- Work redesign has emerged faster than expected as a critical leadership skill.
- Research shows humans do their best work when they feel like it meaning.
- The five elements of meaningful work are task integrity, mastery, autonomy, significance and connectedness.
Wasn’t AI just meant to take the boring work off our hands? To make things like invoicing and data entry go away, not gobble up the parts of work that gave us meaning?
A major research piece by an Australian University has called out work redesign (balancing automation with human expertise and rerouting workflows) as one of the most critical leadership skills for 2026. How to adopt AI at scale, without quietly breaking the human system that sustains it, will be one of the biggest challenges facing organizations across the globe.
Decades of research, and several seasons of Severance, has shown that humans do their best work when they feel like that work has meaning. But what makes work meaningful? And how can leaders redesign workflows to make sure that AI strengthens, rather than strips, the human core of work?
Task integrity: when work stops making sense
In many organizations, AI has improved efficiency at the task level while degrading coherence at the job level. One of the most under-discussed casualty of AI-enabled work, task integrity refers to whether people can see their work as part of a coherent whole rather than a stream of disconnected actions.
Designing roles around outcomes rather than activities, reducing unnecessary hand-offs between humans and systems, and explaining why the work exists in its current form are all ways to protect task integrity. In practical terms, this means redesigning workflows end-to-end when AI is introduced, not just inserting tools into existing processes.
“Organizations unintentionally damage meaning when job-remapping and re-tooling misses linking the employees’ AI literacy and skills to long-term business sustainability,” says Senior Consultant and Great Place To Work, Ha-Minh Chau.
In practical terms, this means redesigning workflows end-to-end when AI is introduced, not just inserting tools into existing processes. It also means leaders spending time explaining trade-offs, not just targets.
“Strong leaders can articulate how the human-plus-AI team’s work directly contributes to core organisational purpose and societal value beyond efficiency metrics.”
Mastery: skills that still belong to humans
One of the quiet anxieties of AI-enabled work is that people are no longer sure which skills are worth improving. Skills compound when work allows them to be used, tested and refined over time. If work becomes too automated, people stagnate. If it becomes too manual, they burn out.
Organizations that approach mastery seriously are explicit about which human capabilities need to deepen as AI scales, and they protect space for learning inside the work itself. They create roles where human judgement, ethical reasoning and relationship management remain central.
To build a culture of human skill evolution, “Leaders should balance investing in technology with developing employees’ AI skills through creative formal and on-the-job learning, such as gamified AI curricula and cross-functional teams exploring AI opportunities,” suggests Ha-Minh Chau.
Employees consistently report higher wellbeing when they feel management understands their work and invests in their growth, and that uplift in wellbeing is strongly linked to higher productivity, better performance, and lower turnover, factors that translate directly into financial gains for the organisation.
Autonomy: employee engagement and AI
Autonomy is often confused with flexibility, but they’re not the same thing. Flexibility is about where and when work happens, autonomy is about who gets to decide how the work is done. In AI-heavy environments, autonomy is often quietly reduced as systems dictate workflows, thresholds and outputs.
Employees disengage when control is imposed without explanation. As systems become more prescriptive, autonomy must be deliberately protected or it quietly disappears.
In practical terms, autonomy shows up in decision rights. Can teams override AI outputs? Can they question metrics? Can they adapt workflows locally? High-performing organisations make autonomy explicit and define decision rights clearly. They allow teams to adapt processes locally, and treat AI outputs as inputs, not commands.
Autonomy is protective, it buffers stress, supports learning, and enables ethical judgement. Removing it in the name of efficiency may deliver short-term gains, but it corrodes engagement over time.
Significance: work that survives scrutiny
Significance answers a simple question. Does this work matter beyond the system that measures it?
Our Well-Being at Work research identifies a sense of purpose as a key contributor to resilience, but as technology mediates more of our work, leaders must actively reconnect people to outcomes, communities and consequences.
Organizations that maintain significance expose teams to customers, close feedback loops, and show how decisions land in the real world. Where that line of sight is lost, meaningful work often goes with it.
Connectedness: work is still a social activity
Collaboration tools are great at keeping projects moving, but they’re poor substitutes for trust, camaraderie and the feeling that you belong. Hybrid and AI-enabled work means that connectedness often needs to be designed into work itself through peer problem-solving and moments of deliberate interaction that remind people they’re part of something bigger than their task list.
In the end, the challenge isn’t whether work happens in offices, homes or clouds of code, but whether we design it in ways that still let people learn from each other, feel seen, and understand why their effort matters.
