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How to Lead a Successful Digital Transformation Project

Louise Cermak
Catapult

Digital transformation is key to ensuring companies keep up with the competitive market landscape. Putting digital at the core of a business can significantly reduce operating expenses and inefficiencies. However, this process often means changing the way internal teams work with one another. To help with the transition, this blog offers chief experience officers (CXOs) advice on how to lead a successful digital transformation project.


According to Statista, two of the leading factors driving digital transformation growth is the increase in customer demand and the need to overtake competitors. Digital transformation not only helps businesses strengthen their presence in a competitive field, but also maintain consistency amongst teams to enable collaboration and flexibility. This transition can be broken down into four core stages, which are essential to get right.

1. Understand the pain points

For effective digital transformation, CXOs need to think about their current organizational structure. It's a good idea to sit down with various teams to create a pain point assessment — a review of every area of the business to see what's working well and what's not. For example, are the teams fragmented or working together? Does everyone understand their role and impact on the overall business?

CXOs should also look at their current technologies and whether there are any additional tools that can help optimise processes. They can then explore optimization and data management tools that can help their business.

2. Remove the blockers to agility

Once teams have identified specific pain points, the next step is creating a clear action plan for implementing solutions. Adopting a continuous improvement approach allows teams to plan activities into sprints and deliver small increments of change compared to larger pieces of work that go nowhere.

Digital transformation should drive the organization to move from project work to product work and avoid teams from stopping and starting work. The move can help reduce costs and prevent loss of product knowledge as teams work on long-term products. This movement turns project-oriented companies that focus on delivery into product-centric teams that focus on business and customer impact. Governance and reporting frameworks will also need to change from the traditional Project Portfolio Management (PPM) approach. Business agility and digital transformation rely on technical innovation, so business leaders must be prepared to invest in modern software delivery practices and tools.

3. Empower teams

To ensure the change can be effectively implemented, it's important to get all teams on board. Businesses can create multi-skilled teams with capacity for infrastructure and DevOps by dispersing large infrastructure teams and forming smaller units that are aligned to specific products or services. Communities of practice can be used to maintain collaboration and share knowledge through dispersed individuals.

However, product managers often do not have the required level of technical knowledge to effectively manage the product team. The digital skills gap is a growing problem for individuals and organizations, but there are ways businesses can close it.

For example, this includes upskilling employees on digital skills that add value to the business. These are often specific to each organization, so understanding what skill gaps are in the team is a crucial first step. Once the foundation has been set, senior management can create a community of practice to help ensure continuous collaboration among colleagues.

4. Sustain the new business model

The key is ensuring new practices continue to be used throughout the company and evolve with changing business and customer needs. Senior management can track the performance of product teams via Google Cloud's DevOps Research and Assessment team's (DORA) five key metrics — deployment frequency, lead time for changes, change failure rate, time to restore service and reliability.

Too often, improving these metrics becomes difficult due to organizational blockers, so senior management should ensure the metrics are applied across the whole delivery cycle. Adding in newer capabilities such as DevOps and associated tools can also help with gathering data and creating a baseline to compare with.

Louise Cermak is a Principal Consultant at Catapult

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 ...

How to Lead a Successful Digital Transformation Project

Louise Cermak
Catapult

Digital transformation is key to ensuring companies keep up with the competitive market landscape. Putting digital at the core of a business can significantly reduce operating expenses and inefficiencies. However, this process often means changing the way internal teams work with one another. To help with the transition, this blog offers chief experience officers (CXOs) advice on how to lead a successful digital transformation project.


According to Statista, two of the leading factors driving digital transformation growth is the increase in customer demand and the need to overtake competitors. Digital transformation not only helps businesses strengthen their presence in a competitive field, but also maintain consistency amongst teams to enable collaboration and flexibility. This transition can be broken down into four core stages, which are essential to get right.

1. Understand the pain points

For effective digital transformation, CXOs need to think about their current organizational structure. It's a good idea to sit down with various teams to create a pain point assessment — a review of every area of the business to see what's working well and what's not. For example, are the teams fragmented or working together? Does everyone understand their role and impact on the overall business?

CXOs should also look at their current technologies and whether there are any additional tools that can help optimise processes. They can then explore optimization and data management tools that can help their business.

2. Remove the blockers to agility

Once teams have identified specific pain points, the next step is creating a clear action plan for implementing solutions. Adopting a continuous improvement approach allows teams to plan activities into sprints and deliver small increments of change compared to larger pieces of work that go nowhere.

Digital transformation should drive the organization to move from project work to product work and avoid teams from stopping and starting work. The move can help reduce costs and prevent loss of product knowledge as teams work on long-term products. This movement turns project-oriented companies that focus on delivery into product-centric teams that focus on business and customer impact. Governance and reporting frameworks will also need to change from the traditional Project Portfolio Management (PPM) approach. Business agility and digital transformation rely on technical innovation, so business leaders must be prepared to invest in modern software delivery practices and tools.

3. Empower teams

To ensure the change can be effectively implemented, it's important to get all teams on board. Businesses can create multi-skilled teams with capacity for infrastructure and DevOps by dispersing large infrastructure teams and forming smaller units that are aligned to specific products or services. Communities of practice can be used to maintain collaboration and share knowledge through dispersed individuals.

However, product managers often do not have the required level of technical knowledge to effectively manage the product team. The digital skills gap is a growing problem for individuals and organizations, but there are ways businesses can close it.

For example, this includes upskilling employees on digital skills that add value to the business. These are often specific to each organization, so understanding what skill gaps are in the team is a crucial first step. Once the foundation has been set, senior management can create a community of practice to help ensure continuous collaboration among colleagues.

4. Sustain the new business model

The key is ensuring new practices continue to be used throughout the company and evolve with changing business and customer needs. Senior management can track the performance of product teams via Google Cloud's DevOps Research and Assessment team's (DORA) five key metrics — deployment frequency, lead time for changes, change failure rate, time to restore service and reliability.

Too often, improving these metrics becomes difficult due to organizational blockers, so senior management should ensure the metrics are applied across the whole delivery cycle. Adding in newer capabilities such as DevOps and associated tools can also help with gathering data and creating a baseline to compare with.

Louise Cermak is a Principal Consultant at Catapult

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 ...