At a glance: AI is changing how employees make decisions and carry out everyday responsibilities. Critical thinking, judgment, communication, and adaptability remain essential skills, but employees are being asked to apply them in new ways. For learning and development leaders, the opportunity is to develop these capabilities through practical, role-specific learning and feedback that helps employees perform confidently in AI-enabled work.
AI can analyze large amounts of data, identify patterns, and recommend next steps in seconds. But what happens when those recommendations don't account for the full situation?
A healthcare professional may need to question an AI-supported clinical recommendation. A restaurant manager may need to adjust an automated staffing forecast when several employees call out. A manufacturing technician may need to investigate an unexpected result from an automated quality inspection.
Each situation requires someone to interpret information, recognize limitations, and determine the appropriate action.
As AI becomes more integrated into everyday work, employees need opportunities to develop these capabilities alongside their technical skills. For HR leaders, that means understanding where human judgment matters, how responsibilities are evolving, and what employees need to practice to perform effectively.
Which human capabilities matter most in AI-enabled work?
Many of the capabilities employees need alongside AI are familiar. What is changing is how often they must apply those capabilities while working with technology.
The World Economic Forum's Future of Jobs Report 2025 identifies analytical thinking among employers' most sought-after core skills, with seven out of 10 companies in 2025 considering it essential, alongside resilience, flexibility, agility, leadership, and creative thinking.
These capabilities take on new importance when employees must evaluate automated recommendations, navigate changing workflows, and make decisions with incomplete information.
Four areas deserve particular attention.
- Critical thinking and judgment: AI-generated recommendations can help employees make faster decisions. Employees still need to evaluate whether the information is accurate, appropriate, and complete. Consider a healthcare professional using an AI-supported tool to identify potential patient risks. The tool may recognize patterns in clinical data, but the professional must consider the patient's history, current condition, and circumstances before determining the appropriate next step. Developing this capability requires practice evaluating information, identifying exceptions, and explaining decisions.
- Communication and empathy: AI can help summarize information or suggest responses, but many workplace interactions require sensitivity to the people and circumstances involved. A restaurant manager may use technology to identify scheduling conflicts or staffing needs. Communicating an unexpected schedule change, supporting an overwhelmed employee, or resolving a difficult guest interaction requires an understanding of individual circumstances. These situations call for listening, empathy, and clear communication. Learning and development programs can strengthen these capabilities through realistic scenarios, role-playing, and feedback on how employees navigate challenging conversations.
- Adaptability and problem-solving: AI-enabled workflows will evolve as organizations introduce new tools and refine existing processes. Employees need to recognize when established approaches are no longer effective and adjust their actions accordingly. In medical device manufacturing, for example, an automated inspection system may flag a potential product defect. A quality professional must investigate the result, determine whether additional testing is necessary, and coordinate with engineering or production teams to resolve the issue. The ability to investigate unexpected outcomes, ask questions, and adapt established processes remains important even as technology becomes more sophisticated.
- Collaboration and accountability: AI-supported decisions frequently affect multiple teams, functions, and stakeholders. A technical professional may identify an issue through an automated system, but resolving it could require collaboration among operations, engineering, quality, and regulatory teams. Employees need to communicate findings, understand their responsibilities, and know when to escalate decisions. These capabilities are especially important in environments where product quality, patient safety, or operational consistency depends on coordinated action.
Learn more about this from InStride’s Chief Product Officer, Michelle Westfort, about what AI didn’t change about workforce strategy.
Human capabilities develop differently across roles
The same capability can look very different depending on the work employees perform.
Critical thinking in a hospital may involve evaluating a recommendation against patient needs and clinical protocols. In a restaurant, it may mean interpreting demand forecasts while balancing labor availability and guest experience. In manufacturing, it may involve identifying the cause of an unexpected production result.
That distinction matters when designing learning programs.
At InStride's IMPACT 2026 summit, HR and L&D leaders explored how organizations can move from AI curiosity to practical capability. During a hands-on AI workshop, participants experimented with building solutions using AI tools, reinforcing the importance of practice, collaboration, and human judgment.
The lesson for learning and development leaders: employees benefit from opportunities to apply emerging skills to realistic problems, receive feedback, and build confidence through experience.
This is relevant as organizations invest in broader AI literacy initiatives. InStride's AI Readiness Illusion research highlights the challenges organizations face when perceived readiness outpaces effective adoption.
Helping employees understand AI is an important starting point. Helping them use it appropriately within their responsibilities requires more targeted development.
How can learning and development leaders develop human capabilities alongside AI?
Learning and development teams can begin by examining how AI is changing the work employees are expected to perform.
Rather than selecting training based only on broad skill categories, consider the situations employees will encounter and the decisions they will need to make.
A practical approach includes four considerations:
- Identify the moments where human capabilities matter. Work with business leaders and managers to understand which responsibilities involve a higher degree of interpretation, judgment, communication, problem-solving, or oversight of AI-generated information.
- Build learning around realistic scenarios. Use role-specific examples, simulations, and exercises that mirror employees' actual responsibilities. A frontline manager and a quality technician may both need critical thinking skills, but the situations they practice should reflect their different roles.
- Create opportunities for application and feedback. Reinforce learning through real assignments, manager coaching, peer discussion, and opportunities to evaluate decisions. Employees need time to practice and refine how they use new capabilities.
- Connect development to performance. Establish what successful application looks like before training begins. That could mean recognizing an incorrect recommendation, appropriately escalating an issue, communicating a difficult decision, or adjusting a workflow when conditions change.
Contextualized, cohort-based learning can support this approach by allowing employees with similar responsibilities to develop capabilities together and apply them to relevant business challenges.
How do organizations know whether capabilities are improving?
Completion rates can show whether employees participated in learning but they offer limited insight into whether employees can apply what they learned.
For learning and development leaders, evaluating applied capability means looking for evidence of changes in behavior and performance.
That might include whether employees can identify inaccurate AI-generated information, explain their reasoning, respond appropriately to exceptions, or demonstrate improved proficiency in a role-specific task.
Managers play an important role in observing those behaviors and providing feedback over time.
At IMPACT 2026, workforce leaders also emphasized the importance of connecting education investments to outcomes that matter to the business. The same principle applies to AI-related development: organizations need to understand whether employees are becoming more capable in the work they perform.
As AI continues to reshape workflows, HR leaders have an opportunity to make the development of human capability more intentional.
The most useful starting point is understanding what employees need to do differently, creating opportunities to practice those responsibilities, and measuring whether their performance improves.
The value of human capabilities becomes clearer when employees can demonstrate them in the work that matters.
