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From Experimentation to Execution: How Generative AI is Transforming Business Priorities

Matt Cloke
Endava

Generative AI represents more than just a technological advancement; it's a transformative shift in how businesses operate. Companies are beginning to tap into its ability to enhance processes, innovate products and improve customer experiences. According to Next Steps in the Era of Digital Business, a new IDC InfoBrief sponsored by Endava, 60% of CEOs globally highlight deploying AI, including generative AI, as their top modernization priority to support digital business ambitions over the next two years. And businesses are shifting from merely experimenting with technology to implementing it strategically.

But IDC found that only 17% of companies have successfully transitioned from testing generative AI to deploying it in live environments. The InfoBrief points to talent shortages and outdated infrastructure as significant challenges for organizations looking to scale their digital capabilities.

And the stakes are high. As a key enabler for innovation, companies that delay AI adoption risk slower time-to-market and reduced operational efficiency. With 38% of organizations making significant investments in generative AI, the race to harness its potential is only intensifying.

Image
Endava

Infrastructure and Strategy: Laying the Foundation for Success

Thriving in an AI-driven world requires more than simply adopting new technology — it demands robust infrastructure and thoughtful planning. The InfoBrief shows that organizations that achieve this level of implementation often attribute their success to high-quality data access and strong partnerships with vendors specializing in AI. On the other hand, nearly one in three organizations cite inadequate infrastructure performance as a barrier to achieving AI project success.

Modernizing legacy systems is critical for businesses looking to thrive in an AI-driven world. Outdated infrastructure often leads to overspending and limits the ability to scale new initiatives. By addressing technical debt, companies can free up resources to focus on strategic priorities, like leveraging AI to enhance operations or create personalized customer experiences.

Upgrading core systems ensures that infrastructure can handle the demands of generative AI, which 84% of organizations recognize as a major new workload. Without modernization, companies risk performance issues that can derail AI projects. A balanced approach is key — ensuring that new capabilities integrate seamlessly with existing operations.

Success in modernization often comes from partnering with external IT service providers who bring the expertise needed to manage complex transitions. These collaborations can streamline the process, reduce costs, and deliver reliable results, positioning businesses to innovate and compete more effectively.

The Human Factor: Bridging the Skills Gap

While AI technology itself is powerful, its success depends on the people behind it. A third of CEOs noted that attracting and retaining skilled talent is crucial to achieving their business goals. Unfortunately, many organizations lack the in-house expertise needed to fully leverage AI.

The report suggests that partnerships with external IT service providers can help bridge this gap. These providers bring the expertise required to implement AI projects effectively. In fact, 47% of companies that successfully deployed generative AI credit their partnerships with strategic vendors for their achievements. As ecosystems grow more complex, the ability to collaborate with skilled and flexible partners will be a defining factor for success.

Moving Forward with Confidence

As we enter the era of "AI everywhere," organizations must take proactive steps to adapt. Embracing change, modernizing infrastructure and ensuring access to high-quality data are pivotal steps to thrive in this evolving landscape. Equally, building strong partnerships and maintaining a clear focus on long-term goals will empower businesses to navigate this transformation with confidence.

Digital transformation is far from a one-time effort, and in fact, 48% of companies globally now consider themselves digital businesses. Those that seize AI's potential while addressing fundamental challenges will position themselves for sustainable growth in a technology-driven future. Agility and a steadfast commitment to innovation will distinguish leaders in this new phase of the digital era.

Success in this AI-driven world requires organizations to embrace the opportunities that come with change. Modernizing infrastructure to meet dynamic business needs and securing high-quality data to fuel AI-powered projects are essential. By partnering with skilled experts who bring fresh insights and proven expertise, businesses can gain a decisive edge — confidently overcoming challenges and staying ahead in an increasingly competitive landscape.

Matt Cloke is Chief Technology Officer at Endava

Hot Topics

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 ...

From Experimentation to Execution: How Generative AI is Transforming Business Priorities

Matt Cloke
Endava

Generative AI represents more than just a technological advancement; it's a transformative shift in how businesses operate. Companies are beginning to tap into its ability to enhance processes, innovate products and improve customer experiences. According to Next Steps in the Era of Digital Business, a new IDC InfoBrief sponsored by Endava, 60% of CEOs globally highlight deploying AI, including generative AI, as their top modernization priority to support digital business ambitions over the next two years. And businesses are shifting from merely experimenting with technology to implementing it strategically.

But IDC found that only 17% of companies have successfully transitioned from testing generative AI to deploying it in live environments. The InfoBrief points to talent shortages and outdated infrastructure as significant challenges for organizations looking to scale their digital capabilities.

And the stakes are high. As a key enabler for innovation, companies that delay AI adoption risk slower time-to-market and reduced operational efficiency. With 38% of organizations making significant investments in generative AI, the race to harness its potential is only intensifying.

Image
Endava

Infrastructure and Strategy: Laying the Foundation for Success

Thriving in an AI-driven world requires more than simply adopting new technology — it demands robust infrastructure and thoughtful planning. The InfoBrief shows that organizations that achieve this level of implementation often attribute their success to high-quality data access and strong partnerships with vendors specializing in AI. On the other hand, nearly one in three organizations cite inadequate infrastructure performance as a barrier to achieving AI project success.

Modernizing legacy systems is critical for businesses looking to thrive in an AI-driven world. Outdated infrastructure often leads to overspending and limits the ability to scale new initiatives. By addressing technical debt, companies can free up resources to focus on strategic priorities, like leveraging AI to enhance operations or create personalized customer experiences.

Upgrading core systems ensures that infrastructure can handle the demands of generative AI, which 84% of organizations recognize as a major new workload. Without modernization, companies risk performance issues that can derail AI projects. A balanced approach is key — ensuring that new capabilities integrate seamlessly with existing operations.

Success in modernization often comes from partnering with external IT service providers who bring the expertise needed to manage complex transitions. These collaborations can streamline the process, reduce costs, and deliver reliable results, positioning businesses to innovate and compete more effectively.

The Human Factor: Bridging the Skills Gap

While AI technology itself is powerful, its success depends on the people behind it. A third of CEOs noted that attracting and retaining skilled talent is crucial to achieving their business goals. Unfortunately, many organizations lack the in-house expertise needed to fully leverage AI.

The report suggests that partnerships with external IT service providers can help bridge this gap. These providers bring the expertise required to implement AI projects effectively. In fact, 47% of companies that successfully deployed generative AI credit their partnerships with strategic vendors for their achievements. As ecosystems grow more complex, the ability to collaborate with skilled and flexible partners will be a defining factor for success.

Moving Forward with Confidence

As we enter the era of "AI everywhere," organizations must take proactive steps to adapt. Embracing change, modernizing infrastructure and ensuring access to high-quality data are pivotal steps to thrive in this evolving landscape. Equally, building strong partnerships and maintaining a clear focus on long-term goals will empower businesses to navigate this transformation with confidence.

Digital transformation is far from a one-time effort, and in fact, 48% of companies globally now consider themselves digital businesses. Those that seize AI's potential while addressing fundamental challenges will position themselves for sustainable growth in a technology-driven future. Agility and a steadfast commitment to innovation will distinguish leaders in this new phase of the digital era.

Success in this AI-driven world requires organizations to embrace the opportunities that come with change. Modernizing infrastructure to meet dynamic business needs and securing high-quality data to fuel AI-powered projects are essential. By partnering with skilled experts who bring fresh insights and proven expertise, businesses can gain a decisive edge — confidently overcoming challenges and staying ahead in an increasingly competitive landscape.

Matt Cloke is Chief Technology Officer at Endava

Hot Topics

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 ...