Move people out of the middle - How AI will reshape the workforce 2025-2035

The Great Resignation was nothing. Prepare for the Great Re-allocation.

Move people out of the middle - AI reshapes the workforce

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In this Issue

🔁 From Great Resignation to Great Reallocation
The job-hopping era is over—AI is now reallocating capital, talent, and opportunity. Leaders must prepare for a workforce realignment, not just attrition.

🤖 AI isn’t just tech—it’s a new economic force
AI is shifting job value away from operations (execution) toward planning (innovation) and outcomes (oversight, governance). Strategic and creative roles will surge while execution-based roles shrink.

📉 Operations shrink, bookends expand
By 2035:

• Operations drop from 72% ➡️ 45%
• Planning & Ideation grow from 20% ➡️ 35%
• Outcomes Measurement jump from 8% ➡️ 20%

🎯 New talent priorities for orgs
Upskill broadly in AI, strategy, compliance & governance. Redesign job families. Focus hiring and development on roles that complement AI, not compete with it.

📚 Future of learning & hiring is in flux
College ROI is falling. Real-time skills matter more than resumes. New learning models—prompt-sharing, tiger teams, AI-human co-learning—will redefine how we train and source talent.

Opinions expressed are those of the individuals and do not reflect the official positions of companies or organizations those individuals may be affiliated with. Not financial, investment or legal advice, and no offers for securities or investment opportunities are intended. Mentions should not be construed as endorsements. Authors or guests may hold assets discussed or may have interests in companies mentioned.

[org strategy]

The Great Resignation was nothing. Prepare for the Great Re-allocation.

The Great Resignation took many leaders by surprise, as workers job-hopped in search of better pay, flexibility, and purpose. That wave is over - welcome to the Great Stay. The prospect of pay raises has been erased by salary deflation.

A much larger shift has begun.

We could call it the Great Reallocation. The Great Resignation caught organizations by surprise but most were able to adjust. The Great Reallocation won't be so forgiving. The time to start preparing is now.

The AI & Jobs conversation: What's getting overlooked

You're probably no stranger to the constant reports on how AI will impact jobs. Most credible economic forecasts indicate a significant restructuring of workforce composition by 2030–2035 due to AI.

A sampling:

• Deloitte - AI at a crossroads: Building trust: Organizations will need massive growth in AI governance and risk management roles.
• Oxford - How Robots Change the World: 20 million manufacturing jobs will be lost to robots by 2030.
• PWC - AI's Impact on jobs, in five stats: 4.8x productivity growth in business operations with 27% drop in hiring.
• WSJ - Tech Jobs Have Dried Up - and Arent' Coming Back Soon - drop in hiring of entry level tech workers.
• HBR - How Gen AI Could Change the Value of Expertise: companies must reshape their orgs and "rethink their talent-management strategies."
• Brookings Study - Generative AI, the American worker, and the future of work: Generative AI will disrupt middle to high pay professions.
• McKinsey Digital Superagency in the workplace: Empowering people to unlock AI’s full potential: Employees need to invest more in their employees.
• IMF - AI Will Transform the Global Economy: "In advanced economies, about 60 percent of jobs may be impacted by AI."
• CMU - National Academies’ report on AI and the future of work: AI is "reshaping demand for different types of human expertise."
• NC Depart of Commerce - Insights on Generative AI and the Future of Work: AI will reshape white-collar professions.
• ADP Research - Most workers think AI will affect their jobs. They disagree on how: urgent need for upskilling and organizational re-design.

The conversation about AI and jobs is often framed in simple terms: Will AI replace jobs? Will it create new ones? Will it all balance out? But this thinking misses crucial details. AI is not just a technology - it's a new operating system, investment theme and increasingly an economic force.

If you’re responsible for talent strategy, hiring, learning & upskilling, or organizational design, now is the time to get ahead of the curve. AI isn’t just making certain jobs obsolete—it’s shifting the entire landscape:

• Where capital is being re-allocated
• Where budgets will grow
• Where talent should be placed,
• Where the best opportunities are most likely to emerge

To prepare, we must move beyond the current approach of surveying how employers and employees expect AI to change things. We need a more complete view of how AI investment, innovation and adoption are actively reshaping the workforce.

The Stakes for Winners vs Losers

This 2024 McKinsey study Rewired and running ahead started to reveal the extreme differences in growth rates between those who lead with AI vs those who lag:

This is a retrospective view but current observations across multiple organizations support the same conclusions: Orgs that lack AI Planning skills spend months and $millions with no luck - in sharp contrast to the rapid value delivery of teams that are strong in four critical AI Planning skills, as described in our paid content further below.

2 ways to learn and prepare this week:

1. S3T Free Edition: understand the key signals & trends: We break down the latest data and insights on AI’s workforce impact, highlighting where shifts are already happening and what they mean for your career and organization.
2. S3T Paid Edition: Take effective action for career and talent strategies: For paid subscribers, we go deeper—mapping out the specific roles, industries, and strategic moves leaders should start making now, to future-proof their teams and investments. We analyze how AI-driven innovation is shaping workforce trends and where proactive leaders should focus now to stay ahead.

👉 Upgrade to full access with a free 30 day trial to get the strategic insights you need.

🔑"Move people out of the middle" - How AI will reshape the workforce 2025-2035

AI will reshape economies by reducing human employment in operations & execution roles but expanding human workloads innovation and oversight/governance. These expanding workloads will likely cause growth in job families in Planning & innovation functions as well as Outcomes measurement functions.

• Business Operations will decline by 27%, as automation takes over blue collar and white collar tasks.
• Strategic & creative roles will grow from 20% to 35%, fueling research, innovation, and AI-empowered decision-making.
• AI governance and compliance will grow sharply, ensuring AI remains ethical, secure, and aligned with human needs.

Companies that upskill their workforce in AI oversight, risk management, and strategic innovation will thrive.

To understand the unique shift that AI is driving, it's helpful to visualize the job market as 3 big segments: Planning, Operations and Outcomes...the before/during/after phases of business. These 3 segments are not the same size - they do not employ the same numbers of people. In today's economy the Operations segment is by far the largest, book-ended by the smaller segments of Planning and Outcomes.

The following section explains.

AI will reshape the workforce…

… the “book-ends” will expand as AI driven automation shrinks the middle:

📌 The Workforce in 2035

1. Business Operations roles will shrink significantly due to automation, falling from 72% of the workforce today to 45% by 2035. AI-driven automation will replace a substantial portion of execution-based jobs, particularly in manufacturing, logistics, customer service, and administrative support.
2. Planning & Ideation roles will nearly double, growing from 20% to 35%. AI dramatically lowers execution costs, allowing more resources to be allocated toward strategic thinking, innovation, and product development. As businesses can now execute ideas faster, more people will be needed to generate and manage these ideas.
3. Outcomes Measurement will experience explosive growth, expanding from 8% to 20% of the workforce. With AI creating output at speeds up to 10x faster than human workers, the need for compliance, security, audits, governance, and AI risk management will increase significantly. The regulatory and oversight workforce will have to scale proportionally to AI's output acceleration to maintain quality, fairness, and safety.

📊 Chart: Projected 2035 Workforce Distribution

🔹 Summary of the Shift:

Operations shrinks from 72% → 45% (due to automation). The human employment aspect of the 2035 economy will be driven more by governance & innovation than sheer execution. Operations will still be crucial, but AI will take over much of the manual execution burden.

Planning & Ideation nearly doubles from 20% → 35% (more human effort shifts to innovation).  This category will see significant expansion driven by:

• The need to address the higher due diligence workload associated with picking the right use cases, ensuring safe ethical use of AI.
• The addition of AI-enabled projects that were unfeasible with previous technologies.

Outcomes Measurement expands from 8% → 20% (AI oversight and compliance skyrockets). This jobs category will have to grow significantly, ensuring AI-driven productivity remains safe, compliant, and aligned with human values.

By 2035 the workforce is expected to be rebalanced: a smaller portion of the workforce in operational work, but a larger portion in the pre and post domains:

• “Planning & Ideation” (creative strategy, R&D, design, use case validation).
• “Outcomes Measurement” (human governance and oversight, quality assurance, compliance)

Now is the time to start preparing.

📌 What to do: Shift the Workforce to Growth Sectors and evolve your job families to include these new required AI roles.

Leaders who understand this shift will work to move workers "out of the shrinking middle" toward the expanding "book-end" segments of Planning and Outcomes.

Top priorities to start on right now:

📚 Upskill for AI & Strategy – Train workers in AI tools, data literacy,, strategic thinking, and change leadership so they can move into Planning & Ideation roles. Don't focus only on tech teams, include data operations, data governance, security, legal, HR and compliance teams.

🛡️ Expand AI Oversight Careers – Invest in compliance, security, and governance training to support the booming Outcomes Measurement sector.

🔄 Redefine Operational Roles – Transition middle-tier jobs into AI-augmented roles, focusing on managing, optimizing, and overseeing automation.

💼 Define New Roles - As shown in the chart below a number of new roles will emerge to address the risks and opportunities driven by AI adoption and normalization in the business sector. Now is the time to begin defining these roles so they can be reasonably mature when the organization needs them in order to achieve quarterly and strategic goals.

The chart below provides suggested starting points for thinking about roles that will become crucial over the next few years. Note that some of the roles in the Outcomes category will have "feedback loop" relationships with many of the Planning roles.

📌 What to expect: headwinds in education and research

• College is prohibitively expensive for most students. Rising tuition costs make higher education financially inaccessible for many. Student debt burdens continue to outpace wage growth, raising questions about ROI.
• High schools & universities struggle to stay relevant. The accelerating pace of technology is shrinking the shelf life of traditional curricula. Many institutions fail to equip students with modern, job-ready skills.
• Government research & education funding is declining. Political trends & rising government debt are leading to budget cuts for education & research. Public funding for universities, STEM initiatives, and innovation programs is shrinking.
• Death of the resume: Companies are realizing they need to validate skills in real-time, which makes the contents of resumes - whether degrees or past employment - less relevant.

In other words, it's increasingly about - what can you do right now, without the support of large budgets or programs, or long education pathways.

📌 What to explore: innovation in education and organizational design

As these traditional approaches of learning and career prep continue to age, new approaches will be required in order to supply the demand for AI-era workers. This raises several key questions:

• Will Team approaches to robotic and Generative AI interaction become a dominant way of learning? The practice of teaching and sharing prompt engineering offers an early glimpse of how education and learning could evolve. Likewise the rise of "tiger teams" - cross-disciplinary teams rapidly building new AI solutions in order to prove out use cases - shows another very promising form of active learning.
• Will online gaming spawn new worker guilds? 3D and AI tools are proliferating so rapidly in online gaming platforms, (Roblox's AI tools for example) one can't help but wonder things like:
  • Are online gaming communities the worker guilds of the future?
  • Can red-teaming and other critical processes be gamified?
  • Will Decentralized Autonomous Organizations (DAOs) merge with gaming communities to create the industrial engines of the future?
• Will companion learning between humans and AI actually scale? We already see examples of humans and AI learning together: Coders get faster/clearer about certain concepts, while the LLMs they're using get smarter thanks to the coder's feedback. Will this phenomenon scale enough to close skill and resource gaps?
• How will online learning (think Khan Academy etc) merge or evolve in the era of generative AI?

The Stakes for Winners vs Losers

This 2024 McKinsey study Rewired and running ahead started to reveal the extreme differential between those who lead with AI vs those who lag:

This is a retrospective view but current observations across multiple organizations support the same conclusions: Orgs that lack AI Planning skills spend months and $millions with no luck - in sharp contrast to the rapid value delivery of teams that are strong in four critical AI Planning skills, as described below.

Survival: 4 AI Planning skills every organization must build

• Data focused use case validation, backed by first-hand, detailed familiarity with key business problems and the data aspects that exacerbate process issues and/or offer solutions. Too often, AI projects and startups get launched before the stakeholders have any idea whether they actually have the data they need to make the idea work. When evaluating an AI use case, start with the data first - see if the data that you have rights to can possibly provide the insight or answer to the problem that needs to be solved.
• Strong AI tech development skills for rapid solution delivery in a secure scalable environment. Rapid build out of ideas goes hand-in-hand with making about those. Build a rough working version as fast as possible test it with people who know firsthand the problem to solve.
• Strong cross disciplinary collaboration on AI governance to quickly navigate issues with clear-eyed understanding of risks in play vs fear-based risk aversion for comfort's sake.
• Tech & analytics driven decision-making. Leadership culture that is comfortable learning from engineers and data SMEs not just management consultants will get to ROI faster than the rest. Picking the right wayfinders for guidance makes all the difference. Smart looking PowerPoint slides always were kind of a liability. Going forward, they’ll be downright dangerous.

Supporting Data on Projected Workforce Composition Changes

The following sources provide data and projections on AI's impact on workforce distribution, as well as a rationale for constructing the estimated shifts in workforce composition 2025-2035.

Looking at the big picture, most credible economic forecasts indicate a significant restructuring of workforce composition by 2030–2035 due to AI.

• This IBM Study based on broad c-suite industry survey's indicates significant workforce shifts. Routine operational roles (manufacturing, assembly, basic office support, etc.) will shrink as a percentage of total employment, while both high-skill creative roles and oversight/regulation roles will grow.
• Early World Economic Forum projections forecast structural shift of millions of jobs: by 2025, 85 million jobs displaced but 97 million new jobs created in fields like data analysis, software development, and new specialties (See Recession and Automation Changes Our Future of Work, World Economic Forum)
• Deloitte - AI at a crossroads: Building trust: Organizations will need massive growth in AI governance and risk management roles. "Organisations with mature AI governance frameworks report a 28 percent increase in staff using AI solutions, and have deployed AI in three additional areas of the business. These businesses achieve nearly 5 percentage points higher revenue growth compared to those with less established governance."
• Oxford - How Robots Change the World: 20 million manufacturing jobs will be lost to robots by 2030.
• PWC - AI's Impact on jobs, in five stats: Review of 500m job postings across 15 countries indicates 4.8x productivity growth in sectors most exposed to AI (business operations value creation), with 27% drop in hiring for these roles, but 3x growth and 25% wage premium in hiring for AI skills.
• WSJ - Tech Jobs Have Dried Up - and Arent' Coming Back Soon - companies are shifting focus toward developing AI and have slowed in their hiring of entry level tech workers.
• HBR - How Gen AI Could Change the Value of Expertise: "In the next few years, the better part of 50 million jobs will be affected" and this will force companies to "reshape their organizational structures and rethink their talent-management strategies."
• Brookings Study - Generative AI, the American worker, and the future of work: 30% of all workers could see at least 50% of their occupation’s tasks disrupted by generative AI, and unlike previous automation of blue collar processes, Generative AI will disrupt middle to high pay professions - many of which are involved in managerial decision-making and operational analysis.
• McKinsey Digital Superagency in the workplace: Empowering people to unlock AI’s full potential: Employees are adapting gen AI 3x faster than their leaders expect, and need to invest more in their employees - training them to succeed in an AI driven era - because skill gaps are slowing them down.
• IMF - AI Will Transform the Global Economy: "In advanced economies, about 60 percent of jobs may be impacted by AI."
• CMU - National Academies’ report on AI and the future of work: "AI will impact jobs by reshaping demand for different types of human expertise, making some types less valuable and others more valuable. This in turn can lead to changes in salaries and even to entirely new jobs requiring expertise people have not yet considered." AI will also change how people teach and learn.
• NC Department of Commerce - Insights on Generative AI and the Future of Work: Average AI Occupational Exposure (AIOE) scoring indicates that AI has high potential to reshape white-collar professions.

• ADP Research - Most workers think AI will affect their jobs. They disagree on how: Survey of 35k private sector workers in 18 countries reveals wide disparities in readiness and understanding of AI in the work force, and underscores need for more upskilling, and more intentional organizational re-design.

In summary, by 2035 the workforce is expected to be rebalanced: a smaller portion of the workforce in in hands-on operational work, but a larger portion in two key domains:

• “Planning & Ideation” (creative strategy, R&D, design, use case validation).
• “Outcomes Measurement” (human governance and oversight, quality assurance, compliance)

Detailed Notes: Calculations & assumptions for AI-driven automation and workforce distribution by 2035

The following sections provide the "homework" used to analyze available data and derive the estimated shifts in the workforce. Recommend careful study of these notes in order to construct your own independent point of view on how the coming changes impact your specific organization, investments or career path.

The Approach

1. Categorizing current allocations across 3 large groups: Planning (forward looking functions) Operations (execution, manufacturing, managing, delivering, supporting) and then Outcomes (auditing, measuring, analyzing, reporting on outcomes, and associated record keeping).
2. Using available sources to estimate the workforce allocation across these three groups. A larger share of the workforce is employed in the Operations group, with smaller shares employed in the Planning and Outcomes categories.
3. Evaluating the impact of AI and modern automation (including robotics) on these 3 categories.
   1. While blue collar jobs have already been experiencing disruption from automation, AI will also begin to disrupt white collar jobs, particularly in operations management and administration - likely reducing the human time spent on today's version of compliance, quality testing, and outcomes measurement.
   2. At the same time, in both the Planning and Outcomes jobs categories, new roles and expertise sets are emerging as AI reshapes the workforce. These are necessitated by the new kinds of threats and opportunities posed by AI, and will drive better opportunities for workers and graduates with the right skills.

The following notes provide detail on how the allocation shifts across the Plan/Operate/Outcome job categories were estimated.

1. Business Operations Workforce Reduction

Automation Displacement Trends: Major studies forecast significant displacement of routine operational jobs by the 2030s as AI and robotics advance.

• McKinsey estimates that 15% (midpoint scenario) of current work hours globally could be automated by 2030 (Jobs of the future: Jobs lost, jobs gained | McKinsey), potentially displacing 400–800 million workers (up to one-third of the global workforce) by 2030 (800 million jobs could be lost to AI and Automation by 2030 - Medium).
• PwC similarly projects that by the mid-2030s automation could impact 30–38% of jobs in advanced economies (about 30% in the UK, ~38% in the US) (PwC asks: Will robots steal our jobs?). These impacts will be uneven across sectors and roles, hitting certain operational fields hardest.

Manufacturing & Logistics: Roles involving physical, repetitive tasks in predictable environments are most vulnerable (Jobs of the future: Jobs lost, jobs gained | McKinsey).

• Oxford Economics research finds robots could eliminate 20 million manufacturing jobs by 2030, roughly 8.5% of the global manufacturing workforce (Robots May Displace 20 Million Manufacturing Jobs by 2030 | IndustryWeek). This “robot revolution” disproportionately affects routine factory work and warehousing/logistics tasks (e.g. assembly line operations, machine operators, warehouse pickers).
• Many displaced manufacturing workers have historically shifted into transport, construction, maintenance, or office support roles – yet those categories (e.g. trucking, shipping, and clerical work) are themselves among the most vulnerable to automation in the next decade (Robots May Displace 20 Million Manufacturing Jobs by 2030 | IndustryWeek). For example, autonomous vehicles and warehouse robots threaten a large share of logistics jobs, and self-checkout or AI-driven kiosks can reduce service roles in retail and food service.

Service & Administrative Support: White-collar routine jobs and administrative support functions are also at high risk. AI-driven software can automate data entry, bookkeeping, basic customer service, and clerical tasks.

• The World Economic Forum’s Future of Jobs analysis projects a sharp decline in roles like data entry clerks, accounting and payroll clerks, and administrative assistants by the mid-2020s as automation accelerates (Recession and Automation Changes Our Future of Work, But There are Jobs Coming, Report Says > Press releases | World Economic Forum).
• Previously the WEF projected that by 2025, 85 million global jobs could be displaced by automation (particularly in these back-office and service roles), according to WEF (Recession and Automation Changes Our Future of Work, But There are Jobs Coming, Report Says > Press releases | World Economic Forum) (Recession and Automation Changes Our Future of Work, But There are Jobs Coming, Report Says > Press releases | World Economic Forum). There are currently no known measures quantifying how this impact has been realized.
• Looking toward 2035, multiple forecasts converge on roughly a quarter to one-third of existing jobs being phased out by AI/robotics. For instance, one analysis suggests automation might substitute for 25–30% of the workforce by 2035 in advanced economies (far higher than in lower-wage countries) (Future of Workplaces With AI | A3Logics Blog) (Future of Workplaces With AI | A3Logics Blog). In sum, occupations centered on routine production, predictable service tasks, transportation, and office support face double-digit percentage declines in employment share due to AI-driven efficiency gains.

The consensus is that job displacement on the order of 20–30% of roles (hundreds of millions of jobs globally) is plausible by early-to-mid 2030s (AI could replace 2.4 million jobs in US by 2030| Forrester's report | Digital Watch Observatory) (Future of Workplaces With AI | A3Logics Blog), with manufacturing, logistics, and clerical sectors seeing some of the largest contractions.

2. Outcomes Measurement Workforce Growth (Governance, Compliance, Testing)

Rationale on 10x Output and Oversight Needs: If AI allows outputs to be generated at 10× (or higher) speed than human workers, a critical question is whether oversight and quality-control functions must scale correspondingly. In principle, workforce devoted to governance, compliance, validation, and testing should grow in proportion to AI output to ensure standards are maintained.

If one AI system can do the work of 10 people, you might need roughly 10 times the auditing/testing effort to review its results, absent further automation of oversight itself. We see examples of output being higher that 10x in come cases (ie a generative AI tool can produce more than 10x the amount of code, documentation, music that a human individual or team could. So 10x is accepted as a reasonable figure for estimating purposes.

Rationale and Industry Perspectives: The need for expanded oversight is highlighted by experts concerned with AI accuracy and risks. For example, Deloitte analysts note that if human fact-checking of AI outputs becomes very labor-intensive, it can erode the productivity gains of using AI (Responsible Use of Generative AI | Deloitte US ). This implies companies must invest in smarter validation processes (potentially including AI-driven QA tools or more human reviewers) to keep up with the flood of AI-generated content/code.

• Many organizations are already increasing roles in AI governance and risk management; IBM’s 2023 report found about 40% of their workforce will require reskilling for AI integration (including adapting to new oversight and coordination tasks) (AI could replace 2.4 million jobs in US by 2030| Forrester's report | Digital Watch Observatory).
• Similarly, the World Economic Forum observes a “surge in demand for trained professionals to address AI governance needs,” as responsible AI adoption becomes a priority (AI governance trends: How regulation, collaboration, and skills demand are shaping the industry | World Economic Forum).
• New job titles like AI auditors, ethicists, compliance officers, and quality analysts are emerging to govern AI systems and their outputs. The AI governance market itself is growing ~45% annually (AI Governance Market Share, Forecast - MarketsandMarkets), reflecting how organizations are ramping up oversight infrastructure.

In practical terms, as AI output scales, companies are likely to increase headcount (and/or tools) in testing, validation, and regulatory compliance to match it.

• For instance, software firms using generative AI for coding are hiring more testers and security reviewers to catch AI-introduced bugs and vulnerabilities.
• Financial institutions deploying AI decision systems are expanding compliance teams to audit algorithmic decisions for fairness and accuracy. The equation above underscores a baseline expectation: a 10× jump in output could warrant on the order of 10× expansion in oversight effort to maintain quality and trust.
• Some oversight tasks will themselves be augmented by AI, but industry consensus is that human judgment remains essential for governance. As one analysis put it, AI will “reshape more jobs than it replaces” (AI could replace 2.4 million jobs in US by 2030| Forrester's report | Digital Watch Observatory) – many workers will shift into new support and supervisory roles that ensure the AI’s supercharged output is correct, safe, and aligned with goals.

3. Planning & Ideation Workforce Growth

Shifting Workforce Share to Planning: As AI reduces human constraints in execution, the balance of work is expected to tilt more toward planning, strategy, and innovation. Currently, “Business Operations” roles make up about 63.3% of the workforce versus 18.1% in “Planning & Ideation” functions (a roughly 3.5:1 ratio of operational jobs to planning jobs). With AI taking over a large portion of execution tasks, that ratio is poised to narrow.

We can model this shift with a scaling relationship: if AI improves execution efficiency by a factor $F$ (i.e. one worker can oversee what $F$ workers used to execute), then the relative share of planning roles could increase by roughly that factor. For example, suppose $F = 2$ (AI doubles execution capacity per worker) – the planning-to-operations ratio might also roughly double in favor of planning. We can generally expect planning workforce grows proportional to execution speed-up while operations workforce shrinks proportional to it.

In less abstract terms, as AI makes execution cheaper and faster, a greater portion of human talent and time can be devoted to creative ideation, strategy, and planning new ventures. Freed from many manual tasks, organizations can tackle more projects and explore more innovations simultaneously – and do better at vetting and selecting use cases - but that requires more planners/designers to conceive and direct those projects.

We already see a trend toward this: non-routine cognitive jobs (creative, strategic roles) have been growing as a share of employment for decades, and this is expected to accelerate. The World Economic Forum notes that by mid-2020s, “analytical thinking, creativity and flexibility” are among the most in-demand skills (Recession and Automation Changes Our Future of Work, But There are Jobs Coming, Report Says > Press releases | World Economic Forum), reflecting an emphasis on human imagination and problem-solving. Many forecasts anticipate new roles in research, product design, strategy, and innovation management to proliferate as automation handles execution. McKinsey, for instance, projects that a sizable chunk (perhaps 8–9% of the workforce by 2030) will be employed in job titles that didn’t exist before – largely in fields driven by new technology and innovation (Jobs of the future: Jobs lost, jobs gained | McKinsey).

Proposed Scaling of Planning Share: Using the current 63.3% vs 18.1% split as a baseline, consider a scenario by 2035 where AI automation has, say, cut the needed Operations workforce roughly in half (through productivity gains and task automation). If total employment remains stable, the freed 30+ percentage points can be reallocated to other functions. Even if only a portion of that goes into planning/ideation roles (with the rest to outcome oversight or entirely new fields), the planning share could increase significantly – potentially on the order of 1.5× to 2× its current level. This implies planning/creative roles rising to perhaps 25–30% (or more) of the workforce, while pure execution roles correspondingly decline towards the 50% range or lower.

Even with more conservative assumptions, a substantial shift is expected – e.g. a 1.5× growth in planning share would make it ~27% of the workforce vs ~45% in operations (bringing the ratio to ~1:1.7, compared to 1:3.5 today).

Feasibility and Supporting Insights: Such redistribution is supported by the notion that innovation becomes more feasible when execution is abundant. As AI lowers the cost and time of doing tasks, companies can undertake more ambitious projects, requiring more human creativity to guide them.

Researchers have observed that AI tools “eliminate repetitive tasks, allowing creators to focus on higher-value creative decisions.” (Generative AI in Creative Work: Shocking Study - Magai) This suggests human workers will move upstream in the value chain – spending more effort on idea generation, planning and overseeing AI-driven execution.

Moreover, entirely new planning-oriented roles are emerging (e.g. AI strategists, prompt engineers, AI project managers (Ethics and security in AI: emerging job profiles for a sustainable future | PALTRON) (Ethics and security in AI: emerging job profiles for a sustainable future | PALTRON)) that capitalize on AI capabilities to drive innovation strategy. The net result envisioned by 2035 is a workforce composition with relatively fewer people “on the factory floor” or performing routine service tasks, and many more in roles that plan, direct, and dream up what those automated systems execute. Economic forecasts generally agree that while automation will shrink some job categories, it will also create demand in others – notably in tech development, data analytics, creative industries, and business strategy. By 2035, the share of jobs devoted to higher-level planning and ideation is expected to expand markedly, potentially approaching one-quarter to one-third of the workforce (up from under one-fifth today), commensurate with AI’s acceleration of execution and an economy increasingly driven by continuous innovation (Future of Workplaces With AI | A3Logics Blog).