Early Wins Emerge as CIOs Find Tangible ROI from Agent Deployments

The promise of AI agents transforming IT operations is moving beyond pilot projects, with early adopters reporting tangible cost relief in specific areas. While widespread ROI remains elusive for many, several use cases are demonstrating clear value for African IT leaders.

Rhonda Baldwin, CIO at LaunchDarkly, highlights coding and service desk agents as key contributors to their cost optimization strategy. “Coding agents expand engineering capacity without proportional headcount growth,” she explains. They’ve also helped optimize cloud spend and reduce workflow consumption costs.

Beyond internal efficiencies, Baldwin cites significant savings from building solutions rather than buying them. For example, the company avoided $1 million in software purchases by developing its own optimization tools with AI coding assistance, plus an additional $120,000 by creating an enterprise asset-management solution.

Key Areas Showing Promise:

  • Coding and cloud cost optimization: LaunchDarkly’s experience suggests significant potential here.
  • Tier 1 IT support: Providing self-service resolution for common requests frees up human agents for complex issues.
  • Process automation: Streamlining workflows reduces manual effort and errors.
  • Knowledge management: AI can create centralized, searchable repositories of information.

Kevin Rooney, CIO at West Monroe Partners, reports a 40% reduction in managed service provider costs after deploying an internal support agent accessible via Teams or Slack. This bot handles routine requests like password resets and software access while routing complex issues to human experts—saving an estimated 2,700 hours annually.

Matthew Wallace, co-founder of KamiwazaAI, found that initial cloud optimization agents cut their spending by 70% by automating monitoring, budget approvals, and resource management.

However, Jeet Pattanaik of Glokal AI cautions against inflated ROI claims. He notes that many organizations fail to account for the cost of verifying agent outputs, which can eat into savings—especially when handling complex or unusual requests.