Editorial
White Paper
Why AI Rarely Improves How Work Actually Gets Done
AI agents are quietly beginning to move from experimental tools to actors in our workplaces. As they are deployed, they will begin to plan, decide and execute across multiple steps. The steps will often move faster than human approval loops were ever designed to accommodate. As this shift happens, we will change from speculative future and lab-bound experiments, to omnipresent technologies. It is already happening inside operational systems, product workflows and organizational processes that rarely attract senior attention.
What makes this moment consequential is not the technology itself, but what it collides with. Organizations have long struggled with diffuse responsibility, informal ownership and decisions that outlive the people who made them. As AI systems become more autonomous, those weaknesses are amplified.
At stake is a hidden curriculum of work: the unspoken rules that determine who owns decisions, how accountability travels and where responsibility dissolves.
The Quiet Truth About AI Failure
AI does not create new organizational failures. It magnifies existing ones. Ambiguous ownership, layered approvals, decision-by-proxy cultures and informal handoffs have always shaped how work gets done. Autonomous systems simply make these dynamics more visible, more scalable and harder to ignore.
When an AI agent produces an outcome no one can fully explain, the failure is rarely technical. It is structural. The system is executing exactly what the organization implicitly taught it to do: act without a clear owner.
The risk is not just about intelligence. It is velocity. Decisions happen faster than organizations are structured to explain, justify or challenge. That gap is where accountability problems emerge.
Delegation Versus Abdication
Delegation involves granting authority while retaining responsibility. Abdication produces outcomes without ownership. Agentic AI makes it deceptively easy to slide from one into the other, particularly when these systems are framed as neutral tools rather than ongoing decision-makers embedded in work.
A leader approves a deployment, one team designs the workflow, another team inherits it and over time accountability becomes diffuse. No one feels fully responsible for outcomes, yet the system continues to act. This is not malice or negligence. It is organizational gravity pulling responsibility away from authority until it disappears.
The Inheritance Problem
AI workflows do not necessarily disappear when teams reorganize, leaders rotate or priorities shift. If they are adopted into technology stacks and critical infrastructure, they persist beyond their creators. They are inherited by new teams, new roles and new leaders who did not design them and may not fully understand their assumptions or limits.
The agent remains active whilst the authority that created it does not travel with it. Responsibility becomes abstract. Ownership quietly shifts to an unnamed “someone.” Decisions made under one set of conditions continue to execute under another, long after their original rationale has faded.
The Questions No One Can Answer
When autonomous systems behave in unexpected ways, the most revealing moments are not technical postmortems but the pauses that follow simple questions. Who originally designed this workflow? Who approves changes today? Who can explain it externally if challenged tomorrow?
In many organizations, these questions are met with silence. Not because people are evasive, but because accountability was never explicitly designed in the first place. This silence is not a governance gap. It is an accountability gap.
The Leadership Shift
As systems become more agentic, the concept of what leadership entails quietly changes shape. Leadership is no longer about hands-on responsibility of a known output. Rather, it becomes stewarding the results that AI-agentic workflows produce, and with that, the conditions under which the algorithmic decisions occurred. This includes setting boundaries, designing escalation paths and inheriting decisions you did not personally authorize.
The uncomfortable reality is that leaders increasingly own outcomes without having touched the original choice. Many organizations are unprepared for this shift, because their leadership models still assume a direct link between authority and action. Agentic systems break that assumption.
Agentic AI is often treated as a technical or innovation problem, delegated downward to engineering or product teams. That framing misses the point. These systems touch product integrity, legal exposure, customer trust, revenue and workforce dynamics simultaneously.
When agentic AI is treated as tooling rather than governance, accountability erodes. Policies multiply, but ownership does not. Executives, who hold the power over the look and feel of organizational design and delivery, cannot outsource this problem without reinforcing it.
What Good Looks Like
Organizations that handle agentic AI well do not rely on perfect foresight. They rely on intentional design. Responsibility for outcomes and changes is visible and named. Interruption points are built into systems so someone can pause or stop them when conditions change. Boundaries around what agentic systems are allowed to do are explicit rather than assumed.
Policy supports this work, but it does not replace it. Ownership cannot be automated.
What Organizations Can Do Now
For many leaders, the question is not whether these risks exist, but where to start. The most effective responses are rarely technical fixes. They are organizational ones.
1. Audit Where Autonomous Behavior Already Exists
Most organizations already have systems making semi-autonomous decisions, often without being labeled as such. Map who designed them, where they operate and what decisions they are empowered to make. The goal is not to slow everything down, but to make invisible agency visible.
2. Clarify Ownership Explicitly
Every agentic workflow should have a named owner accountable for outcomes and changes over time. Even across cross-functional development, the need for someone to ‘own’ the outcome is critical, be it product, HR or even project management. This ownership should persist beyond team restructures and role changes. If accountability dissolves during reorgs, it was never real to begin with.
3. Design Escalation and Interruption Paths
Leaders should be able to answer a simple question: who can stop this system, under what conditions, and how quickly? Escalation should not rely on informal heroics or personal relationships. It should be designed into the workflow.
Webinar
Mar
26
Do Learning Programs Really Work? How to Turn Education Into Engagement In Healthcare
See how leaders are using learning programs to build trust with healthcare professionals and create measurable engagement.
Register
Webinar
On demand
Fuel Your Front Line: Workforce Trends Shaping 2026
Get expert insight into the tools and tactics top frontline teams will need in 2026.
Watch Now
Webinar
On demand
Fix the Disconnect Between Projects and Profit
Your margins are hiding in disconnected data. Let's find them.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
4. Define What Not to Automate
Not every decision benefits from speed or scale. Organizations need explicit boundaries around which decisions remain human-owned, particularly where trust, safety, or long-term relationships are at stake. Deciding what agentic AI is not allowed to do is as important as deciding where it is useful.
These steps do not eliminate risk. They make responsibility legible.
The Real Choice
This moment is often framed as a tradeoff between speed and caution. That is the wrong axis. The real choice is between clarity and convenience, stewardship and throughput, trust and unexamined efficiency.
Agentic AI does not remove responsibility from leadership. It concentrates it.
The question is not whether AI can do the work. It is who owns it when no one remembers why it exists. That question will increasingly define leadership credibility in an agentic world.
Editor's Note: How else are leaders thinking about the introduction of agents into the workplace?
- Humans and AI Agents: Planning the Org Chart of Tomorrow — Why AI agents belong on your org chart — and how to incorporate them.
- The Teammate With No Manager: Who's Accountable for AI? — Why cross-functional leadership, accountability and employee experience design are central to AI’s success at work.
- AI Agents Are Creating a Two-Tier Workplace — As AI agents make their way into workflows, open questions remain around how their presence is reshaping power, labor and responsibility inside the workplace.
Learn how you can join our contributor community.