AI agent sprawl pressures CIOs to recalibrate governance

Every Friday, Bret Greenstein, CAIO at consulting firm West Monroe, holds a company-wide meeting to share what’s happened in AI over the past week. He also spotlights one employee at the firm who’s created their own AI agent from the ground up, which lives in the company’s internal AI store.

Every Friday, Bret Greenstein, CAIO at consulting firm West Monroe, holds a company-wide meeting to share what’s happened in AI over the past week. He also spotlights one employee at the firm who’s created their own AI agent from the ground up, which lives in the company’s internal AI store.

“About 15% of our firm builds all the time now,” Greenstein says. “That’s a huge population.”

Enabling employees to spin out their own agents has become popular at many firms. Staff have built hundreds of agents at software company Blackline, for instance, and Microsoft has deployed more than 500,000 internal agents to help employees streamline workflows. Gartner also anticipates that by 2028, global average Fortune 500 companies will have more than 150,000 agents.

Employees know the intricacies of their work, the biggest pain points, and time drainers, so they can build solutions that address those specific issues, according to Greenstein. It also creates enthusiasm, empowers employees, and fosters innovation among the workforce as they build from the ground up.

That said, there’s been a pivot over the last six months, says Michael Murphy, partner and AI practice lead at global management consulting firm Adaptovate. When agentic AI first came on the scene, companies went all in, pushing to build and agentify nearly anything they could. In recent months, however, the narrative has shifted to getting a handle on agent sprawl, assessing the value agents deliver, and keeping costs in check.

“We’re really at this interesting inflection point where clients are having to figure out if we built the right agents, and are they delivering the value we expected,” Murphy says.

The card

• Industry: Varejo e consumo — Tiendas, comercio electrónico y marcas: demanda, surtido y experiencia.
• Source: CIO
• Published: 24/08/2026

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Cómo leo este caso

En retail la IA se vende como personalización y casi siempre se paga con inventario. Lo que mueve el resultado no es recomendar mejor: es pronosticar la demanda con menos error y que esa cifra llegue a tiempo a quien compra. Un caso que solo habla de la experiencia rara vez cambió el margen.

Las tres preguntas

• Si la mejora se ve en el margen y en el quiebre de stock, o solo en clics.
• Qué tan rápido se recalcula cuando cambia la demanda de un día para otro.
• Si la tienda física y el canal digital comparten el mismo número.

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