Skip to main content

IBM Introduces Apptio AI Value & ROI

Apptio, an IBM company, announced the public preview of IBM Apptio AI Value & ROI, a new set of capabilities developed to give technology and finance executives, AI governance teams and business leaders a single view of AI spending, including token costs, and its connection to measurable business results.

IBM Apptio AI Value & ROI centralizes AI initiatives and tracks metrics across revenue, cost, speed, productivity and risk, connecting AI investments to measurable business outcomes. IBM Cloudability provides visibility and attribution of token spend, while IBM Apptio AI Value & ROI connects that spend to business outcomes, helping enterprises measure the business value of each AI initiative.

“AI is facing a cost crisis, and organizations are under growing pressure to demonstrate business value,” said Bill Lobig, vice president, IBM Apptio. “With Apptio, clients can address waste — with one client seeing a 50% reduction in costs, allowing them to unlock funding for new AI initiatives that accelerate innovation.”

Key capabilities of IBM Apptio AI Value & ROI include:

  • Proof Metrics and Results: IBM Apptio AI Value & ROI connects each AI initiative to one or more business outcomes and customer-selected proof metrics, such as cycle time, cost avoided, conversion rate or incident volume. Baseline, target, actual and realized results can be tracked over time to show whether value is being realized and what action may be needed.
  • Flexible Framework: Customers can use multiple inventory and metric sources to support a range of AI initiatives.
  • Integration with IBM Apptio AI TCO & Usage and IBM Cloudability: Users can incorporate modeled AI cost and consumption inputs from IBM Cloudability and IBM Apptio AI TCO & Usage—including technology, token spend, usage, labor and other cost inputs—to understand and optimize both the cost and business value of AI initiatives.

By connecting AI initiatives to business outcomes and the end-to-end technology and labor investments required to deliver them, IBM Apptio AI Value & ROI helps leaders determine whether AI investments are producing measurable value.

IBM Apptio AI Value & ROI is currently available in Public Preview for all IBM Apptio Costing Standard or IBM Apptio AI TCO & Usage customers and will be generally available in Q3 of 2026.

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

IBM Introduces Apptio AI Value & ROI

Apptio, an IBM company, announced the public preview of IBM Apptio AI Value & ROI, a new set of capabilities developed to give technology and finance executives, AI governance teams and business leaders a single view of AI spending, including token costs, and its connection to measurable business results.

IBM Apptio AI Value & ROI centralizes AI initiatives and tracks metrics across revenue, cost, speed, productivity and risk, connecting AI investments to measurable business outcomes. IBM Cloudability provides visibility and attribution of token spend, while IBM Apptio AI Value & ROI connects that spend to business outcomes, helping enterprises measure the business value of each AI initiative.

“AI is facing a cost crisis, and organizations are under growing pressure to demonstrate business value,” said Bill Lobig, vice president, IBM Apptio. “With Apptio, clients can address waste — with one client seeing a 50% reduction in costs, allowing them to unlock funding for new AI initiatives that accelerate innovation.”

Key capabilities of IBM Apptio AI Value & ROI include:

  • Proof Metrics and Results: IBM Apptio AI Value & ROI connects each AI initiative to one or more business outcomes and customer-selected proof metrics, such as cycle time, cost avoided, conversion rate or incident volume. Baseline, target, actual and realized results can be tracked over time to show whether value is being realized and what action may be needed.
  • Flexible Framework: Customers can use multiple inventory and metric sources to support a range of AI initiatives.
  • Integration with IBM Apptio AI TCO & Usage and IBM Cloudability: Users can incorporate modeled AI cost and consumption inputs from IBM Cloudability and IBM Apptio AI TCO & Usage—including technology, token spend, usage, labor and other cost inputs—to understand and optimize both the cost and business value of AI initiatives.

By connecting AI initiatives to business outcomes and the end-to-end technology and labor investments required to deliver them, IBM Apptio AI Value & ROI helps leaders determine whether AI investments are producing measurable value.

IBM Apptio AI Value & ROI is currently available in Public Preview for all IBM Apptio Costing Standard or IBM Apptio AI TCO & Usage customers and will be generally available in Q3 of 2026.

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...