AI and the End of Information Knowledge Silos

Why am I using a very human image in an article about AI, you may ask.

Because AI is already everywhere, and the future of work will not be defined by how advanced AI becomes, but by how intentionally organisations redesign work, power, and accountability around it. Much of the current conversation still focuses on tools, copilots, and automation, however the deeper shift is structural. When AI enters the flow of work, it shortens the distance between information and action, and in doing so begins to dilute the traditional hierarchies that have shaped organisations for decades.

Historically, hierarchy existed to manage information scarcity and risk. Knowledge sat in functions, seniority signalled access, and decisions flowed upwards through layers of approval. AI disrupts this logic. When people at every level can access relevant insight, policy context, and cross-functional information in seconds, the justification for rigid hierarchies weakens. Authority no longer comes from hoarding information, but from setting direction, defining standards, and creating trust in how decisions are made.

Satya Nadella, Chief Executive Officer of Microsoft, speaking at the World Economic Forum Annual Meeting in Davos, stated that AI will only deliver real value when it is adopted broadly across organisations, and that the real challenge for leaders is trust, not access. People have to use AI to learn how to trust it, and to put guardrails in place that make trust possible. That requires a shift in mindset and skills. Leaders can no longer govern purely through process and escalation. They must govern through context, clarity, and embedded guardrails that allow people to act confidently without constant oversight.

This is where hierarchy starts to thin rather than disappear. AI does not remove the need for leadership, but it changes its role. When information flows freely, leaders move from being decision bottlenecks to being architects of the system. They decide what “good” looks like, what is safe, what is on brand, and what must never happen. The organisation becomes flatter in information flow, but sharper in standards. Nadella described this as a complete inversion, where information no longer trickles up, but flattens across the organisation, forcing leaders to rethink structure.

Context becomes the critical asset. AI represents a new intelligence layer, but it is only as good as the context it is given. As information is increasingly democratised, silos become a structural liability rather than a design choice. Value shifts away from who controls information towards who defines standards, enables coordination, and supports better decisions. This shift has happened with extraordinary speed, in less than five years, and it changes where power and value sit inside organisations.

Most organisational value sits in tacit knowledge, policies, SOPs, playbooks, and lived experience built up across departments. Turning that into usable context for AI is not a technical task alone. It is organisational work. Some describe this as context engineering, but in practice it is about making institutional knowledge explicit, current, and actionable at the point of need. This is exactly where Edify Collective sits. We give frontline teams and managers approved, role-specific answers in seconds, grounded in your SOPs, policies and playbooks, and reinforce them with short practice moments and role plays that build habits. This turns context into operational performance, reducing searching and escalations, speeding up ramp-up, and lowering risk across every site and shift.

This also explains why many organisations struggle to see immediate productivity gains from AI. According to Gallup’s State of the Global Workplace 2025 report, global employee engagement has fallen to 21%, with manager engagement declining from 30% to 27%, a signal that the system is already under strain before AI is fully embedded. AI does not fix disengaged systems. It exposes them. When guidance is fragmented, roles are unclear, and managers are overloaded, AI amplifies confusion rather than resolving it.

The dilution of hierarchy therefore creates both opportunity and risk. When everyone can access answers, briefings, and the same operational context instantly, decision-making accelerates and organisations become more responsive. But without clear guardrails, accountability can blur, inconsistency scales, and risk moves closer to the core. This is why leadership will be the defining variable in AI-powered work. Firms that invest in redesigning workflows, clarifying decision rights, and embedding trusted guidance into daily work will see gains. Others will see noise, rework, and a loss of confidence.

The future of work powered by AI will be uneven. Differences will emerge by sector and by company, driven less by technology choice and more by leadership will. The organisations that succeed will treat AI as performance infrastructure. They will accept that hierarchy based on information control is eroding, and replace it with systems that shorten the gap between knowing and doing. In that world, people are trusted to act because the organisation has done the hard work of making trust possible.