By 2027, global digital transformation spending is expected to approach $4 trillion, with AI and generative AI among the primary forces driving that investment. According to McKinsey's 2025 State of AI survey, 78% of organizations already use AI in at least one business function, up from 55% only two years earlier. Similarly, Gartner predicts that within the next two years, 33% of enterprise software applications will incorporate agentic AI capabilities and that 15% of day-to-day business decisions will be made autonomously. More than half of enterprise GenAI models are also expected to become tailored to a specific industry or business function, signaling a shift from generic experimentation to embedded business capabilities.
As a result, AI is rapidly becoming far more than a technology initiative. It is emerging as a competitive necessity. As more organizations increase their speed, efficiency, innovation capacity, and ability to scale through AI-enabled operations, the pressure on competitors, partners, suppliers, and entire industries continues to intensify.
Organizations that move early and boldly will not simply improve existing processes. They will use this moment to redefine how value is created, delivered, and captured within their industries. They will rethink customer experiences, products, services, workflows, decisions, and even business models. In other words, they will reinvent the organization itself as a source of competitive advantage.
Conversely, organizations that delay—or transform too timidly—will find themselves competing against organizations operating with entirely different capabilities, innovation potential, and adaptability.
Yet starting an AI transformation does not automatically translate into success. More than 40% of agentic AI initiatives are projected to be canceled by the end of 2027 because of unclear business value, rising costs, or immature applications. As a result, organizations now face not only the pressure to begin an AI transformation, but also an even greater pressure to get it right.
Getting AI transformation right extends far beyond technology adoption. It requires rethinking the organization's design so it can operate in fundamentally new ways rather than simply digitizing existing ones. The true challenge is not whether—or even how—to integrate AI. It is avoiding the temptation to build a slightly faster, more cost-effective, or more modern version of today's organization instead of designing tomorrow's.
The following principles can help guide the design of a genuinely next-level, AI-centric organization.
The most successful AI transformations do not begin with technology. They begin by challenging the assumptions that define today's organization.
One of the greatest risks organizations face is approaching AI primarily as an efficiency initiative. The first questions often sound familiar: Which costs can we reduce? Which positions can we automate? Which processes can we make faster? While these questions may generate incremental improvements, they rarely lead to fundamentally new ways of creating value.
The conversation changes when leaders begin with a different set of questions: What new customer experiences can we create? Which new capabilities become possible? What products, services, or business models could AI enable? Rather than beginning with reduction, they begin with expansion.
IKEA offers a compelling example. When its AI assistant, Billie, began handling nearly half of all customer inquiries, the company did not simply calculate how many customer service positions had become redundant. Instead, it recognized that many customers still wanted something AI could not fully provide: personalized advice on designing their homes and creating functional living spaces. Rather than reducing its workforce, IKEA retrained approximately 8,500 customer service employees as interior design advisors, enabling the company to expand its personalized design services while growing a business that generated more than €1.4 billion in revenue during fiscal year 2022 (Reuters, 2023). AI became a catalyst for business expansion rather than workforce reduction.
John Deere illustrates a similar shift from a different industry. Rather than limiting AI to making agricultural equipment more efficient, the company has combined artificial intelligence, autonomous machinery, computer vision, and precision agriculture technologies to redefine how farmers plan, monitor, and optimize their operations. In doing so, John Deere has expanded its role from manufacturing equipment to enabling data-driven farming and intelligent agricultural decision-making. AI strengthened not only the company's products but also its overall value proposition (Reuters, 2025; John Deere, 2025).
The common denominator is not the technology itself. It is the willingness to begin with possibility rather than efficiency.
Beginning with possibility requires organizations to step back before selecting platforms, building AI agents, or automating workflows. This exploration begins by examining where the organization is today, where it intends to compete tomorrow, and which opportunities justify meaningful redesign. It involves challenging assumptions that may no longer hold true, identifying unmet customer needs, exploring emerging market opportunities, and evaluating opportunities through the lenses of desirability, feasibility, viability, and long-term value creation.
Once new forms of value creation have been identified, the organization itself must be designed to deliver them.
If customer experiences, products, services, operating models, or business models change, the organization cannot remain the same. New ways of creating value require new ways of working.
This means redesigning far more than workflows. It requires determining how work will be accomplished, how decisions will be made, and how people and AI will collaborate to create value. It requires organizations to rethink where work should reside, which capabilities become strategically important, and how responsibilities should be distributed across teams, functions, business units, and ecosystems.
Design decisions now extend across every aspect of the organization. Which activities remain uniquely human? Which become AI-enabled? Which require seamless Human-AI collaboration? Which collaboration mechanisms best support the new value proposition? Where does greater decentralization create agility, and where does centralization enable scale, consistency, or governance? Which capabilities become mission-critical, and where should talent be developed, acquired, or redeployed to support the future organization? Equally important, which mindsets and behaviors will enable the organization to succeed? How can leadership practices, people systems, decision-making processes, and incentives reinforce those behaviors so they become the organization's natural way of working rather than another change initiative?
The objective is not to fit the future into yesterday's organization. It is to design an organizational system that enables the future to succeed.
Truly innovative organizational designs require new capabilities. Some of these capabilities will be technical. Many will not. Organizations may need stronger capabilities in systems thinking, data literacy, innovation, experimentation, cross-functional collaboration, AI governance, change leadership, and Human-AI partnership. They may also need to rethink how they attract, develop, reward, motivate, and retain talent in an increasingly AI-enabled environment.
Capability building therefore extends beyond training programs or recruiting approaches. It becomes a strategic effort to ensure that people can successfully contribute to the future organization and continue evolving alongside it.
At the same time, a more fundamental question may have to be revisited: What does expertise look like in the next-level organization? As AI capabilities continue to evolve, which human capabilities become increasingly valuable? Will the future organization require deeper specialization, broader interdisciplinary understanding, or increasingly T-shaped professionals who combine both? The answers will look different from company to company, but the implications for talent, learning, workforce design, and organizational effectiveness are likely to be significant.
One of the most overlooked aspects of designing a next-level organization is the need to transform the very functions responsible for enabling and sustaining it.
If the organization is expected to operate differently, Executive Leadership Teams cannot remain outside that change. A next-level organization requires a next-level ELT. If the organization is being redesigned, the ELT becomes subject to the redesign as well. Governance structures, decision-making processes, collaboration patterns, and leadership assumptions may need to evolve alongside the organization itself.
The same is true for HR and Organization Development. As organizations become more data-centric, AI-enabled, and capability-driven, traditional people practices alone may no longer be sufficient. HR and OD may need to expand their focus toward workforce design, organizational adaptability, capability development, knowledge transfer, and the conditions that enable continuous learning and value creation. Whether ELT or HR/OD, leaders in the next-level organization will need to demonstrate how they create value in fundamentally new ways.
The transformation journey should reflect the organization you are trying to create—not the one you are leaving behind.
If the future organization is intended to be more adaptive, customer-centric, AI-enabled, data-driven, networked, or decentralized, the transformation itself should embody those same characteristics. Otherwise, organizations risk designing one future while reinforcing the structures, decision-making patterns, and behaviors of the past.
Rather than treating the redesign as a project that delivers a future organization, use the journey itself to begin building it. Pilot new governance approaches. Experiment with Human-AI collaboration. Test new decision-making processes, collaboration models, and ways of creating value. Allow teams to learn, adapt, and refine new ways of working before they become part of the formal organization design.
The transformation journey itself becomes the organization's prototype. It provides an opportunity to strengthen new capabilities, build trust, establish new collaboration patterns, and reinforce the mindsets and behaviors that will make your next-level organization and its people thrive.
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