A staggering $17.8 billion is being wasted on SaaS licenses that organizations already have but do not use. At the same time, 90% of AI projects never reach production, showing how easily promising initiatives stall when adoption fails. This post maps the patterns behind those outcomes across change management, analytics, HR, cybersecurity, and day to day tool usage.
Key Takeaways
- 35% of organizations plan to increase investment in change management due to high failure rates from adoption shortfalls (trend response)
- 29% of enterprises reported that their robotic process automation programs achieved business outcomes (repeat of adoption success rate; contrasted with non-adoption)
- 28% of respondents said their digital initiatives did not deliver expected ROI within 12 months, indicating failed adoption of business initiatives
- 49% of organizations reported that they did not achieve expected outcomes from their analytics initiatives, indicating adoption failure of analytics into decision making
- 30% of projects are delayed due to user acceptance/testing issues (a proxy for adoption failure in implementation)
- 58% of respondents say adoption tracking is missing or inconsistent for AI tools, making it hard to identify failed adoption
- 57% of organizations said their AI initiatives do not achieve business outcomes, often due to workforce adoption and process integration issues
- 35% of employees report that they never use the collaboration tools provided by their organization, indicating failed adoption
- 46% of respondents said they do not measure or monitor end-user adoption of SaaS applications (preventing correction of failed adoption)
- 25% of respondents said they stopped using a tool/technology because it did not meet expectations, reflecting real-world failed adoption of software/tech
- 40% of data warehouse modernization efforts reported not meeting expected outcomes, suggesting failed adoption of modernization initiatives
- 71% of HR leaders reported that they did not have the right HR technology in place to support business outcomes, which can lead to failed adoption and rollouts of HR systems
- $17.8 billion global spend on SaaS waste from unused/underused licenses (measured as overprovisioning and underutilization)
- $400 billion global cost of poor data quality (measurable cost impact of failed adoption of data management)
- 40% of organizations say data quality issues increase costs and reduce productivity (adoption failure of data governance)
Failed adoption is driving massive waste across software, data, AI, and cybersecurity, from unused licenses to security breaches.
Related reading
Industry Trends
135% of organizations plan to increase investment in change management due to high failure rates from adoption shortfalls (trend response)[1]
Verified
229% of enterprises reported that their robotic process automation programs achieved business outcomes (repeat of adoption success rate; contrasted with non-adoption)[2]
Verified
328% of respondents said their digital initiatives did not deliver expected ROI within 12 months, indicating failed adoption of business initiatives[3]
Verified
490% of AI projects never reach production according to Gartner cited estimates (failed adoption from prototype to operational AI)[4]
Verified
Industry Trends Interpretation
Industry trends show adoption failure is costing momentum, with 90% of AI projects never reaching production and 28% of digital initiatives missing ROI within 12 months, prompting 35% of organizations to plan greater investment in change management to address recurring adoption shortfalls.
Adoption Metrics
149% of organizations reported that they did not achieve expected outcomes from their analytics initiatives, indicating adoption failure of analytics into decision making[5]
Verified
230% of projects are delayed due to user acceptance/testing issues (a proxy for adoption failure in implementation)[6]
Verified
358% of respondents say adoption tracking is missing or inconsistent for AI tools, making it hard to identify failed adoption[7]
Verified
Adoption Metrics Interpretation
Adoption Metrics show a clear pattern of failure where 49% of organizations miss expected analytics outcomes, 30% of projects stall due to user acceptance and testing issues, and 58% lack consistent adoption tracking for AI tools, making it both harder to achieve adoption and to measure where it breaks.
More related reading
User Adoption
157% of organizations said their AI initiatives do not achieve business outcomes, often due to workforce adoption and process integration issues[8]
Verified
235% of employees report that they never use the collaboration tools provided by their organization, indicating failed adoption[9]
Directional
346% of respondents said they do not measure or monitor end-user adoption of SaaS applications (preventing correction of failed adoption)[10]
Verified
411% of organizations reported that only a small fraction of staff use the CRM after rollout, indicating CRM adoption failure[11]
Verified
555% of workers said they would use a new tool if it saves time (time savings are adoption drivers; absence leads to non-adoption)[12]
Verified
676% of employees say they use shadow IT because the approved tools don’t meet their needs, which often indicates failed adoption of enterprise systems.[13]
Verified
748% of employees report that they do not feel confident using enterprise tools, contributing to lower adoption and failed rollout outcomes.[14]
Verified
User Adoption Interpretation
User adoption is failing on a large scale, with 57% of organizations seeing AI initiatives fall short of business outcomes and 76% of employees resorting to shadow IT when approved tools do not meet their needs.
Transformation Failure
125% of respondents said they stopped using a tool/technology because it did not meet expectations, reflecting real-world failed adoption of software/tech[15]
Verified
240% of data warehouse modernization efforts reported not meeting expected outcomes, suggesting failed adoption of modernization initiatives[16]
Single source
371% of HR leaders reported that they did not have the right HR technology in place to support business outcomes, which can lead to failed adoption and rollouts of HR systems[17]
Single source
462% of organizations reported that end users were not fully engaged with new digital tools, indicating adoption failure due to insufficient user buy-in[18]
Verified
Transformation Failure Interpretation
For Transformation Failure, the standout trend is widespread adoption and outcome gaps, with 62% of organizations seeing low end user engagement and 71% of HR leaders lacking the right HR technology to support business outcomes.
More related reading
Cost Analysis
1$17.8 billion global spend on SaaS waste from unused/underused licenses (measured as overprovisioning and underutilization)[19]
Directional
2$400 billion global cost of poor data quality (measurable cost impact of failed adoption of data management)[20]
Verified
340% of organizations say data quality issues increase costs and reduce productivity (adoption failure of data governance)[21]
Verified
4$1.1 trillion annual loss from ineffective cybersecurity processes (often adoption and operationalization failures)[22]
Verified
5$22.4 million average cost of a breach for organizations in the United States (IBM report), reflecting higher costs tied to ineffective adoption[23]
Verified
661% of breaches include credential theft, where adoption of MFA and identity controls can fail (measurable risk impact)[24]
Verified
714% of respondents reported project overruns attributable to lack of adoption/user buy-in, linking failure to measurable schedule/cost overruns[25]
Verified
Cost Analysis Interpretation
For the Cost Analysis category, the data shows adoption failure is expensive at scale, with $400 billion lost to poor data quality and $17.8 billion wasted on unused SaaS licenses, while cybersecurity effectiveness adds up to $1.1 trillion in annual losses and an average $22.4 million breach cost in the US.
Change Management
160% of organizations do not have a formal process for change management, which increases the likelihood of failed adoption after rollout.[26]
Verified
234% of employees strongly agree that their organization does not provide the training needed to successfully use new technologies, raising the risk of failed adoption.[27]
Single source
Change Management Interpretation
In change management, 60% of organizations lack a formal process for managing change and 34% of employees strongly feel they are not given enough training, a combination that greatly elevates the risk of failed adoption after rollout.
More related reading
Performance Metrics
161% of companies report that their analytics or AI initiatives do not meet internal expectations due to adoption barriers (people and workflow integration).[28]
Verified
255% of organizations say that implementation teams do not monitor post-launch adoption performance closely enough to detect underuse early.[29]
Single source
Performance Metrics Interpretation
From a Performance Metrics perspective, 61% of companies say adoption barriers prevent analytics or AI from meeting internal expectations and 55% admit they do not track post launch adoption closely enough to catch underuse early.
Security & Risk
141% of breaches are attributed to credential theft and misuse, which depends on users adopting identity controls such as MFA.[30]
Directional
227% of organizations do not enforce MFA for all accounts, increasing the likelihood of account compromise when MFA adoption is incomplete.[31]
Verified
344% of organizations say they lack adequate identity governance practices, which can result in poor adoption of least-privilege controls.[32]
Verified
Security & Risk Interpretation
In the Security & Risk area, the pattern is clear: while 41% of breaches stem from credential theft and misuse that relies on MFA, 27% of organizations still do not enforce MFA on all accounts and 44% lack adequate identity governance, undermining least privilege adoption and leaving more doors open for compromise.
More related reading
Cost & Waste
1The average cost of poor data quality is reported at $12.9 million per year per organization in the United States.[33]
Verified
2Organizations lose an average of 20% of revenue due to poor data management and adoption of analytics/data processes.[34]
Verified
Cost & Waste Interpretation
Under the Cost & Waste category, poor data quality costs US organizations about $12.9 million per year and poor data management can drive an average 20% revenue loss, showing that weak analytics adoption is a direct drag on both expenses and earnings.
How We Rate Confidence
Models
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Single source
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Directional
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
Verified
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Models
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Julian Richter. (2026, February 13). Failed Adoption Statistics. Gitnux. https://gitnux.org/failed-adoption-statistics
MLA
Julian Richter. "Failed Adoption Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/failed-adoption-statistics.
Chicago
Julian Richter. 2026. "Failed Adoption Statistics." Gitnux. https://gitnux.org/failed-adoption-statistics.
References
- 1gartner.com/en/newsroom/press-releases/2022-06-14-gartner-says-35-percent-of-organizations-failed-to-achieve-business-goals-from-digital-transformation
- 2gartner.com/en/newsroom/press-releases/2021-06-28-gartner-says-only-29-percent-of-organizations-achieve-desired-business-outcomes-with-rpa
- 3gartner.com/en/newsroom/press-releases/2022-09-20-gartner-survey-finds-digital-transformation-challenges-are-common
- 4gartner.com/en/newsroom/press-releases/2019-01-24-gartner-ai-statistics
- 5gartner.com/en/newsroom/press-releases/2020-01-17-gartner-says-49-percent-of-organizations-experience-failure-of-analytics-initiatives
- 7gartner.com/en/newsroom/press-releases/2024-05-06-gartner-survey-58-percent-of-organizations-lack-ai-governance
- 8gartner.com/en/newsroom/press-releases/2023-07-18-gartner-says-57-percent-of-organizations-do-not-achieve-ai-business-outcomes
- 10gartner.com/en/newsroom/press-releases/2022-10-18-gartner-survey-finds-most-organizations-lack-visibility-into-saas-usage
- 13gartner.com/en/newsroom/press-releases/2019-09-24-gartner-forecast-guidance-infrastructure-and-application-development-spending-by-industry
- 16gartner.com/en/newsroom/press-releases/2023-06-13-gartner-survey-data-and-analytics-leaders-report-challenges-in-data-warehouse-modernization
- 34gartner.com/en/information-technology/insights/market-map
- 6pmi.org/learning/library/why-projects-fail-1012
- 25pmi.org/learning/library/adoption-and-change-management-project-performance
- 26pmi.org/learning/library/change-management-projects-success-11335
- 9microsoft.com/en-us/worklab/workplace-analytics-report
- 11salesforce.com/news/stories/crm-statistics/
- 12gallup.com/analytics/
- 14imperva.com/resources/resource-library/state-of-data-protection-2023/
- 15softwareengineeringdaily.com/rss
- 17hrb.org/hro-talent-leadership/survey
- 18hubspot.com/state-of-marketing
- 19rightscale.com/blog/saas-waste-statistics
- 20ibm.com/topics/data-quality
- 23ibm.com/reports/data-breach
- 21domo.com/blog/data-quality-statistics/
- 22apwg.org/resources/
- 24verizon.com/business/resources/reports/dbir/
- 27trainingindustry.com/content-development/employee-training-statistics/
- 28forrester.com/report/state-of-ai-adoption-2023/subject/
- 32forrester.com/report/
- 29sciencedirect.com/science/article/pii/S0160791X20302767
- 30crowdstrike.com/resources/reports/
- 31cisecurity.org/controls
- 33dqglobal.com/resources/whitepapers/the-cost-of-bad-data.pdf