How to build an organization that wins in the agent economy

Successful AI transformation depends on organizational readiness

Every company is at a different point in its AI journey. That matters. Some leadership teams already understand that AI changes how work gets done. Others still see it as another technology investment or another transformation program. Those are very different starting points, and they require different actions.

The biggest mistake is thinking AI transformation can be delegated. Budget approval is not leadership. AI changes how decisions are made, how products are built, how customers are served, and how teams operate every day. If senior leaders have never used AI to solve a real business problem themselves, they will struggle to recognize where the biggest opportunities are. That slows the entire organization.

The companies moving fastest have leaders who spend time experimenting with AI personally. They write documents with it. They analyze data. They review strategy drafts. They test workflows. This creates firsthand understanding instead of secondhand opinions. Once leaders experience meaningful productivity gains themselves, conversations become much more practical. The discussion shifts from “Should we use AI?” to “Where should we redesign work first?”

This is important because AI is not a one-time technology upgrade. The models improve constantly. New capabilities appear every few months. Organizations that wait for stability will always be behind. The competitive advantage comes from building an organization that learns continuously instead of one that tries to build the perfect long-term plan.

Progress should also be measured differently. Many executives focus on reaching the final destination, but the value begins much earlier. Every stage of adoption creates measurable improvements in speed, quality, and capacity. A company that moves from limited experimentation to structured AI adoption is already creating value, even if it has not fundamentally redesigned its operating model.

For executive teams, adaptability becomes a core capability. Strategy cycles become shorter. Decisions rely more on experimentation than prediction. Teams need permission to test new approaches quickly, measure results, and improve continuously. That requires leadership to become comfortable making decisions with incomplete information while maintaining clear business objectives.

One important nuance is that readiness is rarely uniform across an organization. Different business units, functions, and leaders will move at different speeds. Trying to force everyone into the same adoption timeline often creates unnecessary resistance. A better approach is to identify the parts of the business that are ready to move, generate measurable success there, and allow those results to influence the rest of the company.

Six foundational conditions are essential for AI transformation

Technology is rarely the reason AI transformations fail. Most failures happen because the organization continues operating with systems and management practices designed for a much slower world. Before scaling AI, leaders need to establish several conditions that allow change to happen consistently across the business.

The first requirement is leadership commitment. The executive team must genuinely believe AI is one of the most important opportunities facing the company. That belief cannot stop at approving funding. Leaders need enough personal experience with AI to recognize both its strengths and its limitations. Their behavior sets expectations for the rest of the organization.

The second requirement is ownership by the business. AI should not become the responsibility of a central innovation office or technology team alone. Business leaders need to own productivity improvements within their own functions. Corporate leadership should define the direction, establish common metrics, remove obstacles, and maintain accountability. The actual transformation happens where the work is performed.

Measurement is equally important. Organizations need visible dashboards that show adoption, business outcomes, and examples of successful use. Perfect metrics are not required at the beginning. Useful metrics are. Publicly recognizing successful teams helps create internal momentum, especially when respected employees demonstrate better ways of working. People are often influenced more by trusted colleagues than by corporate announcements.

Speed also becomes a strategic capability. Many governance processes were designed when software changed slowly. AI evolves much faster. If vendor approvals, security reviews, procurement, and governance still operate on quarterly timelines, the business will continuously fall behind. That does not mean reducing security or compliance standards. It means redesigning those processes so they support rapid and responsible deployment.

Another requirement is protecting the people driving change. Every successful transformation depends on a relatively small group of employees who are willing to redesign workflows, challenge established processes, and experiment with new tools. These individuals are not always the most senior people. They are often experienced operators who understand both business problems and technology. Leaders need to remove unnecessary bureaucracy around them and give them enough authority to move quickly.

Finally, organizations should present AI as a way to increase the impact of their best people. Top performers usually want to spend less time on repetitive work and more time solving difficult problems, building products, serving customers, and creating value. AI expands that capacity when implemented well. If employees only hear discussions about efficiency and headcount reduction, adoption will slow because trust declines.

For executive teams, these six conditions reinforce each other. Strong leadership without measurement creates confusion. Good metrics without business ownership create compliance rather than transformation. Fast governance without committed leaders often results in isolated pilot projects that never scale. Success comes from treating these elements as one operating system rather than six independent initiatives.

Stage 1 – “The light switch”: sparking early adoption through personal breakthroughs

Every AI transformation starts with a small group of people who discover that their way of working has fundamentally changed. They stop seeing AI as an interesting tool and start relying on it every day because it helps them complete work faster, improve quality, or solve problems they could not solve as efficiently before.

At this stage, broad adoption is not the objective. The objective is creating enough genuine success stories that the rest of the organization begins paying attention. Only about 5% to 10% of employees initially reach this point. That is normal. Trying to convince everyone at once usually wastes time and energy.

The priority is to identify those early adopters and give them visibility. They become the organization’s first AI champions because they speak from experience rather than theory. Their examples carry credibility. When they show how AI improved a proposal, accelerated software development, strengthened customer analysis, or produced a better product specification, other employees begin asking practical questions instead of debating whether AI matters.

Leadership should make these experiences easy to create. Giving employees access to AI tools is only the beginning. Many people open an AI application, see an empty prompt, and have no idea where to start. Clear business challenges remove that uncertainty. Asking teams to draft a competitive analysis, redesign a sales playbook, create customer segments, write technical specifications, or build a working prototype gives AI an immediate connection to real work.

Hands-on learning is especially valuable. Allow experienced users to teach colleagues directly, with everyone working on actual business tasks rather than demonstrations. This approach helps employees understand both the strengths and the limitations of AI. It also encourages better questions, which often determine the quality of AI outputs.

Executives should also accept that adoption will not happen evenly. Some employees will become enthusiastic almost immediately. Others will remain skeptical until they see repeated evidence from trusted colleagues. That difference should not become a distraction. Organizations gain more by accelerating employees who are ready than by focusing most of their attention on those who are resistant.

This stage also provides valuable management information. Early adoption reveals where enthusiasm already exists, which functions are experimenting successfully, and where additional support is needed. Those signals help leaders decide where to invest time, resources, and executive attention during later phases of the transformation.

Stage 1 delivers up to a 5% blended productivity improvement across the organization, with most of that gain coming from the relatively small group of early adopters. Beyond productivity, these employees help identify future change leaders and uncover areas of strong demand for AI across the business.

Stage 2 – “Raise the floor”: embedding AI in workflow through structured pilots

Once early enthusiasm has been established, the next objective is consistency. AI should no longer depend on a handful of highly motivated individuals. It should become part of how teams perform everyday work across the organization.

Begin with structured pilots involving 10 to 20 teams working on real business activities. These should not be exploratory experiments disconnected from operations. Teams should use AI on active projects, customer work, software development, operational processes, and existing backlogs. Success becomes much easier to evaluate when the work already has established performance measures.

Measurement is critical during this stage. Organizations need both leading and lagging indicators. Leading indicators show whether employees are actually adopting AI and using it effectively. Lagging indicators demonstrate whether that adoption produces meaningful business outcomes. Looking at only one category creates blind spots. High usage without measurable business improvement indicates poor implementation. Strong business outcomes without understanding adoption patterns make scaling much more difficult.

Engineering organizations can measure AI adoption, usage depth, pull request velocity, feature throughput, mean time to recovery (MTTR), and engineering expense as a percentage of revenue. Sales organizations can monitor adoption quality, customer-facing time, pipeline coverage, conversion rates, and revenue generated per sales representative. These metrics connect AI activity directly to operational performance.

An important part of Stage 2 is preventing every team from solving the same problems independently. As pilot teams discover effective workflows, prompts, context files, agent configurations, and operating practices, those methods should be documented and shared across the company. Standardization reduces duplication, improves quality, and allows organizations to expand from a small number of successful teams to hundreds of teams without repeating the same learning process.

Leaders also need to decide how they will convert productivity gains into business value. Increased efficiency can create additional capacity, improve customer responsiveness, accelerate product development, reduce technical debt, or support future growth initiatives. Without clear objectives, AI risks becoming another technology expense rather than a driver of measurable business performance.

Structural barriers often become visible during this phase. Compensation systems, performance incentives, outsourcing contracts, agency relationships, and legacy business processes may all discourage new ways of working. These issues cannot always be solved immediately, but they should be identified early because they can significantly slow broader transformation if left unchanged.

For executives, Stage 2 is often the most demanding phase because it requires disciplined execution rather than isolated innovation. Pilot programs must produce evidence that builds confidence across the organization. Once that evidence exists, scaling becomes a management challenge instead of a technology challenge.

Organizations can achieve productivity improvements of up to 25% across adopting teams. It also estimates that a company with 10,000 employees and fully loaded employment costs of $150,000 per employee could free approximately $300 million in annual capacity through a 20% productivity improvement. These gains may be redirected toward higher growth, increased operational speed, backlog reduction, or funding continued AI investments.

Stage 3 – “Raise the ceiling”: redesigning work around an AI-first operating model

Most organizations stop after improving existing workflows. That creates meaningful gains, but it does not unlock AI’s full potential. Stage 3 starts with a different question: if AI existed before today’s processes were designed, would the organization build those processes the same way? In many cases, the answer is no.

This stage is about redesigning work instead of optimizing it. AI is no longer an assistant added to existing workflows. It becomes the foundation for creating entirely new operating models. Teams become smaller, decisions become faster, and work is organized around outcomes rather than traditional structures.

Select one leader with a significant organization who fully understands AI’s capabilities and is willing to challenge existing assumptions. This leader should receive a clear mandate, executive sponsorship, and direct access to the CEO and COO to remove obstacles quickly. The goal is not to create another pilot project. The goal is to prove that a different operating model works.

That requires real authority. Leaders cannot redesign work if they remain constrained by existing reporting structures, job definitions, compensation systems, or hiring practices. They need the flexibility to reorganize teams, redefine responsibilities, promote different skills, and experiment with new ways of delivering results. Without that freedom, organizations simply automate existing inefficiencies.

Protect these frontier teams from organizational resistance. Every large company develops routines, approval processes, and cultural expectations that naturally reinforce existing ways of working. Those mechanisms often slow transformational initiatives. Executive support becomes essential because visible backing signals that experimenting with new operating models is an organizational priority rather than an isolated exception.

Another critical element is documenting what works. AI capabilities continue evolving, which means successful workflows will also change over time. Organizations should capture proven methods, reusable processes, technical configurations, and institutional knowledge so future teams can build on existing experience instead of starting from the beginning. Over time, this knowledge becomes valuable intellectual property that strengthens the organization’s competitive position.

Executives should recognize that Stage 3 is not intended to transform the entire enterprise immediately. It creates a model that demonstrates what becomes possible when organizational constraints are intentionally removed. That evidence helps leadership make more informed decisions about broader structural changes.

This phase also changes the role of management. Leaders spend less time supervising individual activities and more time designing systems, removing barriers, allocating resources, and enabling high-performing teams to move quickly. As AI assumes more routine work, human leadership shifts toward judgment, prioritization, creativity, and organizational direction.

Organizations reaching Stage 3 can achieve productivity improvements of two to three times within transformed teams. It cites examples including five people completing product refactoring work that had originally been expected to require 500 people, and three-person sales teams managing twice as many customer accounts. If approximately 10% to 15% of an organization reaches this level, costs could decrease by roughly 30% while operational velocity doubles or triples in key business functions.

Stage 4 – “The new normal”: scaling an AI-First enterprise

Stage 4 represents the point where AI-first operating principles become standard across the organization rather than existing in isolated teams or business units. No large enterprise has fully reached this stage yet because AI capabilities have only recently become mature enough to make this level of transformation realistic.

The defining characteristic of Stage 4 is continuous adaptation. Organizations stop treating transformation as a project with a finish date. Instead, they build processes that continually evaluate new AI capabilities, test them in production environments, standardize successful practices, and deploy them across the business. This cycle becomes a permanent operating capability rather than an occasional strategic initiative.

That shift has important implications for executive leadership. Long planning cycles become less effective when technology evolves every few months. Leaders need governance systems that support frequent decision-making, rapid learning, and disciplined execution. Competitive advantage increasingly comes from how quickly an organization can absorb new capabilities while maintaining operational stability and strong governance.

At this stage, AI becomes integrated into every major business function. Product development, engineering, customer support, finance, sales, marketing, operations, legal, and corporate services all evolve around AI-enabled workflows. The objective is not simply increasing automation. It is improving the quality, speed, and consistency of decision-making across the enterprise.

Organizational culture also changes. Continuous learning becomes a business requirement rather than an individual preference. Employees are expected to update skills regularly, adopt new workflows, and contribute to improving how work is performed. Companies that establish these habits are likely to respond more effectively as AI capabilities continue advancing.

Executives should also recognize that reaching Stage 4 does not eliminate uncertainty. New AI models, regulatory requirements, cybersecurity risks, and market dynamics will continue changing. Organizations therefore need strong governance alongside operational agility. Responsible deployment, data security, compliance, and human oversight remain essential even as AI becomes deeply embedded in business operations.

The companies that move toward this stage are building a capability that extends beyond technology adoption. They are creating an organization that improves continuously without waiting for major transformation programs. Over time, that ability becomes increasingly difficult for competitors to replicate because it combines leadership, culture, operating processes, and accumulated organizational knowledge.

In this future operating model, approximately 70% of today’s operating expenses could generate more value and higher organizational velocity than 100% of current spending under traditional operating models. While this remains a forward-looking projection rather than an observed industry benchmark, it illustrates the scale of change the authors believe AI-first organizations may ultimately achieve.

Balancing rapid transformation with organizational capacity and avoiding superficial adoption

Once an organization commits to AI, the next challenge is deciding how fast to move. There are two broad approaches. One is a gradual transformation over several years. The other is to deliberately create pressure by maintaining ambitious business targets while reducing team sizes or changing operating models more quickly. The authors argue that companies at the leading edge increasingly favor the second approach because AI capabilities are improving too rapidly for slow implementation cycles.

Speed, however, should not be confused with rushing. Moving quickly requires focus. If AI becomes one initiative among many competing priorities, meaningful transformation is unlikely. Organizations need to create enough capacity for employees to learn new tools, redesign workflows, and build new habits. Without dedicated time and executive attention, AI adoption often remains limited to isolated experiments.

One of the biggest risks is mistaking activity for progress. Many organizations begin tracking AI usage through metrics such as the number of prompts submitted or the frequency of logins. These measurements provide visibility, but they do not prove that work has fundamentally changed. Employees can satisfy usage targets without improving productivity or changing how they operate.

Leaders should evaluate adoption through both behavioral and business outcomes. The most important questions are straightforward. Has the quality of work improved? Has cycle time decreased? Have employees replaced older workflows with AI-enabled ones? If the answer is no, then high usage numbers alone should not be interpreted as successful adoption.

This distinction matters because organizations often create incentives around metrics. When people are rewarded for usage rather than outcomes, they naturally optimize for the easiest measurement. Executives should design performance indicators that encourage meaningful business improvements rather than simple compliance.

Transformation also requires leaders to accept a degree of short-term disruption. Teams will initially experiment with different workflows. Some approaches will succeed, while others will not. The objective is to shorten learning cycles so successful methods spread quickly across the organization. Organizations that treat every early setback as a reason to slow down risk falling behind competitors that continue learning and adapting.

For executive teams, governance should focus on business outcomes instead of technology adoption alone. AI initiatives should be reviewed using familiar operational measures such as customer satisfaction, product quality, revenue growth, operational efficiency, delivery speed, and profitability. This keeps AI aligned with strategic objectives rather than allowing it to become a standalone technology program.

Workforce composition and talent strategy become strategic competitive advantages

AI changes the economics of talent. As organizations become more productive, they may require fewer people to produce the same, or greater, business output. That does not reduce the importance of people. It increases the importance of having the right people in the right roles.

The employees who create the greatest long-term value will be those who continuously learn, adapt to changing technologies, exercise sound judgment, solve complex problems, and work effectively alongside AI systems. These capabilities become increasingly valuable because AI can automate many repetitive or structured activities, allowing people to focus on higher-value decisions and innovation.

This requires a different approach to workforce planning. Rather than hiring exclusively for technical expertise, organizations should seek individuals who combine business knowledge, operational experience, curiosity, and a willingness to adopt new ways of working. AI proficiency becomes an important capability, but it should complement strong domain expertise rather than replace it.

Reskilling existing employees is equally important. Many experienced professionals already possess deep institutional knowledge that AI cannot replicate. Teaching these employees how to use AI effectively often delivers greater value than replacing them with entirely new teams. Organizations that invest in continuous learning are more likely to retain critical expertise while increasing overall productivity.

Leaders should use objective performance data when evaluating workforce readiness. Assumptions about who will adapt are often inaccurate. Some employees adopt AI quickly regardless of seniority or job title, while others struggle despite extensive experience. Measuring actual behavior provides a more reliable basis for investment, development, and workforce decisions.

Recruitment strategies will also need to evolve. Competition for AI-capable talent continues to intensify, making hiring a strategic responsibility rather than a task that can be delegated entirely to recruiting teams. Senior leaders should remain directly involved in identifying critical roles, attracting high-performing candidates, and ensuring the organization offers an environment where ambitious talent wants to build its career.

Becoming an AI-first organization can also strengthen an employer’s position in the talent market. Skilled professionals increasingly look for workplaces where they can develop modern capabilities, work with advanced technologies, and contribute to meaningful innovation. Organizations that demonstrate genuine commitment to AI transformation are likely to become more attractive to these candidates.

Executives should remember that workforce strategy is no longer separate from business strategy. Decisions about hiring, reskilling, organizational design, and performance management directly influence how quickly the business can capture AI’s potential. Companies that align talent strategy with AI transformation are likely to build a stronger competitive position over time.

The bottom line

The biggest question is no longer whether AI will reshape your business. It is whether your organization can adapt faster than the market around it.

Technology will continue improving. Better models, more capable AI agents, and lower costs are almost guaranteed. Those advances will be available to everyone. What will separate companies over the next few years is not access to AI. It will be leadership, execution, and the ability to redesign how work gets done.

That requires a different mindset. AI is not another digital initiative to assign to a single department or manage through a steering committee. It is a business transformation that affects strategy, operations, talent, governance, and culture at the same time. The organizations that recognize this early will have more opportunities to shape the future instead of reacting to it.

The journey also does not need to begin with a complete organizational redesign. Every stage creates value. Start by helping leaders build firsthand experience with AI. Measure business outcomes instead of activity. Scale what works. Remove unnecessary friction. Give your strongest teams the freedom to challenge existing assumptions. Then repeat the process as the technology evolves.

The pace of change will remain uncomfortable for many organizations. Waiting for certainty is unlikely to reduce that uncertainty. It simply gives competitors more time to build capabilities that become increasingly difficult to match.

For executives, the priority is clear. Build an organization that learns faster than the environment changes. If you can do that consistently, AI becomes more than a productivity tool. It becomes a capability that strengthens every part of the business and positions your organization to compete in a market that will continue evolving for years to come.