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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

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In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

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

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Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ...