AI And Competitive Advantage In The Next Era

The focus is now shifting to AI and competitive advantage, as companies rethink what will set them apart in the years ahead.

Even without significant new advances in the technology, AI will change the nature of competitive advantage.

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A New Era of Competition

Business history is marked by shifts in how companies create advantage. In the industrial age, efficiency ruled, as Henry Ford transformed car manufacturing by lowering costs through assembly lines. In the late 20th century, Walmart became a giant by driving relentless supply chain efficiency, while McDonald’s standardized processes to deliver speed and consistency worldwide.

The digital era added a new edge: speed and customer access. Amazon, for example, can introduce new features dozens of times a day, while Netflix can spot changes in viewer preferences and launch new shows before its rivals even notice the trend.

We now stand on the edge of another transformation. As McKinsey recently noted in its paper “The Agentic Organization: Contours of the Next Paradigm for the AI Era” (Sept. 2025), artificial intelligence is shifting the source of competitive advantage once again. This is not about clever tools or chatbots. It is about rethinking the fundamentals of how value is created.

The companies that understand this shift and act on it will win market share. Those that wait risk being left behind.

From Efficiency to Intelligence

The introduction of AI into core business operations does more than speed things up. It changes the math of business itself. For most of history, the cost of serving more customers rose in step with growth. To sell more cars, Ford had to build more factories. To expand retail, Walmart had to hire more people and open more stores. Digital businesses gained efficiency in how they reached customers and processed transactions, removing some of the friction of physical operations, but costs still rose as they scaled.

AI-first workflows would shift that equation. Once a process is redesigned to run primarily through intelligent systems, the cost of serving one more customer becomes minimal. McKinsey researchers describe this effect as driving “marginal costs toward the cost of compute.” In plain English, this means that whether you handle one customer or one million, the extra cost is almost negligible.

Consider customer service. In the past, doubling call volume meant doubling staff. With AI systems handling the bulk of routine questions, a company can scale to millions of interactions while reserving its people for only the most complex cases. Human attention becomes more valuable, because it is spent on higher-order problems, not on volume.

This is why AI doesn’t just improve efficiency. It rewrites the rules of how companies grow.

This is a fundamental break from the past. For most of history, growth and cost were tied together. To serve more customers, you needed more employees, more equipment, or more stores. In the AI era, the relationship between growth and cost loosens. This changes how growth works, allowing companies to get bigger without the traditional jump in costs.

Data Becomes the Raw Material of Advantage

If AI is the engine, data is the fuel. But not all data is equal. Public data (the information available to anyone) is now accessible through shared AI systems. That means it offers no real competitive edge to anyone.

What matters is the data only your company can capture: the customer histories, usage patterns, performance records, and sensor readings that reflect how your products and services work in the real world. McKinsey describes this as building a “walled garden” of proprietary data that competitors cannot easily replicate.

Consider a manufacturer that tracks performance across thousands of machines in its plants. By analyzing that data, it can predict failures, reduce downtime, and design better products faster than competitors. Or picture a bank that uses decades of transaction data to create financial services tailored not just to a demographic group, but to individual households. These are insights no competitor can simply copy.

Unfortunately, many companies are still struggling with more than messy data. They are running on outdated systems that can’t talk to each other, patching gaps with spreadsheets, and piling on shadow software (often not integrated) just to keep things moving. Critical platforms like ERP systems are often long past their prime, but leaders hesitate to upgrade because the work will be expensive and overwhelming. The choice, however, is stark: do the hard work of modernization now, or lose the chance to compete while waiting a little longer. Companies that invest in fixing these foundations will be the ones ready to turn their data into tomorrow’s competitive edge.

IT Team Capabilities Will Be a Differentiator

If data becomes the raw material of advantage, then the teams that build, operate, and maintain the systems around that data, especially IT, will play a defining role in competitive differentiation. In an AI-first world, success won’t come just from having data or intelligent systems; it will come from how well IT can translate between technology and operations.

That means IT can no longer sit in the “back office.” IT teams must become fluent in process: in how work actually happens, from the user’s perspective, and not just in theory. They must understand how customers move through the business, how work is handed off inside the company, where bottlenecks occur, and how day-to-day decisions are actually made. Without that fluency, you risk building brilliant AI atop broken processes, and amplifying the chaos.

In many organizations, IT and operations live in separate worlds. IT designs and/or implements systems; operations lives with their consequences. The result is friction, rework, user complaints, and wasted potential. Research on business–IT alignment confirms this: when IT fails to support real business processes, the value of IT to the organization plummets. A recent survey by Harvard Business Review found that 77% of the respondents said the gap between strategy and IT implementation imposes significant costs and results in lost opportunities.

To make matters worse, most IT teams are trained in technology—code, infrastructure, architecture—not in mapping organizational workflows or translating them into system logic. It’s rare for companies to have truly capable bridging roles. Where they exist, they are often accidental or underdeveloped. For example, a business analyst who happens to know systems, or an operations manager who “speaks IT.” More often, companies rely on outside advisors or consultants to fill that translation gap.

But in the AI era, that role becomes even more critical. As processes change rapidly and AI needs clear input to function effectively, bridging “what the business needs” and “how the system should act” becomes a core capability.

Business leaders have a choice. They can invest now to build IT teams that understand people and processes as well as technology and can bridge users and systems internally. Or, if that capability isn’t present, they can create strong translation layers through analysts, product leads, or external partners.

This raises a glaring problem: most IT professionals today are not educated to do this work. Traditional IT training emphasizes technology mastery, not process fluency or translating operational needs into system design. Similarly, most business leaders lack training to convert operational goals into system requirements. In between, the skill of translation is often missing. Education must catch up. Universities and training programs should teach technologists to understand how work really gets done, and managers to translate business needs into system requirements. Without that, companies will struggle to align AI-first ambitions with real-world execution.

Either way, firms whose IT cannot cross that divide risk falling behind. Those that reorient IT around process fluency will be far better positioned to convert data and AI into sustainable advantage.

Hyperpersonalization as the New Standard

We are moving toward a world where many consumers will rely on their own AI assistants: software that can negotiate, compare, recommend, and even make decisions on their behalf. As these personal assistants become more capable, customers will measure every interaction against that standard. They’ll expect companies to know them, anticipate their needs, and solve problems quickly and personally.

That raises the bar for every company in every competitive space. Retailers will no longer be able to design for broad categories like “women age 35 to 50.” They’ll need to design for the individual: Kathleen, who buys muted colors for travel and prefers sustainable fabrics. Healthcare providers won’t be able to rely on generic treatment plans for “patients with diabetes.” They’ll need to tailor care to James, who works night shifts, eats on the run, and struggles with keeping regular appointments.

And it doesn’t stop at consumer goods or healthcare. Consider a recent experience I had with a national security provider. A sensor on one of our doors broke: an essential part of the system. I called to request a replacement, but instead of simply shipping the part, I was scheduled to speak to another representative the next day. That person then asked me to use my phone camera to prove the sensor was broken, most likely to avoid sending unnecessary parts or prevent fraud. But here’s the problem: if the representative had looked at my customer history, or if an intelligent agent had done it in seconds, they would have seen that we have spent tens of thousands of dollars with them over the years, and this was the very first peripheral we had ever requested. The risk of sending one small sensor was negligible compared to the cost of frustrating a loyal customer.

An agentic system could have weighed the history, the cost, and the risk instantly, and made the obvious decision: send the part. That would have been faster, cheaper, and would have left me more satisfied.

In the AI era, companies will be judged on whether they can make decisions that fit each customer’s situation, quickly and at scale, rather than running everyone through the same generic process. The companies that master this will not only delight customers, but will also reduce friction and costs along the way. Those that don’t will lose ground to competitors who can.

Decoupling Growth from Cost

One of the most powerful implications of AI is that it allows growth without proportionate increases in cost.

Here McKinsey’s research provides useful evidence. They describe a global bank’s “agent factory” that uses squads of AI agents to handle compliance and know-your-customer processes, supervised by a small team of humans. The bank achieved both higher accuracy and lower costs, while freeing human staff to focus on higher-value oversight.

The report also cites a European utility that rolled out an AI assistant for three million customers. The company cut handling times, increased satisfaction, and resolved more calls without human involvement. What once was a cost center became a channel for stronger customer relationships.

These examples demonstrate that businesses that implement AI in these ways will be able scale without simply adding more headcount. And that changes the rules of competition.

Six Imperatives for Leaders

For CEOs and business owners, these shifts raise urgent questions. Competitive advantage in the AI era will not be determined by who buys the latest software. It will be determined by who makes the right strategic choices.

Here are six imperatives:

1. Redesign value creation. Ask where AI-first workflows could lower your marginal costs to near zero. Which processes could be reimagined so they scale like software, not like headcount?
2. Build and protect proprietary data. Treat your data as a strategic asset. What streams of information do you already own that could be refined into distinctive products, services, or insights?
3. Deliver personalization at scale. Customers will expect individualized experiences. How will you deliver services that feel personal, not generic, in a world where personal assistants are doing the shopping?
4. Treat IT as a strategic function. IT can no longer operate as the back office. Competitive advantage will hinge on whether your technology teams understand processes from the user’s perspective and can translate business needs into system design. Leaders must either build these capabilities in-house or create strong bridging roles to close the gap.
5. Decouple growth from costs. Look for ways to grow without simply adding more people or more overhead. Where can intelligence replace brute force?
6. Develop your people for an AI-first world. Success will depend on employees who can manage hybrid workflows, learn new skills quickly, and apply judgment where systems fall short. Investment in upskilling and reskilling is not optional.

These are not questions for your IT department. They are questions for your executive team. They are at the heart of business strategy.

A Leadership Gap, Not a Technology Gap

In my latest book, “Straight Talk: The No-Nonsense Guide to Strategic AI Adoption,” I argue that the greatest barrier to effective AI adoption is not technical knowledge. It is leadership.

McKinsey’s framing of the “agentic organization” reinforces this point. AI may be the technology, but the advantage comes from how leaders choose to wield it. The challenge is not learning prompts or mastering software tools. The challenge is rethinking what your company’s edge will be in a landscape where cost structures, customer expectations, and data ownership look entirely different.

AI and Competition Moving Forward

Every business leader now faces a choice. Some will wait for more clarity, hoping to copy best practices once they emerge. But by then, the leaders who acted early will already have reshaped their markets.

The next competitive advantage belongs to those who see this moment as an opportunity to redefine how they create value. That requires boldness, urgency, and a willingness to make decisions before all the answers are clear.

We are entering a new era of competition, defined by AI and competitive advantage. Success will belong to the leaders who guide their organizations through this transition with foresight and conviction.

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Executive Briefing Notes by Role Available Here