Skip to main content

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 The InfoBrief points to talent shortages and outdated infrastructure as significant challenges for organizations looking to scale their digital capabilities.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 Outdated infrastructure often leads to overspending and limits the ability to scale new initiatives.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

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

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

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...