Picture a serene factory floor in Germany, where machines are not just functioning but communicating with each other. They predict their maintenance needs and reduce downtime, and their implementation has returned a positive ROI within one year (Ingenu, 2024). It’s not just a technological leap; it's a transformation.
In a London lab, AI sifts through millions of medical research papers, identifying a promising COVID-19 treatment in just 48 hours—a task that would have taken researchers months or years (Drees, 2020). It’s not just fast; it’s redefining possibilities.
Meanwhile, in New York, an AI system for JPMorgan Chase processes 12,000 commercial credit agreements in seconds, a task that previously demanded 360,000 hours of human review (Son, 2017). It’s not just efficient; it’s revolutionizing how we work.
An AI-driven supply chain In Singapore anticipates global disruptions before they hit production (Koerber, 2024). It’s not just about adapting; it’s about instilling a sense of security and resilience.
Netflix discovers 80% of the content through AI-personalized recommendations, changing how we consume content and how content is created and distributed (Krysik, 2024). It’s not just about sales; it’s about meaningful customer experiences.
This isn’t science fiction. It isn’t next year or next decade. It’s happening today in companies that have realized the full implications of AI early and acted fast. These organizations aren’t just improving their operations but redefining what’s possible in their industries.
The global AI revolution isn’t coming—it’s already here and moving at lightning speed. The global AI market is heading towards $826.70 billion by 2030, and the EU AI Act has arrived to set the rules of the game. Some see this as a threat, another regulatory burden in an already complex world. But they’re missing the bigger picture.
The Act isn’t just another set of rules to follow—it’s a roadmap to build trusted, sustainable AI capabilities that will define market leaders for years. Think of it as the GDPR moment for AI. Yes, GDPR requires much investment in time and effort. Nevertheless, organizations that got in early didn’t just comply—they built deeper relationships with their customers and created competitive advantages that still exist today.
This is the first of a four-part series inspired by Encora’s whitepaper on what the EU AI Act means, why this is an opportunity, and how to leverage it (Hollyoak, 2024). In this series, you’ll be taken on a journey through the current state of AI to see how organizations across industries are using AI to drive innovation, efficiency, and competitive advantage. You’ll see real-world AI applications, learn about generative AI's transformative power, and understand the strategic considerations for successful AI adoption. From case studies in healthcare, banking, and manufacturing to a deep dive into the EU AI Act, this series will provide a roadmap for why AI is not just a technology trend but a fundamental reimagining of business capabilities. Whether you’re a business leader, strategist, or tech enthusiast, you’ll leave with a clear understanding of how to approach AI transformation, the cost of hesitation, and the framework for building long-term sustainable AI strategies.
The Perfect Storm of Opportunity
We have a perfect storm of three factors: technological capability, market readiness, and regulatory framework. This is a once-in-a-lifetime opportunity for organizations willing to transform. The numbers are mind-blowing: With a 36.6% CAGR in the global AI market through 2030, generative AI alone will add $2.6 trillion to $4.4 trillion to the worldwide economy.
But these numbers are just the tip of the iceberg. The real story is happening in businesses across every industry, where AI is pushing the boundaries of what’s possible. Take UPS’s AI-powered route optimization system, which saves the company $400m a year and 100,000 tons of carbon (Roundtrip.ai, 2024).
It’s the same across other industries. For example, banking is being transformed using Generative AI: 40% increase in employee productivity through AI-assisted task automation, up to 50% faster completion of customer-facing processes, enhancement of client experience, and up to 25% reductions in operational costs through targeted process optimization (Revvence, 2024).
The healthcare sector is where AI’s potential is most compelling. Beyond the headline-grabbing achievements in drug discovery, AI is changing everything from diagnostic accuracy to patient care management. AI tools like Medtronic’s GI Genius, designed to process colonoscopy images, have been trained to process colonoscopy images and detect signs of cancer by up to 14.4% and reduce missed rates by nearly 50% (Dept Health & Social Care, 2023). In radiology, AI algorithms have achieved a diagnostic accuracy rate of 94% in detecting lung nodules, significantly outperforming human radiologists who scored 65% accuracy in the same task (Team DigitalDefynd, 2024). Hospitals are using AI to schedule patients, reduce wait times, and predict potential complications before they happen. Mental health professionals are using AI to identify at-risk patients and intervene earlier.
A retail client who implemented AI-driven inventory management three years ago didn’t just reduce stockouts by 30%—they built a predictive understanding of customer behavior so well that they can see trend changes weeks before their competitors (Owczarek, 2023).
In manufacturing, AI isn’t just improving efficiency. It’s enabling entirely new business models. Companies are moving from selling products to selling outcomes, using AI-powered predictive maintenance to guarantee uptime and performance, reducing downtime by 20% and maintenance costs by 10% in their first year (Core BTS, 2024). Smart factories are self-optimizing their operations in real-time, responding to changes in demand, supply chain disruptions, and energy costs automatically. The result isn’t just lower costs—it’s greater resilience, sustainability, and customer satisfaction.
The Hidden Costs of Hesitation
While some organizations are debating when to start their AI journey, early adopters are building a lead that may be impossible to catch up with. The gap between AI leaders and laggards isn’t growing linearly—it’s growing exponentially. Each day of delay compounds the problem of catching up, creating what economists call “path dependence”—a situation where early decisions (or indecisions) limit future options dramatically.
The cost of waiting goes far beyond missed efficiency gains. Organizations that delay their AI adoption will face many problems that become increasingly difficult to solve. Their best employees, who want to work with the latest technology, start looking elsewhere. This brain drain accelerates as AI skills become more valuable in the job market, creating a negative feedback loop that makes it harder to attract new talent.
Customers, too, are moving towards organizations that offer more advanced, more personal experiences. As they get used to AI in some areas of their lives, they expect it everywhere. Organizations that can’t meet those expectations are fighting a losing battle for customer loyalty.
Perhaps most importantly, organizations that delay accumulate technical debt, making future implementations even more complicated and expensive. This isn’t just about old systems–it’s about missed learning and data collection that can never be recovered. The knowledge gap is most acute in machine learning, where success is all about accumulated experience and refined data.
Take two retailers during the pandemic. The first had invested in AI-powered demand forecasting and supply chain optimization and could adapt quickly to the dramatic shift in consumer behavior. The second used traditional forecasting methods, but that decision resulted in inventory imbalances and missed opportunities. The difference wasn’t just in their short-term performance but in their ability to learn and adapt to changing circumstances.
A Framework for Long-Term Success
Success in the AI era requires more than technology investment—it requires a holistic approach to strategy, data, ethics, and business innovation. The most successful organizations know that AI transformation is a journey that touches every part of their business—from customer experience to supply chain management, from product development to employee engagement.
This starts with a hard look at current capabilities and future opportunities. Data strategy is critical. Understanding that the quality and governance of data will be as important as the AI algorithms themselves is essential. Ethics and governance are also essential, recognizing that trust and transparency are not barriers to innovation but enablers of long-term competitive advantage.
Take JPMorgan Chase’s AI transformation. They didn’t just automate existing processes—they reimagined how work could be done. Their AI system for reviewing commercial loan agreements didn’t just save time—it changed how their professionals worked, freeing them up to focus on higher-value activities that require human judgment and creativity. Most importantly, they built their system with governance and ethics at the heart so their AI would be powerful while being trustworthy.
The most successful AI implementations have three things in common:
- They start by clearly understanding the business problem they aim to solve. The technology serves the strategy, not the other way around. When BenevolentAI set out to find potential COVID-19 treatments, they weren’t just applying AI to a database—they were using it to solve a specific, pressing medical need.
- They know that data quality and governance are crucial to success. Organizations that treat data as a strategic asset and invest in its collection, curation, and protection outperform those that see data as a by-product of their business.
- They build for scale from the start. They may start with pilots, but their architecture and approach are designed for enterprise-wide deployment. This prevents AI silos and means early wins can be rapidly rolled out across the business.
The Regulatory Opportunity
The EU AI Act is not a barrier but a blueprint for responsible innovation. Forward-thinking organizations see regulatory compliance and competitive advantage not as opposing forces but as two sides of the same coin of a sustainable AI strategy.
The Act’s risk-based approach provides a framework for organizations to build trust with customers, partners, and regulators. By baking principles like transparency, fairness, and human oversight into their AI systems from the start, they can build sustainable competitive advantages that will last as regulatory frameworks evolve globally.
This is especially important given the global nature of modern business. The Act’s influence will go far beyond Europe’s borders, setting de facto international standards for data protection, just as GDPR did for data protection. Organizations that get ahead of the curve on these standards can operate globally. Those who wait will find themselves locked out of critical markets.
For a deeper dive into the EU AI Act, see “Seizing the AI Advantage – Using the EU AI Act as a Catalyst for Innovation"
Charting Your Course
The journey to AI leadership begins with knowing where you are and where you need to go. It requires a clear view of how AI can transform your business and a practical plan to get there.
That’s where Encora’s Engineer for AI Framework comes in. Over years of working with organizations at every stage of AI adoption, we’ve developed a practical approach that turns obstacles into opportunities. It’s not just about technology; it’s about sustainable competitive advantage.
Think of it as building a house. It would be essential to have a solid foundation (your data strategy), strong walls (your governance framework), and a clear view of what you’re building (your enterprise architecture and strategic roadmap). However, unlike traditional construction, you can start small and scale up quickly as you figure out what works for your business.
Success in AI adoption isn’t about having an enormous budget or advanced technology but having an excellent framework to approach the topic. Our framework breaks this down into manageable steps:
First, we help organizations assess their AI readiness across three key areas: technical infrastructure, data, and organizational culture. This is not a theoretical exercise—it’s about finding real opportunities and challenges.
Next, we work together to identify high-impact, low-risk areas for initial AI deployment. This might be automating mundane tasks, enhancing customer service, or improving forecasting accuracy. The key is to start where you can show quick wins while building capability for more significant projects.
We begin by asking three simple questions:
- What processes in your business have the most time-consuming mundane tasks that can be automated or augmented with AI? Where are your best people spending time on non-value-added activities?
- Where’s your unused data? Most organizations have goldmines of insight into their customer interactions, operational metrics, and financial data.
- Where in your business would predictive insights be most valuable? Where could better forecasting help you improve service, reduce costs, or capture new opportunities?
We then help establish governance frameworks and ethical guidelines so that AI development aligns with regulatory requirements and organizational values. It’s not just about compliance–it’s about building trust with customers, employees and stakeholders.
Along the way, we stress the importance of data strategy. As one of my CTO friends says, “AI without good data is like a Ferrari without fuel.” We help organizations collect data and make it valuable and accessible while keeping privacy and security in mind.
Conclusion: The Power of AI in Transformation
The AI revolution is more than just a technology shift – it’s a complete rethinking of how businesses operate, innovate, and create value. From manufacturing to healthcare, banking to retail, AI is not an incremental improvement but a game changer for entire industries.
What sets successful companies apart is their tech and their AI approach. It’s about not seeing AI as a standalone technology but as a way to solve complex business problems. The most forward-thinking companies understand that AI is about efficiency and rethinking business models, customer experiences, and organizational capabilities.
We have a unique opportunity with technological maturity, market readiness, and a supportive regulatory framework like the EU AI Act. This moment requires bold thinking and strategic action. Companies that wait risk more than technological obsolescence—they risk becoming irrelevant in a fast-changing business world.
The way forward is not about significant, transformational changes but small, intentional steps. It’s about building an innovation culture, investing in data quality, establishing robust governance frameworks and continuous learning and adaptation. The AI journey is not a destination but a journey of discovery, innovation and value creation.
So, the message is simple: AI is for those who see it as an opportunity, not a problem.
Key Takeaways
As we look to the future, the question isn’t whether AI will change your industry–it’s whether you’ll be leading that change or trying to catch up. The technology is here, the regulatory framework is emerging, and the competitive advantage is clear.
You don’t have to transform everything at once to be an AI leader. You have to start wisely, strategically, and immediately. Begin by understanding where you are. Identify where AI can deliver value now, build your data foundations, develop your talent, and create your governance frameworks.
Every organization’s journey will be different, shaped by its own context, capabilities, and objectives. But the need to start is universal. The AI revolution is here, and the benefits from early, thoughtful adoption are growing.
The future belongs to those organizations that see this as a once-in-a-lifetime opportunity to reimagine how they create and deliver value. The only question is: Will you be one of them?
The next few months and years will divide organizations into two camps: those that seize this moment to build sustainable competitive advantage and those that wait too long to catch up. The choice—and the opportunity—is yours.
In my next post, the second in the series, I'll explore how organizations are moving beyond essential compliance to building comprehensive AI strategies that drive sustainable competitive advantage. We'll examine real-world cases of successful AI transformations and provide practical guidance for building the capabilities needed for long-term success.
References
Core BTS, 2024. Predictive Maintenance with AI: Reducing Downtime and Costs. [Online]
Available at: https://corebts.com/blog/predictive-maintenance-with-ai/
Dept Health & Social Care, 2023. Thousands of patients to benefit from quicker diagnosis and more accurate tests from ground-breaking AI research. [Online]
Available at: https://www.gov.uk/government/news/thousands-of-patients-to-benefit-from-quicker-diagnosis-more-accurate-tests-from-ground-breaking-ai-research
Drees, J., 2020. AI spots potential COVID-19 treatment drug within two days. [Online]
Available at: https://www.beckershospitalreview.com/innovation/ai-spots-potential-covid-19-treatment-drug-within-two-days.html
Hollyoak, M., 2024. Seizing the AI Advantage: Using the EU AI Act as a Catalyst for Innovation. [Online]
Available at: https://insights.encora.com/insights/seizing-the-ai-advantage-using-the-eu-ai-act-as-a-catalyst-for-innovation? utm_campaign=AI+Thought+Leadership&utm_content=313901999&utm_medium=social&utm_source=linkedin&hss_channel=lcp-68198
Ingenu, 2024. Return on an M2M Investment. [Online]
Available at: https://www.ingenu.com/portfolio/calculate-m2m-roi/?doing_wp_cron=1732555510.5736529827117919921875
Koerber, 2024. AI in Supply Chain Resilience: Lessons from global disruptions. [Online]
Available at: https://www.koerber-digital.com/blog/ai-in-supply-chain-resilience
Krysik, A., 2024. Inside the Netflix Algorithm: AI’s Role in Personalizing User Experience. [Online]
Available at: https://stratoflow.com/how-netflix-recommendation-system-works/
Owczarek, D., 2023. Accurate Inventory Forecasting with AI: A Game-Changer for Managing Future Demand and Streamlining Inventory Management. [Online]
Available at: https://nexocode.com/blog/posts/ai-driven-inventory-forecasting/
Revvence, 2024. Unlocking the Potential of Generative AI in Banking: How GenAI Drives Compliance, Efficiency, and Innovation. [Online]
Available at: https://revvence.com/blog/generative-ai-in-banking
Roundtrip.ai, 2024. ORION: How Route Optimization Keeps UPS Drivers On Time. [Online]
Available at: https://www.roundtrip.ai/articles/ups-route-optimization-software
Son, H., 2017. JPMorgan software does in seconds what took lawyers 360,000 hours. [Online]
Available at: https://www.independent.co.uk/news/business/news/jp-morgan-software-lawyers-coin-contract-intelligence-parsing-financial-deals-seconds-legal-working-hours-360000-a7603256.html
Team DigitalDefynd, 2024. 10 AI in Healthcare Case Studies [2024]. [Online]
Available at: https://digitaldefynd.com/IQ/ai-in-healthcare-case-studies/
