3 Key Challenges for Enterprise Technology Investment in 2025

Eugene Khvostov
Apptio
Learn more about IBM

What kind of ROI is your organization seeing on its technology investments?

If your answer is "it's complicated," you're not alone.

According to a recent study conducted by Apptio in collaboration with Hanover Research, there is a disconnect between enterprise technology spending and organizations' ability to measure the results. The survey of more than 1,000 global IT decision-makers found that while more than 90% of surveyed companies are planning on increasing their technology budgets in 2025, over half (55%) of business leaders lack the information necessary to evaluate their technology spend decisions effectively.

This opacity is concerning, given the mounting pressure on organizations to realize meaningful transformation from AI, on top of existing challenges like meeting sustainability goals. In order to remain competitive going forward, businesses will need to get a handle on their IT complexity with a framework for data-driven decisions.

Here are three key findings from the study, along with recommendations for how organizations can solve their challenges.

1. Organizations aren't making the most of their data

The first step in managing a technology budget is getting an accurate measurement of where your money is going. For many organizations, this is complicated by difficulties in bringing together data in a way that enables insights. The study found that two of the top three factors affecting businesses' confidence in their tech decisions were distrust in data (56%) and data silos (49%).

The latter presents a particular challenge for many larger enterprises, where technology provisioning is increasingly being handled by individual departments rather than under the centralized purview of IT. This tends to result in an accumulation of shadow IT, with redundant applications, inefficient resource usage, and underutilized software licenses. Recognizing the criticality of budgetary management, organizations' finance departments are becoming more deeply involved in IT investment decisions. More than half (53%) of respondents said their finance teams had a significant influence on their technology purchasing.

Still, when it comes to solutions for managing spending, more advanced tools are going relatively underutilized. The study found that customer relationship management (CRM) and enterprise resource planning (ERP) systems were the primary single source of truth for data-driven decisions in 97% and 93% of organizations, respectively. On the other hand, only 71% or respondents said their companies use business intelligence tools, and only 52% use IT financial management tools. These solutions can be useful in aggregating and deriving actionable insights from the data housed within CRM and ERP systems, leading to a better understanding of how spending is driving business outcomes.

Recommendations:

  • Strengthen communication: Stakeholders from across business, IT, and finance should collaborate more closely on business objectives and how technology can support them.
  • Eliminate silos: Establishing a data foundation that provides unencumbered visibility into organization-wide spending will help to reduce shadow IT and misaligned resources.
  • Utilize advanced tools: Implement tools that collect operational and financial data from across the enterprise and translate it into business outcomes that stakeholders can understand.

2. AI returns are being measured in more than just dollars

Since the advent of generative AI a few years ago, businesses have been working to infuse the power of large language models (LLMs) into a range of processes to drive improvements. And while many organizations have taken caution in moving from experimentation to deployment, the study found that all 1,004 companies surveyed are currently harnessing AI for at least one application in their business.

The most common current use cases among these companies are data analysis/decision support (71%), process automation (55%), and fraud detection/cybersecurity (46%). To fund these AI deployments, 50% of survey respondents are pulling from internal capital allocation from existing budgets, while 36% have a dedicated AI innovation fund. Interestingly, 39% of organizations say they're funding AI costs with the savings generated by AI-driven efficiencies — an indication that a sizable contingent of businesses is seeing substantial hard-dollar ROI from their AI applications.

As one might expect, increased revenue is the top criterion surveyed organizations listed for valuing the return on their AI investments, with 90% of respondents classifying it as very or extremely important. However, the study also found that many businesses place equal value on non-monetary gains from AI like improvements in operational efficiency (86%), decision making (84%) and employee productivity and satisfaction (81%). As such, even enterprises that are having difficulty achieving or measuring hard-dollar ROI are still finding successes to justify further investment.

Recommendations:

  • Strategic funding: Utilize a combination of internal capital allocation, cost savings from AI-driven efficiencies, and dedicated AI funds to support AI initiatives.
  • Implement frameworks: Establish FinOps and Technology Business Management (TBM) frameworks and practices to free up more budget for AI.
  • Establish metrics: Standardize on a system of KPIs to better quantify the non-monetary benefits of AI like productivity and operational efficiency.

3. Sustainability initiatives are complicating IT strategies

As companies continue to strive toward their carbon emissions reduction targets, technology is presenting opportunities for improvement as well as challenges and complications for IT investment.

As enterprise technology estates are major contributors to their organizations' carbon footprints, businesses are prioritizing equipment like energy-efficient hardware (49%) as well as a greater use of cloud computing (33%) to cut the carbon footprint of their premises. Organizations are also increasingly looking to AI and machine learning technologies (37%) to drive a reduction in energy consumption.  

However, challenges persist in organizations' ability to measure and track their progress. Among these, data complexity (37%) and data availability (28%) are leading difficulties among survey respondents.

Recommendations: 

  • Focus on data quality: Implement systems to gather reliable and consistent energy and carbon data from organizational assets and supply chain partners.
  • Use green clouds: Shift resources to cloud providers that leverage carbon-free energy sources like solar and wind to offset their power requirements.
  • Leverage software: Using carbon accounting software in conjunction with overarching strategies like TBM frameworks, organizations will have the visibility required to optimize their resources as well as their budgets. 
Eugene Khvostov is Chief Product Officer at Apptio

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3 Key Challenges for Enterprise Technology Investment in 2025

Eugene Khvostov
Apptio
Learn more about IBM

What kind of ROI is your organization seeing on its technology investments?

If your answer is "it's complicated," you're not alone.

According to a recent study conducted by Apptio in collaboration with Hanover Research, there is a disconnect between enterprise technology spending and organizations' ability to measure the results. The survey of more than 1,000 global IT decision-makers found that while more than 90% of surveyed companies are planning on increasing their technology budgets in 2025, over half (55%) of business leaders lack the information necessary to evaluate their technology spend decisions effectively.

This opacity is concerning, given the mounting pressure on organizations to realize meaningful transformation from AI, on top of existing challenges like meeting sustainability goals. In order to remain competitive going forward, businesses will need to get a handle on their IT complexity with a framework for data-driven decisions.

Here are three key findings from the study, along with recommendations for how organizations can solve their challenges.

1. Organizations aren't making the most of their data

The first step in managing a technology budget is getting an accurate measurement of where your money is going. For many organizations, this is complicated by difficulties in bringing together data in a way that enables insights. The study found that two of the top three factors affecting businesses' confidence in their tech decisions were distrust in data (56%) and data silos (49%).

The latter presents a particular challenge for many larger enterprises, where technology provisioning is increasingly being handled by individual departments rather than under the centralized purview of IT. This tends to result in an accumulation of shadow IT, with redundant applications, inefficient resource usage, and underutilized software licenses. Recognizing the criticality of budgetary management, organizations' finance departments are becoming more deeply involved in IT investment decisions. More than half (53%) of respondents said their finance teams had a significant influence on their technology purchasing.

Still, when it comes to solutions for managing spending, more advanced tools are going relatively underutilized. The study found that customer relationship management (CRM) and enterprise resource planning (ERP) systems were the primary single source of truth for data-driven decisions in 97% and 93% of organizations, respectively. On the other hand, only 71% or respondents said their companies use business intelligence tools, and only 52% use IT financial management tools. These solutions can be useful in aggregating and deriving actionable insights from the data housed within CRM and ERP systems, leading to a better understanding of how spending is driving business outcomes.

Recommendations:

  • Strengthen communication: Stakeholders from across business, IT, and finance should collaborate more closely on business objectives and how technology can support them.
  • Eliminate silos: Establishing a data foundation that provides unencumbered visibility into organization-wide spending will help to reduce shadow IT and misaligned resources.
  • Utilize advanced tools: Implement tools that collect operational and financial data from across the enterprise and translate it into business outcomes that stakeholders can understand.

2. AI returns are being measured in more than just dollars

Since the advent of generative AI a few years ago, businesses have been working to infuse the power of large language models (LLMs) into a range of processes to drive improvements. And while many organizations have taken caution in moving from experimentation to deployment, the study found that all 1,004 companies surveyed are currently harnessing AI for at least one application in their business.

The most common current use cases among these companies are data analysis/decision support (71%), process automation (55%), and fraud detection/cybersecurity (46%). To fund these AI deployments, 50% of survey respondents are pulling from internal capital allocation from existing budgets, while 36% have a dedicated AI innovation fund. Interestingly, 39% of organizations say they're funding AI costs with the savings generated by AI-driven efficiencies — an indication that a sizable contingent of businesses is seeing substantial hard-dollar ROI from their AI applications.

As one might expect, increased revenue is the top criterion surveyed organizations listed for valuing the return on their AI investments, with 90% of respondents classifying it as very or extremely important. However, the study also found that many businesses place equal value on non-monetary gains from AI like improvements in operational efficiency (86%), decision making (84%) and employee productivity and satisfaction (81%). As such, even enterprises that are having difficulty achieving or measuring hard-dollar ROI are still finding successes to justify further investment.

Recommendations:

  • Strategic funding: Utilize a combination of internal capital allocation, cost savings from AI-driven efficiencies, and dedicated AI funds to support AI initiatives.
  • Implement frameworks: Establish FinOps and Technology Business Management (TBM) frameworks and practices to free up more budget for AI.
  • Establish metrics: Standardize on a system of KPIs to better quantify the non-monetary benefits of AI like productivity and operational efficiency.

3. Sustainability initiatives are complicating IT strategies

As companies continue to strive toward their carbon emissions reduction targets, technology is presenting opportunities for improvement as well as challenges and complications for IT investment.

As enterprise technology estates are major contributors to their organizations' carbon footprints, businesses are prioritizing equipment like energy-efficient hardware (49%) as well as a greater use of cloud computing (33%) to cut the carbon footprint of their premises. Organizations are also increasingly looking to AI and machine learning technologies (37%) to drive a reduction in energy consumption.  

However, challenges persist in organizations' ability to measure and track their progress. Among these, data complexity (37%) and data availability (28%) are leading difficulties among survey respondents.

Recommendations: 

  • Focus on data quality: Implement systems to gather reliable and consistent energy and carbon data from organizational assets and supply chain partners.
  • Use green clouds: Shift resources to cloud providers that leverage carbon-free energy sources like solar and wind to offset their power requirements.
  • Leverage software: Using carbon accounting software in conjunction with overarching strategies like TBM frameworks, organizations will have the visibility required to optimize their resources as well as their budgets. 
Eugene Khvostov is Chief Product Officer at Apptio

Related Links

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For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

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