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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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Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

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Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...