Putting AI to Work
The AI revolution is an opportunity to redesign work that is more human-centric. Here are tips for leading this new AI workflow change.
In many industries today, talent scarcity poses a significant challenge, particularly as the demand for new skills escalates. For many professional roles, internal factors have further contributed to the problem, including the neglect of deliberate talent planning, inadequate investment in development and support, and a lack of clarity on workload adjustments in the era of modern generative artificial intelligence (GenAI).
In a world where CEOs are looking to talent, technology and innovation for growth, human resource professionals have the opportunity—and the enterprise imperative—to rethink the architecture of work. This requires a new way of looking at talent and skills, as well as a thoughtful approach to how work gets done.
So, where do you start? In this article, we will zoom in on one sector to lay out a particular approach, then pull back to look at how those core principles might apply in other sectors and professions.
Health care is our starting point. The U.S. is experiencing a severe shortage of skilled nurses, with projections indicating a deficit of between 200,000 and 450,000 nurses. Factors contributing to this skills shortage include an aging population, retirements in the nursing workforce and pandemic-induced burnout. The persistent, high demand for health care services exacerbates the scarcity of skilled nurses, especially in acute care settings.
McKinsey & Company research last year found that 45 percent of inpatient nurses (about 2 million of the 4.2 million nurses in the U.S.) reported they are likely to leave their role in the next six months. The main reasons: a lack of feeling valued by their organizations and the increasing burden of an unmanageable workload.
To address the nursing shortage in the midterm to long term, we can take actions such as attracting more students to nursing and expanding access to nursing education. These interventions are beneficial, but closing the supply gap requires a paradigm shift in our approach to nursing work that incorporates technology, especially AI.
Prioritize Human Potential
Embracing AI as part of a skills-centric approach that places human potential at its core is imperative for organizations addressing skills shortages, particularly in professions with steep training requirements. But before delving into the implementation details, let’s explore the rationale. Why should an organization view human potential as the linchpin of its performance and growth?
People are complex learning organisms capable of adapting, leading and continuously evolving. Viewing the existing and potential workforce from this perspective not only nurtures innovation, agility and joy but also contributes meaningfully to on-the-job sustainability and overall societal well-being.
When applied to the nursing shortage, this approach guides the health and life sciences sector in attracting, developing and empowering nurses to fulfill the roles they have chosen to do. Concurrently, AI, technology and innovation lay the groundwork for a more effective and fulfilling way of working.
What Is a Skills-Based Approach?
As individuals, we have two types of skills: learned and inherent. Learned skills often take the spotlight in talent considerations because they’re easier to measure and because they relate to tasks and activities. But they are not the most reliable performance indicators. They have a limited shelf life in terms of relevance, and they don’t always relate to expected outcomes.
Therefore, at a time when the tasks that we do are continually evolving or disappearing in line with technological and societal change, it is our core inherent human skills—and what motivates us—that hold more value. If we shift to viewing individuals as a composition of their inherent and learned skills combined with their inner motivations, organizations need to adopt a skills-centric approach. That means placing skills, not jobs, at the center of workforce strategy.
Unilever is one organization doing just that. The company is breaking work into projects and tasks to allow for flexibility and view individuals as multifaceted contributors. This approach unleashes an internal talent marketplace.
However, to gauge the true currency of your people, it is essential to consider their human potential. This involves overlaying each person’s skills with their motivations, aspirations, and unique ability to learn and adapt.
A New Architecture of Work: Outcomes Drive Tasks
As you integrate AI and automation, there’s a pressing need to examine and redesign job roles. Aim for a seamless integration of human skills and technological capabilities.
Adopting such a “work architecture” approach means looking at jobs through a specific analytical lens. Begin by deconstructing each role into tasks and activities (the doing) and outcomes (the expected impact). Outcomes take the highest priority.
In the context of nursing, the primary expected outcome is clear: caring for humans, saving lives, nurturing health and building trust with those in distress. This outcome-driven perspective prompts a closer look at the tasks and activities assigned to nurses.
Once you have identified the desired outcome, the subsequent phase involves scrutinizing tasks and activities. Surprisingly, you may find that a significant portion—in many cases up to 50 percent of the list—comprises bureaucratic, administrative and quality-control tasks. These duties are essential for patient welfare, but a question arises: Why burden nurses with these responsibilities?
Applying the Human Potential model to this scenario will likely reveal that administrative tasks do not align with nurses’ motivations or their learned and core inherent skills. In the contemporary technological landscape, AI and automation should shoulder most of this administrative burden, liberating nurses to focus on activities and tasks directly related to their primary expected outcome while at the same time appealing to their core motivations for entering the profession.
Striking the right balance between human and AI collaboration is crucial. Clearly delineate tasks for humans, identify those suitable for automation and call out areas where collaboration is most effective. Designing workflows that enable seamless collaboration will then allow each entity to complement the strengths of the other.
Wharton School professor Ethan Mollick suggests two strategies for leveraging AI: “Centaur” work involves dividing tasks strategically between both humans and AI, whereas “cyborg” work involves seamlessly integrating AI and human efforts. Deciding which tasks are exclusively human, which can be augmented and which can be delegated exclusively to AI will shape the future of work.
Taking a step further in the context of nursing, the work architecture approach prompts critical questions:
- What tasks align with nurses’ skills, motivations and aspirations?
- What should they learn to progress in their profession?
- What knowledge can they share with their peers?
- Which aspects of their work could be efficiently delegated to others, whether human or AI, to enhance overall efficiency?
Breaking down work from predefined roles to a focus on individual skills becomes imperative. This approach not only optimizes the efficiency of health care delivery but also enhances job satisfaction and the ability of nursing professionals to work at the top of their licensure. By aligning tasks with human expertise and leveraging AI for routine and data-centric activities, organizations can redefine the future of nursing work, creating a symbiotic relationship between humans and technology.
The evolution of skills extends beyond bullet points. It involves an awareness of individuals’ motivations, along with a profound decoding of talent to gain a proper understanding of how it aligns with the work that needs to be done. This process aims to reintroduce joy and satisfaction into work when possible.
In summary, in tackling nursing shortages, reimagining work architecture requires a deep understanding of nursing workflows, tasks and the work environment. The input of nurses sheds light on challenges and areas where their skills can be better utilized. During the reconstruction phase, the emphasis is on aligning tasks with nurses’ expertise, which redirects focus toward direct patient care and clinical decision-making.
AI Improves Triage at Yale New Haven Hospital
Amid the nursing crisis, health care institutions are turning to AI tools to make the work of nursing work better. Yale New Haven Hospital (YNHH) exemplifies this trend in its effort to enhance emergency department triage. For ER nurses at YNHH, triage has been enhanced through an AI-enabled clinical decision-support tool that considers patient information, compares it to historical emergency department data and recommends an acuity level. This assists clinicians in making decisions about patient care.
Since implementing this AI tool in early 2023, YNHH has seen improved triage accuracy, more efficient allocation of resources such as staff and space, reduced variability in triage decisions, and shorter triage times.
One key point: The AI solution is a decision-support tool, not a decision-making tool. The human/AI partnership is essential, but the ultimate decision and responsibility remains with the nurses.
“Nursing is not about completing tasks,” said Chris Chumra, who has been an ER nurse at YNHH for 17 years and is now leading the implementation of various clinical solutions. “The job of a nurse involves taking a step back and looking at the full picture while taking in a lot of data, managing a dynamic situation and serving as a health care translator. AI can be a great partner in this work.”
Chumra points to the importance of engaging practitioners who deeply understand the current workflow, operational context and multiple decision-making factors. He also advises on the importance of applying sound change management, recognizing that these fundamental changes to ways of working are also significant changes to culture.
Design Human-Centric Work
Fundamentally, our goal is to design work that highlights and elevates human strengths within job roles. Nurses will need emotional intelligence, problem- solving and sound decision-making more than other inherent skills that relate to repetitive administrative work. The same can be said of many professions for which complex, in-the-moment problem-solving is the reason the roles exist.
Technology enablement, especially through AI, strategically creates room for professionals to concentrate on tasks and activities aligned with their motivations and core inherent skills. Automating everyday tasks such as data entry and scheduling frees up time from routine activities so people can focus on high-value, meaningful work. This promotes adaptability, teamwork, creativity and innovation—all distinctly “human” qualities that contribute significantly in areas where AI may have limitations.
It’s critical to remember that AI is not simply a tool of organizational efficiency. It should be harnessed to support and enhance human well-being, driving progress with a people-centered focus and purpose. For highly skilled professionals, higher salaries and enhanced benefits are, of course, crucial. But restoring the intrinsic purity of their profession would also significantly contribute to attracting and retaining people.
Utilizing AI to liberate the human potential of your workforce goes beyond introducing new technologies—it’s a cultural rewiring. Picture a workplace that embraces uncertainty, one where adaptability is the lifeblood of the culture. This isn’t just adapting to change; it’s purposefully shaping our organizations with adaptability at their core.
Transitioning to a technologically advanced work environment requires more than just introducing new tools. It necessitates a cultural shift toward continuous learning, experimentation and innovation. Change management strategies can help you address apprehensions about new technologies, emphasizing their role as tools to augment human expertise. Ongoing training and support are crucial for employees to adapt to evolving work processes, ensuring they stay abreast of advancements.
AI-Enabled, People-Powered
We focused on nursing in this article because the profession offers near-term opportunities for implementation, not a three-year to five-year further evolution of AI. This adaptation involves preparing nurses to work alongside AI tools effectively. Ongoing training ensures that nurses evolve with technological advancements, effectively applying their specialized training to improve patient care and outcomes.
The cultural shift toward adaptability is essential for managing the integration of AI into nursing practices, where feedback mechanisms and continuous learning play a pivotal role in refining workflows.
Ultimately, the true essence of AI, even near term, lies in liberating human potential. In a workplace context, this involves transitioning to an organizational structure that values skills, motivations and aspirations over traditional qualifications.
The synergy between humans and AI goes beyond optimizing processes; it’s a harmonious collaboration that leverages the strengths of both. Done well, the business case is clear: Highly engaged employees who are emotionally committed to their organizations are 87 percent less likely to leave, according to Gallup, and their companies are 21 percent more profitable than those with less engaged staff. AI, coupled with a work architecture approach and a human-centric mindset, is an enhancement to making such outcomes possible.
Samantha Schlimper is the managing director of Randstad’s global talent advisory business, bringing strategy from concept into actionable reality.
Judith Scimone is an HR executive, futurist and editor-at-large for People + Strategy.
5 Tips for Leading AI Workflow Change
1. Gather feedback from your people.
Any journey into understanding workflow should directly involve people who do the work and understand it deeply. While outputs are visible in the form of actions, understanding inputs can give you useful insights into where critical thinking, judgment, contextualization and feedback play a key role. Direct feedback from those doing the work also provides insights into the relevant work context and day-to-day challenges. Involving users drives more process credibility and ease of adoption by peers.
2. Focus on human potential and prioritize inherent core skills as learning needs evolve.
As AI continues to evolve, the role of human intelligence in the workplace will evolve, too. To stay ahead, cultivate a talent ecosystem that fosters continuous learning, adaptability and agility. As organizational roles shift, seizing opportunities for expanded learning and career advancement becomes paramount.
Building this ecosystem necessitates a deep understanding of the skills and aspirations of your present and future workforce (the Human Potential approach). This approach ensures ongoing development by aligning your talent practices with the dynamic demands of the evolving workplace landscape.
3. Adopt a forward-thinking mindset.
Identify and address your organization’s most pressing challenges today and anticipate those of tomorrow. Think “leapfrog,” not “catch-up.” While maintaining a strategic pace, prioritize the pursuit of forward-thinking solutions, avoiding a regression to outdated fundamentals. This proactive mindset ensures that your organization advances with innovation, staying ahead of the curve instead of retracing steps from the past.
4. Implement targeted learning to enhance employee skills in areas that complement AI.
Ensure that your workforce is equipped to work alongside AI technologies effectively. The human-centric approach also advocates for substantial investment in training and development to upskill employees in areas crucial for fostering innovation and adaptability.
5. Review and iterate.
If your organization is at the initial stages of AI integration, don’t be discouraged. Start with small, impactful changes rather than immediate wholesale transformation. Identify areas for modest adjustments, then monitor their effectiveness and gather feedback. Iterate strategies based on outcomes and evolving organizational needs. Stay agile in adapting to changes in technology and the business landscape.
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