Skip to main content

Almost Half of Businesses Have Implemented Machine Learning, but What Is Next for the Technology?

Bartek Roszak
STX Next

The popularity of machine learning (ML) has skyrocketed in recent years, driven largely by its ability to process data at much faster speeds than humans and produce invaluable insights to unlock business value. By 2026, Gartner predicts that over 80% of enterprises will have used GenAI APIs and models and/or deployed GenAI-enabled applications in production environments, up from less than 5% in early 2023.

Recent STX Next research found that at present, almost half of businesses have now implemented machine learning into business processes in some way, with the most common application being image detection/segmentation, followed by recommendation systems and optical character or text recognition.

Despite the growth in popularity of artificial intelligence (AI) and ML across a number of industries, there is still a huge amount of unrealized potential, with many businesses playing catch-up and still planning how ML solutions can best facilitate processes. Further progression could be limited without investment in specialized technical teams to drive development and integration.

Room for Growth

At present, STX Next data suggests that 50% of CTOs still do not have a single member of staff employed in an AI, ML or data science role at present, underlining the scale of the progress that still needs to be made. To add to this, just a quarter of companies have a separate AI/data division and 38% have between just one and five team members in a dedicated AI/ML or data science role.
Clearly, while many leaders acknowledge AI's potential, there is still a need for more investment in specialized resources to support its development. Implementing machine learning in one form or another will soon be crucial in keeping pace with changes in the industry and meeting customer expectations. As with the roll out of any new technology, its success relies on investment in time, headcount and finances.

This will no doubt become more prominent over the next year and beyond as organizations look for more ways to economically and efficiently scale their business and tackle new challenges. In many cases, leaders will need to assess the extent to which off-the-shelf ML solutions can support their businesses, and work out how much they need to invest in R&D to deliver the required level of expertise.

AI ≠ ChatGPT

AI's popularity and constant presence in headlines this year has been driven largely by the success of large language models like ChatGPT. However, AI has many use cases beyond models like these and can support many business functions that organizational leaders may not yet be aware of.

In 2024, we'll no doubt see an increase in uptake of AI and ML in other business processes. While large language models serve a valuable purpose, they are just one part of AI and ML's arsenal.
The most common applications of AI at the moment are largely unsurprising, as AI's ability to tackle repetitive processes and recognize patterns within images and text is clear and evident. What is surprising is that these are still only adopted by a quarter of businesses. AI can and will revolutionize many industries, but there is still work to be done in educating the market on its capabilities.

Striking the Balance

AI and ML's popularity shows no sign of slowing. CTOs looking to stay ahead of the curve should embrace its potential, remaining careful to balance the needs of the business with the unique needs of clients and customers.

There is also the need to balance the implementation of AI with support for existing employees. In many cases, AI can enable people to exceed in their roles and create new efficiencies, rather than replacing them altogether. Businesses that are able to leverage its potential by enhancing their skillsets will reap the rewards in 2024.

Bartek Roszak is Head of AI at STX Next

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Almost Half of Businesses Have Implemented Machine Learning, but What Is Next for the Technology?

Bartek Roszak
STX Next

The popularity of machine learning (ML) has skyrocketed in recent years, driven largely by its ability to process data at much faster speeds than humans and produce invaluable insights to unlock business value. By 2026, Gartner predicts that over 80% of enterprises will have used GenAI APIs and models and/or deployed GenAI-enabled applications in production environments, up from less than 5% in early 2023.

Recent STX Next research found that at present, almost half of businesses have now implemented machine learning into business processes in some way, with the most common application being image detection/segmentation, followed by recommendation systems and optical character or text recognition.

Despite the growth in popularity of artificial intelligence (AI) and ML across a number of industries, there is still a huge amount of unrealized potential, with many businesses playing catch-up and still planning how ML solutions can best facilitate processes. Further progression could be limited without investment in specialized technical teams to drive development and integration.

Room for Growth

At present, STX Next data suggests that 50% of CTOs still do not have a single member of staff employed in an AI, ML or data science role at present, underlining the scale of the progress that still needs to be made. To add to this, just a quarter of companies have a separate AI/data division and 38% have between just one and five team members in a dedicated AI/ML or data science role.
Clearly, while many leaders acknowledge AI's potential, there is still a need for more investment in specialized resources to support its development. Implementing machine learning in one form or another will soon be crucial in keeping pace with changes in the industry and meeting customer expectations. As with the roll out of any new technology, its success relies on investment in time, headcount and finances.

This will no doubt become more prominent over the next year and beyond as organizations look for more ways to economically and efficiently scale their business and tackle new challenges. In many cases, leaders will need to assess the extent to which off-the-shelf ML solutions can support their businesses, and work out how much they need to invest in R&D to deliver the required level of expertise.

AI ≠ ChatGPT

AI's popularity and constant presence in headlines this year has been driven largely by the success of large language models like ChatGPT. However, AI has many use cases beyond models like these and can support many business functions that organizational leaders may not yet be aware of.

In 2024, we'll no doubt see an increase in uptake of AI and ML in other business processes. While large language models serve a valuable purpose, they are just one part of AI and ML's arsenal.
The most common applications of AI at the moment are largely unsurprising, as AI's ability to tackle repetitive processes and recognize patterns within images and text is clear and evident. What is surprising is that these are still only adopted by a quarter of businesses. AI can and will revolutionize many industries, but there is still work to be done in educating the market on its capabilities.

Striking the Balance

AI and ML's popularity shows no sign of slowing. CTOs looking to stay ahead of the curve should embrace its potential, remaining careful to balance the needs of the business with the unique needs of clients and customers.

There is also the need to balance the implementation of AI with support for existing employees. In many cases, AI can enable people to exceed in their roles and create new efficiencies, rather than replacing them altogether. Businesses that are able to leverage its potential by enhancing their skillsets will reap the rewards in 2024.

Bartek Roszak is Head of AI at STX Next

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...