Making AI Agents Work Inside Your Organisation

Diagram of human-centered AI workflow showing problem definition, data curation, model training, writing, evaluation, deployment, and feedback loop with human roles.

For technology leaders, the hard question is not whether AI agents can execute tasks, but which work should be re-engineered so they create measurable business value. That shifts the conversation from model selection to operating model design: who owns the workflow, who approves exceptions, and where human review remains mandatory.

Governance matters more than demos. Agentic tools can amplify speed, but they also amplify ambiguity if decision rights are unclear. CIOs and transformation leaders should define the boundaries up front: what an agent may do independently, what requires human sign-off, and what evidence must be retained for audit, compliance and incident review. Without that discipline, pilots can succeed technically while failing operationally.

The portfolio challenge is to fund a few workflow-specific use cases with clear economics rather than spreading effort across generic experimentation. Good candidates are high-volume, rules-heavy processes where cycle time, error rates or service backlogs are already painful. The next management question is simple: what metric will improve, by how much, and who owns the benefit? If benefits cannot be linked to a named process owner, the use case is probably too vague.

Adoption is the final constraint. AI agents alter roles, not just tasks, so leaders need to plan for new supervision, escalation and skill requirements. The practical test is whether frontline teams trust the system enough to use it under pressure. That means investing in training, exception handling and feedback loops, not just deploying another tool. The organisations that win will be those that treat AI agents as a redesign of work, not an automation add-on.

To unlock value from AI agents, organisations need to focus less on replacing people and more on redesigning workflows around real business problems.
A recent webinar moderated by Nathan Furr, Professor of Strategy at INSEAD, explored where agentic AI is already creating value within companies, and why the real challenge lies not in the technology itself but in its deployment. In conversation with Jur Gaarlandt (MBAโ€™10D), partner at Artefact, the discussion cut through the hype to focus on what makes AI agents work inside an organisation.ย 
Although AI agents are often presented as autonomous systems capable of replacing human labour, Furr challenged the narrative of mass disruption. Drawing on research and historical parallels with earlier technological revolutions, he argued that technology tends to eliminate tasks rather than jobs, shifting roles towards more analytical, technical and creative activities.ย  Where organisations are seeing the strongest results today is in internal processes. AI agents are particularly effective when embedded into repetitive, rules-based workflows such as coding support, contract processing and logistics operations. The key, however, is to keep the human operator at the centre.ย  Rather than designing systems to automate entire roles, the starting point should always be: Where does the human operator need help? Answering that question requires more than experimentation. It calls for strong data foundations, clear governance and the internal capabilities to scale use cases across the business. The real competitive advantage lies not in access to AI tools, but in the ability to integrate them into how work actually gets done.

Edited by:

Verity Ashton

About the author(s)

INSEAD Knowledge
is the expert opinion and management insights portal of INSEAD, The Business School for the World.
https://knowledge.insead.edu/leadership-organisations/making-ai-agents-work-inside-your-organisation

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