233% 3-year return on investment and 13 months to payback with Red Hat AI

Legacy infrastructure is the primary barrier to AI strategy. Current systems simply weren't built for these workloads. Platforms lack native AI support, leaving expensive GPUs underutilized at rates as low as 30%. While teams often customize models, the accuracy of those models tends to drop. In some situations, slow provisioning times force data scientists to build their own shadow environments—unauthorized tools and systems that IT and security teams don't know about. It's clear that AI success requires a platform that helps simplify workload management and boost productivity.

To quantify the value of such a platform, Red Hat commissioned an independent Total Economic Impact™ (TEI) study from Forrester Consulting. This research moves beyond theory to analyze the real-world financial benefits, risks, and return on investment (ROI) experienced by customers using Red Hat AI. By providing a third-party framework for evaluating economic impact, this study helps organizations build a rigorous business case for their own AI initiatives. We're sharing these findings to show how your peers have turned infrastructure challenges into measurable financial gains.

What Forrester found

Forrester Consulting conducted a February 2026 TEI study examining the financial and operational impact of deploying Red Hat AI across 4 enterprise organizations in financial services, manufacturing, government, and telecommunications. The findings are quantified using a rigorous methodology and for organizations still stitching together point solutions, worth reading carefully. To synthesize these findings, Forrester uses a composite organization.

A composite organization is a hypothetical model representing the collective experiences of interviewed Red Hat AI customers. It aggregates findings into a single representative profile that reflects shared challenges like underused hardware and slow deployment cycles. This framework establishes a standardized benchmark for ROI. The following figures detail the 3-year impact for the modeled enterprise.  

For the composite organization, Forrester modeled a global, industry-agnostic enterprise with $100 million in annual revenue, 3,000 employees, and 80 data scientists. The 3-year results were:

• 233% return on investment (ROI)  (p. 5)
• $4.4 million net present value (NPV)  (p. 5)
• $6.2 million in total benefits against $1.9 million in costs  (p. 5)
• 13-month payback period  (p. 5)

Improving GPU utilization unlocked US$3 million in savings

Forrester first quantified the value of optimizing existing infrastructure. This shift improved GPU utilization from 30% to 70% by year 1 and reached 80% by year 3.

The head of DevOps at a financial services organization in the study said:

“[Flexible infrastructure resources are] not only a cost saver but also a way to do more with the power we have. This gives us flexibility to change resources depending on our priorities... Infrastructure management therefore becomes much more meaningful to meet the business objectives of the financial institution and deliver value.”

In a market where GPU availability remains constrained and server costs remain high, doing more with existing capacity is a meaningful competitive advantage. Before deploying Red Hat AI, the interviewed organizations averaged GPU utilization rates of just 30% to 40%. The government organization's Chief Information Officer (CIO) noted that each GPU server cost upward of $400,000. (p. 11)

Red Hat AI's centralized visibility into AI infrastructure and inference optimization capabilities changed the calculus. With a clear view of which GPUs were allocated, idle, or oversubscribed, IT operations teams could make informed decisions about resource assignment. This transparency reduced the guesswork that previously drove incremental, and often unnecessary purchases.

75% faster MLOps provisioning helped data scientists accelerate 400 AI projects

Before Red Hat AI, data scientists at the interviewed organizations weren't primarily blocked by data quality or model complexity. They were blocked by infrastructure. 

"In 10 minutes, if you want to develop a new model, your repository is ready, your workbench is ready, and your infrastructure resources are available. Your working environment is ready and totally isolated. The data scientists reallocate that time to develop more accurate Al and ML models. They can start more projects, or they can take more time for themselves to upskill or hone existing skills."

 — Head of DevOps, financial services

The Forrester study found that machine learning operations (MLOps) provisioning and environment preparation consumed an average of 3 days per AI project. For organizations running dozens or hundreds of projects simultaneously, that adds up fast. Red Hat AI standardized the deployment approach and introduced automation for repeatable, pre-approved workflows, helping accelerate a total of 400 AI projects over 3 years. (p. 14)

60% less time on model training and rework unlocked $2.5 million

Once data scientists have their environments provisioned, the focus shifts to model quality and iteration. The Forrester study found that prior to Red Hat AI, 15% of data scientist time went strictly to model training and rework. (p. 13)

The CIO at a government organization pointed to the compounding effect directly: 

"Even if we have a 10% to 20% accuracy gain—and we saw a 90% accuracy improvement in some areas—it means less rework on the IT side. This time savings compounds as we build more trust with the models, which also increases adoption of the models we build."

Red Hat AI enables this shift by providing self-service access to the latest models, resources, and specific frameworks for predictive and generative AI. This immediate availability accelerates development and leads to models with superior response rates and accuracy. Because these models perform better, data scientists spend significantly less time on manual corrections. Rework time dropped by 40% in the first year and reached a 60% reduction by year 3. Instead of troubleshooting, teams redirected this reclaimed time toward building complex models and starting new projects, unlocking $2.5 million in total productivity gains. (p. 13)

AI that generated up to 2% annual profit growth 

Infrastructure optimization and increased developer productivity are necessary but not sufficient. The question enterprise leaders ask is whether AI actually drives business outcomes. 

For the composite organization, Forrester modeled a conservative 0.5% to 2% annual top-line improvement from customer-facing AI models. Even at conservative assumptions, customer-facing AI built on Red Hat AI contributed measurable profit improvement within 3 years. For the organizations Forrester interviewed, the examples are specific:

• Manufacturing: Built customer-facing models generating over $500,000 in additional revenue, plus an internal natural language processing (NLP) model that saved over $1 million annually in maintenance costs.
• Telecommunications: Deployed a customer service model that reduced incident response times by 50%.
• Government: Deployed forecasting, fraud detection, and research summarization models into production.

The operational efficiency gains in some of the interviewed customers has also uncovered new revenue stream initiatives: 

"Our organization plans to scale the Al solution to other critical areas, such as satellite management, and eventually turn the implementation into a commercial product for other telecom service providers." 

— CEO, Telecommunications

Qualitative value beyond the numbers

Forrester identified 4 additional benefits that were not quantified in the financial model but can significantly impact enterprise decision-making:

Infrastructure management time savings up to 60%: Organizations can reduce time spent on environment setup by consolidating to a unified stack. (pp. 4, 16–17)

Reduced shadow IT risk: Access to sanctioned tools in minutes rather than days reduces the incentive to build outside the platform, improving audit trails and data governance. (p. 4)

Emerging AI compliance support: Red Hat AI’s transparency and audit capabilities help organizations prepare for emerging regulations like the EU AI Act. (p. 17)

Improved stakeholder relationships: Self-service capabilities decrease internal resource politics and help technical teams more consistently meet business expectations. (p. 17)

Security: The non-negotiable requirement

One theme runs through every interview Forrester conducted—these organizations could not move their AI development to the public cloud. Data privacy regulations, internal security policies, and the sensitivity of training data made on-premises deployment a requirement, not a preference.

"Shift left is our first philosophy and [Red Hat Al] aligns with that philosophy because I can easily manage everything, including both infrastructure and Al, through software development practices. The mentality is simple, but the value is strong."

 — Head of DevOps, financial services

Red Hat AI's ability to deploy on-premises while preserving flexibility for future hybrid or cloud deployments was a primary selection criterion. Equally important was that on-premises deployment didn't mean sacrificing data access for data scientists or developers. The platform maintained security and governance requirements without restricting the people who needed to build models. 

What this means for your organization

The Forrester composite is a hypothetical model, but its assumptions are grounded in the experiences of 4 real organizations across diverse industries. The pattern they describe—fragmented infrastructure, low GPU utilization, slow provisioning, security constraints that rule out cloud-only approaches—is recognizable to anyone building enterprise AI today.

For CIOs and IT leaders: GPU utilization is a proxy for AI infrastructure ROI. If your utilization rates are below 50%, you are purchasing hardware you are not fully using. Centralized visibility into resource allocation is the first step toward reversing that, and inference optimization is a strong second step.

For heads of AI and data science: Provisioning time is a hidden tax on every AI project your team runs. Self-service, standardized tooling changes the math, reducing provisioning delays from days to minutes per project.

For MLOps and platform engineers: Repeatability and automation are not just efficiency tools, they are governance tools. Pre-approved, automated pipelines reduce compliance risk and free your team from reactive provisioning work.

For business stakeholders: The 13-month payback period means Red Hat AI began returning value after the first year of deployment.

Where to go next

Forrester's TEI methodology is designed to give readers a framework they can adapt to their own assumptions. The study includes detailed modeling tables for each benefit category, explicit risk adjustments, and the underlying assumptions for the composite organization. 

• Download the full Forrester TEI study to draft your own business case
• Learn more about Red Hat AI and how it supports enterprise AI development and deployment

This blog post is based on "The Total Economic Impact™ Of Red Hat AI," a commissioned study conducted by Forrester Consulting on behalf of Red Hat, published February 2026. Forrester makes no assumptions as to the potential ROI that other organizations will receive. The composite organization is a construct designed to illustrate potential financial impact and does not represent any specific customer. See the full study for complete methodology, assumptions, and disclosures.