Many of the futures we have explored are no longer hypothetical. They are showing up in how work is organized and experienced today. The fundamental question now is whether organizations are shaping those changes deliberately or simply reacting as they unfold.

combines monitoring of more than 500 business, academic, government, and economic resources with original global mixed methods research involving 2,718 employees, 500 C-suite leaders, 60 HR executives, and founders of AI-native companies. It points to a fundamental concern: organizations may be optimizing the future of work for efficiency when they should be designing it for value.

The productivity trap

In October 2025, we explored 10 predictions for work, workforces, and work practices, each with two possible futures. A year later, the evidence is strongest for what we call the “AI Upgrade” future, in which AI鈥檚 value is measured through efficiency, productivity gains, and time savings. Signals of broader work transformation are emerging, but they remain less established.

The emphasis on productivity is understandable because time savings, output, and cost reductions are relatively easy to measure. The harder question is what organizations do with the capacity AI can create. They can ask employees to produce more of the same work, or they can redesign work, roles, teams, and talent systems around what people and AI can achieve together.

Work transformation needs a north star

Related research from our found that 88% of employees said their jobs have changed because of AI, yet half of those changes occurred without meaningful guidance from managers or their organizations. Employees are using AI to simultaneously offload tasks and expand the scope of their work, often without a shared playbook or a clear view of the intended outcome. 

Before organizations can transform work intentionally, they need a clear north star for what work should become. Yet between 55% and 70% of employees said they had received no clear organizational guidance on the work shifts they are expected to navigate. At the same time, 72% of C-suite leaders said that most organizations claiming to transform work with AI are really automating existing jobs faster. Without a shared vision for work transformation, organizations risk defaulting to isolated experimentation and efficiency-first use cases, creating uneven outcomes and limiting the combined potential of people and AI.

Six shifts to guide better work design

We endorse six shifts in work and organizational design that are both enabled by AI and associated with work-enriching characteristics well established in organizational psychology as leading to increased employee motivation, effort, performance, and retention:

• From specialist to generalist: Apply core expertise across a broader range of problems and tasks.
• From static jobs to dynamic roles: Organize work around flexible responsibilities and outcomes that evolve with business needs.
• From pyramid to diamond organizational design: Reduce coordination layers and concentrate more people closer to where core work happens, enabling them to own end-to-end delivery.
• From output to impact: Evaluate work by the value it creates, not simply by activity or deliverables.
• From centralized authority to decentralized decision-making: Move decisions closer to the people and teams with the context to act.
• From people management to self-leadership: Give employees greater ownership of their direction, performance, and growth.

Through an experimental study, we validated how all six shifts can enrich work. For example, employees perceived 77% greater autonomy under decentralized decision-making, while dynamic roles were perceived as 49% more challenging than static jobs. The full report examines these effects across all six shifts, compares where organizations are today with where employees believe they need to be, and offers a prioritized path for leaders to get started.

Work transformation requires HR transformation

If work shifts this substantially, the function responsible for the people agenda cannot assume its own role and structure will remain the same. HR was built for fixed jobs, predictable career paths, established hierarchies, and talent systems designed for consistency. AI challenges each assumption.

With this context, we see HR鈥檚 new mandate as focused on three interconnected pillars:

• Automate: Use automation and intelligent systems to release capacity from administrative work and create space for more strategic contribution. C-suite leaders estimate that 49% of HR work will be fully automated within five years, compared with 35% of enterprise work overall.
• Redesign: Lead enterprise work redesign by determining how tasks, workflows, roles, and teams are structured across people and AI. Executives ranked work redesign as the transformation pillar that will create the greatest enterprise value over the next five years and identified HR as its leading owner.
• Reinvent: Overhaul recruitment, development, mobility, performance, and rewards to support how work now gets done and who succeeds in the new way of working. Seventy-five percent of executives agree that organizations can succeed in the AI era only if they reinvent how they measure, reward, and grow people.

In the full report, we also explore four plausible futures for how these pillars might come together to shape HR鈥檚 strategic contribution, depending on where leaders believe the people agenda can create the greatest value.

The future of work is a choice

The future of work is already being built. The opportunity now is to build it intentionally, creating better work for people and greater value for organizations. 

Explore how our 10 predictions from 2025 are tracking, the evidence behind six work shifts for better employee and business outcomes, and four possible futures for how HR can create strategic value in an AI-enabled enterprise. .

Dr. Autumn D. Krauss is chief scientist of the Future of Work Research Lab for 麻豆原创 SuccessFactors.

Fewer opportunities for early talent 

The job marketplace has contracted significantly for those with less than five years of professional experience. Research published by 麻豆原创 shows that openings in the 10 most common entry-level job titles declined by 35% in just one year, from 2024 to 2025.*

Budget constraints, hiring freezes, and uncertainty around the ROI of early talent as AI increasingly takes on routine and manual tasks are among the reasons cited by HR leaders today.  

Applications skyrocket  

AI is also having a major impact on the recruitment process. With such limited opportunities available, more than half of early talent candidates use AI to help them land a job鈥攁 process that now takes eight months on average and involves more than 300 job applications.*

HR is feeling the pressure managing the candidate pipeline, with聽the number of applicants per early talent job opening doubling since 2021.聽A聽high volume of candidates聽are聽submitting聽AI-generated r茅sum茅s and applications, and it鈥檚 becoming much harder to detect high-fidelity signals around skills, fit, and potential.聽

It鈥檚聽a painful聽scenario for everyone involved. Fragile and unsustainable. And the聽implications聽will be聽profound for enterprises that聽don鈥檛聽act quickly.聽聽

HR leaders voice concerns  

Playing out the current聽trajectory聽to its natural conclusion, what happens when all the聽fresh talent eventually dries up? Already,聽HR leaders聽are alarmed by this risk.聽鈥淯ltimately, if we stop investing in early talent, we will wind up eliminating our talent pipeline,鈥� one聽global head of early talent聽programs at a high-tech organization聽told researchers.*

A senior HR director at a high-tech organization commented: 鈥淚f we continue down this path and don鈥檛 provide a way for early talent to get started, it鈥檚 going to lead to massive skill shortages in the future.鈥� 

Widespread reductions in early talent hiring will lead to skills gaps that聽will聽prove expensive to remedy. Organizations will struggle to build company capabilities,聽retain聽knowledge, and develop future leaders.聽聽

But by far the most common concern from leaders was around not seeing early talent as AI-native. If the AI capabilities of this cohort are overlooked, companies may miss out on a key opportunity to scale AI innovation and adoption across the business.  

What鈥檚 the answer?  

Today,聽there鈥檚聽an opportunity for HR leaders to be more intentional聽and strategic, to reimagine聽their approach聽to early talent from the ground up. With the right early talent strategy, organizations can gain a competitive advantage.

Here are three steps to consider. 

Step 1: Rethink entry-level roles 

Traditionally, junior employees have mainly been given routine, repetitive tasks. Combined with frustratingly slow career progression, the result is eroding morale and commitment.  

This聽approach must evolve.聽The nature of work is changing rapidly,聽and early talent no longer need to take on those routine tasks. These employees need the opportunity to develop at speed and to be supported in performing聽work that聽meaningfully addresses聽business challenges.聽聽

HR has the chance to reshape entry-level positions,聽to provide support, guidance, and tools to enable聽junior staff聽to contribute聽in聽more聽impactful聽ways. This may involve them聽working聽with proper guidance聽to support聽more critical聽projects, interacting with customers,聽and聽even聽owning some tasks end to end.聽

This approach not only enables early talent to contribute more positively to the business at an earlier stage, but when combined with clear goals, regular feedback loops, and occasional coaching, it also fosters greater engagement and commitment. 

Step 2: Support your strategy with technology 

Hiring and developing early talent have become more complex鈥攆rom deciphering AI-generated applications, to redesigning roles and meeting their aspirations in a fast-changing business context. And with the nature of early talent work shifting, leaders need tools to help understand the new capabilities that will predict long-term success and demonstrate the value of early talent initiatives.  

Here鈥檚 where technology can help. During the hiring process,聽technology聽can help聽employers聽see beyond the聽noise of聽AI聽applications聽and rediscover the聽meaningful聽signals聽they聽need to create candidate shortlists and strengthen hiring decisions. Meanwhile, technology can also help to maintain engagement with other high-potential聽candidates聽who applied鈥攆or when the next opportunities arise.聽聽

Once early talent begin work, today鈥檚 technology can help you track their participation in early talent programs and progression towards their goals. It also helps facilitate individualized learning opportunities and demonstrate the ROI of your early talent investments. For research-based recommendations on the role of technology in early talent selection and development, check out this . 

Step 3: Reframe the business case for early talent 

As the nature of early talent work is changing alongside the technology used to support them, HR leaders agree that the old business case for early talent investments needs to be reimagined. Many organizations are focused on mitigating critical skill gaps and engaging in large-scale AI transformations. While early talent lack experience, they are eager to engage in continuous learning and understand how to work effectively alongside AI. 

Research also reveals that鈥攁s they work alongside modern tools and technologies鈥攅arly talent can contribute to high-value, meaningful work much faster than in the past.  

A modern early talent business case is one that involves focusing on faster time to meaningful work, reducing critical skill gaps, and leveraging the AI-native capabilities of today鈥檚 entry-level workers.  

Build your early talent strategy 

Will HR leaders watch on as a generation of AI-savvy talent remains underused, or act now and build the skills pipelines necessary for a future-ready workforce? 

Get further insights on this topic by reading our report, 鈥�.鈥�&苍产蝉辫;痴颈蝉颈迟&苍产蝉辫;辞耻谤&苍产蝉辫; to stay tuned for when phase two of this research gets published later this year. 

Dr. Autumn D. Krauss is chief scientist at 麻豆原创 SuccessFactors.

*, 麻豆原创, 2026. 

麻豆原创 surveyed over 4,000 managers and employees globally about how AI is reshaping workplace dynamics and HR practices, and paint a complicated picture.聽

The AI Literacy Divide Is Shaping Workforce Perceptions of AI  

According to the survey data, the biggest factor influencing the workforce鈥檚 opinions of AI is their level of AI literacy, or their ability to detect, understand, and evaluate the technology. Compared to people with high AI literacy, people with low AI literacy were over six times more likely to feel apprehensive, seven times more likely to feel afraid, and over eight times more likely to feel distressed about using AI at work. Additionally, nearly 70% of people with high AI literacy expected to see positive outcomes from the use of AI at work, compared to 29% of people with low AI literacy. 

Respondents with high AI literacy were also more likely to have positive or egalitarian perceptions about how AI usage should 鈥� or should not 鈥� impact people decisions like performance reviews, career advancement, and compensation.  

When presented with a hypothetical scenario where two employees have exactly the same level of performance in the same job, with one using AI to complete their work and the other not, research participants expressed divergent views on how AI usage should be considered when making important people decisions:  

• Should AI usage improve performance reviews? More than half (55%) of people believed that employees who use AI should have better performance reviews than those who don鈥檛 use AI. This sentiment was even higher (64%) for workers with high AI literacy.  
• Should AI usage factor into compensation? Forty-four percent of people with low AI literacy believed that employees who use AI should be paid less than those who don鈥檛 use AI. Conversely, 46% of people with high AI literacy reported they believed compensation should be equal, regardless of AI use. 
• Should AI usage influence promotion? Forty-five percent of people believed that employees should have the same chance of promotion regardless of AI usage. Those with high AI literacy felt similarly, with the majority (57%) believing promotion odds should be equal regardless of AI use. 

It is a long-standing premise in management that better performance reviews should yield higher compensation and better chances of promotion. These mixed results show that the use of AI has complicated this, and they provide a clear reflection of the complex moment in time organizations and employees are navigating.  With any new technology comes a period of adapting our knowledge, attitudes, and behaviors related to it. These results show that even though AI adoption is accelerating, some employees are still grappling with foundational questions about the use of AI at work and forming their own assumptions 鈥� not just about the technology, but also about the people who use it.  

AI in Hiring Practices 

Interestingly, the findings also revealed that the majority of people want to work for companies that use AI in their hiring practices. Between 45% to 57% of people said that they would react positively if a company used AI tools in the hiring process, such as being more likely to apply for and accept a job offer, feeling more confident in their fit with their new job, and believing the hiring process to be more fair. This positivity was even higher for those with high AI literacy, with between 66% and 75% of the most AI-literate employees endorsing these positive reactions.  

The previous results about the impact of using AI on work outcomes showed that people have strong mixed opinions about employees using AI to do their jobs. However, these results indicate that employees are much more universally accepting when it comes to organizations using AI to improve practices like hiring. Organizations hoping to improve their practices by increasing efficiency and reducing biases are likely to be more successful at attracting talent, and especially talent with sought-after AI skills.  

What This Means and How to Address AI Literacy in Your Organization  

From this research, it鈥檚 clear that as AI becomes a more widely used tool at work, organizations must focus on and invest in AI literacy to help employees understand this new technology, increasing adoption and ensuring everyone is equipped to benefit from it. Our data shows that the most important aspects of AI education to improve sentiment and adoption are knowing how to use AI to achieve one鈥檚 goals and make tasks easier and being able to detect when a technology uses AI.  

Organizations can enhance AI literacy 鈥� and consequently AI adoption 鈥� through a variety of strategies, including:  

• Hands-on experience: Provide opportunities for employees to work with AI tools in practical settings, encouraging experimentation and familiarity.  
• Training and resources: Offer structured training sessions and resource libraries that cover AI fundamentals, specific tools, and real-world applications relevant to your organization. 
• Change communication: Whan adopting a new tool, be clear about how it works and the expected impact it will have on an employee鈥檚 experience.  
• Showcase wins: Share success stories about teams that have benefitted from the use of AI in their work and how it has positively impacted the organization.  
• Peer learning: Identify employees with high AI literacy and create structured learning sessions designed for early adopters to help upskill their peers through mentorship and knowledge sharing. 
• Promote a growth mindset: Cultivate an organizational culture that values curiosity and learning, helping employees to feel comfortable exploring AI technologies, asking questions, and providing feedback on their experiences.

Autumn Krauss is chief scientist at 麻豆原创 SuccessFactors.