AI

Strong leadership is essential for AI transformation. Leaders like Piyush Gupta at DBS Bank champion AI initiatives, aligning them with business strategy. The authors stress that long-term commitment, backed by significant investments, is critical to sustain momentum and avoid reverting to old habits. “A decision by a company’s senior executives to be transformed by AI is not a casual one. They are making a decision that will have a major influence on the company for decades and ultimately involve hundreds of millions or billions of dollars.”

9. Ethical and Trustworthy AI Practices

AI-fueled companies prioritize ethical frameworks to ensure fairness and transparency. The book cites Ping An’s AI ethics governance policy and Deloitte’s Trustworthy AI Framework as models. Ethical AI builds customer trust and mitigates risks like bias, making it a strategic imperative for sustained success. “If a company is relying heavily on AI in its business, it needs to ensure that the AI systems it uses are ethical and trustworthy, or it’s likely to lose more from AI than it gains.”

10. Becoming an Organizational Learning Machine

AI-fueled companies operate as “organizational learning machines,” continuously learning from AI deployments and data. Ping An’s experimentation with AI-generated art and DBS Bank’s chatbot improvements exemplify this approach. The authors advocate for scalable learning through rapid experimentation, model monitoring, and a culture of innovation. “One way of summarizing all these attributes is to think of all-in on AI companies as organizational learning machines. In such businesses, many aspects of AI-related learning are institutionalized and well oiled.”

Conclusion

All-In on AI provides a compelling roadmap for legacy companies to transform through aggressive AI adoption. Davenport and Mittal highlight the strategies of AI-fueled pioneers, emphasizing bold commitments, systematic deployment, and ethical practices. By learning from these leaders, organizations can navigate the complexities of AI integration and position themselves for long-term success. As the authors note, “AI is here to stay, and the companies that apply it with vigor and intelligence will likely dominate their industries over the next several decades.” Embracing these lessons equips businesses to thrive in an AI-driven world, turning potential into performance.