2026 Global Human Capital Trends

Organisations are twice as likely to exceed ROI expectations when they deliberately design work and the interactions between humans and machines. Effective design must embrace the levels and the type of interaction needed. The level of interaction should consider both the macro (organisational strategy, governance, ethics) and micro (specific work, roles, team composition) perspectives, while the human-machine interaction types can range from AI as a boss, coach, collaborator, assistant, direct report, doppelgänger, or autonomous worker. Choosing the right one at the right time for specific work and workers is essential.

AI-generated content blurs authorship, amplifies bias, and makes it difficult to distinguish real from fake. The erosion of agency makes it hard to determine human vs. AI contributions, leading to concerns about productivity and fair evaluation. Over-reliance on AI can erode critical judgment and domain expertise as well as impacting talent acquisition with fake resumes and deepfake candidates. Organisations need to shift from cybersecurity to "disinformation security" by implementing AI lineage mapping, conducting AI risk simulations, and using real-time identity authentication.

AI has the potential to transform decision-making, but AI use in decisions is outpacing organisational oversight, leading to unclear responsibility chains and reduced human ownership. Organisations need to elevate the human decision-making as a discipline by using decision frameworks, encouraging sound decision basis and culture, and modernising decision rights and governance for AI. It's crucial to grow human decision-making skills, train and enable managers to manage AI, and rigorously evaluate AI performance through continuous monitoring and accountability anchors.

AI is raising fundamental questions for workers about fairness, effort, and job security, which, if unanswered, lead to cultural debt. Organisations must set a strong foundation and prioritisation for cultural evolution through leadership alignment, anchoring on purpose and values, and transparent communication. Building trust and human connection is crucial, including redesigning work to embed AI with a focus on human-to-human interactions.

Orchestration allows organisations to be both big and fast, improving speed, quality, and cost simultaneously. Effective orchestration requires quicker, more effective decision-making and creating "plug-and-play modularity" for diverse human-AI teams. Further this requires applying strategic levers to create capability and capacity through defining a “build, buy, borrow, bot" strategy, leveraging "multipliers" like blending human and AI capabilities, boosting productivity, and bridging talent across boundaries. AI can also help orchestrate capabilities and capacity through tools like digital twins and agentic AI for real-time monitoring.

Functions were designed for dependability and specialisation but now struggle with speed, orchestration, and cross-functional collaboration needed for challenges like AI implementation and innovation. Organisations should separate "run the business" activities (which can be centralised and automated) from "grow the business" activities (which require multidisciplinary, outcome-focused teams). Options for transformation range from enhanced collaboration to solution-based teaming and full reimagination of work. This often involves integrating workflows and potentially whole functions such as HR and IT to enable  new capabilities within the business.

Workers experience a dizzying pace of change, leading to decreased well-being and increased workload. "Changefulness" cultivates workers' abilities to adapt, experiment, and learn as a daily muscle, rather than a disruption. Organisations can achieve this by creating a "surround-sound system" of adaptive experiences embedded in the flow of work, such as real-time AI coaching and micro-challenges. AI enables a shift to "change and learning at the unit of one," localising and hyper-personalising initiatives based on individual motivations and needs.

Trust in institutions is eroding, and board decisions affect human sustainability (skills, employability, well-being, belonging), labour market health, truth and trust (data integrity, transparency), and general economic health. Each trend discussed in the report has long-term societal implications. Poorly designed human-machine relationships can hollow out empathy, while strong data governance preserves institutional legitimacy. Unchecked AI decision-making threatens trust, but deliberate governance can extend human capability. Boards must actively engage with these long-term considerations and extend their influence beyond organisational boundaries to shape a more human future.