Summary
The integration of AI agents into workplace environments raises critical questions about autonomy and governance. The Chief Products Officer at Wrike emphasizes that autonomy for AI should be viewed as a dial rather than a binary switch, allowing organizations to gradually increase the responsibilities of AI agents as they demonstrate reliability. This approach ensures that high-risk decisions remain under human control while enabling AI to handle lower-risk tasks, ultimately fostering a collaborative environment between humans and AI.
The discussion highlights the importance of distinguishing between AI activity and genuine business value. Many organizations fall into the trap of measuring success through vanity metrics, such as the number of tokens used or tools deployed, rather than focusing on metrics that reflect real impact, such as cycle time and customer satisfaction. Effective AI implementation requires a clear understanding of existing workflows and the documentation of processes to facilitate AI's integration and productivity.
Moreover, the conversation addresses the challenges organizations face when adopting AI within complex workflows. It is crucial to avoid simply layering AI onto outdated processes; instead, organizations must ensure that their workflows are well-structured and documented. Change management and employee training are essential to help teams adapt to new AI tools and maximize their potential.
As AI agents become more prevalent, establishing clear roles, permissions, and governance structures is vital. Organizations should treat AI agents like employees, gradually increasing their autonomy based on performance and the complexity of tasks. This careful approach to governance not only enhances trust but also enables organizations to leverage AI effectively while maintaining accountability for outcomes.
Show Notes
How much autonomy should an AI agent receive before it has proved that its work can be trusted? In this episode of AI at Work, I speak with Alexey Korotich, Chief Product Officer at Wrike and a former Dell software leader. Wrike supports around two million users, along with a growing number of agents, giving Alexey a close view of how enterprise teams are trying to move from AI experiments into daily work. Alexey challenges two popular measures of progress. A dashboard built in an hour may look impressive, but enterprise software creates lasting value through trust, context, and reliable delivery. The same problem appears in AI adoption metrics. Prompts fired, tokens burned, and tools deployed can show activity without revealing whether cycle time improved, margins changed, customers became happier, or a workflow produced a better result. The conversation then turns to the reason many AI projects stall. Alexey does not believe every process must be rebuilt from the beginning. He sees a more immediate problem in work that remains undocumented, unstructured, or divided across teams and systems. When employees repeatedly explain how work should be done, much of the time saved by AI disappears. Historical work, process artifacts, permissions, and accountability all give an agent the context it needs to produce a useful result. We also discuss how leaders can set boundaries for autonomous AI agents. Alexey compares agent onboarding with employee onboarding. Start with high-frequency, low-risk tasks such as meeting summaries, request triage, status updates, and risk flags. Review the output, build trust, and raise autonomy gradually. Higher-stakes decisions should remain with people, supported by identity controls, limited access, review channels, and a way to recover when an agent makes a mistake. For Wrike, shared context comes through what Alexey calls a work intelligence graph. It records how work moves, who participates, who owns each stage, and what each person or agent can access. That organizational memory can help agents learn from completed work and human feedback instead of treating every request as an isolated prompt. Should companies measure AI adoption by how often employees use it, or by what their workflows deliver? Listen to the episode and share your thoughts with me.
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