Every organization has two versions of its processes: the one documented, if they are lucky, and the one people actually follow.
If you’ve been reading this series, you know the thesis: AI doesn’t fix dysfunction; it multiplies it. Article 2 examined what happens when AI is deployed without strategic clarity. This article moves to the operational core of the Galbraith Star Model: Processes — the flows of information, decisions, and work that connect structure to execution. Because even when the strategy is sound, AI will fail if it’s pointed at processes that don’t work the way anyone thinks they do.
The Workaround Economy
Every mature organization runs on workarounds. Not because people are undisciplined, but because formal processes never fully account for the complexity of human judgement Over time, experienced employees develop informal practices in the form of judgment calls, handshake agreements, and undocumented shortcuts. These adaptations accumulate quietly, become invisible, and keep things running.
This is the workaround economy. It is not a failure of discipline. It is a rational human response to process design that doesn’t match operational reality. And in most organizations, it is far larger than leadership realizes.
AI has no visibility to reality. Teams architect AI solutions based on a mix of outdated assumptions and duct tape. AI trains on data based on these legacy workarounds when the “Deploy AI” mandate pushes for automation quickly.
What This Looks Like in Practice
The trend we see looks the same at every company, regardless of the industry or domain. At least 90% of companies do not have a true understanding and documentation of their operational and decision-making processes.
When we are brought in to develop agents or automate existing workflows, the scope of work expands as we uncover hidden workarounds.
A recent client engagement unearthed multiple accountable teams that owned a single process. When it came time to define who the “human-on-the-loop” escalation point should be when reports surfaced irregularities, the lack of alignment of process ownership resulted in delayed implementation.
What To Do Instead
Map the process as it is conducted today, not the one you documented months or years ago. Before AI touches any workflow, understand how work genuinely gets done. Shadow the people who do it. Interview across levels. Identify where formal process and actual practice diverge, and understand why they diverge. Those divergence points are exactly where AI will either enforce something that doesn’t work or automate something nobody fully understands.
Distinguish expertise from drift. Not all workarounds are dysfunction. Some represent accumulated expertise that senior people have learned through years of pattern recognition. Others are pure drift, practices that evolved without intention and persist because no one questioned them. The goal isn’t to eliminate all informal practices. It is to encode your best process thinking, not just your most common process behavior.
Reconcile your system of record with your operating reality. Before you train a model, close that gap by updating the documentation and systems to reflect the process that produces results. Train the model on data that represents how the process flows to reduce the need for unnecessary human-in-the-loop interaction.
Treat process readiness as a prerequisite, not a parallel track. Most organizations run process remediation alongside AI deployment and hope they converge. They rarely do. The discipline is uncomfortable but straightforward: understand the process deeply enough to know what you’re automating before you deploy technology that will enforce whatever it finds at speed and at scale.
The Bottom Line
AI doesn’t just automate processes. It automates the assumptions embedded in those processes, including the gap between the process as designed and the process as actually practiced. Where humans learned to navigate that gap through judgment and relationships, AI has no such insight. It will enforce the documented fiction or scale the undocumented reality, and neither outcome is what you intended.
The organizations that avoid the execution trap aren’t the ones with perfect processes. They’re the ones willing to reconcile the formal and informal process variations before they hand the keys to a system that can’t tell the difference.
Does this resonate with you? Unify is here to help. Our consultants are ready to facilitate the right conversations to bring your processes into alignment before AI amplifies what’s already broken.
Next in the series: “The Human Variable: AI Readiness Is People Readiness,” a deep dive into the skills, fears, and trust dynamics that determine whether AI is adopted or resisted.
About The Shift Series
Shift Happens is a series exploring how organizations can turn disruption into direction. We write about the real, human side of work, where change, technology, behavior, and leadership collide in ways no framework fully captures.
Every article follows one of the five currents that shape modern work:
The Human Side of Transformation, the heartbeat beneath the strategy.
Change Management as the Missing Discipline, the discipline hiding in plain sight, quietly determining who succeeds.
Technology, Tools + Human Behavior, the space where logic meets instinct, and where most rollouts live or die.
Organizational Structure, Power & Governance, the lines, ladders, and tensions that decide how work truly flows.
Leadership Micro, Shifts, Governance & Operating Models, the small shifts that create disproportionate impact.
We combine lived experience with practical insight. The kind you can apply the same day, not someday.
Shift happens! But with the right mindset, it happens through you.
If your organization is navigating a shift in technology, structure, or culture and needs practical, human, centered support, reach out.
This is the work we love! And the work we do best.
