Published January 30, 2025 in Technology • 9 min read • 
Audio available
While previous artificial intelligence (AI) systems have excelled at specific tasks, the next generation of AI is poised to become the primary actor in professional roles, fundamentally reshaping how knowledge work is performed. For existing organizations, the critical question isn’t how to compete with AI-native organizations but how to manage their own transition as their human-centered operations model becomes increasingly uncompetitive.
The evolution of AI can be characterized by three overlapping yet distinct phases, each representing a significant leap in capability and scope. This progression underscores how AI is advancing beyond mere task support to perform roles with increasingly comprehensive responsibilities.
Most of our astonishment about AI concerns the ability of neural networks to perform a wide range of disparate tasks at super-human levels with super-human speed and at negligible cost. These include:
While impressive and transformative, these early phase incarnations of AI are assistants for specific tasks and are inherently siloed. They exhibit deep but narrow expertise, excelling in isolation but requiring human mediation for deployment, applying findings, bridging disciplines, and interpreting results within a broader business context.
Professional roles demand more than isolated expertise. Consider a management consultant leading a project. This requires seamless coordination across multiple domains, such as strategic analysis, project planning, client engagement, and team leadership. Until now, AI could support these functions piecemeal but could not integrate them in a coordinated, end-to-end manner. The need for human oversight in weaving these threads into a cohesive strategic vision kept AI in a subsidiary role.
Today’s AI-forward manager leverages AI whenever she can. She might employ AI to help her conduct an industry and SWOT analysis. She might have an AI assistant draft a strongly worded email to her boss asking for additional resources for a project. She might even use recent AI models to run statistical analyses and generate charts for a presentation. But while our consultant embraces AI to help with specific tasks, she still controls, manages, and oversees the span of activities, pulling in AI expertise as appropriate for narrow tasks.
All of this explains why the dominant rhetoric today is that AI will be a companion, copilot, or assistant for knowledge workers rather than a replacement. However, recent advances suggest a different and very disruptive future.
The newest models handle more than just specific tasks. Like OpenAI’s o1, they demonstrate unprecedented capabilities in complex chain-of-reasoning tasks. These systems can break down intricate problems into smaller components, solve each part systematically, and synthesize comprehensive solutions – all with minimal human intervention.
They can be given a very high-level task and autonomously carry out appropriate research and statistical analyses, draft a report including charts and graphs, and prepare the texts for personalized emails to stakeholders. This capacity for holistic task management foreshadows a future where AI could replace roles rather than just assist us. Returning to our consultant, with AI as task expert and holistic task manager, her role really begins to shrink.
Are we facing a future where AI manages all the tasks associated with knowledge work? The implications are profound: moving beyond the paradigm of AI as a tool toward AI as the primary actor, with humans increasingly serving in supportive or administrative roles. The potential scale of the disruption is significant. It can cost $100,000 a year for each human expert or manager a company hires, whereas another “instance” of an AI agent in an AI “organization” would cost close to zero.
It now becomes clear why Microsoft, Meta, OpenAI, and Google plan to spend hundreds of billions of dollars developing the next generations of AI.
Today’s AI-forward professional workers primarily interact with generative AI through chat interfaces alongside other digital tools in their workflow. These include Office applications, video conferencing platforms, specialist applications, databases, and internet-based services.
For AI systems to evolve beyond assistant or copilot roles, they must learn to navigate and utilize these digital tools and interfaces independently. We’re already seeing early demonstrations of AI systems with “computer use” capabilities – the first glimpses of AI that can interact autonomously with software. These systems are beginning to access and navigate the internet through browsers, modify file systems, and write and execute code.
Ilya Sutskever and other AI visionaries envision models functioning as operating systems for our computers. In this scenario, users would communicate high-level, long-term goals through natural speech, and AI would break down complex tasks into manageable sub-tasks. The system would call upon existing applications as needed, build new applications when required, and manage task execution autonomously.
The digital interfaces between AI systems and computer applications remain limited, requiring API access rather than direct chat interface integration. However, given the rapid learning capabilities of digital AI systems, we can expect significant advancement in this area. As with “task manager” capabilities, “digital system manager” capabilities promise to supercharge AI to vastly outperform traditional human-first organizations (where all of us currently live).
A phenomenon observed in climate change biology is known as the “escalator to extinction”. As global temperatures rise, species on mountains gradually migrate upward to maintain their preferred climate conditions. Each generation moves slightly higher until, eventually, they reach the summit, and there is nowhere left to go.
This powerful metaphor resonates with AI advancement. Just as species ascend to escape rising temperatures until they run out of mountain, human workers are moving toward increasingly sophisticated cognitive tasks as AI capabilities expand from below. We first abandoned routine manual labor to machines, then basic cognitive tasks to computers, and now increasingly complex knowledge work to AI systems. With each advance in AI capability, humans move “up the mountain” toward what we believe to be the uniquely human domains of creativity, strategic thinking, emotional intelligence, and ethical judgment. Yet, like the species approaching the summit, we may find ourselves running out of higher ground as AI capabilities advance into these supposedly safe havens of human cognition.
This transformation rips up the narrative of AI as a copilot. While current discussions often focus on preserving uniquely human skills or emphasizing relationship-based roles, this framing misunderstands the nature of the change. The progression from narrow task expertise to comprehensive role management isn’t merely an expansion of AI capabilities but a categorical shift in how knowledge work is performed. This isn’t about humans learning to work alongside AI but about AI systems becoming the primary performers, with humans supporting technology that demonstrates superior capabilities in complex cognitive tasks.
“When AI systems can perform knowledge work more effectively at near-zero marginal cost, the traditional model of human-centered organizations becomes fundamentally uncompetitive.”
Incumbent organizations face more than adaptation challenges – they confront an existential threat to their operating model. At the core lies structural inertia, where existing processes, systems, and cultural norms create resistance to change. This inertia is reinforced by substantial investments in current infrastructure and deeply embedded organizational hierarchies. However, the most significant barrier isn’t resistance to change; it’s that the very nature of the traditional organization is rendered obsolete in an AI-centric world. When AI systems can perform knowledge work more effectively at near-zero marginal cost, the traditional model of human-centered organizations becomes fundamentally uncompetitive.
Today’s usual organizational transformation strategies – calls for greater agility, cultural change, or innovation initiatives – may be inadequate in the face of what’s to come. They assume organizations can evolve to meet new competitive pressures while maintaining their basic structures. But the AI revolution is much more than a competitive pressure; it’s an existential threat to the human-centric nature of knowledge work.
In contrast, organizations built from the ground up around AI possess inherent structural advantages that go beyond mere technological superiority. Their architecture assumes AI as the primary actor, with human roles designed specifically to support and enhance AI capabilities rather than the reverse. This critical difference allows for exponential scaling at minimal cost, creating an insurmountable efficiency gap that traditional organizations cannot bridge through incremental adaptation.
Given these realities, incumbent organizations face stark choices. Strategic exit – realizing value through sale or merger before market position deteriorates – may often be the most rational response. Those seeking to maintain relevance might pursue parallel innovation by creating autonomous AI-first subsidiaries, essentially starting anew without the constraints of legacy operations. Strategic partnerships with AI companies offer another path, though this likely means accepting a subordinate role.
The rise of autonomous AI represents more than a technological shift; it marks the emergence of a new organizational species better adapted to performing knowledge work in the digital age. The question for most incumbent organizations isn’t how to adapt. It’s how to manage the transition out of an increasingly uncompetitive and outdated business model as more AI-native companies emerge and transform their industries. This transformation will likely accelerate as AI capabilities advance. The organizations that succeed will be those that recognize the magnitude of this shift and take decisive action.
AI's true revolution isn't in chatbots but in transforming industrial operations through dark factories and predictive maintenance. This will require leaders to balance technological advancement with responsible societal impact....
In a world of deepfakes, hallucinations, and bias, we offer practical guidance on how you can trust your AI systems....
Audio available
Companies are rushing to embrace the game-changing opportunities to improve operations and scale that AI offers; yet many are unaware of the pitfalls. Test your knowledge of the risks here – and...
In an era defined by advanced AI and behavioral data, Apple’s 'Evil Steve' test exemplifies a proactive approach to ethical decision-making and responsible data governance....
Explore first person business intelligence from top minds curated for a global executive audience