Every few years, a new technology promises
to transform work.
Agentic AI is the latest and perhaps the most consequential.
Yet many organizations are approaching it in familiar ways. They begin with the tools and platforms they already have. Then they try to fit new ways of working inside those existing boundaries. That approach is understandable. But it may be limiting.
Early experience with AI initiatives suggests why. Across industries, a large share of AI projects never make it into production or deliver meaningful value. Many efforts stall long before they scale because organizations focus on technology first and organizational clarity later.
Agentic AI is not simply an automation upgrade. It represents a deeper structural change, what I call the Agentic Work Shift. Intelligent systems are beginning to reason, decide, and act alongside humans. They are no longer only executing predefined steps.
I use this term deliberately. What is changing is not only software. The design and governance of work itself are changing.
If that is the case, then adopting Agentic AI requires more than selecting platforms or extending current architectures. It requires a change in thinking and mindset at multiple levels of the organization.
Before any agent is built, there are harder questions worth sitting with.
- What outcomes are we truly trying to achieve, independent of tools?
- Which decisions are we prepared to delegate to intelligent systems?
- Which decisions must remain human owned?
- How do we define accountability when a system can decide and act?
- What boundaries should limit autonomy?
- When should an agent act alone, and when should it pause or escalate?
- How do we supervise intelligent behavior without turning autonomy back into rigid automation? And
- How do these answers differ across leadership, architecture, delivery, and operations teams?
Without clarity on these questions, discussions about agents’ risk of becoming trapped in current structures. Instead of reshaping work, we simply automate the present.
The broader history of AI projects shows why this matters.
“Most AI projects, fail to deliver meaningful value or reach production at scale. This is less a failure of technology and more a failure of organizational design and clarity.”
Many initiatives begin with enthusiasm and pilots, but stall when they meet real data, real customers, and real regulations. Organizations discover that models do not fit their processes, that ownership of decisions is unclear, and that risk controls were designed
for traditional software, not for systems that can act with judgment.
When Decision Ownership Becomes Unclear
Consider customer service. An agent may offer exceptions, modify orders, or propose refunds. To the customer, this feels like a decision by the organization, not by a machine.
When the outcome is wrong, responsibility becomes blurred. Similar tensions appear in operations, where agents prioritize work, trigger payments, or schedule actions. The action is digital, but the consequences are human.
Traditional software risk assumed predictable outputs. Agentic systems create probabilistic outcomes, which require different governance. Trust in agents is built through transparency, not through model accuracy alone.
The Agentic Work Shift: A Six-Lens Framework
Intent and Path
- Start with the decision you want to improve, not the agent you want to build.
- Avoid the big-bang approach. Begin with assisted modes and iterate toward autonomy only as confidence grows.
Boundaries and Gates
- Every agent needs explicit limits. What may it decide? What must it escalate?
- Autonomy without gates becomes risk rather than progress.
Governance and Accountability
- Someone must own outcomes when an agent acts.
- Accountability cannot be delegated to a model, and audit paths must be visible from day one.
Security and Identity
- Agents act on behalf of organizations. Verification, impersonation risk, and access controls are design questions, not afterthoughts.
Operational Economics
- Architecture shapes cost. Poorly designed agent loops can multiply usage and turn promising pilots into unsustainable systems.
Fairness and Impact
- Agents inherit biases from data and objectives.
- Organizations must monitor not only accuracy but social and ethical consequences.
This framework is not a technical blueprint. It is a way to think before building, so that work design leads technology design.
Leadership Responsibilities
Leaders will need to play a different role as agents become part of daily operations.
Their task is not only to approve technology but to define where human judgment must remain central. They must set boundaries, create safe spaces for learning, and accept responsibility for systems they do not fully control.
Governance in an agentic world is less about command and more about stewardship.
Learning From Early Reality
Not everyone sees early setbacks as a problem. Some argue that abandoned projects are a sign of maturity, forcing organisations to move from enthusiasm to discipline. Others note that many so-called failures are judged by the wrong measures.
Traditional ROI looks for immediate savings, while the real value may lie in better decisions, risk reduction, and gradual productivity that compounds over time.
Experience also suggests that small, assisted agents often succeed where large autonomous programs struggle. Teams that begin with limited, repeatable tasks build trust and understanding before expanding scope.
These perspectives do not contradict the Agentic Work Shift. They reinforce the idea that progress will be iterative and organizational, not sudden and purely technical.
Looking Ahead
What makes this moment particularly important is speed. The conversation around Agentic AI is accelerating quickly.
Organizations that delay the mindset shift may find themselves reacting under pressure instead of designing deliberately.
The promise of agents is real: systems that can collaborate with humans, handle complexity, and adapt to changing
conditions. But that promise will be realized only if we treat Agentic AI as a question of work design first and technology second.
I have been reflecting on these questions, exploring how organizations might think more clearly about autonomy, decision boundaries, and responsibility in an agentic world. I do not yet have all the answers. The patterns, however, are becoming difficult to ignore.
In simple terms, Agentic AI will change how organizations decide. Those that design for that change will benefit. Those that only add tools may struggle.