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Gartner: Majority of Technology Purchases Come with High Degree of Regret

As technology continues to become more critical to the business, technology customers have access to more options and information than ever before leading to more instances of buyer remorse.

56% of organizations said they had a high degree of purchase regret over their largest tech-related purchase in the last two years, according to a new survey by Gartner, Inc.

"The high regret feelings are at their peak for tech buyers that have not started implementation, indicating significant frustration with the buying experience," said Hank Barnes, distinguished VP analyst at Gartner. "In the past, it was relatively easy for product leaders to predict who buyers were, but no longer. Buying team dynamics are changing and customers can find buying to be a real challenge."

Barnes identified key changes in tech buying behavior: "There can be significant downside to regret associated with enterprise technology decisions. The survey found that the organizations that indicated they had high regret for their purchase took, on average, 7 to 10 months longer to complete that purchase. Slow purchase decisions can lead to frustrated teams, wasted time and resources and even, potentially, slower growth for the company."

According to the survey, 67% of people involved in technology-buying decisions are not in IT which means that anyone could be a tech buyer for their organization. In this environment, a new technology adoption chasm is emerging. This new chasm divides organizations that are confident adopters and buyers of technology from the vast majority that are not. High-tech providers need new approaches to identify and engage these different types of B2B customers and predict which type of customer they are dealing with to improve the odds of winning good business.

"To shift strategies, we need to think about psychographics beyond the motivations for buying to also include how decisions are approached and which groups are driving the strategy," said Barnes. "Gartner has developed a psychographic model called Enterprise Technology Adoption Profiles (ETAs) that revealed seven specific customer segments. Using ETAs is one element that can help high tech providers move from a product/market fit strategy towards a product/customer fit strategy."

Enterprise Technology Adoption Profiles (ETAs) are a proprietary model developed by Gartner that assesses the psychographics that drive how and when organizations make technology decisions.

Additionally, high tech providers should create a model to help identify "best fit" situations and "should avoid" situations. "Best fit" situations should be captured in an ideal customer profile — an enterprise persona — which focuses on the characteristics of the organizations being targeted, not the individuals within those organizations. It can include a variety of factors including the technologies they use, their business situation, the resources available to them and psychographic ETAs.

"There will be a big grey area in between that you have to be thoughtful in evaluating whether to commit to pursuing the opportunity. This is all about improving your odds and allocating resources and investments effectively," said Barnes.

Having a keen understanding of the ideal customers will help high tech providers shape their strategies. With this insight, Gartner recommends that high tech providers do three things:

1. Focus the bulk of investments and effort toward supporting the "best fit" situations with the right offering, the right messaging, and the right type of content and engagement activities.

2. Train customer-facing teams on how to recognize the customer characteristics that indicate a "best fit."

3. Train customer-facing teams on how to adjust their approach when encountering prospects that fall into the grey area between "best fit" and "should avoid."

Methodology: In November and December 2021, Gartner surveyed 1,120 respondents in North America, Western Europe and Asia/Pacific to understand how organizations approach large-scale buying efforts for enterprise technology. Respondents were required to be at a manager level or higher, aware of large-scale buying efforts for technology occurring during the past two years, and directly involved in the evaluation or selection of products or services for technology projects.

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Gartner: Majority of Technology Purchases Come with High Degree of Regret

As technology continues to become more critical to the business, technology customers have access to more options and information than ever before leading to more instances of buyer remorse.

56% of organizations said they had a high degree of purchase regret over their largest tech-related purchase in the last two years, according to a new survey by Gartner, Inc.

"The high regret feelings are at their peak for tech buyers that have not started implementation, indicating significant frustration with the buying experience," said Hank Barnes, distinguished VP analyst at Gartner. "In the past, it was relatively easy for product leaders to predict who buyers were, but no longer. Buying team dynamics are changing and customers can find buying to be a real challenge."

Barnes identified key changes in tech buying behavior: "There can be significant downside to regret associated with enterprise technology decisions. The survey found that the organizations that indicated they had high regret for their purchase took, on average, 7 to 10 months longer to complete that purchase. Slow purchase decisions can lead to frustrated teams, wasted time and resources and even, potentially, slower growth for the company."

According to the survey, 67% of people involved in technology-buying decisions are not in IT which means that anyone could be a tech buyer for their organization. In this environment, a new technology adoption chasm is emerging. This new chasm divides organizations that are confident adopters and buyers of technology from the vast majority that are not. High-tech providers need new approaches to identify and engage these different types of B2B customers and predict which type of customer they are dealing with to improve the odds of winning good business.

"To shift strategies, we need to think about psychographics beyond the motivations for buying to also include how decisions are approached and which groups are driving the strategy," said Barnes. "Gartner has developed a psychographic model called Enterprise Technology Adoption Profiles (ETAs) that revealed seven specific customer segments. Using ETAs is one element that can help high tech providers move from a product/market fit strategy towards a product/customer fit strategy."

Enterprise Technology Adoption Profiles (ETAs) are a proprietary model developed by Gartner that assesses the psychographics that drive how and when organizations make technology decisions.

Additionally, high tech providers should create a model to help identify "best fit" situations and "should avoid" situations. "Best fit" situations should be captured in an ideal customer profile — an enterprise persona — which focuses on the characteristics of the organizations being targeted, not the individuals within those organizations. It can include a variety of factors including the technologies they use, their business situation, the resources available to them and psychographic ETAs.

"There will be a big grey area in between that you have to be thoughtful in evaluating whether to commit to pursuing the opportunity. This is all about improving your odds and allocating resources and investments effectively," said Barnes.

Having a keen understanding of the ideal customers will help high tech providers shape their strategies. With this insight, Gartner recommends that high tech providers do three things:

1. Focus the bulk of investments and effort toward supporting the "best fit" situations with the right offering, the right messaging, and the right type of content and engagement activities.

2. Train customer-facing teams on how to recognize the customer characteristics that indicate a "best fit."

3. Train customer-facing teams on how to adjust their approach when encountering prospects that fall into the grey area between "best fit" and "should avoid."

Methodology: In November and December 2021, Gartner surveyed 1,120 respondents in North America, Western Europe and Asia/Pacific to understand how organizations approach large-scale buying efforts for enterprise technology. Respondents were required to be at a manager level or higher, aware of large-scale buying efforts for technology occurring during the past two years, and directly involved in the evaluation or selection of products or services for technology projects.

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As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

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