In this excerpt from Beacon: The Definitive Business Guide to AI Strategy and Transformation, Ray Bordogna, a veteran IT and corporate strategist who is Chief AI Officer at management consultancy QuantumPivot, lays out a playbook for business leadership in the age of artificial intelligence.
Part of AI’s power lies in bridging two ongoing revolutions. The first is the business transformation, which includes reinventing your operating model, value proposition, and ways of working. The second is the digital transformation, which involves harnessing software, data analytics, cloud, and automation.
AI intensifies both revolutions. For instance, a company rethinking its customer engagement (business transformation) can use AI-based personalization and natural language chatbots (digital transformation) to deliver drastically better customer experiences.
Consider the case of an insurance company that has already digitized some processes but in a piecemeal fashion. By leveraging AI to connect and optimize each silo—predictive underwriting, automated claims triage, and personalized coverage recommendations—the company could transform from a reactive insurer into a proactive partner in risk management.
Scaling AI is as much a leadership challenge as it is a technical one. Executives set the ambition level, sponsor cross-functional collaboration, and nurture a culture that embraces data-driven experimentation. A CEO who publicly trusts AI-based forecasts sends a potent message across the organization. Conversely, an executive team that withholds funding or dismisses AI-driven insights can derail even the most promising initiatives.
Leaders also arbitrate how failures are perceived. In AI projects, iterative trial and error is a natural path to breakthroughs. Without executive support, teams may shy away from experimentation, stifling progress at its inception.
This book uses real and composite case studies to illustrate AI’s potential across various functions—from insurance underwriting and claims processing to retail merchandising and healthcare diagnostics. I reference multiple frameworks, including design thinking, the Open Group Architecture Framework for enterprise architecture (TOGAF), and the Information Technology Infrastructure Library ( ITIL) to minimize jargon and focus on their strategic essence rather than deep technical detail. I use the same approach in discussions of technical subjects such as machine learning techniques, cloud architecture, or data engineering. The focus remains on the broader, business-level outcomes that AI can unlock.
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Leadership in Action Checklist
For executives forging their AI strategy.
Reflect on your aspiration. Think about what winning looks like for your organization in an AI-enabled future. Consider how AI could enhance your value proposition or redefine customer experiences.
Analyze competitor activity. Observe how competitors are using AI to gain an edge. Identify areas where they are excelling and where gaps may provide opportunities.
Research AI pacesetters in other industries. Identify companies that are successfully leveraging AI in innovative ways. Study how leaders in retail, healthcare, manufacturing, or finance are using AI to transform their operations and create new value. Apply relevant lessons to your own strategy.
Start the conversation. Begin discussing AI with your leadership team. Share insights on how AI might align with your business transformation and digital goals.
Prepare to evaluate organizational readiness. Consider whether your organization has the foundational elements—data quality, technical infrastructure, and culture—to support AI initiatives.
Hypothesize key use cases. Brainstorm areas within your business where AI could drive immediate impact, such as automating routine processes or improving customer insights.
Lead by example. Use AI tools in your own decision-making processes, showcasing their potential value to your team.
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The book is structured around a cohesive four-phase roadmap. First, reimagine your business. This calls for recognizing AI is a strategic imperative and source of new business value, then defining an AI vision aligned with business strategy and reassess your business model in light of AI’s possibilities. Second, rearchitect. This focuses on building the organizational and technical foundations for AI, including team structures, application and data architectures and security frameworks. Third, rewire, focusing on how to operationalize AI across day-to-day workflows, including embedding AI agents, establishing ethical guidelines, managing change, and instilling a culture of continuous improvement. And fourth, renew, centering on defining strategies to sustain AI momentum, integrating sustainability goals, forging strategic partnerships and shaping AI’s long-term role in enterprise strategy.
Each part builds on the last, carrying the reader from vision through execution and into a forward-looking renewal of capabilities.
Throughout these sections, you’ll find checklists, such as the “Leadership in Action Checklist” (see box), as well as action plans, and summary takeaways to help you tailor insights to your context— whether you’re the CEO of a century-old insurance firm or a digital-native start-up.
The path to AI-driven success follows a similar pattern: Reimagine bold possibilities, rearchitect for scalable delivery, and rewire for daily impact—while always keeping an eye on the future. This book speaks to a cross section of business leaders, from C-level executives to directors overseeing AI or digital innovation. It’s also relevant to mid-level managers in marketing, operations, or finance who need a strategic vantage point to guide their technical teams. Technical professionals—such as CIOs or heads of analytics—will find a high-level blueprint for linking AI deployments to overarching business goals. Meanwhile, less technical executives can use the framework to ask sharper questions, shape budgets, and orchestrate enterprise-wide buy-in.
The era of AI-driven business is unfolding rapidly, and with the right strategy, your organization can thrive on these shifting tides.
For leaders, the urgency is clear. Competitors are rapidly adopting AI to capture market share, boost revenue, and reshape customer expectations. This intensifying environment creates a mandate for action—those who fail to adapt risk irrelevance.
Every effective AI transformation begins with clarity of purpose. A robust AI vision is not an abstract ideal—it is the guiding star that prioritizes investments, invigorates teams, and steers your organization boldly through the complexities of AI-driven change.
