AI for Managers: How Managers Can Use AI to Improve Work and Team Performance

Updated August 21, 2026

Managers are being asked to do more than manage work. They have to translate changing business priorities, coach employees, develop skills, improve performance, make talent decisions, and now help their teams adapt to AI.

That pressure is showing up in the workforce. Global manager engagement fell from 27% to 22% in 2025, the steepest year-over-year decline Gallup has recorded. At the same time, Microsoft’s 2025 Work Trend Index found that 80% of the global workforce says it lacks enough time or energy to do its work.

AI for managers can help, but its value goes beyond saving time. 

Artificial intelligence (AI) may be the most powerful and readily available tool managers have to reduce their burnout, increase their effectiveness, and reimagine their roles. Instead of being consumed by administrative tasks, managers can leverage AI to focus on what truly matters — building relationships, developing employees, and helping drive their organizations’ strategic initiatives.

There’s another reason managers matter to AI. Betterworks’ 2026 State of Performance Enablement research found that 41% of managers and supervisors use generative AI tools daily or weekly, but only 18% say their team goals frequently include expectations around using AI tools. Fewer than 16% of managers and employees understand their organization’s AI vision.

So the challenge is two-sided. Managers need to learn how to leverage AI effectively themselves, and they need to help employees understand where AI belongs in everyday work.

How AI elevates managers

AI works best when it removes friction around management, not when it tries to automate management itself. That frees up more time for the parts of the job that require human context and judgment.

AI tools can help managers:

• Summarize information before a 1:1 or coaching conversation 

• Identify themes across goals, feedback, and recent work 

• Draft and refine goals connected to business priorities 

• Reduce time spent assembling performance documentation 

• Surface potential skills gaps and development opportunities 

• Identify goal progress and risks that need attention 

• Organize follow-ups after conversations 

• Analyze patterns that would be difficult to spot manually

These practical applications go beyond a writing assistant. Machine learning and other AI technologies can organize large amounts of information, and generative AI can turn that information into a useful starting point for managers.

AI can provide context, but the manager remains responsible for interpreting it, asking questions, and making decisions.

That is especially important in performance management. AI-assisted performance management can help managers reduce manual work around goals, feedback, and reviews while maintaining an evidence-based view of performance. Instead of relying on what they remember at the end of a review period, managers can work from information gathered throughout the performance cycle.

The measure of success is better management, not more AI output.

AI makes skills and career development more actionable

Career development is one of the most promising applications of AI for managers because effective development requires both information and human judgment.

AI-driven systems can help identify skills demonstrated through actual work, detect gaps between current capabilities and future needs, and surface learning experiences or projects that may support an employee's growth.

Managers can then add the context that technology lacks.

An employee may have the technical ability to take on a new responsibility but needs confidence, visibility, or experience working across functions. Another employee may be ready for stretch work that an algorithm alone would not understand from a job title.

The strongest model combines AI with manager judgment.

Rather than relying only on static skills inventories or self-reported capabilities, organizations can use Skills Intelligence to create a more current picture of workforce capability. Managers can use those signals to inform development conversations, recommend relevant opportunities, and identify high-impact projects that connect employee growth to business needs.

Managers, once freed from unnecessary administrative burdens, can use these insights to provide personalized coaching, assign stretch opportunities, and connect employees with work that supports both their ambitions and the organization's priorities.

"Managers are vital to the future of work. AI doesn't replace them — it amplifies their impact," says Jamie Aitken, VP of HR transformation at Betterworks. "By automating routine tasks, AI frees managers to focus on uniquely human roles like coaching, guiding skills development, and driving innovation. This transformation empowers managers to elevate their teams, unlock potential, and shape the organization's strategic future."

AI helps managers build stronger relationships

One risk of poorly implemented AI is that it creates more distance between managers and employees, but thoughtful implementation can close that distance instead.

AI can reduce the preparation and documentation surrounding a conversation so the manager can focus on the conversation itself.

Before a 1:1, for example, a manager might use AI to pull together recent goal progress, recognition, open follow-ups, and relevant feedback. During the meeting, the manager can spend less time reconstructing what happened and more time discussing what the employee needs next.

Afterward, AI can help summarize themes and action items so both people have a shared record.

AI can support the human side of management by providing real-time information, but empathy, trust, context, difficult conversations, and judgment still belong to people.

This mirrors how Betterworks approaches performance management: continuous signals from goals, feedback, skills, and conversations give managers better evidence for coaching, while people remain responsible for interpretation and action.

How managers can use AI to improve everyday work

For managers deciding where to start, the highest-value AI projects are usually connected to recurring work where better information or less manual effort will improve an important outcome.

A simple way to identify opportunities is to ask three questions:

1. What work takes time without requiring much managerial judgment? Meeting preparation, information gathering, initial summaries, status updates, and first drafts may be candidates for AI assistance.
   

2. Where would better context improve a decision or conversation? Examples include preparing for a 1:1, checking goal progress, spotting recurring feedback themes, or understanding where an employee may need development.
   

3. Which activities are close enough to real work to produce measurable value? The best AI initiatives connect to actual team priorities. Managers should avoid implementing AI simply because a tool is available. Identifying high-impact use cases first makes adoption more practical and gives teams a clearer way to evaluate whether AI is actually helping.

Managers can then start with a small number of real-world workflows, evaluate the results, and expand what works.

How managers can elevate AI adoption

Giving employees access to AI tools does not mean they will understand when, why, or how to use them.

Betterworks' 2026 research illustrates the disconnect. While 81% of executives say AI adoption is required or encouraged, only 16% of employees report using it regularly. Managers and supervisors fall between those groups, with 41% reporting daily or weekly generative AI use.

That puts managers between the organization's AI strategy and employees' daily experience, translating one into the other.

The most effective managers translate broad AI initiatives into specific expectations rather than simply telling employees to "use more AI." That means defining what AI should help the team do better, where it shouldn't be used, what information employees can share with approved systems, how the team will judge the quality of AI-assisted work, and which outcomes matter most.

That clarity helps move AI adoption from experimentation to a repeatable way of working.

Encourage focused AI experimentation

Experimentation works best when it's tied to a clear purpose.

Managers can create space for employees to test approved tools against real work: preparing research, synthesizing information, improving a workflow, generating alternatives, or reducing repetitive effort.

Then the team should compare results. That comparison should look at whether the AI-supported approach saved meaningful time, improved accuracy or quality, still involved employee verification, produced a better customer or employee outcome, and is worth repeating.

These conversations turn experimentation into organizational learning rather than a collection of disconnected AI tricks.

Managers can also ask team members to share useful prompts, lessons, and failures. That makes AI learning social and gives employees examples rooted in the work their peers actually perform.

Use performance management to reinforce AI adoption

AI adoption is more likely to stick when employees see how it connects to goals, expectations, feedback, and growth.

Betterworks' 2026 research found that 90% of HR leaders say AI has changed the definition of a high performer, yet only 42% say their organization currently includes AI expectations in goal-setting.

That gap creates ambiguity. Employees hear that AI matters, but the systems used to define success may tell a different story.

Managers can help close that gap by incorporating responsible AI learning into goals and development plans. Depending on the role, that might mean learning an approved AI tool, improving a workflow with AI, developing stronger verification practices, or testing an AI-assisted approach to a measurable business problem.

Goals should connect AI use to a real outcome, like “Reduce time spent on data analysis by at least 25% in Q4.” 

Managers can also use 1:1s and feedback conversations to check in on what's working, where the employee is struggling, what new capability they're developing, and where human review matters most.

This creates a feedback loop between AI adoption, learning, and performance.

Build an AI-learning culture

AI capabilities will continue to change, so long-term success depends less on mastering one tool than on building the ability to learn continuously.

Managers can work with HR and L&D leaders to provide learning experiences tied to real tasks instead of relying only on generic AI training. They can also make AI learning part of regular team conversations so employees have a place to share questions, practices, and new use cases.

This matters because employees need clarity and relevance, not just access.

Betterworks' 2026 research found that while 49% of HR leaders rank AI use as a top influence on employee performance, only 9% of employees believe AI skills have become more important to their success. Managers can help close that gap by showing employees how new capabilities connect to better work, career development, and meaningful outcomes.

AI fluency grows through use, reflection, feedback, and repetition. Managers are well-positioned to create that cycle.

“Leaders can mandate AI adoption, but sustainable momentum and adoption come from showing employees how it will make their work more meaningful,” says Andrea Lagan, Chief Operating Officer at Betterworks. “When people see how AI helps them focus more of their time on activities that energize them — not just faster work — they start to lean in. That’s the tipping point for culture change. HR’s role is to make that connection visible every day.”

The future of AI for managers is human-led

AI can give managers better information and more capacity to manage well without taking their place.

The strongest applications of AI for managers make important human interactions more informed, timely, and useful, rather than removing people from performance management. That means:

• Reducing administrative tasks without outsourcing judgment

• Using data-driven insights to ask better questions rather than treating an algorithm as the answer

• Helping employees build new skills while still recognizing aspirations and context that data can't fully capture

• Making AI part of real work, not a separate initiative

That shift is already underway. With the Betterworks MCP Server, managers can connect Betterworks performance context to AI assistants like ChatGPT, Claude, or Gemini. They can check goal progress, prepare for a 1:1, review recognition and follow-up areas, and create or update goals from the AI tools they already use.

AI can make managers faster. But even better, it can help them become more informed, consistent, and focused on the work only people can do: coaching, judgment, connection, and leadership.