9 Trends Shaping Work in 2026 and Beyond

CEO expectations for AI-driven growth remain high in 2026—at the same time their workforces are grappling with the more sober reality of current AI performance. Gartner research finds that only one in 50 AI investments deliver transformational value, and only one in five delivers any measurable return on investment.

In 2026, executive teams will have to navigate this difficult tension: delivering on today’s growth targets while nurturing a workforce capable of driving future value amid AI transformation. To do that, they must prioritize:

• Navigating new realities of the AI era: The basic tenets of the employment deal are in flux, propelled by rapid technological advancement, economic volatility, and political uncertainty.
• Mitigating emerging threats to organizational performance: The rapidly evolving AI landscape makes it difficult to predict and prioritize the most significant threats the organization will face.
• Seizing opportunities for a blended human-machine workforce: As organizations look for new and innovative ways to generate value from their AI investments, the evolving human-machine workforce will create new opportunities for differentiation.

To help leaders prepare for the unexpected—or underappreciated—risks that organizations may encounter in 2026, Gartner offers the following nine predictions.

1. AI layoffs outpace AI productivity gains.

In 2025, we witnessed large, high-profile layoffs attributed—directly or indirectly—to AI. But in reality, people aren’t actually losing their jobs to better-performing AI, at least not yet. Gartner analysis finds that less than 1% of layoffs in the first half of 2025 were the result of AI increasing employees’ productivity.

Instead, many of these CEOs are making workforce decisions in anticipation of AI returns that may never come. They’re embracing anticipated efficiency gains, assuming that a shrinking human workforce indicates, or even helps foster, innovation and productivity.

In 2026, these organizations must navigate the painful choices that come with reducing headcount, which will be even more difficult without proven AI-driven productivity gains. If the pace of AI productivity lags behind the work organizations need to get done now, they will have to rehire talent they prematurely laid off, often at greater cost, to meet their targets.

As organizations navigate this messy middle between the onset of the AI era and the technology reaching a level of maturity where it can deliver on its promise, HR will be responsible for ensuring that the workforce’s size, structure, and skills are able to support the organization’s current core priorities without sacrificing its ability to achieve an AI-infused business model.

2. Culture dissonance holds organizations back from performance goals.

In 2025, a few organizations grabbed headlines by publicly embracing a more hard-edged culture characterized by longer hours, more aggressive performance management, and reduced employee flexibility.

But while it’s true that CEOs are seeking a culture that “performs,” in an AI-driven world, these large shifts are the exception, not the rule. Instead, most organizations are more subtly but consistently expecting more from employees without offering more in return. And employees are noticing.

This top-down performance pressure is leading to profound cultural dissonance in which many organizations’ stated culture no longer reflects the realities of employees’ everyday experience.

This cultural dissonance has real consequences, even in an employer-friendly labor market, including lower performance, declining engagement, and degraded employer brand, all of which can make it harder to deliver on the CEO’s performance goals.

In 2026, the most successful organizations will be forthright, both internally and externally, about the reality of their culture and what they expect from employees (e.g. hours, output, and location). Culture can, and should, evolve to meet the realities of the current work environment, but employees must know what they’re signing up for.

3. AI takes a toll on employees’ mental fitness.

Organizations are dedicating enormous effort into determining how work will change because of AI, but they’re devoting far less energy into understanding how people themselves will change. Gen AI adoption in organizations has reached near ubiquity. At the same time, the evidence is mounting of emotional and cognitive damage that can result from prolonged gen AI use, from cognitive atrophy to AI psychosis.

In 2026, AI’s impact on employees’ mental fitness—their emotional, psychological, and cognitive well-being will become an urgent problem in the workplace. This is something organizations are currently largely ignoring: A recent Gartner survey found that 91% of CIOs and IT leaders said their organizations dedicate little to no time scanning for behavioral byproducts of AI use. This gap will lead to well-being, performance, and productivity costs.

Companies will also be vulnerable to legal actions that are not currently included in most risk assessments—for example, a lawsuit from a grieving family after AI encourages an employee towards destructive behavior, or an employee contesting their dismissal for inappropriate behavior after they followed guidance from an organization-provided tool. In cases like these, organizations will be asked to face questions of who is responsible for the actions employees take after using employer-provided AI tools.

As AI becomes even more deeply embedded in daily workflows, the most successful organizations will work to mitigate the psychological and cognitive risks of sustained AI use before these costs undermine both employee well-being and organizational performance.

4. AI workslop becomes a top productivity drain.

In pursuit of ever greater levels of employee productivity, organizations are encouraging—and, in some cases, mandating—the use of AI tools. Unfortunately, in doing so, these same organizations have inadvertently incentivized the proliferation of “workslop,” or quickly-produced but low-quality work generated by or with AI that is riddled with errors and adds minimal or negative value.

Workslop is causing significant pain: In one study, employees reported spending an average of nearly two hours dealing with each case of workslop they encounter. This is a key reason why many organizations continue to struggle to realize any financial value from their AI investments.

Throughout 2026, ever-growing pressure to use AI will combine with rising expectations for individual employee efficiency, leading to even more workslop. Employers who have invested heavily in AI but have not prioritized investing in AI change management will find that their employees are using AI more when told to and producing more output, but that output is low quality or inaccurate.

In contrast, organizations that focus their AI investments toward substantively addressing employees’ biggest pain points will see lower rates of adoption than those that mandate employee AI use, but higher-quality work and higher financial returns from their AI investments.

5. Forward-thinking employers restore humanity to the hiring process.

AI has made modern hiring an arms race. Candidates use AI for easier application; organizations use AI to sift through higher volume; candidates lean more into AI to stand out, leading organizations to implement even more AI to detect genuine, qualified matches and avoid malicious actors.

It’s a recipe for low trust and inauthenticity all around: A Q424 Gartner survey of nearly 3,300 candidates found that only one-half think the jobs they’re applying to are legitimate. Gartner estimates that by 2028, 25% of job candidates will be fake.

One option is to lean into this race and fully remove human recruiters from the process. This is especially appealing at organizations facing pressure to cut headcount. However, it’s hard to imagine a fully automated hiring process that doesn’t drastically worsen offer rejection rates and first-day no-show rates.

The most successful AI-age recruiting strategies will marry “high-touch” approaches like in-person events and experiential skills assessments with emerging AI tools (e.g., interview agents) to maintain the quality of candidate pools while also maximizing the impact of recruiter activities.

6. Insider corporate espionage risks increase.

The use of AI in the hiring process has increased the risk of insider threats, specifically in the form of corporate espionage. For example, Crowdstrike identified more than 320 incidents over the past 12 months—up by 220% from the previous year—in which North Koreans gained fraudulent employment at Western companies working remotely as developers. To appear legitimate, they use AI-generated profile photos, deepfakes during remote interviews, and stolen personal information.

Gartner’s 2025 Cybersecurity Innovations in AI Risk Management and Use Survey suggests organizations are seeing more employee deepfakes at work: 43% of security leaders reported at least one incident involving a deepfake during an audio call with an employee, and 37% of security leaders reported at least one incident of a deepfake during a video call.

High-priority industries, such as technology, energy, manufacturing, and utilities, have already begun hardening their potential targets, by instituting additional identity checks, requiring in-person account recovery for high-risk roles, and increasing technological sovereignty (by shifting to vendors located in the same country or countries as their operations.) In 2026, additional security incidents will lead adjacent industries to do the same.

HR is not traditionally considered to be at the front lines of information security, but they must be in 2026. While CIOs will focus efforts on threat detection and increased cybersecurity measures, CHROs will need to invest in deterring insider threats by building employee readiness for identifying and reporting malicious behavior.

7. Tech-to-trades career paths blossom.

A decade ago, coding bootcamps retrained frontline or manual workers as software engineers. In 2026, we’ll see retraining and apprenticeship programs emerge to help digital workers transition into skilled trade professions.

As workers feel that their careers are threatened by AI, many of them will look to pivot to “AI-proof” careers—often in hands-on, skilled trade work that is unlikely to be fully automated in the near to medium-term.

These career changers will create a new talent pipeline for skilled tradespeople—an attractive option at a time when many organizations find that these skills are in short supply. However, companies could feel the impact of talent shortages in the roles these employees are fleeing.

Forward-thinking employers will head off a potential exodus by making plans to upskill and retain key digital talent, addressing anxieties about job security, and being transparent with employees about how AI investments will affect their jobs.

8. Process pros—not tech prodigies—unlock AI value.

Organizations are scrambling to hire talent with the latest AI skills and to upskill their existing talent to use existing AI tools effectively. This is a top priority for most organizations: In a recent Gartner survey of CIOs, 81% said AI skill gaps impede their ability to meet objectives.

Yet chasing after AI prodigies is a fool’s errand. Technical AI skills are not necessarily generalizable, so success with one tool will not automatically result in quality output from another. Meanwhile, AI tools are evolving so quickly that the pool of external talent with experience in new-to-world tools and platforms is simply not deep enough.

Instead of seeking out employees with skill in particular platforms or tools, the most successful organizations in 2026 will prioritize finding work process experts—employees whose creativity and systems thinking allow them to redesign entire processes, not just optimize individual tasks. Gartner research finds business units that redesign how work gets done with AI are twice as likely to exceed revenue goals.

9. Employees demand compensation for training their digital doppelgangers.

Digital twins and AI avatars are just beginning their ascent. Although they may not enter the mainstream for several years, some organizations are beginning to explore these types of digital doppelgangers to replicate high-performing employees and leaders. In 2026, we expect to see this trend expand from digital “actors” and “musicians” to the broader workforce, as AI is used to replicate high-performing employees, or even CEOs.

Digitally replicating employees opens uncharted territory in terms of employee rights and employer obligations. Already, legislation is emerging outlining conditions under which “digital replicas” of workers can be created and used. Organizations also face novel questions about compensation: We will begin to see more employees demand to be compensated, not just for training AI tools, but for the ongoing use of their digital likenesses or data long after they’ve left the organization.

Employers can get ahead of this trend by ensuring their AI governance policies address employee name, image and likeness rights, and by reviewing these policies regularly to account for rapidly evolving technological and regulatory environments.

. . .

In 2026, many executives find themselves in a liminal space between immediate business need and AI-driven reinvention just over the horizon. Most organizations will not have the capacity or necessity to act on each of these nine trends; to help target those most likely to help navigate this transition period, executive teams should ask:

• Which of these trends will disproportionately impact your industry, organization, or geography specifically?
• Which trends represent the most substantial threats to your critical talent, especially as your AI strategy evolves?
• Which trends provide the greatest opportunity to differentiate your organization, either with your critical talent or key customer segments?

Selecting the most relevant trends for your organization is the first step in moving beyond aspiration to action in the human-machine era.

Authors’ Note: We’d like to thank Brent Cassell, Ben Cook, Trisha Rai and CV Viverito who contributed to this piece.