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Emerging Technology Priorities Go Beyond AI

Matt Cloke
Endava

Over the past decade, the pace of technological progress has reached unprecedented levels, where fads both quickly rise and shrink in popularity. From AI and composability to augmented reality and quantum computing, the toolkit of emerging technologies is continuing to expand, creating a complex set of opportunities and challenges for businesses to address.

In order to keep pace with competitors, avoiding new models and ideas is not an option. It's critical for organizations to determine whether an idea has transformative properties or is just a flash in the pan — a challenge tackled in Endava's new 2024 Emerging Tech Unpacked Report.


The main takeaway from the report is that, due to the ongoing uncertainty within the tech sector, most decision makers are choosing technologies that offer more noticeable, near-term benefits — such as AI, which nearly 50% of respondents ranked as a top-three initiative. However, despite the space that AI now takes up in the conversation around emerging technology, there are other areas of focus that also show ROI potential.

Additional insights from the report include:

■ Unsurprisingly, AI and generative AI were the top two priorities for organizations included in the study, with only 3% declaring AI not relevant to their business. It is understandable that companies are putting large amounts of resources into AI, particularly generative AI, as many leaders expect it to drive near-term and long-term benefits.

■ After AI, big data and predictive analytics emerged as the third and fourth-highest priorities among organizations. Over 30% of respondents have already implemented both technologies, and a further 30% are in the process of doing so.

■ Internet of things (IoT) was the fifth-highest priority for the study's participants. 40% of respondents already use IoT in some capacity, making it the most implemented technology in this year's study. Though IoT has been around for a while and is not as buzzy as other topics, organizations still see its appeal and application to their business.

■ Virtual reality is one area of technology that is still being treated with caution, as leaders struggle to see it driving business results. 53% of respondents said that the metaverse would be moderately or very relevant, yet only 17% have actually implemented a strategy. With Apple's recent high-profile launch of the Vision Pro, it remains to be seen whether the technology will ever meet the hype it received a few years ago.

All the technologies outlined in the report need quality, well-structured data to be successfully implemented. Data is crucial for training AI models, delivering accurate predictive data, facilitating IoT decision-making, and more. While organizations have different choices for what to invest in, many will start to prioritize data infrastructure and management.

With so many different options for adopting technology, organizations face a daunting landscape filled with promising opportunities and difficult challenges. As businesses navigate these complexities, they must consider how these technologies fit their company's unique circumstances and the timeline for return on investment. As the digital toolkit will undoubtedly further expand in the coming years, businesses will need foresight, adaptability and creativity to use technology to problem solve, and those that think outside of the box will be the most successful.

Matt Cloke is Chief Technology Officer at Endava

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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Emerging Technology Priorities Go Beyond AI

Matt Cloke
Endava

Over the past decade, the pace of technological progress has reached unprecedented levels, where fads both quickly rise and shrink in popularity. From AI and composability to augmented reality and quantum computing, the toolkit of emerging technologies is continuing to expand, creating a complex set of opportunities and challenges for businesses to address.

In order to keep pace with competitors, avoiding new models and ideas is not an option. It's critical for organizations to determine whether an idea has transformative properties or is just a flash in the pan — a challenge tackled in Endava's new 2024 Emerging Tech Unpacked Report.


The main takeaway from the report is that, due to the ongoing uncertainty within the tech sector, most decision makers are choosing technologies that offer more noticeable, near-term benefits — such as AI, which nearly 50% of respondents ranked as a top-three initiative. However, despite the space that AI now takes up in the conversation around emerging technology, there are other areas of focus that also show ROI potential.

Additional insights from the report include:

■ Unsurprisingly, AI and generative AI were the top two priorities for organizations included in the study, with only 3% declaring AI not relevant to their business. It is understandable that companies are putting large amounts of resources into AI, particularly generative AI, as many leaders expect it to drive near-term and long-term benefits.

■ After AI, big data and predictive analytics emerged as the third and fourth-highest priorities among organizations. Over 30% of respondents have already implemented both technologies, and a further 30% are in the process of doing so.

■ Internet of things (IoT) was the fifth-highest priority for the study's participants. 40% of respondents already use IoT in some capacity, making it the most implemented technology in this year's study. Though IoT has been around for a while and is not as buzzy as other topics, organizations still see its appeal and application to their business.

■ Virtual reality is one area of technology that is still being treated with caution, as leaders struggle to see it driving business results. 53% of respondents said that the metaverse would be moderately or very relevant, yet only 17% have actually implemented a strategy. With Apple's recent high-profile launch of the Vision Pro, it remains to be seen whether the technology will ever meet the hype it received a few years ago.

All the technologies outlined in the report need quality, well-structured data to be successfully implemented. Data is crucial for training AI models, delivering accurate predictive data, facilitating IoT decision-making, and more. While organizations have different choices for what to invest in, many will start to prioritize data infrastructure and management.

With so many different options for adopting technology, organizations face a daunting landscape filled with promising opportunities and difficult challenges. As businesses navigate these complexities, they must consider how these technologies fit their company's unique circumstances and the timeline for return on investment. As the digital toolkit will undoubtedly further expand in the coming years, businesses will need foresight, adaptability and creativity to use technology to problem solve, and those that think outside of the box will be the most successful.

Matt Cloke is Chief Technology Officer at Endava

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...