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In an October 5, 2026 interview with Unite.AI, Perry discusses workflow redesign, human oversight, organizational structure, and Andus Labs’ approach to assessing AI adoption. His recommendations and company figures are claims made in the interview, not independently validated findings.
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Who is Chris Perry?
The Unite.AI interview describes Perry as a communications, digital strategy, and innovation executive with nearly three decades of experience. It says he spent more than 22 years at Weber Shandwick in senior roles, including Chairman of Futures and Chief Innovation Officer, worked in technology communications at General Motors, and served as a vice president at Edelman. The interview says he founded Andus Labs in 2025; these biographical details are reported there and are not independently verified here.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Andus Labs says it helps organizations translate enterprise AI capabilities into organizational outcomes by focusing on the people and work systems around the technology. The company describes its “Human OS for AI” as an operating layer for coordinating work across people and agents, alongside programs and products intended to reinforce organizational learning. These are the company’s descriptions, as presented in the interview.
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A November 2025 Workestration episode listing also names Perry and Andus Labs colleague Jennifer McTiernan as guests in a discussion of human agency, workplace AI, change management, and generative thinking.
How does Perry distinguish AI deployment from transformation?
Deployment is easy to count: licenses purchased, agents configured, logins recorded. Transformation is harder to measure. Perry’s proposed test is whether the organization has changed how work gets done and gained a capability it previously lacked.
That distinction matters because adding AI to an existing process may make the process faster without changing its purpose, ownership, or effect. Perry argues that organizations should ask what they can now do that they could not do before, then connect the answer to a business outcome. Tool usage is evidence of access or activity; it is not, by itself, evidence of transformed work.
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In the interview, Perry recounts an unnamed company where he says adoption reached 80% while change in how work got done was “something closer to 5%.” The company, measurement method, and underlying evidence are not supplied, so this is an anecdote rather than a general benchmark.
What does AI-native workflow redesign look like?
Perry’s example is a weekly report. Rather than simply asking AI to produce the report faster, he suggests asking why the report exists. If its purpose is to detect a problem early, an AI system might flag that problem when it occurs, making the periodic report unnecessary.
The example is illustrative, not a documented client result. Its practical lesson is to begin with the job the process is meant to perform—not the existing form, meeting, or approval sequence—and then consider whether AI changes the best way to perform that job.
- Start with the outcome: Identify what decision or action the workflow is supposed to enable.
- Question the inherited steps: Check whether reports, handoffs, and approvals still serve that outcome.
- Redesign around the new capability: Consider whether AI can surface a signal sooner, coordinate work differently, or remove a step rather than merely speed it up.
- Measure the change: Look for a changed process or business capability, not only higher usage.
Perry also invokes an electric-motor analogy: he says electric motors reached American factories in the 1880s, while productivity gains did not appear until the 1920s, concluding, “The rearrangement was the revolution.” The interview does not provide a historical source for that timeline, so it should be understood as Perry’s analogy rather than an independently established historical account.
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What should human accountability mean in practice?
Perry’s standard is explicit: “Accountability means a named leader who sees what the system produces and can explain the outcome, correct it or stop the system.” In practice, that requires assigning responsibility to a person with enough visibility and authority to intervene—not simply declaring that a human remains “in the loop.”
Before deploying an agent, an organization should decide what the agent may do on its own and which actions require human review or approval. Perry argues for closer human control where decisions affect a person’s work, earnings, or legal standing. The more serious or difficult to reverse a mistake would be, the less suitable it is to leave the decision entirely to an agent.
- Name the accountable leader: Make clear who can explain the system’s output and is empowered to correct or stop it.
- Set decision boundaries in advance: Distinguish routine actions an agent may take from consequential decisions that require a person.
- Match oversight to consequences: Consider the cost and reversibility of error, how quickly an error will be visible, and the effect on people or legal standing.
What organizational structures does Perry expect to change?
Perry argues that organizations built around functions and sequential approvals can obstruct work shared by employees and agents. He recommends small, problem-oriented teams with clear ownership instead of relying on departmental handoffs to coordinate every task. That is his forecast and recommendation, not an established outcome for all organizations.
| Organizational approach | How work is coordinated | Key trade-off in Perry’s framework |
|---|---|---|
| Functional structure | Work moves across departments and sequential approvals. | Specialized responsibilities may create handoffs and slow decisions that span functions. |
| Problem-oriented teams | A small team organizes around a business problem, with clear ownership. | Clear decision rights can help people and agents coordinate, but the interview does not establish that this structure is universally superior. |
The underlying question is not whether every organization should abandon its org chart. It is whether existing roles, approval chains, and job boundaries still help the organization deliver the outcome—or mainly preserve a process designed for different conditions.
What is Andus Labs’ Ground Truth Index?
Perry describes the Ground Truth Index as a methodology intended to identify and assess patterns in how organizations approach AI. In the interview, he says Andus Labs has documented 300 patterns through engagements and conversations with leaders across 595 organizations. The article provides no underlying dataset or audit, so those counts should be treated as company-reported figures, not as a representative or independently validated sample.
As Perry describes it, the process uses five independent analyst agents to score patterns across five dimensions. The top 50 patterns are then sent for human review, with a final 25 selected. The interview does not provide the underlying scoring documentation or independently audited results; the figures describe the company’s account of its process.
What is the central idea in Perry’s interview?
Perry’s argument is that AI adoption becomes meaningful when organizations redesign work around new capabilities, assign real human accountability, and reconsider structures that impede coordination. His examples offer a way to frame those questions, while the interview’s company metrics and anecdote remain claims attributed to him and Andus Labs rather than independently established evidence.
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