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Most Companies Unable to Achieve Project-to-Product Transformation

Many companies are unable to complete transformational work due to immature operating models and management systems, according to the 2023 Project to Product State of the Industry Report from Planview.

The report found that only 8% of organizations have successfully operationalized their project to product transformation. The low success rate contradicts the previously optimistic outlook from a 2018 report claiming that 85% of executives said they had either adopted or had plans to adopt a product-centric model, motivated by a desire to improve speed to market and agility and to support the move to digital business. Five years later, the report's data underscores the complexity of this multi-year process and its required buy-in and commitment across all levels of the organization.

"In boardrooms across the globe, executives are being mandated to prioritize technology investments that ensure their companies transform and emerge from the current downturn stronger. While some enterprise initiatives are managed as projects, a product-based operating model holds the key to increased efficiency, better customer outcomes, and profitable growth for digital portfolios," said Dr. Mik Kersten, CTO, Planview and author of Project to Product: How to Survive and Thrive in the Age of Digital Disruption with the Flow Framework. "The consequences of slow delivery and technical debt can be seen in very public profit losses and system-wide malfunctions. While there are no shortcuts, there is a roadmap of best practices that accelerates the transition, which the report outlines."

The report reveals that five years into the shift from project to product:

■ 92% of businesses do not have the foundation for a product-oriented model, causing their digital transformation efforts to fail.

■ Business leaders believe their IT and software development teams, in charge of transformation efforts, can deliver 10x more than their actual capacity, leading to team burnout.

■ 40% of digital innovation work from IT and engineering teams are wasted due to shifting priorities at the C-level.

■ Only 8% of what's planned by IT and software development teams gets delivered, inefficiency that can no longer be ignored given today's cost and performance pressures.

Given the criticality of this shift for the future success of traditional companies versus early adopters, the report outlines the critical steps needed to accelerate this transition: a partnership between executives and senior product and development leadership to do the following:

1. Benchmark their progress versus their competitors.

2. Understand and implement best practices to shorten the time it takes to capture the ROI of transformation efforts.

3. Identify the organizational attributes that increase the likelihood of operationalizing the product model by learning which efforts to prioritize.

Methodology: The report combines survey data from 326 respondents with systems data from 3,600+ software development value streams in 34 of the world's leading enterprises

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 ...

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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 ...

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 ...

Most Companies Unable to Achieve Project-to-Product Transformation

Many companies are unable to complete transformational work due to immature operating models and management systems, according to the 2023 Project to Product State of the Industry Report from Planview.

The report found that only 8% of organizations have successfully operationalized their project to product transformation. The low success rate contradicts the previously optimistic outlook from a 2018 report claiming that 85% of executives said they had either adopted or had plans to adopt a product-centric model, motivated by a desire to improve speed to market and agility and to support the move to digital business. Five years later, the report's data underscores the complexity of this multi-year process and its required buy-in and commitment across all levels of the organization.

"In boardrooms across the globe, executives are being mandated to prioritize technology investments that ensure their companies transform and emerge from the current downturn stronger. While some enterprise initiatives are managed as projects, a product-based operating model holds the key to increased efficiency, better customer outcomes, and profitable growth for digital portfolios," said Dr. Mik Kersten, CTO, Planview and author of Project to Product: How to Survive and Thrive in the Age of Digital Disruption with the Flow Framework. "The consequences of slow delivery and technical debt can be seen in very public profit losses and system-wide malfunctions. While there are no shortcuts, there is a roadmap of best practices that accelerates the transition, which the report outlines."

The report reveals that five years into the shift from project to product:

■ 92% of businesses do not have the foundation for a product-oriented model, causing their digital transformation efforts to fail.

■ Business leaders believe their IT and software development teams, in charge of transformation efforts, can deliver 10x more than their actual capacity, leading to team burnout.

■ 40% of digital innovation work from IT and engineering teams are wasted due to shifting priorities at the C-level.

■ Only 8% of what's planned by IT and software development teams gets delivered, inefficiency that can no longer be ignored given today's cost and performance pressures.

Given the criticality of this shift for the future success of traditional companies versus early adopters, the report outlines the critical steps needed to accelerate this transition: a partnership between executives and senior product and development leadership to do the following:

1. Benchmark their progress versus their competitors.

2. Understand and implement best practices to shorten the time it takes to capture the ROI of transformation efforts.

3. Identify the organizational attributes that increase the likelihood of operationalizing the product model by learning which efforts to prioritize.

Methodology: The report combines survey data from 326 respondents with systems data from 3,600+ software development value streams in 34 of the world's leading enterprises

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 ...