A disconnect between executives and workers is emerging, with leaders preparing for more agile ways of working and workers favoring stability. The challenge is that the ability for organizations to evolve and adopt new models of work depends largely on the ability of the workforce to do the same. How can leaders find the right balance between the stability that workers crave and the agility that organizations need to create stagility?

Traditional anchors, such as static job descriptions, defined teams, and linear, internal career pathways, are being challenged. As these anchors become upended, organizations and workers must find new sources of stability to reinforce the connection between worker and organization, putting both in a position to adapt and thrive. These new anchors will come in the form of reimagined organization structures, greater intentionality in the design and resourcing of work, a better understanding of workers as individuals, and stronger networks within and beyond the four walls of the organization.

To realize both business and human outcomes, organizations will likely need to address meeting overload, outdated processes, and the myriad of nonessential work that drains focus and keeps people from achieving the outcomes that matter most. They will also need to be mindful of unintended consequences, where new tools meant to increase productivity and efficiency add new layers of complexity instead: more notifications to check, more dashboards to update, and more digital busywork.

“Work getting in the way of work” comes with real costs for organizations: reduced performance; damage to employee morale and well-being; and erosion of organizational capacity, culture, and innovation. And as we move further into the era of generative artificial intelligence, getting stuck in the nonessential work may also inhibit our ability to fully unleash its potential benefits, since advancements inevitably slow down when they reach humans who don’t have the capacity or bandwidth to learn, implement, or master the tools. Breaking this cycle often hinges upon implementing a new corporate mindset about how the organization defines and values slack—unscheduled, unassigned time that workers have autonomy over how to use—and a new mechanism to evaluate the best path to reducing or streamlining the tasks and processes that create unnecessary work.

People are at the heart of AI’s potential. Technology’s value does not come from replacing human labor; it’s working more closely than ever with humans, amplifying their ability to discover and capture opportunities for innovation and growth. As AI becomes increasingly intertwined with workers, it’s changing their experience—often through silent, unintended impacts on the work they do and the ways they do it.

An organization’s employee value proposition (EVP)—sometimes called a workforce or human value proposition—crystallizes the reasons people come to an organization and stay with it. An updated EVP for the world of human and machine collaboration can account for AI-driven changes in the work experience and support a healthy, mutually beneficial relationship between organizations and their workers. To move toward a new EVP that accounts for how AI is transforming work and the worker-organization relationship, organizations can study workers’ use of AI and its silent impacts; share the plan for AI with workers; and forge a relationship between the HR and tech functions.

The experience gap—the gulf between what employers demand and what workers bring—presents a thorny and ever-present riddle: Workers can’t get jobs without having the required experience. But they can’t acquire the necessary experience without foothold jobs or equivalent opportunities. While this gap has always been a challenge, the ability to gain experience is being complicated by new developments such as AI taking on certain tasks, the erosion of the apprenticeship model with remote work, and the growing complexity of work, which increasingly demands more—and more varied—types of experience.

Closing the experience gap is possible, but it will require changes on both the supply and demand sides of the talent market. Hiring organizations, job seekers, and educational institutions all need to reflect on the capabilities organizations truly seek when they impose experience requirements so they can determine how to meet those underlying needs—including new approaches they might take.

Technology is not what it used to be. The shift from automating work to be more efficient to augmenting people’s capabilities to unlock their performance has broad implications for work, and it complicates organizations’ decisions about which tech to purchase, what they can expect to get out of their investments, and how to recognize and measure value.

To make effective tech investments in today’s world where people and smart machines increasingly work together, leaders will need to navigate difficult tensions in building their value cases. These tensions include how to balance focusing on the immediate and predictable return of automation versus the potential larger value from augmentation use cases. In addition, they will need to decide whether to focus on harder-to-quantify outcomes (e.g., innovation, capabilities) in the value case versus more traditional outputs (e.g., efficiency)—not to mention having to organize and orchestrate new technologies in a world where traditional boundaries between them are falling away.

To truly connect with workers in ways that move them most toward positive outcomes, organizations should tap into what inspires the individual worker as a unique human being. A good first step is embracing a multimodal approach to collecting and understanding workers’ varied motivations. Self-reported worker data—through traditional approaches such as surveys, focus groups, or prompts to enter information into a workforce portal—while useful, has limitations. With intelligent technologies, organizations can go beyond self-reporting to observe and infer individual worker motivations passively, in real-time and at scale. However, collecting data from workers should be performed responsibly to build trust.

Whether the organization uses AI, analytics, or simple human judgment, insights into what makes a person tick can help drive the hyper-personalization of a wide variety of workforce strategies and outcomes. Furthermore, motivating at the unit of one doesn’t necessarily require big budgets, massive change, or large technology investments. Organizations can get started now by simply asking managers to better understand their workers’ unique motivations and tailor their feedback, development plans, or other practices accordingly. No matter the strategy used, organizations that let the data speak can gain a better understanding of what drives each individual to act the way they do, giving them the power to unlock human performance.

For years, headlines have made clear how much workers and their leaders dislike performance management, largely because they think it is ineffective. Nonetheless, performance management is an unavoidable reality. Organizations need some way to make defensible talent decisions like allocating rewards, promoting people, and providing developmental feedback to improve workforce performance. However, given lack of trust in the process and persistent negative perceptions, the answer may lie not in reinventing performance management but instead in engineering human performance—taking an evidence-based and holistic approach to elevating human and business outcomes together.

Despite years of reinvention and retooling, performance management processes still fall short when they’re relied on as the sole means to unlock human performance. That’s because one singular process isn’t big enough to encompass the many factors that drive both human and business outcomes. Rather, organizations need to expand beyond the performance management process if they want to meet the very goals that they hoped performance management would deliver. Engineering human performance includes a robust performance management process, but it goes beyond process to incorporate organizational culture and design, manager and people connections, technology and data, workforce practices, talent management, and workplace design.

Some organizations are either eliminating the role of manager completely or greatly reducing the number of middle manager roles. This shift toward “bossless” organizations is likely being driven by a renewed focus on efficiency, agility, and worker empowerment with “zero distance” to the customer. Economic pressures also have organizations looking to reduce costs, and AI and other technologies are poised to take on many administrative tasks. However, for most organizations, eliminating managers altogether isn’t the solution. But neither is simply retaining or elevating the role of the manager as it has existed for over a century. Instead, organizations should seek a third path: reinvention of the role entirely. Because in an age in which work is increasingly complex and volatile, people-powered, and more AI-augmented, the traditional role of the manager may no longer be fit-for-purpose.

AI, along with new support structures, is a key enabler to this reinvention. Not only can AI automate much of the administrative work that takes the time of managers today, but it can also become a powerful ally in assisting them with focusing on and leading what matters most to organizational results: developing, coaching, motivating, and nurturing people; redesigning work, reallocating resources, and optimizing human and machine interactions to drive human performance in the age of AI; and enabling agility, strategic problem-solving, and innovation.