Will AI Substantially Reshape Jobs in 2026?

Will AI Substantially Reshape Jobs in 2026? Over the next two to three years, 50% to 55% of jobs will be reshaped by AI. Boston Consulting Group in its analytical research made to that conclusion .

Key Takeaways

Task automation doesn’t equal job loss. Most roles will remain—but will change substantially.

• • Our microeconomic model reveals that over the next two to three years, 50% to 55% of jobs in the US will be reshaped by AI.

• • Workforce strategy cannot sit downstream of automation. It must be embedded in strategy.

• • For CEOs, the imperative is to focus on achieving the right balance of automation, upskilling, and deliberate talent planning

Amplified Roles. When AI augments human capabilities and demand expands, employment may remain stable or grow. Humans remain central to value creation, and wage inflation may develop as higher productivity increases competition for skilled talent. This type of role represents 5% of current jobs.

Software engineering falls under this category. (For a “what if” scenario, see “What Would It Take for Software Engineering to Move from an Amplified to a Divergent Role?”) So do many lawyers, particularly those in advisory and judgment-intensive areas of law. Investment in legal tech startups, like Harvey AI, reached record-high levels in 2025, spurring significant discussion of how AI will reshape this field. As AI accelerates research, drafting, and case preparation, legal services could become more accessible. In domains where unmet demand exists, such as compliance, regulatory review, and contract management, lower costs may increase the volume of legal work. Senior legal judgment would remain central, with AI amplifying professional capabilities.

Rebalanced Roles. When AI augments work but demand is bounded, headcount may remain steady while roles are redesigned. Routine tasks automate while more complex responsibilities expand. As work shifts toward higher-value activities, skill requirements rise and upskilling becomes essential. We estimate that 14% of current jobs fall under this category.

One example is content marketing, where demand is bounded by marketing budgets and strategic priorities. At the same time, with the fragmentation of audiences and with influencers and large language models reshaping customer journeys, brands need more, not less, content delivered in a more targeted way to the right audience at the right time. Marketer roles—instead of being broken up by channel, as they are now—will expand, with marketers and creative directors becoming omnichannel specialists who can think through end-to-end campaigns. Academic research is a similar case. Demand is constrained by institutional funding, but with increased productivity driven by agentic AI, the need increases for hypothesis formation, data interpretation, and scholarly judgment—which remain the province of humans.

Divergent Roles. Where AI substitutes for human tasks but demand remains expandable, the effect on employment becomes uneven; our analysis suggests that this applies to 12% of current jobs. In these roles, entry-level and junior positions are more exposed to automation in the short term. A sizable portion of the structured tasks traditionally performed at these levels are among the first to be automated, which means there will be some roles lost in this category. At the same time, responsibilities at senior levels persist and may grow to include higher volumes of work as demand expands. This creates a structural tension. The roles that remain require contextual judgment, oversight, and coordination skills that are typically built through on-the-job experience. This is a clear example of a scenario where the numbers alone can’t tell the full story.

Insurance sales agents fall into this category. AI automates routine activities such as lead qualification, quote generation, and policy comparisons—tasks often handled by entry-level employees or sales assistants, especially for standardized products such as auto or basic life insurance. At the same time, significant protection gaps remain, particularly in broader life insurance coverage and among small businesses that remain underinsured. By lowering distribution and servicing costs, AI allows insurers to reach previously underserved customers, expanding overall market participation and unlocking latent demand. As a result, some routine roles decline, while others shift toward higher-value activities such as policy advisory for more complex products (commercial insurance or annuities, for example) and long-term client relationship management.

IT support technicians provide a similar example. AI can resolve routine tickets and automate diagnostics. As digital infrastructure expands and systems grow more complex, the need for advanced troubleshooting and systems oversight may rise. Entry-level resolution work may decline unless workers can be quickly upskilled, while higher-skilled technical coordination roles persist or grow.

Substituted Roles. Only when demand is capped and AI directly substitutes for human workers in core tasks do roles fall into this category—an outcome that our analysis estimates affects about 12% of current jobs. Efficiency gains convert into net job losses, and downward wage pressure develops for the positions that remain.

Certain financial-analyst roles fall under this category (as do call center representatives). The volume of financial analysis is largely tied to existing reporting cycles, investment mandates, and internal decision processes. When AI automates routine modeling, data aggregation, and initial interpretation, the output does not expand proportionally. In this case, productivity gains are more likely to reduce the number of analysts required than to drive additional hiring.

It is important to note that substitution does not imply permanent exclusion from the workforce. As augmented, less automatable, and emerging roles expand elsewhere in the economy, workers in substitution-exposed positions may transition to such roles with targeted reskilling and mobility support. Starting to plan now for these redeployments—even though they will not happen immediately or all at once—will be critical, as transitions require time, coordination, and investment.

Enabled Roles. AI will become embedded in the day-to-day activities of 23% of jobs, reshaping how tasks are performed, but not fundamentally altering how work is structured. Workers will be expected to use AI to improve efficiency, accuracy, and decision making. While routine elements may be streamlined, core responsibilities—often involving the physical presence of a human worker, interpersonal interactions, or domain-specific expertise—will remain human-led. As AI becomes a baseline capability, skill requirements will rise, making continuous upskilling essential.

Clinical assistants and lab technicians illustrate this dynamic. Their work remains hands-on or patient facing, but AI increasingly supports documentation, workflow coordination, and aspects of diagnostic analysis. For example, clinical assistants may use AI for real-time note taking and patient intake, and lab technicians can leverage AI to interpret test results or flag anomalies. Over time, effective use of these AI tools becomes part of the role, raising expectations for productivity and accuracy without fundamentally changing its nature.

Limited-Exposure Roles. In the remaining share of lower-automation jobs—34% of current jobs—both the technical feasibility of automation and the scope for AI-driven productivity gains remain limited. (Our analysis does not account for potential future breakthroughs in technologies such as humanoid robotics.) Work is often highly contextual, relationship driven, or dependent on a physical human presence in ways that are difficult to codify or scale through AI.As a result, these roles are less likely to be significantly reshaped in the near term.

Physicians and teachers are illustrative. Both require the ability to form complex judgments, engage in interpersonal interactions, and adapt in real time to individual needs. AI may assist in limited, task-specific ways, but it will not meaningfully reshape either role. The core of the work—patient care and instruction—will remain fundamentally human.

The Scale of Impact. Across all six segments, 10% to 15% of jobs are vulnerable to elimination; these are the jobs in the substituted and divergent categories, weighted by their automation potential and adjusted for demand expansion. At the same time, 50% to 55% will be reshaped; these are the jobs in the amplified, rebalanced, and enabled categories, as well as the remaining portions of jobs in the divergent and substituted categories where work is not vulnerable to elimination. The remaining roles are largely lacking in significant automation potential in the near term. Meanwhile, an unspecified but meaningful number of jobs are likely to be created as new demand emerges and new capabilities are enabled by AI.

The Side Effects of Agentic AI Role Transformation. First, a key challenge in the AI era will not be simply the number of jobs affected, but how quickly workers can be upskilled and redeployed into redesigned roles. Absorbing a workforce shift of this magnitude will therefore require deliberate investment in reskilling, mobility, and capability building, making workforce development a leadership priority.

In the near term, some organizations may respond by slowing or freezing hiring as they reassess workforce needs and push for higher productivity with AI—creating additional pressure on existing employees to adapt and upskill. In some cases, these measures are being taken ahead of realized AI impacts and should be viewed as a temporary phase rather than a new status quo. Companies whose products and services are in high demand will continue to hire over time as productivity gains translate into growth.

Second, as AI absorbs much of the routine work that has historically justified large entry-level hiring cohorts, fewer execution-focused positions will be required. In the short term, the volume of entry-level jobs may decrease. Some companies will still choose to invest in junior workers in order to create a talent pipeline, while others may cut back. Over time, though, these jobs will be redefined and filled by candidates prepared to take on higher-order tasks, such as supervising AI outputs, managing exceptions, and contributing to more complex problem solving earlier in their careers.

In this environment, AI fluency may become an increasingly important complement to tenure in assessing readiness for augmented responsibilities, with companies prioritizing candidates who proactively adopt AI tools and rethink how work is done. In some cases, this will create opportunities for junior candidates—including recent graduates—who may be more familiar with AI than workers with more experience on the job.

Third, skill thresholds will rise. Redesigned roles will demand that employees demonstrate greater expertise, oversight, and accountability, increasing the premium on domain knowledge and sound judgment. As Exhibit 7 illustrates, the more durable roles tend to require higher credentials and greater seniority. Moving into these roles requires new capabilities and, in many cases, additional training or formal credentials. Even where total employment remains steady, these higher thresholds can create barriers to entry and friction in workforce transitions.

What CEOs Need to Do Now to Prepare

There are four critical starting points for leaders.

Embed workforce strategy into competitive strategy. AI will reshape competitive dynamics and enable new business models. Companies that respond by accelerating innovation, redesigning offerings, or reconfiguring their delivery of value at the enterprise, business line, product, and functional levels will fundamentally change the nature of work inside their organizations. Workforce strategy cannot sit downstream of automation. It must be embedded in strategic planning. Remaking the workforce is a competitive imperative.

Leaders should avoid reactive actions on cost driven by headlines or peer behavior. Workforce decisions must reflect the specific mix of automatable and augmentable work within the specific enterprise. Copying another company’s reductions without understanding one’s own exposure risks undermines both productivity and long-term competitiveness.

Refocus automation on redesign, not just cost reduction. Agentic AI isn’t a blunt instrument. Different roles require different approaches. Sometimes cost is a dominant factor. At other times, other priorities dominate, such as speed of work and quality. Cost actions—such as headcount freezes or reductions—are visible and straightforward, with explicit OpEx impact. But when AI drives productivity rather than cuts, ROI becomes harder to define and defend.

In these situations, leaders must redesign workflows and rethink how performance is measured. Capturing value requires new, domain-specific KPIs that link productivity gains to tangible outcomes, such as higher revenue per FTE, more product shipped, or stronger customer impact. Task turnover within a role will be a measure of how fast roles can evolve toward higher-value work. Realizing AI’s upside often means growing into the improvement—translating productivity gains into financial results through budgeting and workforce planning.