Identifying operational savings through rapid process intelligence

Challenge 

The insurance firm was undergoing a multiyear modernization program with savings targets tied to cost reduction and productivity targets. Technology investments were underway, but end-to-end insight into how work flowed across systems and teams hadn’t fully emerged. 

As the program progressed, a savings gap emerged relative to committed targets. Traditional approaches, process-mapping workshops, point automation, and system upgrades didn’t produce sufficient, sustainable savings. Highly siloed teams understood only portions of the end-to-end process, making it difficult to identify where rework, delays, and system failures were eroding value realization. 

The organization turned to Guidehouse to develop a fast, low‑risk way of uncovering tangible savings opportunities and determine whether deeper process intelligence could support its broader transformation goals. 

Approach 

We proposed a focused proof of value using process intelligence to analyze a single, high-volume operational function. By applying process intelligence as part of our partnership with Celonis, we aimed to deliver rapid, data-driven visibility into how work flowed across the firm’s systems and teams. That visibility would help leaders decide where to apply AI, automation, or advanced analytics to generate the most value.

Working from a narrow, one‑time data extract provided by the client, our team quickly ingested and structured the data into the Celonis platform to build a digital twin—providing a clear view into actual system activity across the process. This data‑driven view went beyond assumptions and revealed how work truly moved across intake, updates, exceptions, escalations, and resolution. 

Our analysis exposed significant process variations, including error queues, rework loops, unnecessary handoffs, and system failures that dramatically increased cycle times. To translate these inefficiencies into quantifiable financial impact, we applied conservative estimates related to labor effort, escalation rates, service‑level delays, and rework. 

Impact 

Intentionally designed to be fast, low-cost, and minimally disruptive, the pilot produced decision‑ready insights that leadership could trust. In just four days, we identified between $500,000 and $1 million in potential annual savings from a limited dataset covering only 107 cases within a single operational process.  

Beyond those quantifiable savings, the engagement delivered immediate qualitative value. Visualizing the end‑to‑end process enabled teams to see handoffs, delays, and workarounds that had gone unrecognized across organizational silos. This shared, data‑driven view shifted conversations from anecdotal explanations to evidence‑based discussions about root causes and improvement opportunities. 

Our analysis also surfaced underlying data quality and control issues, highlighting opportunities to strengthen enterprise data hygiene and reduce downstream operational and compliance risk. Most importantly, it validated process intelligence as a low‑risk and quick proof of value that is extensible across the wider organization.