Delivering $10m+ cost savings through agentic AI

1 min read

Team reviewing dashboards and automation metrics

A large enterprise services organization wanted to turn AI ambition into measurable cost savings. The opportunity was clear: identify and deliver $10m+ in gross savings through agentic AI, with $1.1m+ targeted in 2026 from initial deployments. We helped move the program from opportunity identification into build-ready delivery, prioritizing 20+ workflows across six functional areas and establishing a repeatable AI Enablement Program to scale delivery.

Outcome 1: Identified a clear line of sight to $10m+ in gross cost savings through agentic AI
Outcome 2: Realized $900K+ in 2026 savings from initial AI deployments with another $9M+ projected in FY 2027 as momentum across the program accelerates
Outcome 3: Prioritized 20+ workflows across five functional areas for AI automation

The challenge

Our client had no shortage of AI ideas, but needed a disciplined way to separate viable opportunities from low-value experimentation. The real challenge was knowing which workflows could be automated safely, which had the strongest value case, and where data, risk or delivery constraints could slow progress.

With multiple functions in scope, our client needed a clear prioritization model and delivery structure that could turn early use cases into scalable automation.

The approach

We began with a four-week use case and business case sprint to rapidly identify and size AI opportunities. Each opportunity was assessed for value, feasibility, data readiness and risk, creating a clear view of which workflows could deliver meaningful savings and move quickly into build.

From there, we prioritized high-impact workflows across the organization and developed build-ready specifications for the first wave of delivery. We established the governance, architecture and delivery patterns needed to support implementation, while setting up value tracking against the business case.

As the program moved towards agentic AI implementation, we mobilized a multi-pod delivery model to manage and coordinate priority workflows through build, test and deployment. Working as product owners alongside the client’s internal build teams, we helped shape requirements, manage delivery, track value and remove blockers. Information security, change management and adoption planning were built into the process from the start, helping our client move beyond isolated use cases towards a repeatable AI Factory that could scale automation across functions.

The impact

The organization moved from a broad AI cost-saving ambition to a clear, prioritized delivery program. Discovery was completed, the first agentic workflows moved into build and our client gained a stronger view of where automation could unlock material savings.

The work also created the foundations for repeatable AI delivery. Rather than treating each use case as a standalone experiment, our client established an AI Factory with shared governance, delivery patterns and value tracking. This gave teams a more scalable way to build, test, deploy and measure agentic AI workflows.

Ultimately, the program was positioned to deliver meaningful savings over time, with a repeatable model in place to scale AI automation across the organization.