All-in On AI

When Piyush Gupta took over as CEO of DBS Bank in 2009, the institution was mockingly known as "Damn Bloody Slow" among customers frustrated with its sluggish service. Today, DBS stands as one of the world's most digitally advanced banks, winning prestigious global awards and serving as a beacon for AI-powered transformation. This remarkable journey from industry laggard to global leader illustrates a profound shift happening across organizations worldwide.

We are witnessing the emergence of a new breed of companies that don't merely use artificial intelligence as a tool, but have fundamentally rewired their DNA around intelligent systems. These AI-fueled enterprises represent less than one percent of large organizations today, yet they consistently outperform their competitors in efficiency, innovation, and customer satisfaction. Through intimate conversations with executives who have led these transformations, we discover that becoming AI-powered isn't about technology alone—it's about reimagining how humans and machines can work together to create extraordinary value. Their stories reveal the courage required to bet everything on a future where data-driven decisions, predictive insights, and intelligent automation become the foundation of competitive advantage.

At radius financial group, a modest 200-employee mortgage company in suburban Boston, Keith Polaski made an audacious decision in 2016. While most small businesses barely had AI on their radar, Polaski embarked on an intensive search for artificial intelligence tools to transform what he calls "loan manufacturing." His conviction was simple yet profound: if he could measure everything in his mortgage processing operation, he could optimize it with intelligent systems. Despite skepticism from peers who viewed AI as the exclusive domain of Silicon Valley giants, Polaski pressed forward, implementing predictive models to streamline underwriting, automate document processing, and optimize workflow management.

The transformation exceeded even Polaski's ambitious expectations. Today, radius operates with substantially higher productivity and profitability than industry averages, processing loans faster and more accurately than competitors many times its size. Meanwhile, in China, Ping An—starting as a modest insurance company in 1988—has evolved into a $200 billion financial ecosystem powered entirely by AI-driven insights. From two-minute car accident claims processing to AI-assisted medical consultations serving 400 million users, Ping An demonstrates how intelligent systems can create entirely new business models.

At Airbus, the aerospace giant faced a stark choice: embrace digital transformation or watch competitors gain decisive advantages. The company invested not just in AI technology but in retraining over a thousand employees in advanced analytics skills. Their Skywise platform now connects airlines worldwide, using machine learning to predict maintenance needs and optimize flight operations. These pioneering organizations share a common realization—AI isn't about replacing human intelligence, but amplifying it to achieve what neither humans nor machines could accomplish alone.

These early adopters discovered that becoming AI-fueled requires more than implementing new technology; it demands fundamental shifts in culture, strategy, and human capability. Their courage to venture into uncharted territory has created blueprints that other organizations can follow, proving that the AI revolution belongs not to a select few tech companies, but to any organization willing to reimagine its future.

Shell's transformation into an AI-powered energy company began with a simple recognition: their engineers already possessed deep expertise about equipment and processes—they just needed new tools to scale their knowledge. Rather than hiring an army of data scientists, Shell partnered with Udacity to train over 5,000 engineers in machine learning techniques. These engineers, who intimately understood pumps, compressors, and control systems, became the perfect candidates to build and maintain predictive maintenance models. Dan Jeavons, Shell's head of digital innovation, realized that combining domain expertise with AI capabilities created something far more powerful than either alone.

The results were extraordinary. Shell now monitors over 10,000 pieces of equipment daily using AI-powered predictive maintenance, with the number growing by several hundred each week. Engineers who once followed rigid maintenance schedules now use machine learning models to predict exactly when equipment needs attention, dramatically reducing downtime and costs. More importantly, these engineers found their work more engaging and meaningful—freed from routine tasks, they could focus on complex problem-solving and strategic decision-making.

At DBS Bank, CEO Piyush Gupta took a similar approach but with a twist. The bank created "translators"—quantitatively oriented professionals who bridge the gap between business stakeholders and AI developers. For every two data scientists on a project, DBS assigns one translator to ensure that sophisticated algorithms actually solve real business problems. This human-centered approach to AI implementation has enabled DBS to deploy over 150 AI applications across the organization while maintaining strong employee engagement and customer satisfaction.

The most successful AI transformations recognize that technology alone cannot drive lasting change. Organizations like Shell, DBS, and Deloitte are discovering that the future belongs to companies that can seamlessly blend human insight with machine intelligence. They invest as heavily in reskilling and cultural transformation as they do in algorithms and computing power, understanding that sustainable competitive advantage comes from empowering people to work alongside intelligent systems rather than competing against them.

The Kroger Co. faced an existential challenge: how to compete in an era where digital natives like Amazon were redefining customer expectations for grocery shopping. Rather than simply digitizing existing processes, Kroger embarked on a comprehensive AI-powered reimagining of retail through its data science subsidiary, 84.51°. The strategy, called "Restock Kroger," placed artificial intelligence at the center of every major business function, from supply chain optimization to personalized customer experiences.

Today, Kroger delivers over 11 billion personalized recommendations per week—an impossible feat without machine learning. The company uses AI to optimize inventory across 2,500 stores, predict demand for individual products, and create customized shopping experiences for millions of customers. Their partnership with Ocado brings robotics and AI into fulfillment centers, while their Precision Marketing platform helps consumer packaged goods companies reach customers with unprecedented accuracy. What began as a traditional grocery chain has evolved into a sophisticated AI-powered ecosystem that generates revenue from data insights, targeted advertising, and operational efficiencies.

Ping An took an even more radical approach, using AI to create entirely new business models across financial services, healthcare, automotive services, and smart cities. Their healthcare ecosystem connects patients, doctors, hospitals, pharmacies, and insurers through intelligent platforms that can diagnose diseases, recommend treatments, and process claims in seconds rather than weeks. By 2021, 36 percent of Ping An's 37 million new customers came through these AI-powered ecosystems, demonstrating how artificial intelligence can become the foundation for exponential growth.

At Morgan Stanley, the wealth management division developed a "Next Best Action" system that transforms how financial advisors serve clients. Using machine learning to analyze market conditions, client portfolios, and individual preferences, the system generates personalized investment recommendations in seconds—work that previously required 45 minutes of manual analysis. This AI-powered approach enabled Morgan Stanley to enhance client relationships while improving advisor productivity.

These strategic transformations reveal that AI's greatest power lies not in automating existing processes, but in enabling entirely new ways of creating value. Organizations that view AI as merely a cost-cutting tool miss the profound opportunity to reimagine their industries and create sustainable competitive advantages through intelligent business model innovation.

Capital One's journey from analytics-focused bank to AI-powered financial institution illustrates the complex path from vision to reality. CEO Rich Fairbank had championed data-driven decision making since the company's founding in 1994, but the transition to AI required far more than upgrading existing systems. The bank had to rebuild its entire technology infrastructure, moving all applications and data to the cloud while simultaneously developing machine learning capabilities across every business function.

The transformation demanded extraordinary commitment. Capital One invested billions in cloud infrastructure, hired thousands of machine learning engineers and data scientists, and developed internal training programs to upskill existing employees. Their Center for Machine Learning now oversees thousands of models in daily use, from fraud detection and credit decisioning to customer service and product recommendations. The bank's Eno intelligent assistant helps customers manage their financial lives, while sophisticated algorithms analyze real-time transaction data to prevent fraud and provide personalized offers.

At DBS Bank, the implementation journey began with failure. CEO Piyush Gupta's early AI experiments in 2013 produced no successful outcomes, but these failures became valuable learning experiences. The bank established a goal of conducting 1,000 experiments annually, creating a culture where failure was viewed as a stepping stone to success. Over time, DBS developed systematic approaches to data management, model deployment, and change management that enabled successful AI implementation across the organization.

CCC Intelligent Solutions demonstrates how mid-size companies can achieve AI transformation through focused execution. CEO Githesh Ramamurthy spent nearly a decade developing AI-powered image analysis for automobile insurance claims, patiently building capabilities while waiting for technology to mature. When smartphone cameras became sophisticated enough to capture high-quality damage photos, CCC was ready with trained models and established workflows. Their billion-dollar investment in AI capabilities now processes insurance claims in minutes rather than days.

These implementation journeys reveal that successful AI transformation requires patience, persistence, and systematic capability building. Organizations must be prepared for initial failures, continuous learning, and significant long-term investments in technology, talent, and cultural change. The companies that persist through inevitable setbacks and maintain unwavering commitment to their AI vision ultimately achieve competitive advantages that transform entire industries.

Well, a health behavior startup led by former Caesars Entertainment CEO Gary Loveman, represents the next generation of AI-native companies. Unlike legacy organizations that must retrofit existing systems and processes, Well was designed from inception to use machine learning for personalizing health interventions. The company analyzes insurance claims data, electronic health records, and user interactions to deliver precisely targeted behavioral nudges that help people adopt healthier lifestyles. Their AI systems don't just predict health outcomes—they actively intervene to change them.

Loveman's experience transitioning from a large corporation to an AI startup illuminates the challenges facing traditional organizations. At his previous company, implementing basic changes like collecting customer mobile phone numbers would have required $30 million in systems modifications. At Well, modular architecture and API-based integrations enable rapid innovation and deployment. This architectural flexibility allows the startup to experiment with new approaches, iterate quickly based on results, and scale successful interventions without technical constraints.

The contrast between legacy and AI-native organizations extends beyond technology to fundamental business model differences. Well's systems were built to predict what interventions will motivate behavior change, while traditional healthcare companies' systems were designed merely to record transactions. This predictive orientation permeates every aspect of the business, from user interface design to reward mechanisms and clinical pathway development.

As AI technology continues advancing at exponential rates, the competitive advantages of AI-native organizations will only intensify. These companies benefit from clean data architectures, predictive business models, and cultures optimized for continuous learning and adaptation. However, their success also creates opportunities for forward-thinking legacy organizations willing to make bold investments in transformation.

The future belongs to organizations that can seamlessly integrate human wisdom with machine intelligence, creating value that neither could achieve alone. Companies that embrace this human-AI partnership while building the necessary technological and cultural foundations will discover unprecedented opportunities for growth, innovation, and positive impact. The transformation frontier remains wide open for organizations with the vision and courage to reimagine their possibilities.

The stories of these pioneering organizations reveal a profound truth: becoming AI-fueled is fundamentally a human endeavor that requires technology to succeed. From Piyush Gupta's personal leadership of DBS Bank's data transformation to Shell's engineer-led approach to predictive maintenance, successful AI initiatives depend on leaders who understand both the technical possibilities and human dynamics of change. These organizations invested as heavily in cultural transformation, employee development, and change management as they did in algorithms and computing infrastructure.

The path forward for any organization lies in recognizing that AI's greatest power comes not from replacing human capabilities, but from amplifying them. Start with your people—identify champions, invest in education, and create cultures that embrace experimentation and learning from failure. Focus on solving real business problems rather than implementing technology for its own sake, and be prepared for a journey measured in years rather than months. Most importantly, maintain unwavering belief that the combination of human wisdom and artificial intelligence can create extraordinary value for customers, employees, and society. The transformation frontier awaits those ready to take the first courageous step toward an AI-powered future where technology serves to enhance rather than replace the uniquely human capacity for innovation, empathy, and growth.