Organisations around the world are gearing up to roll out AI initiatives.
So much so, that a recent Riverbed survey reveals that 94% of organisations view it as a strategic priority. These businesses have a balancing act on their hands, tasked with delivering seamless digital employee experiences (DEX) while managing increasingly complex IT estates – a challenge for which AI is clearly perceived as a solution.
However, the same survey shows the reality that whilst only 37% of business and IT decision-makers claim to be fully prepared to implement AI projects now, 86% expect their organisation to be fully prepared by 2027, proving that the next three years will be a period of rapid expansion. However, strategic decision-makers worldwide are seeking answers to concerns around data quality, security, and system visibility – without which, the potential of AI will be difficult to turn into a practical reality.
Why managing complex IT estates creates challenges for AI adoption
Of course, managing modern IT estates is no small task. With the emergence of cloud services and hybrid infrastructures, IT leaders are now feeling the pressure to maintain performance while meeting changing expectations. The complexity of these systems often causes a fragmentary effect, where operational visibility becomes blurred and troubleshooting processes default to reactive rather than proactive.
Data is posing the most pressing challenge for AI teams and IT leaders on their adoption journey. Leaders understand that data accuracy and completeness is critical to high-quality AI – in fact, 85% of them say so. Yet only 43% consider their data to be excellent in completeness, and a mere 40% rate its accuracy highly. This looming question over data integrity not only slows down AI implementation but also diminishes trust in the eventual outcomes, with 69% of organisations questioning their data’s effectiveness for AI usage.
The integration of AI has become tightly woven with the newfound need to provide smooth and reliable digital interactions at every touchpoint, to the extent that Zendesk describes AI as a ‘foundational element in any modern DEX strategy’. Modern employees and customers both demand delay-free systems on a daily basis, meaning low performance in this area can damage not only customer satisfaction but also employee productivity and retention.
Further doubts regarding online safety are also doing nothing to allay AI hesitation, with 41% of enterprises admitting fears around cybersecurity and 38% doubtful about meeting an ever-evolving field of regulatory and compliance standards. What’s more, over three-quarters (76%) are apprehensive about AI potentially accessing their proprietary data in the public domain. Without remedying these systematic ailments, full AI implementation will be harder to achieve.
Harnessing the power of unified observability
Organisations everywhere are exploring the potential power of technology to address their challenges. Of all the solutions available, unified observability offers the most direct stepping stone for achieving both AI adoption and operational efficiency.
In this context, ‘unified observability’ refers to digital platforms with the ability to monitor and comprehend the health of an entire IT estate through data-driven information. No matter the complexity, this end-to-end visibility captures and analyses telemetry data, providing businesses with priceless insights into the health and performance of their systems.
Using the insights it offers; IT engineers can construct a digital estate that consistently detects issues and preserves data reliability. The ‘successful deployment of observability platforms’ leads to ‘faster product development cycles’, according to Gartner, meaning that organisations with systems that proactively identify and resolve problems are more prepared to accelerate their AI integration.
To this effect, 84% of decision-makers are now prioritising a unified AI observability platform, compared to a previous preference for disparate point products. This shift reflects the growing sentiment that comprehensive, real-time visibility is a key foundation for improving system reliability and, by extension, the likelihood of AI success.
Optimising IT performance with AIOps
In the same way, AIOps (Artificial Intelligence for IT Operations) can augment the impact of unified observability – enhancing operational efficiency by automating routine tasks and optimising IT workflow. A streamlined system like this reduces an organisation’s propensity for human error, freeing up staff to focus on strategic initiatives. Again, these AI-driven analytics – which 85% of decision-makers agree improve user experience – are crucial for identifying patterns, detecting anomalies, and preventing system failures before they escalate into critical errors.
Likewise, the majority (86%) of organisations believe AI automation is essential for improving IT efficiency and driving user satisfaction. Tolerance for legacy technology is low, with 68% of decision-makers believing poor DEX would drive younger staff to quit their company according to Riverbed’s 2023 survey. Protecting the next generation’s morale with AIOps and observability could therefore be a pertinent strategy for achieving business growth, retaining employees and cultivating a forward-thinking reputation.
This represents an ideal springboard, both demographically and technologically, for nurturing a workforce ready to immediately embrace AI – and to continue that momentum into the future.
Learning from the best to achieve success
Doubtful IT decision-makers need only to look at their high-performing competitors to find a blueprint for successful AI adoption. There’s a clear correlation between AI usage and commercial success, with over two-thirds (67%) of businesses – that reported a 10.5% or more revenue change – also happen to be leveraging AI to its full capabilities to improve the user’s digital experience. In simple terms: establishing an adaptable and robust AI-adjacent infrastructure can unlock new commercial heights for an organisation.
These industry leaders are reaping the rewards of investing in the tools and processes that enable AI to deliver on its promise. It’s only logical, then, that emulating this successful approach by embracing AIOps and unified observability can help organisations to stay well ahead of the curve – in terms of ensuring operational efficiency, providing superior user experiences, and gaining a competitive advantage.
About the Author
John Atkinson is Director, Solutions Engineering UKI, at Riverbed Technology. Riverbed is the only company with the collective richness of telemetry from network to app to end user that illuminates and then accelerates every interaction so that users get the flawless digital experience they expect across the entire digital ecosystem. Riverbed provides two industry-leading solutions: the Riverbed Unified Observability portfolio, which integrates data, insights, and actions across IT to enable customers to deliver seamless digital experiences; and Riverbed Acceleration, which offers fast, agile, and secure acceleration of any application over any network to users, whether they are mobile, remote, or on-premises. Together with our thousands of partners, and market-leading customers across the world, we empower every click, every digital experience. Riverbed. Empower the Experience.
Featured image: Adobe Stock
