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Gartner: 30% of GenAI Projects Will Be Abandoned After Proof of Concept by End of 2025

At least 30% of generative AI (GenAI) projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value, according to Gartner, Inc.

"After last year's hype, executives are impatient to see returns on GenAI investments, yet organizations are struggling to prove and realize value,” said Rita Sallam, Distinguished VP Analyst at Gartner. "As the scope of initiatives widen, the financial burden of developing and deploying GenAI models is increasingly felt."

A major challenge for organizations arises in justifying the substantial investment in GenAI for productivity enhancement, which can be difficult to directly translate into financial benefit, according to Gartner. Many organizations are leveraging GenAI to transform their business models and create new business opportunities. However, these deployment approaches come with significant costs, ranging from $5 million to $20 million.

"Unfortunately, there is no one size fits all with GenAI, and costs aren't as predictable as other technologies," said Sallam. "What you spend, the use cases you invest in and the deployment approaches you take, all determine the costs. Whether you're a market disruptor and want to infuse AI everywhere, or you have a more conservative focus on productivity gains or extending existing processes, each has different levels of cost, risk, variability and strategic impact."

Regardless of AI ambition, Gartner research indicates GenAI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment (ROI). Historically, many CFOs have not been comfortable with investing today for indirect value in the future. This reluctance can skew investment allocation to tactical versus strategic outcomes.

Realizing Business Value

Earlier adopters across industries and business processes are reporting a range of business improvements that vary by use case, job type and skill level of the worker. According to a recent Gartner survey, respondents reported 15.8% revenue increase, 15.2% cost savings and 22.6% productivity improvement on average. The survey of 822 business leaders was conducted between September and November 2023.

"This data serves as a valuable reference point for assessing the business value derived from GenAI business model innovation," said Sallam. "But it's important to acknowledge the challenges in estimating that value, as benefits are very company, use case, role and workforce specific. Often, the impact may not be immediately evident and may materialize over time. However, this delay doesn't diminish the potential benefits."

Calculating Business Impact

By analyzing the business value and the total costs of GenAI business model innovation, organizations can establish the direct ROI and future value impact, according to Gartner. This serves as a crucial tool for making informed investment decisions about GenAI business model innovation.

"If the business outcomes meet or exceed expectations, it presents an opportunity to expand investments by scaling GenAI innovation and usage across a broader user base, or implementing it in additional business divisions," said Sallam. "However, if they fall short, it may be necessary to explore alternative innovation scenarios. These insights help organizations strategically allocate resources and determine the most effective path forward."

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Gartner: 30% of GenAI Projects Will Be Abandoned After Proof of Concept by End of 2025

At least 30% of generative AI (GenAI) projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value, according to Gartner, Inc.

"After last year's hype, executives are impatient to see returns on GenAI investments, yet organizations are struggling to prove and realize value,” said Rita Sallam, Distinguished VP Analyst at Gartner. "As the scope of initiatives widen, the financial burden of developing and deploying GenAI models is increasingly felt."

A major challenge for organizations arises in justifying the substantial investment in GenAI for productivity enhancement, which can be difficult to directly translate into financial benefit, according to Gartner. Many organizations are leveraging GenAI to transform their business models and create new business opportunities. However, these deployment approaches come with significant costs, ranging from $5 million to $20 million.

"Unfortunately, there is no one size fits all with GenAI, and costs aren't as predictable as other technologies," said Sallam. "What you spend, the use cases you invest in and the deployment approaches you take, all determine the costs. Whether you're a market disruptor and want to infuse AI everywhere, or you have a more conservative focus on productivity gains or extending existing processes, each has different levels of cost, risk, variability and strategic impact."

Regardless of AI ambition, Gartner research indicates GenAI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment (ROI). Historically, many CFOs have not been comfortable with investing today for indirect value in the future. This reluctance can skew investment allocation to tactical versus strategic outcomes.

Realizing Business Value

Earlier adopters across industries and business processes are reporting a range of business improvements that vary by use case, job type and skill level of the worker. According to a recent Gartner survey, respondents reported 15.8% revenue increase, 15.2% cost savings and 22.6% productivity improvement on average. The survey of 822 business leaders was conducted between September and November 2023.

"This data serves as a valuable reference point for assessing the business value derived from GenAI business model innovation," said Sallam. "But it's important to acknowledge the challenges in estimating that value, as benefits are very company, use case, role and workforce specific. Often, the impact may not be immediately evident and may materialize over time. However, this delay doesn't diminish the potential benefits."

Calculating Business Impact

By analyzing the business value and the total costs of GenAI business model innovation, organizations can establish the direct ROI and future value impact, according to Gartner. This serves as a crucial tool for making informed investment decisions about GenAI business model innovation.

"If the business outcomes meet or exceed expectations, it presents an opportunity to expand investments by scaling GenAI innovation and usage across a broader user base, or implementing it in additional business divisions," said Sallam. "However, if they fall short, it may be necessary to explore alternative innovation scenarios. These insights help organizations strategically allocate resources and determine the most effective path forward."

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Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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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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 ...

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