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AI is changing how work gets done faster than many organisations are redesigning how capability is built. As AI reshapes roles and workflows, organisational development must move beyond managing change after the fact. It must help leaders make informed decisions about capability, workforce design and the human systems that make transformation sustainable.
Automation Speed Outpacing Talent Strategy
In our work, we've observed a critical misalignment across different organisations implementing AI. The pressure to automate is real and urgent in order to keep up with the market. According to Harvard Business Review, entry-level job pipelines are thinning globally, and the consequences of eliminating these roles in favour of AI are compounding.[1] Yet most organisations are making automation decisions based on a single metric: cost savings. They're not asking what they're actually losing.
Consider what happens when an organization eliminates entry-level roles without a parallel talent strategy. The balance sheet improves immediately. But within two to three years, the organization struggles to fill middle management positions. Senior leaders realize that no one in the organization understands how critical decisions are actually made. The tacit knowledge that once lived in those entry-level roles has simply vanished. The efficiency gains of year one becomes the capability crisis of year three. This isn't a hypothetical concern. It's a pattern occurring across organisations of all sizes, particularly those facing talent retention challenges and rapid digitalization pressures.
The “Capability Debt”: What Gets Lost When Entry-Level Roles Disappear
To understand why this matters, we need to look at how professional development actually works. Research from the Center for Creative Leadership reveals a fundamental truth that most organisations overlook: 70 percent of professional development comes from on-the-job experience, 20 percent from relationships with experienced colleagues, and only 10 percent from formal training.[2] When we automate entry-level roles, we're not just eliminating tasks. We're eliminating 90 percent of the development model that creates competent middle managers and future leaders.
The World Economic Forum's Future of Jobs Report 2025 identifies the skills most critical for the AI era: analytical thinking, creative thinking, resilience and adaptability, leadership and social influence, and curiosity and lifelong learning.[3] These aren't skills that can be downloaded through a training module or acquired by prompting a language model. They're developed through doing: Navigating ambiguity, managing relationships under pressure, recovering from mistakes, and learning to influence without authority.
Entry-level roles, at their best, were the crucible where these capabilities were forged. They were also where tacit knowledge, i.e. the knowledge that lives in people rather than in systems, was transferred. How do you navigate a difficult client? When do you escalate an issue? How to manage conflict? These questions have answers that exist nowhere except in the minds of experienced practitioners. When we automate those roles, we don't just lose efficiency gains. We lose the mechanism for transferring irreplaceable knowledge to the next generation.
This is what we call "capability debt", a systemic obligation an organization creates when it breaks the environments where critical capabilities are developed. Unlike a skills gap, which suggests individuals need to catch up, capability debt names an organizational responsibility. It shifts accountability from the employee to the institution.
The Cost of Doing Nothing
The stakes are higher than most organisations realize. According to the World Economic Forum, 39 percent of core skills will become obsolete by 2030.[4] Organisations that aggressively automate without investing in their people aren't just facing a skills gap. They're accumulating capability debt while simultaneously damaging the talent pipelines that would repay it.
In regions experiencing significant talent mobility, where skilled professionals are leaving for opportunities elsewhere, this challenge is particularly acute.[5] Organisations are already losing senior practitioners who carry irreplaceable tacit knowledge. When they then automate entry-level roles, they're further weakening the pipeline.
The organizational structure in many companies compounds this problem. Many organisations have a thin middle management layer, a structure that was sustainable when there was a strong pipeline from entry-level to middle management. When that pipeline breaks, the organisation becomes top-heavy and brittle.
Being passive about this challenge means to continue automating without strategic talent planning. This leads to a cascade of problems: difficulty filling leadership roles, loss of organisational knowledge, deeper silos, slower decision-making, and ultimately, reduced competitive advantage. The organisations that will struggle most in the next five years won't be those that automated too little. They'll be those that automated without talent strategy.
The Counter-Productivity of Speed Without Strategy
Succeeding in transforming organisations for AI adoption isn’t synonymous with aggressive and speedy automation. Success in this case means automating the most intelligently. This requires a different approach. Rather than making automation decisions based on cost calculations alone, organisations need to use data to understand the true impact of those decisions. This is where organisational network analysis becomes relevant and essential.
When we conduct organisational analysis that maps the actual network flow of information, influence, and collaboration within an organisation, we often discover that entry-level and junior roles are critical connectors between silos.[6] A junior analyst might be the person who understands how finance connects to operations and strategy. An associate might be the bridge between two departments that rarely communicate. These aren't senior positions on the org chart, but they're central nodes in the organizational network that are not clearly visibly in the formal hierarchy of the organization.
When those roles are automated without understanding their network function, the organization doesn't just lose individual contributors. It loses connectors. Silos deepen. Decision-making slows. The organization becomes more efficient in the short term and less effective in the long term.
Mapping Real Roles and Dependencies through Organisational Network Analysis
Through Organisational Network Analysis, an organisation can identify where capability debt is most dangerous. We can see which roles are central to knowledge flows, which individuals are at risk of leaving because they lack development pathways, and where organizational resilience is most vulnerable. The data reveals what intuition often misses. This is where strategy replaces guesswork. Rather than automating based on assumptions, organisations can use three complementary diagnostic approaches.
In a nutshell, Organisational Network Analysis reveals the actual flow of information and influence. It identifies bottlenecks, key connectors, and silos that might otherwise remain invisible.
Diagnostic tools such as Organisational Network Analysis or People Analytics help in answering the questions that matter:
• Who could perform this work without AI if required?
• Who can reliably evaluate AI outputs for accuracy and nuance?
• What developmental pathways no longer exist?
• Where are the critical network nodes we cannot afford to disrupt?
• Where is capability most concentrated and most at risk?
These aren't rhetorical questions. They're diagnostic questions that separate intelligent automation from automation for its own sake.
Workload Redistribution, Not Headcount Reduction
Organisations that succeed in AI transformation use automation to free up human capacity, not to eliminate people. They redistribute workload to create space for strategic work. They invest in building new capabilities such as how to work alongside AI, how to evaluate AI outputs, how to think creatively in an AI-augmented world, while protecting the core capabilities that enable organizational survival.
This requires building what we call a "distributed apprenticeship pipeline." Since traditional entry-level roles are shrinking, organisations need new, structured ways to transfer tacit knowledge and develop critical capabilities. Depending on the organisational context this may mean identifying senior practitioners who carry irreplaceable tacit knowledge, particularly those nearing retirement, and recognizing them as strategic assets rather than costs to be managed. To work on building a solid pipeline, building structured, cross-functional shadowing programs for mid-level managers with these senior practitioners is essential for tacit knowledge retention before transitions occur.
The goal is straightforward: ensure that tacit knowledge is transferred, that capability is developed, and that the organization doesn't lose what it needs to survive.
Digital-First Culture Requires Balance
A true digital-first culture isn't about deploying the latest software or having the most advanced algorithms. It's about understanding the synergy between human capability and artificial intelligence. It's about balancing automation with intentional capability investment. It's about being pro-AI and pro-transformation while remaining pro-people.
The organisations that will win the next decade won't be the ones with the best algorithms. They'll be the ones with the most resilient, adaptable, and deeply knowledgeable human leaders. This means being intentional about retaining talent. It means offering development pathways that keep people engaged.
The question for your organization is clear: Are you automating to cut costs, or are you automating to create space for capability building?
Resources
[1] Harvard BusinessReview. (2026). Your Talent Strategy Has to Keep Up with YourAI Transformation by Jenny Fernandez. https://hbr.org/2026/06/your-talent-strategy-has-to-keep-up-with-your-ai-transformation
[2] Center for Creative Leadership. (n.d.). The 70-20-10 Framework for Learning and Development. Referenced in multiple organizational development research and cited in professional development literature.
[3] World Economic Forum.(2025). Future of Jobs Report 2025: Skills for the AI Era. https://www.weforum.org/publications/the-future-of-jobs-report-2025/
[4] World Economic Forum. (2025). Future of Jobs Report2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/
[5] Harvard Business Review. (2026). Your Talent Strategy Has to Keep Up with Your AI Transformation by Jenny Fernandez, discussing global talent mobility patterns and brain drain dynamics.
[6] Teamspective. (2026).Organizational Network Analysis: Complete Guide to MappingWorkplace Networks. https://teamspective.com/blog/organizational-network-analysis/