Skip to main content

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...