As artificial intelligence moves from being a copilot to a collaborator – and potentially an autonomous “employee” — inside large organizations, it will change the very nature of organizational structure, strategy, and leadership. That’s the argument that Itai Green makes in this excerpt from his forthcoming book, Innovation or Elimination: Winning in a World of Constant Change.
Green is a lecturer on open innovation at Tel Aviv University, CEO of Innovate-Israel, and the former Head of Business Development and Innovation at Amadeus IT Group, a tech company that serves the travel industry.
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Executives must understand that encouraging employees to use GenAI is not just about efficiency but also shifting the company’s DNA toward an innovation mindset. Employees who are comfortable with GenAI tools have become more open to adopting transformative technologies from external innovators. This shift is essential, as McKinsey estimates that traditional analytics, machine learning, and deep learning contribute approximately $11–17.7 trillion to the global economy, while new generative AI use cases could add $2.6–4.4 trillion, representing a 15–40 percent increase. When applying GenAI to workforce productivity, the incremental impact is even higher, reaching $6.1–7.9 trillion, which translates to a 35–70 percent increase in economic potential.
These figures underscore a critical point: competitive advantage does not come from small efficiency improvements but from adopting breakthrough technologies capable of reshaping entire industries. Not only that, but Generative AI is also becoming more powerful every day. Across leading AI labs, such as Anthropic, Google, Meta, Microsoft, and OpenAI, capabilities have advanced dramatically in just two years. Early versions released in 2022–2023 mainly were text-only, displayed limited contextual understanding, struggled with long or complex conversations, and offered little or no multimodal functionality. By 2025, frontier models had become fully multimodal, achieved advanced reasoning capable of multistep problem-solving, demonstrated strong coherence during long interactions, integrated real-time data, and introduced more sophisticated personalization and agentic behaviors.
Beyond that, not only is it becoming more powerful, but its use, and the way humans interact with it, are also transforming. This is particularly true when considering the evolution of the workforce. While GenAI is empowering humans, we are quickly moving toward an era where human and AI collaboration is at the core of new organizational models. The AI Autonomy Levels Framework provides a clear framework for understanding this evolution. Level 1, “User as an operator,” is where the user is in full control, and the AI serves as a “copilot,” providing on-demand support. In Level 2, “User as a collaborator,” the user and AI work together, planning and executing tasks in parallel. Level 3, “User as a consultant,” shifts more responsibility to the AI, with the user providing high-level feedback and guidance. Level 4, “User as an approver,” is a more passive role where the user only intervenes to resolve blockers or approve consequential actions. Finally, Level 5, “User as an observer,” represents a fully autonomous AI that plans and executes tasks without any user involvement.
This gradual shift is likely to result in new organizational structures, particularly the flattening of the organization and thinning of junior roles, as well as the need for supervisors for AI agents who can better work with this technology, as well as employees who will be empowered by AI, and take on new and combined roles, leading to changes also in ideation and product development. A good example is Wand, whose platform enables organizations to build hybrid workforces made up of both human professionals and AI agents. These agents can be recruited, trained, managed, and even continuously evolve, effectively replacing some employee roles while collaborating with others in a shared workspace. This demonstrates how the future of work will not only require humans to adapt to AI but also to learn how to manage AI colleagues directly.
Despite the excitement surrounding GenAI, its adoption has faced challenges. Recent surveys indicate that 36 percent of companies reported no change in growth due to AI, and only 19 percent of companies have seen revenue growth exceeding 5 percent from their AI initiatives. However, this is not due to the limitations of AI itself but rather to a lack of strategy, training, and cultural readiness within organizations. Despite that, 92 percent of companies plan to increase their AI investments over the next 3 years, and 82 percent expect revenue growth from GenAI during this period.
Generative AI is also likely to result in the restructuring of processes and workflows into two possible patterns: The factory pattern and the artisan pattern.
Many companies also focus on trends, such as GenAI, and overlook not only other critical technological advancements, but also what is common to them all – innovation. The common solution to staying ahead of the technological curve is effectively adopting relevant emerging technologies through open innovation. Organizations that master this methodology are unlikely to be surprised by the “next cool thing,” or disrupted by technology or future competitors. Open innovation allows organizations to successfully implement AI and other technologies, each according to its maturity and value to the company. Once this methodology is embedded, it guarantees the seamless integration of advanced technologies, including AI, but not limited to it.
Generative AI is also likely to result in the restructuring of processes and workflows into two possible patterns: The factory pattern and the artisan pattern. The first model allows organizations to deploy AI agents that can collaborate and navigate work, from end to end. This is particularly relevant for routine processes. In the second model, GenAI tools are implemented at scale to serve as assistants and copilots, enabling them to aid and enhance human work. This is especially relevant in more complex cases that require human judgment.
For example, startups like Wonderful demonstrate the factory model in action: their AI customer-facing agents can seamlessly manage tasks across chat, voice, and email while integrating into complex enterprise systems.
The significance of AI, especially GenAI, extends far beyond automation and efficiency – it is also a catalyst for open innovation, as AI-driven platforms enable innovation on an unprecedented scale, breaking down barriers between organizations and democratizing access to cutting-edge insights. GenAI is likely to result in a skills revolution with new workforce and career development considerations, requiring upskilling and new learning journeys for employees.
At the same time, it is also likely to result in new organizational structures, particularly the flattening of the organization and thinning of junior roles, as well as the need for supervisors for AI agents who can better work with this technology, as well as employees who will be empowered by AI, and take on new and combined roles, leading to changes also in ideation and product development. If leaders do not understand how to use such tools themselves, provide their employees with training, and incentivize them to experiment with new solutions, they are likely to fall behind more innovative competitors.
Additionally, GenAI has a profound impact on open innovation itself, where one of its most significant contributions is its ability to analyze and synthesize vast amounts of information. An excellent example of this is BridgeWise, a startup that leverages AI to analyze over 90 percent of the global securities market. The company provides insightful analysis, uncovering hidden opportunities and making information accessible, replacing the need for entire departments of investment analysts.
Traditional research and development cycles, which often took years or even decades, can now be accelerated through AI-driven analytics and predictive modeling. By processing scientific literature, patents, market trends, and user feedback in real-time, AI-powered systems can generate insights that would take human researchers exponentially longer to uncover. This enables organizations to iterate more rapidly, reducing the risk and cost of experimentation.
It is not only research that is being transformed, but product development is equally disrupted. Take Base44, a one-person startup acquired by Wix just months after its founding. Base44’s platform can turn ideas into fully functioning applications within minutes,
showcasing how AI dramatically increases efficiency in product development and shortens cycles that once took months or years.
AI…enables leaders to process complex market dynamics in real time, identify emerging trends, and pressure-test strategic assumptions with unprecedented speed and accuracy.
Generative AI takes this further by offering tangible, creative solutions in real-time. Whether designing new materials, generating code for software development, or simulating innovative business strategies, generative AI significantly enhances the ideation and prototyping phases of innovation. Companies like NVIDIA, Microsoft, and IBM have leveraged generative AI to create novel products, optimize industrial processes, and even co-develop solutions with external partners in open innovation ecosystems.
Moreover, AI-driven platforms and ecosystems have facilitated the rise of collaborative innovation hubs. These platforms, such as Kaggle, GitHub with GitHub Copilot, and OpenAI’s research collaborations and data partnerships, enable experts from various domains to contribute their knowledge and collectively co-create solutions. Such collaborations enhance creativity and foster a culture where intellectual capital is shared rather than hoarded, resulting in mutually beneficial value for all.
Finally, GenAI is transforming not only innovation but also strategic decision-making, fundamentally reshaping how organizations analyze, plan, and adapt to their environment. Just as it accelerates open innovation by synthesizing vast datasets, AI is now redefining the strategy development process. It enables leaders to process complex market dynamics in real time, identify emerging trends, and pressure-test strategic assumptions with unprecedented speed and accuracy. AI-driven tools, particularly generative AI tools, can map competitive landscapes, uncover hidden opportunities, and generate adaptive insights. While human judgment remains central to defining long-term vision, AI augments decision-making in several ways, broadening the range of possibilities, enabling more agile responses to change, and facilitating better, faster, and improved decision-making. In this regard, GenAI is not just a tool, but a partner in shaping the future of business.
Excerpted from the 2026 book Innovation or Elimination: Winning in a World of Constant Change, by Itai Green. (Featured image by Abbe Sublett on Unsplash.)
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