For many workers, learning artificial intelligence tools has quietly become “a second job”—one layered onto already full workloads, unclear expectations and rising anxiety about job security. Instead of freeing time and cognitive energy, AI initiatives often increase pressure, leaving employees feeling overworked or even disposable.
A 2024 McKinsey report on generative AI adoption found that employees are more likely to experience burnout when AI tools are introduced without role redesign or workload reduction, even as productivity expectations rise. Similarly, a recent study from The Upwork Research Institute reveals that while 96% of execs expect AI to improve worker productivity, 77% of employees feel it’s only increased their workload (with an alarming 1 in 3 employees saying they will quit their jobs within the next six months due to burnout).
Members of the Senior Executive AI Think Tank—a curated group of leaders in machine learning, generative AI and enterprise AI applications—note that this growing problem is not necessarily due to employee resistance or lack of technical ability, but how organizations sequence AI adoption, structure learning and communicate intent.
Below, Think Tank members offer a clear roadmap for introducing AI as a system-level change—not an extracurricular obligation—to help ensure this technology empowers people rather than exhausts them.
Create Capacity Before Capability
Anand Santhanam, Global Principal Delivery Leader at Amazon Web Services (AWS), says the perception of AI as a “second job” stems from a fundamental sequencing failure: Organizations often launch AI training before doing the harder work of redesigning jobs.
“Most AI rollouts require employees to learn new tools while performing existing jobs at full capacity,” Santhanam says. “This results in task accumulation.”
Santhanam argues that successful AI transformations follow a different sequence. Leaders first “identify and remove low-value work before introducing AI,” then create “capacity before capability” by freeing 10 to 15% of employee time for experimentation. Adoption must also be tied to a visible reduction in workload and not abstract efficiency promises.
“When leadership frames AI as ‘doing more with less,’ people see a threat,” Santhanam says. “When they frame it as ‘doing different work, better’ and actually remove tasks from plates, adoption follows naturally.”
“AI augments judgment and creativity; it doesn’t replace them.”
Redesign Work, Not Just Roll Out Tools
Su Belagodu, GTM Operator and Managing Partner at Intellectus Advisors, says AI stress often starts at the top. Leadership teams treat AI as a tool rollout instead of a work redesign.
“They ask people to ‘learn AI’ on top of their existing responsibilities, without changing priorities, incentives or how success is measured,” Belagodu says. That approach, she adds, “creates stress and resentment.”
Effective organizations, she says, anchor AI training to real workflows and are explicit about what work AI is meant to remove. Learning is treated as part of the job, with protected time and fewer competing demands. Just as important, leaders must define the human role clearly.
“AI augments judgment and creativity,” Belagodu says. “It doesn’t replace them.” Without that shift, she says, AI feels like pressure. With it, AI becomes empowerment.
Pair Institutional Knowledge With AI Fluency
Tim Maliyil, CEO and CTO Advisor at PerkyPet, takes a more optimistic view. He says AI can energize teams when leaders intentionally create cross-generational collaboration.
“I’ve brought in younger, AI-fluent team members to help train senior staff—including myself,” Maliyil says. Tasks like creating product specification diagrams are now “faster, more intuitive and genuinely enjoyable” for his team.
By pairing deep institutional knowledge with emerging AI expertise, Maliyil says organizations unlock productivity and morale at the same time. AI becomes a shared learning experience, not a top-down mandate.
“We’ve created a highly productive and energizing collaboration,” he says.
Lead With Communication, Not Silence
Chris Chambers, Principal at The Einstein Bridge, says AI change management must begin with internal communication. With public narratives equating AI to job loss, leaders cannot afford to stay silent.
“Companies have to start with their own internal public relations campaigns,” Chambers says, educating employees on the strategic rationale behind AI adoption. Communication should address emotional responses head-on and highlight the upskilling opportunities AI creates.
When employees see AI as a “win-win instead of an outright loss,” Chambers says, resistance fades.
Make Learning an Organizational Responsibility
Aditya Vikram Kashyap, Vice President of Firmwide Innovation at Morgan Stanley, says AI adoption fails when learning is framed as personal upskilling rather than organizational responsibility.
“Learning cannot be treated as a side hustle layered onto already saturated roles,” Kashyap says. “It must be financed with time, protected attention and the deliberate removal of obsolete work.”
Kashyap emphasizes that strong programs pair tool fluency with judgment fluency—teaching not just how to use AI, but when to question or override it. Performance metrics must also evolve, shifting from speed and volume to quality and outcomes.
“When AI is deployed as pressure, people feel replaceable,” Kashyap says. “When it is deployed as leverage, organizations compound both human dignity and capability.”
“Companies must restore harmony by embedding AI learning into paid work.”
Restore Balance by Embedding Learning Into Real Work
Dileep Rai, Manager of Oracle Cloud Technology at Hachette Book Group (HBG), says when AI learning feels like a second job, it is a signal that “imbalance has entered the system.” Technology designed to reduce cognitive load on the mind is instead “being added to its load,” undermining trust and adoption.
“Companies must restore harmony by embedding AI learning into paid work,” Rai says, aligning training to “real tasks and real relief—not after-hours effort.”
He emphasizes that effective programs are “gentle, role-aware and progressive,” allowing employees to experience usefulness before complexity. This sequencing builds confidence and prevents early overwhelm.
Rai also highlights the emotional dimension of change management. Leaders must communicate with clarity and compassion, reinforcing that “AI exists to support human judgment, not replace it.”
“When learning is rewarded with time, trust and growth,” Rai says, “people feel strengthened, not threatened.”
Embed AI Learning in Daily Workflows
Chandrakanth Lekkala, Principal Data Engineer at Narwal.ai, says AI training fails when it is treated as a standalone initiative rather than an extension of daily work.
“Companies should embed AI training within existing workflows,” he says, so learning happens where value is created.
Protected learning time during work hours is essential, Lekkala adds, but so is demonstrating immediate value. Leaders should focus on “quick wins” and “role-specific use cases showing tangible productivity gains,” helping employees see results early. Peer mentorship networks also play a critical role, reducing isolation and normalizing experimentation.
Lekkala encourages organizations to publicly celebrate early adopters, adjust performance metrics to account for learning curves and ensure leaders visibly use AI tools themselves.
“Framing AI as augmentation with adequate support transforms perception from burden to opportunity,” he says.
Teach Fundamentals Before Tools
Sinan Ozdemir, AI thought leader and Founder of Crucible, says many organizations inadvertently create the “second job” problem by starting in the wrong place.
“Most AI training jumps straight to tools: click here, prompt like this, watch the demo,” he says. “That’s backwards.”
According to Ozdemir, people need fundamentals first: an understanding of how AI systems work and where their limitations lie. Without that foundation, employees will be lost.
“The ‘second job’ feeling comes from memorizing interfaces without grasping the concepts or seeing real results,” he says.
Effective programs, Ozdemir argues, prioritize applied learning through “mini-projects tied to real job tasks, not passive walkthroughs.” When leaders “teach the why before the how” and make learning hands-on, employees become energized rather than exhausted.
Set Supportive Expectations at the Manager Level
Sathish Anumula, Enterprise and Business Architect at IBM Corporation, says even well-designed AI programs can fail if managers reinforce unrealistic expectations with accelerated delivery demands.
“Companies must give people protected time to learn, offer role-specific training and ensure managers set supportive—not accelerated—expectations,” he says.
AI adoption should also come with ready-to-use workflows and ongoing coaching, Anumula adds, alongside a clear message that the technology exists “to reduce busywork, not replace talent.” When employees feel supported at the team level, AI shifts from overwhelming to empowering.
Fix the Design, Not the People
Divya Parekh, Founder of executive coaching brand DivyaParekh.com, feels the problem isn’t a training one, but a design one. Organizations, she says, too often “teach tools and call it transformation,” leaving employees to stack AI on top of already full workloads.
“AI training must be integrated into real-world work through redesigned workflows, clear ownership, fewer tasks and permission to relinquish tasks that AI now handles,” Parekh says. Without that permission, stress is inevitable.
Parekh stresses that change management matters more than technical capability. Leaders must communicate clearly about how AI affects expectations, performance and job security—“Silence breeds fear.” She argues that the goal is not faster people, but “better systems.” When AI removes friction instead of adding responsibility, employees feel supported rather than disposable.
“We’re asking workers to change how they think and work without giving them a proven methodology or framework to do it safely and effectively.”
Provide Structure and Safety
Jim Liddle, entrepreneur, investor, advisor and enterprise AI strategist, says the stress surrounding AI adoption has far less to do with the technology itself.
“The real issue isn’t the technology,” Liddle says. “It’s that we’re asking workers to change how they think and work without giving them a proven methodology or framework to do it safely and effectively.”
Too often, he explains, companies default to a sink-or-swim approach: “Here’s a new tool, figure it out,” or “Go self-learn on this course.” That lack of structure fuels anxiety, particularly when employees feel they must be AI-literate simply to remain employable.
Liddle argues that AI must be treated like any other professional skill. A structured approach should function as a critical-thinking framework, not just a training program, with accountability built in from day one. When employees are given clear guardrails and a repeatable way to work with AI, learning stops feeling risky and starts making day-to-day work more productive and efficient.
Lower the Stakes of Learning
Uttam Kumar, Engineering Manager at American Eagle Outfitters, says burnout is inevitable when AI training is treated as an extracurricular activity. Retail leaders, in particular, must integrate AI learning directly into the paid workday.
“They should establish ‘Innovation Sprints,’” Kumar says, “where staff are given dedicated, on-the-clock hours to experiment with tools without the pressure of their standard KPIs hanging over them.” By lowering the stakes of the learning curve, organizations transform AI from a perceived threat into a career-enhancing capability.
Kumar also notes that when employees see AI handling routine tasks like inventory counts or scheduling, the value becomes immediately tangible, and they reclaim their time for high-value customer engagement.
“This structural shift ensures that AI is viewed as a supportive co-pilot rather than a replacement,” he says.
Treat AI as Craft, Not Overtime
Pradeep Kumar Muthukamatchi, Principal Cloud Architect at Microsoft, says organizations must confront a hard truth: AI learning feels like unpaid overtime for many employees, driven by dense content, unclear benefits and unrealistic timelines.
The fix, Muthukamatchi argues, is straightforward but often overlooked.
“Companies should redesign work so AI training is embedded into normal hours, role-specific, and paced in small, practical sprints supported by peer ‘AI champions’ and psychological safety,” he says.
When companies treat AI fluency as an upgrade to an employee’s craft—not a countdown to their replacement—adoption accelerates.
Easy Steps to Ease the Stress
- Remove low-value work before introducing AI. Free up capacity first so AI learning replaces tasks rather than piling on more work.
- Redesign jobs, not just training programs. Anchor AI learning to real workflows and redefine success so learning is part of the role.
- Pair AI fluency with institutional knowledge. Use cross-generational collaboration to turn AI adoption into a shared productivity win.
- Lead AI adoption with proactive internal communication. Clearly explain the strategic rationale, address fears early and frame AI as a win-win.
- Make AI learning an organizational responsibility. Fund adoption with time, attention and the removal of obsolete work, not personal sacrifice.
- Embed AI learning into paid work with role-aware pacing. Design training to be progressive, compassionate and focused on mastery over volume.
- Integrate AI training directly into daily workflows. Show immediate value through quick wins, peer mentorship and leadership participation.
- Teach AI fundamentals before tools. Focus on the “why” and applied mini-projects so learning feels empowering, not exhausting.
- Set supportive expectations at the manager level. Protect learning time and reinforce that AI exists to reduce busywork, not replace talent.
- Redesign systems so AI removes responsibility, not adds it. Give employees permission to relinquish tasks AI now handles.
- Replace ad hoc learning with a structured framework. Treat AI as a professional skill built on critical thinking, accountability and control.
- Lower the stakes with on-the-clock experimentation. Create dedicated learning sprints without KPI pressure to prevent burnout.
- Normalize AI learning as part of the job. Embed training into work hours, pace it in small sprints and support it with peer champions.
Turning AI From a Burden Into a Force Multiplier
AI does not have to feel like a second job. When leaders redesign work, protect learning time and communicate with clarity, technology becomes a lever for human capability rather than a source of stress.
As members of the Senior Executive AI Think Tank assert, the future of AI at work is not about faster people—it is about better systems that respect time, judgment and dignity. Done right, AI does not make people disposable. It makes their best work more possible.
