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We like an interviewee who is unafraid to make predictions, and that’s exactly what we have in John Atkinson, Director of Solutions Engineering for UK & Ireland at Riverbed. The biggest challenges facing IT leaders? “This will the successful adoption and implementation of AI,” he said, no hesitation. “Organisations are eager to move beyond the hype and focus on AI solutions that deliver measurable, scalable outcomes. However, the rapid evolution of IT landscapes brings new challenges, including blind spots and increased strain on infrastructure.”
This is a theme throughout the interview, with the phrase “AIOps” never far away from John’s lips. Standing – as can be easily guessed – for AI-powered operations, this market is expected to grow threefold in the next five years. The corollary: if your IT team hasn’t prepared for this then you’re beginning the race 20 metres behind the start line.
But there’s good news. “A significant portion of [IT teams’] time is spent on manual tasks such as identifying, diagnosing and fixing issues,” John said. “AIOps will automate many of these repetitive and time-consuming tasks… IT teams will be able to use predictive analytics to enhance performance and security.”
You could argue that John has skin in this particular game. Riverbed’s AI-powered Observability Platform is delivering exactly this kind of insight to its enterprise customers. But keep reading and we think you’ll find there’s plenty of good reasoning to back up the claims.
Without a doubt, the adoption of AI-powered operations, or AIOps, will be one of the most significant shifts in IT over the next 5-10 years. The AIOps market is on track for significant expansion, projected to increase from $13.5 billion in 2023 to $40.9 billion by 2028. This growth underscores the demand for platforms that leverage AI and automation to streamline IT operations and minimise reliance on manual processes.
Simultaneously, we’re witnessing a rapid increase in the volume of data generated by IT systems, applications and digital platforms. Traditional data management methods are struggling to keep pace with this explosion of information, leading to overwhelming workloads for IT teams. The sheer scale of data, combined with the complexity of digital infrastructures, makes manual IT management unsustainable. This is where data stores come into play, providing a unified, scalable, and secure ecosystem that allows IT teams to shift their focus from managing capacity to driving innovation and getting the right data, at the right time, at the right quality.
Meanwhile, AIOps platforms provide the ability to intelligently manage IT operations, reducing resource strain. By leveraging AI and machine learning, AIOps can detect patterns and blind spots, identify anomalies, and predict potential system failures before they impact business operations. It can also optimise the digital experience across PCs, devices, apps and networks.
Enabled by AIOps, the IT industry will move from reactive, manual processes to proactive, predictive, and automated systems that resolve issues before they escalate. This will redefine how IT departments operate, and ensure agility, efficiency and resilience.
The role of IT professionals will drastically evolve due to the rise of AIOps. Currently, IT teams are overwhelmed by the volume of data, alerts, and service desk tickets they face. A significant portion of their time is spent on manual tasks such as identifying, diagnosing, and fixing issues.
AIOps will automate many of these repetitive and time-consuming tasks, allowing IT professionals to focus more on strategic projects and innovation. Rather than constantly firefighting, IT teams will be able to use predictive analytics to enhance performance and security.
With this shift, IT professionals will need to develop new skill sets, including managing AIOps platforms, interpreting AI-driven insights, and making data-informed decisions. Instead of being primarily responsible for ‘keeping the lights on,’ IT teams will play a crucial role in driving business growth and long-term success.
AI will have the greatest impact on IT in the coming decade, driven by its ability to automate, optimise and enhance decision-making at scale. However, as Apple’s decision to halt AI-generated news alerts demonstrates, even the most advanced companies face challenges with AI implementation.
AI is only as effective as the data it processes and the safeguards supporting it. Without strong operational oversight, blind spot mitigation and the introduction of predictive and generative AI solutions, errors can quickly hinder proactive decision-making, slow troubleshooting, and force IT teams into a reactive state.
At the same time, many businesses need to increasingly move data at a faster pace to support their AI ambitions. This demand is driving a redesign of network infrastructure to handle the scale and complexity of AI workloads effectively.
Most businesses have large budgets set aside for AI initiatives, recognising the importance of investing in the necessary infrastructure. In fact, 91% of businesses recognise AI as a competitive advantage and demand for it to be integrated into the fabric of operations. From day-to-day tasks to long-term strategic planning, its potential to drive innovation and growth is undeniable.
Open AI platforms will be central to this evolution, providing flexibility and accessibility so businesses can customise and integrate AI solutions into various operations.
IT, particularly with the adoption of AIOps, will revolutionise industries by improving digital experience and enabling innovation.
In healthcare, for example, it ensures that critical systems such as patient monitoring operates seamlessly, allowing medical professionals to focus on patient outcomes rather than IT disruptions. Just as healthcare follows the principle that ‘prevention is the best cure’, AIOps help to prevent system failures before they occur.
In the finance and insurance sector, it plays a pivotal role in fraud detection, real-time anomaly detection, and transaction monitoring. By centralising data and leveraging AI for insights, financial institutions can ensure seamless operations while bolstering security and reducing risk. This not only safeguards sensitive financial data, it also enhances customer trust and experience.
This will be the successful adoption and implementation of AI. Whilst recent surveys reveal that 94% of leaders view AI as a top C-suite priority, only 37% of enterprises feel fully prepared to implement AI projects today. Similarly, Gartner reports that only around 10% of AI proof-of-concepts move into production, underscoring the significant gap between AI ambitions and practical execution.
Organisations are eager to move beyond the hype and focus on AI solutions that deliver measurable, scalable outcomes. However, the rapid evolution of IT landscapes brings new challenges, including blind spots and increased strain on infrastructure. IT teams will need advanced visibility into modern architectures and tools to manage and derive insights from vast data sets effectively.
At the same time, businesses are under pressure to streamline costs and optimise resources, creating a demand for AI-driven automation solutions that enhance efficiency and reduce operational burdens. Balancing these priorities will be critical to IT’s success in the coming years.
The complexity of data will also present significant obstacles. Traditional data management methods are increasingly inadequate for handling this growth. Therefore, IT teams must find new ways to store, process, and access vast amounts of data while ensuring it remains secure and easily accessible.
Data quality will also be a major concern. Many organisations face challenges around the accuracy and completeness of their data, which can lead to mistakes and incorrect conclusions, as well as have an impact on decision-making. To address these challenges, IT professionals will need to prioritise observability, data integrity and compliance and get ahead of evolving security regulations.
In addition, keeping pace with technological innovation will be crucial. IT teams will be tasked with integrating new platforms while continuing to learn how to use and adapt to emerging technologies. IT professionals will also need to communicate the strategic value of IT to business leaders, showcasing how effective IT management drives business success.
IT innovations hold huge potential to bridge the digital divide by making technology more accessible, reliable, and user-friendly. One major barrier is the generational divide in AI adoption. Research shows that 52% of business leaders believe Gen Z is the most comfortable with AI, followed by Millennials at 39%. In contrast, older generations may struggle with AI adoption due to a lack of familiarity or training. If left unaddressed, this gap could lead to growing digital inequality.
Organisations can mitigate this by implementing AI training programmes that equip employees of all ages with the skills to leverage AI effectively. In fact, high-performing companies are already more likely to invest in comprehensive AI training, highlighting the importance of structured learning in adoption.
