Impact of AI on business transformation

introduction

Since 2023, when our team first began publishing analytics¹ When it comes to AI development, there has been unprecedented technology acceleration and dramatic inflation of expectations, creating the greatest hype and potential for value creation since the dot-com era. A broader trajectory is now clear, including some long-term structural changes in how AI will transform business.

Over the past two years, we have been observing the market approach to AI. Some companies bought into the hype and made undisciplined investments. Others who chose to remain on the sidelines accumulated debt due to AI intuition. This, combined with a lack of understanding of AI at all levels of the workforce, not only hinders the use of AI for day-to-day efficiency, but also prevents new ideas for future value creation. While the lack of ROI with the former approach was evident, the consequences of the latter, while not immediately measurable, can also be detrimental to companies, as periods of disruptive change throughout history suggest.²

Expectations for artificial intelligence and the potential for value creation

Over the past year, two key areas have become clearer and are expected to impact how enterprises approach AI development and adoption over the next year.

• Long-term structural changes in the way businesses operate are becoming more apparent. As technology continues to change the way business is done, companies are increasingly recognizing the first-principles thinking needed to successfully navigate AI transformation.
• The hype and expectation curves approach equilibrium. The effervescence of the peak hype is fading, and despite the potential for continued local fluctuations and many surprises, the maturity of AI value creation has progressed to the point where we are seeing widespread and systemic value creation. In fact, in a 2025 survey, a majority of companies cited AI as both their biggest value creator and their biggest execution challenge.³

7 long shifts

We are looking forward to advancement through AI. 7 long-term structural changes Economics about how business is done. Depending on the underlying industry economics and market mechanisms, sectors may experience changes differently, at different speeds and timescales, including: They include:

• Accelerating the separation of income from labor: The digitalization of business over the past two decades has increasingly separated value creation from human labor. As AI automates cognitive work, an AI-powered workforce could drive revenue growth and accelerate the separation of different functions and departments. This brings to light early evidence from emerging AI-native companies that could lead to a long-term rise in revenue per employee as a competitive indicator.
• Evolving cost structure: Marginal costs for many services (support, content, analytics, design, etc.) tend toward zero over time, driving EBITDA growth. Many fixed cost items will shift from labor to computing and data with automation, and businesses will face increased upfront fixed costs to support their transformation obligations. However, in the long run, variable costs per unit of revenue growth will decrease.
• Continuous disruption of the customer journey: Advances in digital, social, and mobile technology have disrupted the customer journey over the past decade. Agentic AI has dramatically reduced information arbitrage and made the conversion funnel of product and service discovery faster, automated, and non-linear, triggered by a wide variety of new AI surfaces. As information becomes more commoditized, the focus must be on differentiation through better experiences, more personalized service, and overall service quality to drive customer engagement, conversion, willingness to pay, and loyalty.
• Change in pricing strategy: As efficiency becomes a key factor in AI, exposed sectors and functions will experience price compression and EBIT pressure. This leads to pricing and margin expansion strategies driven by differentiation, such as unique assets, data, brands, domain knowledge, and patents. New pricing models, such as those based on outcomes, success, and quality metrics, will continue to be tested with greater frequency and maturity.
• Transforming your revenue generation model: As AI transforms the nature of products, services, sales/fulfillment channels, customer segments, and perceived value, it will have a far-reaching impact on additive and net new revenue growth opportunities. Companies with proprietary content, data, and domain knowledge IP will find new ways to leverage AI to monetize it. AI engagement surfaces (such as chatbots) are already experimenting with new advertising models through their interfaces, acting as new channel mediators that businesses can monetize to improve product and service discovery. For example, a leading media company was able to apply AI to its existing content products and develop product categories for new markets and customer segments in just a few weeks.
• Strengthen your workforce and operating model: The mix of human and AI workforces will increase the clock speed of execution and decision-making for businesses, increasing output and outcome-based expectations from employees. As AI takes over interstitial workspaces and takes over tasks, current job definitions will become blurred and consolidated (for example, field and service operations teams may be merged, and roles between product and R&D teams may become blurred). We expect this shift to lead most companies to consider flatter organizations, fewer management hierarchies, and greater decentralization through federated governance.
• Formation of a new competitive moat: Traditional moat generation based on process, information arbitrage, capital and labor access becomes a weak proposition as AI changes the economics of each of these categories, allowing competitors to more easily enter adjacent businesses. The effects of software-like scalability will increasingly impact non-digital industries as AI natives take functional expertise and turn it into digital infrastructure, extending first-principle digital platforms without the traditional operational burden. Building a competitive moat will increasingly depend on an organization’s ability to establish unique data, unique domain knowledge, unique brand equity, and a level of AI integration that drives rapid feedback iteration and operating model change.

how to move forward

AI has rapidly evolved from a revolutionary technology cycle to a structural economic force that is reshaping how value is created, captured, and retained. However, despite sector-specific differences, broad competitive disparities are steadily beginning to emerge among companies. This will continue to manifest itself in the rapid and serious recognition of structural changes across the business and the application of first principles thinking to operational and business model transformation.

Companies that treat AI as a tool of marginal efficiency risk increasing profits during times of exponential change. Meanwhile, companies that redesign their cost structures, revenue models, talent strategies, and competitive environments will define the next generation of market leaders. In part two of this series, we examine the key drivers of the structural changes described above and five priorities for executives in 2026.