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robot evolution


The seven levels of human capability

Published 23 January 2024 in Technology • 12 min read

Given the rapid evolution of artificial intelligence, business leaders need a dynamic framework to anticipate the impact of AI on their business.

The rapid development of artificial intelligence has ignited interest in the potential impacts on business and the economy, particularly employment. It’s challenging for business leaders, however, to make sense of artificial intelligence (AI) without a framework to understand the nature of human cognitive and emotional capabilities and how artificial intelligence progressively will equal or exceed them.

Based on research beginning in the late 1980s at Harvard Business School by Professors Ramchandran Jaikumar and Roger Bohn and their doctoral students (of which I was one) on the impact of automation and “stages of knowledge,” I developed a seven-level framework of human capabilities that AI will progressively replicate. Each level represents an increase in AI capability and builds on the development of previous ones.

The seven levels are a framework for understanding the progression of AI and its potential impact on your business. By mapping AI’s progress across these levels to your business, you can anticipate which roles within your organization might be automated, aiding in strategic workforce planning and reskilling efforts. This will help guide your business toward a future where AI will play a progressively more comprehensive role.

The framework also provides insights into where investing in AI technologies in your business might be most beneficial. By understanding where AI currently stands on the capability scale and where it is likely to progress, you can make more informed decisions about AI investments that align with your strategic goals.

Finally, you can develop risk mitigation strategies by highlighting potential social and economic disruptions that could result from AI’s advancement—such as job losses due to automation. This can help ensure your business’s sustainability in the face of AI-driven change.

Understanding the impact of AI at each level

The development of AI capability at each level has or will substantially impact jobs historically done by humans. Below, I use software development to illustrate the progressive impact of the successive levels of AI development and identify other occupations that either have or likely will be substantially impacted or eliminated.

A critical nuance in this progression is that AI at lower levels will likely amplify the capabilities of people who will eventually be replaced as higher-level capabilities are developed.

Level 1: Basic information processing and task execution

At this foundational level, AI excels at handling vast amounts of data and executing repetitive tasks with superior efficiency and precision compared to human capabilities. This proficiency in basic information processing is a cornerstone of AI applications in everyday scenarios, leading to significant automation in various fields.

In software development, AI’s role in automating tasks such as code formatting, simple debugging, and certain aspects of code generation has been transformative. This automation streamlines the development process and significantly reduces the time and labor traditionally required, particularly from entry-level developers and code reviewers. The implication is a shift in the focus of human developers toward more complex and creative tasks as AI handles routine aspects.

The impact of AI at this level extends far beyond software development. Occupations characterized by repetitive, rule-based tasks are experiencing a profound shift due to AI automation. Data entry clerks, warehouse workers, and assembly line workers are prominent examples of roles undergoing this transition. AI performs tasks more efficiently and accurately in these fields, minimizing the errors often associated with human fatigue or oversight. This increase in efficiency and accuracy has significantly reduced the need for human labor in these roles. The automation of such tasks by AI is not just a matter of replacing human labor but also enhancing productivity and reliability in various industries.

This development heralds a new era where AI’s role in executing basic tasks is both a driver of efficiency and a catalyst for reshaping the workforce, compelling a re-evaluation of job roles and the skills required in the evolving job market.

Level 2: Basic decision-making and problem-solving

At this stage in AI development, the focus is on decision-making based on predefined rules and problem-solving within set parameters. This capability is particularly transformative in software development, where AI can take over a substantial portion of automated testing tasks. This includes unit, integration, and system tests, traditionally labor-intensive processes that require meticulous attention from manual quality assurance (QA) testers.

By automating these processes, AI reduces the workload and the demand for such human resources. Additionally, AI’s role in code optimization and algorithm selection is increasingly significant, which could lead to a decreased necessity for mid-level developers who generally undertake these tasks. The impact is not just in efficiency but also in enhancing the quality and reliability of the software developed.

robot emotions
AI can mimic emotion recognition and social understanding based on programmed cues, facilitating the development of chatbots and virtual assistants

The implications of AI at this level extend to various other fields. Financial analysts, tax preparers, and traffic controllers are examples of professions where decision-making is often rule-based or involves solving problems with known solutions. AI’s ability to process vast amounts of data swiftly and accurately can lead to the automation of many aspects of these jobs. For financial analysts, AI can automate market analysis and data interpretation. AI can streamline tax preparation by efficiently handling routine cases in taxation. Traffic control, another area heavily reliant on rule-based decision-making, could see AI systems optimizing traffic flow and managing complex networks more effectively than human controllers.

As AI continues to evolve at this level, it is expected increasingly to augment or replace human roles in similar professions, reshaping the workforce and prompting a reevaluation of skills and training in the affected fields.

Level 3: Basic social and emotional intelligence

At this stage, AI can mimic emotion recognition and social understanding based on programmed cues, facilitating the development of chatbots and virtual assistants. It can identify a user’s emotional state from text and respond with programmed social etiquette. The true complexity of human social and emotional intelligence, however, lies in nuances that AI cannot yet fully grasp.

In software development, this could influence user interface design and the role of developers in creating socially engaging applications. Software developers’ role in creating socially engaging and user-friendly applications would also be affected as AI begins to understand and adapt to social norms.

AI at this level also would impact occupations such as social media managers, sales associates, and human resources specialists. An AI that understands social norms and emotional cues could potentially automate various tasks in these roles, leading to substantial shifts in job descriptions and requirements.

Level 4: Advanced decision-making and learning

At this level, AI’s capabilities in decision-making under uncertain conditions and its creative problem-solving skills are developing rapidly, albeit not yet fully matured.

This evolution is increasingly allowing it to handle more complex and nuanced tasks in software development. For instance, AI can begin to automate identifying and resolving software bugs by learning from historical data and past fixes. This capability could significantly alter the roles of bug fixers and maintenance engineers as AI takes over tasks that were once heavily reliant on human expertise and experience.

The impact of AI at this level would extend far beyond technical fields. In areas like stock trading and advertising, where advanced decision-making and adaptability are crucial, AI’s growing ability to analyze complex market data and learn from trends could lead to significant automation. For stock traders, AI systems could make rapid, informed decisions based on market conditions, potentially outperforming human traders in speed and efficiency. In advertising, creatives could see AI playing a more prominent role in generating innovative campaign ideas and strategies based on consumer behavior analysis and market trends.

This shift towards AI-driven decision-making and learning represents a transformative change, which suggests that AI technologies could automate or augment significant components of these professions. The prospect of AI handling such advanced tasks underscores the evolving nature of these roles and the need for professionals to adapt and integrate AI into their workflows.

Level 5: Advanced social and emotional intelligence

Currently, this level of cognitive capability remains mostly beyond the reach of AI. AI would need significant advancements in its ability to understand, interpret, and mimic human emotion and social nuances to achieve this level. Genuine empathy, a fundamental human trait, presently is beyond AI’s capability.

cognitive abilities
At the moment, the cognitive abilities required for goal-setting and strategic planning remain a uniquely human domain

Should AI develop an understanding of and responsiveness to complex human emotions, this would revolutionize user experience design and impact or eliminate roles for user experience (UX) designers. In addition, roles focusing on developing collaborative tools could be impacted as AI facilitates more effective communication and interaction among teams.

Other occupations, such as negotiators, diplomats, and therapists, could be affected. The ability to handle intricate negotiations, understand complex social norms and show empathy are critical components of these roles. When AI develops these capabilities, it will transform these professions.

Level 6: Goal setting and planning

At the moment, the cognitive abilities required for goal-setting and strategic planning remain a uniquely human domain. This level involves complex, abstract thinking and a deep understanding of long-term objectives and the nuances of various strategies. If AI progresses to this stage, it would mark a significant leap in its capabilities.

Such AI could understand and interpret high-level project requirements in fields like software development, fundamentally altering the roles of system analysts and senior developers. These professionals, who excel in breaking down complex concepts into actionable plans, might find their roles evolving or becoming redundant as AI takes on these responsibilities.

Moreover, the impact would extend beyond technical fields. Strategy consultants and business executives, whose expertise lies in abstract reasoning, long-term planning, and self-awareness, would also face substantial changes. Their roles, which involve crafting strategies based on deep market understanding and foresight, could be significantly supplemented or even replaced by advanced AI capable of analyzing vast datasets to make strategic decisions and set goals. This development would revolutionize how businesses operate and challenge our understanding of human intuition and strategic thinking in the professional world.

Level 7: Consciousness and agency

At this apex of AI development, the achievement of consciousness and agency represents the most profound theoretical leap, particularly in fields requiring deep cognitive and emotional understanding. Currently, AI cannot experience consciousness and does not possess intrinsic motivation, rendering it incapable of fully autonomous complex decision-making and creative innovation.

In software development, this advancement could lead to the automation of the entire software lifecycle, from design to deployment. It would enable AI to execute tasks, understand, and innovate in ways currently exclusive to human developers. This would drastically reshape the industry, potentially reducing the need for human intervention in all but the most creative or ethically nuanced aspects.

Similarly, professions like artists, researchers, and novelists, which thrive on the unique human capacities for consciousness, deep curiosity, and intrinsic motivation, would undergo a seismic shift. AI with consciousness could create art that resonates on a human level, conduct research with an understanding of complex variables and human implications, and write novels that capture the intricacies of human experience and emotion. This development would challenge our understanding of creativity and originality, questioning what it means to be human in a world where machines can replicate and surpass our most intrinsic qualities. The ethical, philosophical, and practical implications of such a development are vast, opening up debates about the nature of consciousness, the value of human work, and the limits of AI.

A dynamic planning approach

Given the rapid evolution of AI capabilities, businesses need a dynamic planning approach that anticipates both the current state and potential future trajectories of development.

At the core of the approach is a level-specific impact assessment. This involves a detailed analysis of how each AI level could impact various aspects of a business, identifying both challenges and opportunities. For instance, at the basic information processing level, a company should evaluate how AI can automate and streamline data management. In contrast, at more advanced levels, the focus might shift to AI’s role in decision-making and strategic planning. This progression planning enables a business to stay agile, adapting its strategies as AI technologies evolve.

AI brain
“Focused research and development efforts at each AI level are also essential, enabling businesses to stay at the forefront of AI technology and leverage its capabilities effectively.”

Future scenario development is another crucial aspect. Businesses should create scenarios for each AI level, envisioning how advancements might influence operations, customer relations, and market positioning. These scenarios help in crafting dynamic response strategies that are flexible enough to adjust to the changing AI landscape, ensuring businesses anticipate technological shifts.

A critical element in this planning approach is the evolution of skills and the workforce. As AI capabilities grow, the demand for specific skills will shift. Businesses must forecast the skills that will be crucial at each AI level and invest in training and developing their workforce accordingly. Moreover, the collaboration models between AI and human workers need to be tailored to each level, maximizing efficiency and harnessing the unique strengths of both human and artificial intelligence.

Integrating ethical considerations and governance across all levels of AI development is also vital. Each AI level brings unique ethical challenges and implications, and businesses must establish guidelines that address these concerns. This includes implementing governance structures that evolve with AI advancements, ensuring compliance with ethical standards and regulations.

Technological infrastructure is another pillar of this approach. Businesses must invest in infrastructure that can support AI applications across different levels of development, ensuring scalability and flexibility. Focused research and development efforts at each AI level are also essential, enabling businesses to stay at the forefront of AI technology and leverage its capabilities effectively.

Continuous monitoring and level-based adaptation form the backbone of this approach. Businesses need to keep a vigilant eye on the advancements in AI technology, tracking progress through the seven levels. Regular feedback and adjustment mechanisms are crucial for assessing the effectiveness of AI strategies and making necessary adjustments.

Stakeholder engagement and communication, tailored to each AI level, are indispensable. Internally, this involves educating and informing employees about AI’s implications and how it affects their work. Externally, collaborating with industry partners, customers, and suppliers ensures a comprehensive understanding of AI developments and opportunities at each level.

In implementing this approach, businesses should begin by assessing the current level of AI capability relevant to their industry and business. This assessment forms the basis for level-by-level strategy workshops, where potential impacts are discussed, and strategies are formulated. Creating a cross-functional AI task force ensures a holistic approach to AI integration across the business. Regular review and update sessions are essential to keep strategies relevant and effective as AI progresses through the levels.

How to work with an uncertain timeline

It’s important to remember that the timeline for the development of AI remains uncertain. Progress will continue to be influenced by numerous factors, including technological breakthroughs, ethical considerations, societal acceptance, and legal regulations. Despite the uncertainties, understanding the potential trajectory of AI’s capabilities can help us prepare for a future where AI plays an increasingly integral role in our lives and work.

By deeply integrating the seven levels of AI capability into their planning, business executives can create a dynamic, anticipatory model that responds to current AI developments and proactively prepares for future advancements. This approach ensures that businesses remain agile and forward-thinking, ready to harness the full potential of AI while navigating the complexities and opportunities of a rapidly changing technological landscape.


Michael Watkins - IMD Professor

Michael D. Watkins

Professor of Leadership and Organizational Change at IMD

Michael D Watkins is Professor of Leadership and Organizational Change at IMD, and author of The First 90 Days, Master Your Next Move, Predictable Surprises, and 12 other books on leadership and negotiation. His book, The Six Disciplines of Strategic Thinking, explores how executives can learn to think strategically and lead their organizations into the future. A Thinkers 50-ranked management influencer and recognized expert in his field, his work features in HBR Guides and HBR’s 10 Must Reads on leadership, teams, strategic initiatives, and new managers. Over the past 20 years, he has used his First 90 Days® methodology to help leaders make successful transitions, both in his teaching at IMD, INSEAD, and Harvard Business School, where he gained his PhD in decision sciences, as well as through his private consultancy practice Genesis Advisers. At IMD, he directs the First 90 Days open program for leaders taking on challenging new roles and co-directs the Transition to Business Leadership (TBL) executive program for future enterprise leaders.


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