How a Global Organization Reduced AI Token Consumption by 35% Without Limiting AI Adoption
As AI adoption accelerated across the organization, understanding rising token consumption became increasingly difficult. Artic conducted a comprehensive AI cost optimization assessment that uncovered usage patterns, optimized retrieval strategies, strengthened governance controls, and reduced token consumption by 35%, creating a scalable framework for responsible AI growth.
Client Overview
Our client is a global manufacturing organization with operations across multiple regions and business units. To improve productivity and streamline access to information, the company had invested in several AI-powered solutions, including employee assistants, knowledge search tools, and workflow automation applications.
As adoption increased, AI became part of everyday operations across engineering, operations, and corporate functions. Different teams introduced new use cases, and usage continued to expand as employees found practical ways to incorporate AI into their work.
While these initiatives delivered measurable value, leadership started to question rising AI costs. They wanted a clearer understanding of token consumption, usage patterns, and whether spending was aligned with the business outcomes being achieved.
Requirements
The client wanted a clearer understanding of what was driving AI costs across the organization. They needed better visibility into token consumption, user activity, application usage, and opportunities to improve efficiency without slowing AI adoption.
Cost Visibility
Understand where AI spending was occurring
Token Consumption Analysis
Identify major drivers of token usage
User & Application Review
Evaluate adoption, access, and utilization
Optimization Opportunities
Uncover areas to improve AI efficiency
Challenges:
As AI adoption accelerated, the organization saw a steady increase in usage across users, applications, and workflows. While overall spending was visible, the underlying drivers of token consumption were difficult to identify and control.
Limited Cost Visibility
AI spending was increasing, but identifying the largest contributors remained challenging.
Growing User Adoption
More employees gained access, creating broader usage across teams and applications.
Inefficient RAG Retrieval
Retrieval systems often returned excessive content for relatively straightforward requests.
Expanding AI Agent Activity
Growing numbers of AI agents increased background processing and token consumption.
AI Cost Complexity
4 Key Drivers
Users, applications, retrieval systems, and AI agents were contributing to rising token consumption, creating limited visibility into AI spending and optimization opportunities.
Our solution
Artic performed a comprehensive AI cost optimization assessment to understand how AI was being adopted across the organization and identify the factors contributing to rising token consumption. The review focused on usage patterns, application design, governance, and opportunities to improve efficiency while supporting continued AI adoption.
AI Cost & Usage Analysis
Assessed token consumption patterns across users, applications, and business workflows.
Prompt & RAG Optimization
Reviewed prompts, retrieval strategies, chunking methods, and context delivery to reduce token consumption while maintaining response quality.
Governance & Access Review
Evaluated user access, adoption trends, and governance controls across AI solutions.
Monitoring & Optimization Roadmap
Developed recommendations for visibility, cost control, and long-term AI efficiency.
As our AI initiatives expanded, we knew we needed better visibility into what was driving costs. Artic helped us uncover the factors behind token consumption, improve our retrieval efficiency, and establish stronger governance practices. The result was not only reduced AI spending but also greater confidence in our ability to scale AI responsibly across the organization.
Director, Digital Transformation Dpt.
Organizational benefits
35%
Reduction in Token Consumption
Reduced unnecessary token usage through prompt optimization, retrieval tuning, workflow improvements, and better management of AI agents across high-consumption business processes.
50%
Faster Cost Visibility & Reporting
Improved monitoring and reporting capabilities, enabling stakeholders to quickly identify spending trends, usage patterns, and key drivers of AI consumption.
25%
Improvement in RAG Efficiency
Improved retrieval accuracy by reducing irrelevant content and optimizing context selection, resulting in lower token consumption and better responses.
Improved Cost Visibility
Established clearer insights into AI spending across applications, users, and workflows, helping teams understand where costs originated and increased.
Stronger AI Governance
Implemented governance controls that improved oversight of AI usage, access management, adoption patterns, and ongoing optimization initiatives.
Scalable AI Operating Model
Created a sustainable framework for expanding AI adoption while maintaining visibility, accountability, and control over future consumption and costs.
Conclusion
The client wasn’t dealing with a single cost issue or an underperforming AI solution. The challenge was understanding what was happening beneath the surface as AI usage continued to grow.
More users, more applications, more retrieval activity, and more AI agents were all contributing to token consumption in different ways. While overall spending was visible, the reasons behind that spending were not always clear.
By taking a closer look at how AI was being used across the organization, the client gained a better understanding of where costs were coming from and where efficiency could be improved. The result was not just lower consumption, but better visibility, stronger governance, and a clearer path for scaling AI responsibly across the business.
Gain Visibility Into Your AI Costs
Understand what is driving token consumption across your AI applications, users, retrieval systems, and agents.
