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The IT Investment Paradox: Why Bigger Budgets Are Creating Slower Decisions

Eugene Khvostov
Apptio

It's the paradox at the heart of enterprise IT in 2026: teams secure a record-level budget, but instead of immediately innovating and evolving with new tools and seeing results, decision-making has slowed to a crawl. They're flying blind, unable to connect massive investments in areas like AI and cloud to the one thing the business actually cares about: value.

For technology leaders, 2026 is the year to bring business context and value back into the IT conversation. Apptio just released its 2026 Technology Investment Management Report, which revealed 74% of organizations are increasing their IT budgets, yet there is a staggering disconnect. While priorities are clear — with 94% of leaders focusing on cybersecurity and 91% on AI — nearly half (49%) struggle with cloud cost volatility and 43% are concerned about the costs of AI/ML. As budgets scale, the sheer complexity of the IT landscape is outpacing the tools to manage it, making it difficult for leaders to obtain the forecasting needed to make confident decisions and translate investments into results. This isn't just a temporary challenge; it's a sign of a much deeper evolution.

By building IT Financial Management (ITFM) resilience, shifting from project-based to product-based operating models, and incorporating AI-powered financial intelligence, an agile, outcome-driven approach is possible, helping teams work efficiently and eliminate ROI uncertainty. The old playbook for managing technology is outdated, and a new one is taking its place, defined by three seismic shifts that are already separating the leaders from the laggards.

The Confidence-Capability Gap

Reliance on outdated tools is directly impacting leader confidence and slowing down critical decisions, presenting a significant, untapped opportunity for organizations. The report found that 90% of leaders say uncertainty about ROI has a moderate to major impact on their investment decisions — an increase from 85% in 2025. This doubt is fueled by a disconnect between perception and reality. While nearly 59% of ITFM professionals believe their forecasts are highly accurate, only 35% use purpose-built ITFM tools. Nearly 47% still rely on generic ERP systems, with others still dependent on manual spreadsheets.

Disconnected systems and unclear metrics compound the confidence problem. Distrust in data (84%) and persistent data silos (80%) make it even harder to find the insights necessary to justify spend. These manual and generic tools introduce delays and inaccuracies that limit scalability, making it difficult for leaders to achieve the transparency and agility stakeholders now expect. When nearly every leader questions ROI, investment decisions stall, and the gap between what leaders think they know and what they can prove continues to widen. By integrating IT Services Management (ITSM) anchored in Technology Business Management (TBM) practices, enterprises can increase visibility and understand the true ROI across labor, licenses and infrastructure.

Building ITFM Resilience

Many teams continue to struggle with visibility, alignment and forecasting, creating not only operational and confidence gaps but also major business risks, including runaway cloud spend, stalled innovation, and diminished stakeholder confidence. Closing the confidence gap requires more than just optimism; it requires building ITFM resilience through deliberate, strategic action.

First, start with the basics. Establish foundational capabilities like complete cost transparency and accurate forecasting. Second, modernize the toolkit to automate data integration and finally reduce the risky reliance on spreadsheets, as they create delays and impact decision-making capabilities. From there, adopt dynamic planning, moving beyond rigid annual cycles to an agile, iterative model that can keep pace with the business and evolve alongside it. Finally, build stakeholder alignment by engaging finance, IT, and business leaders with a common, data-driven language for IT spend.

ITFM maturity isn't about perfection — it's about progress. Taking these steps creates a single source of truth that can shift IT from operating as a siloed cost center to becoming a strategic and connected business asset driving innovation and growth.

The Shift from Projects to Products

The data reveals why a fundamental shift from project-based to product-based operating models is no longer optional — it's critical. The pressures forcing this change are already clear. With FinOps teams growing in both size and cross-functional diversity (61% are now over six people) and AI funding becoming more dynamic (67% of organizations are reallocating internal capital), the rigidity of static, annual project plans has become a primary roadblock to innovation. Without strong FinOps fundamentals — visibility, forecasting, and accountability — organizations risk runaway cloud spend, delayed innovation and diminished stakeholder confidence.

To manage this new reality, teams must operate like product development engines, with defined sprints, clear OKRs, and intense scrutiny on business performance to unlock more agile ways of working and allow funding and resources to track product success in real time. This is where integrating ITSM with TBM becomes critical. The integrated framework provides the real-time visibility needed to track product performance, understand ROI across labor, licenses and infrastructure, and ultimately unlock a more accountable, outcome-driven way of working that closes the gap between strategy and execution.

AI as the Enabler for Real-Time Intelligence

This agile, product-centric model is impossible without an equally agile approach to financial data — one powered by AI. The AI era requires near real-time, integrated financial and operational information to make sound decisions at high speed. AI-powered financial intelligence is the key that eliminates data silos, connecting disparate costs to direct business value and democratizing insight for every user. By pairing this powerful financial insight with agile ways of working, IT leaders and their teams can finally close the confidence-capability gap and take definitive control over their decisions.

The path forward for technology leaders is clear: those who cling to spreadsheets and rigid annual plans will fall behind and become paralyzed when it comes to proving business value. The leaders who will thrive in 2026 and beyond will be those that focus their priorities on building ITFM resilience, shift towards a product-centric mindset, and leverage AI to transform data into strategic insights. By making these changes, IT leaders can close the gap between budget and value, reclaim their role as essential drivers of business innovation, and gain their confidence back for good. 

Eugene Khvostov is Chief Product Officer at Apptio

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The IT Investment Paradox: Why Bigger Budgets Are Creating Slower Decisions

Eugene Khvostov
Apptio

It's the paradox at the heart of enterprise IT in 2026: teams secure a record-level budget, but instead of immediately innovating and evolving with new tools and seeing results, decision-making has slowed to a crawl. They're flying blind, unable to connect massive investments in areas like AI and cloud to the one thing the business actually cares about: value.

For technology leaders, 2026 is the year to bring business context and value back into the IT conversation. Apptio just released its 2026 Technology Investment Management Report, which revealed 74% of organizations are increasing their IT budgets, yet there is a staggering disconnect. While priorities are clear — with 94% of leaders focusing on cybersecurity and 91% on AI — nearly half (49%) struggle with cloud cost volatility and 43% are concerned about the costs of AI/ML. As budgets scale, the sheer complexity of the IT landscape is outpacing the tools to manage it, making it difficult for leaders to obtain the forecasting needed to make confident decisions and translate investments into results. This isn't just a temporary challenge; it's a sign of a much deeper evolution.

By building IT Financial Management (ITFM) resilience, shifting from project-based to product-based operating models, and incorporating AI-powered financial intelligence, an agile, outcome-driven approach is possible, helping teams work efficiently and eliminate ROI uncertainty. The old playbook for managing technology is outdated, and a new one is taking its place, defined by three seismic shifts that are already separating the leaders from the laggards.

The Confidence-Capability Gap

Reliance on outdated tools is directly impacting leader confidence and slowing down critical decisions, presenting a significant, untapped opportunity for organizations. The report found that 90% of leaders say uncertainty about ROI has a moderate to major impact on their investment decisions — an increase from 85% in 2025. This doubt is fueled by a disconnect between perception and reality. While nearly 59% of ITFM professionals believe their forecasts are highly accurate, only 35% use purpose-built ITFM tools. Nearly 47% still rely on generic ERP systems, with others still dependent on manual spreadsheets.

Disconnected systems and unclear metrics compound the confidence problem. Distrust in data (84%) and persistent data silos (80%) make it even harder to find the insights necessary to justify spend. These manual and generic tools introduce delays and inaccuracies that limit scalability, making it difficult for leaders to achieve the transparency and agility stakeholders now expect. When nearly every leader questions ROI, investment decisions stall, and the gap between what leaders think they know and what they can prove continues to widen. By integrating IT Services Management (ITSM) anchored in Technology Business Management (TBM) practices, enterprises can increase visibility and understand the true ROI across labor, licenses and infrastructure.

Building ITFM Resilience

Many teams continue to struggle with visibility, alignment and forecasting, creating not only operational and confidence gaps but also major business risks, including runaway cloud spend, stalled innovation, and diminished stakeholder confidence. Closing the confidence gap requires more than just optimism; it requires building ITFM resilience through deliberate, strategic action.

First, start with the basics. Establish foundational capabilities like complete cost transparency and accurate forecasting. Second, modernize the toolkit to automate data integration and finally reduce the risky reliance on spreadsheets, as they create delays and impact decision-making capabilities. From there, adopt dynamic planning, moving beyond rigid annual cycles to an agile, iterative model that can keep pace with the business and evolve alongside it. Finally, build stakeholder alignment by engaging finance, IT, and business leaders with a common, data-driven language for IT spend.

ITFM maturity isn't about perfection — it's about progress. Taking these steps creates a single source of truth that can shift IT from operating as a siloed cost center to becoming a strategic and connected business asset driving innovation and growth.

The Shift from Projects to Products

The data reveals why a fundamental shift from project-based to product-based operating models is no longer optional — it's critical. The pressures forcing this change are already clear. With FinOps teams growing in both size and cross-functional diversity (61% are now over six people) and AI funding becoming more dynamic (67% of organizations are reallocating internal capital), the rigidity of static, annual project plans has become a primary roadblock to innovation. Without strong FinOps fundamentals — visibility, forecasting, and accountability — organizations risk runaway cloud spend, delayed innovation and diminished stakeholder confidence.

To manage this new reality, teams must operate like product development engines, with defined sprints, clear OKRs, and intense scrutiny on business performance to unlock more agile ways of working and allow funding and resources to track product success in real time. This is where integrating ITSM with TBM becomes critical. The integrated framework provides the real-time visibility needed to track product performance, understand ROI across labor, licenses and infrastructure, and ultimately unlock a more accountable, outcome-driven way of working that closes the gap between strategy and execution.

AI as the Enabler for Real-Time Intelligence

This agile, product-centric model is impossible without an equally agile approach to financial data — one powered by AI. The AI era requires near real-time, integrated financial and operational information to make sound decisions at high speed. AI-powered financial intelligence is the key that eliminates data silos, connecting disparate costs to direct business value and democratizing insight for every user. By pairing this powerful financial insight with agile ways of working, IT leaders and their teams can finally close the confidence-capability gap and take definitive control over their decisions.

The path forward for technology leaders is clear: those who cling to spreadsheets and rigid annual plans will fall behind and become paralyzed when it comes to proving business value. The leaders who will thrive in 2026 and beyond will be those that focus their priorities on building ITFM resilience, shift towards a product-centric mindset, and leverage AI to transform data into strategic insights. By making these changes, IT leaders can close the gap between budget and value, reclaim their role as essential drivers of business innovation, and gain their confidence back for good. 

Eugene Khvostov is Chief Product Officer at Apptio

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...