AI in IT Operations: A Balancing Act

The conversation concerning the rise of generative AI is filled with both excitement and apprehension. While some envision a future where humans transcend routine tasks to focus on more profound value-driven endeavors, others worry about potential job losses at scale due to AI integration. When it comes to cybersecurity, the narrative oscillates between one in which AI emboldened security makes cyberattacks a thing of the past and a grim reality in which hackers and nation states will leverage AI to launch unstoppable attacks. There is a lot of opinion, speculation, and uncertainty.

At Evolving Solutions, our lens is squarely focused on AI’s influence on IT operations, and we agree the transformative impact of AI in this domain will prove substantial. We have an advantage over many companies in that we have assisted a broad range of enterprises over the years and witnessed firsthand how AI is being utilized. We are also focused on understanding the potential opportunities and challenges that this technology brings to IT environments. As with any innovation, AI presents a mix of benefits and challenges, and I’ve delineated our insights into this transformational technology and its most significant impact on IT operations.

Creating Information from Data

Data on its own, much like isolated musical notes, lacks significant utility. Consider how these notes, when thoughtfully arranged and orchestrated, culminate in a symphony – a rich and emotive composition that resonates and lingers. Similarly, the raw data emanating from your IT systems is just a collection of facts and metrics, offering limited insight in its unprocessed form. It’s the transformation of this data into information that imbues it with context, meaning, and actionable insights, enabling operations teams to make informed decisions and grasp the nuances of complex situations.

At its core, AI plays a pivotal role in transforming raw data into information that provides insightful information. Using advanced algorithms and machine learning techniques, data can be dissected and interpreted in ways that humans cannot begin to match. By analyzing data from multiple dispersed sources, AI can identify patterns, anomalies, and trends that might be invisible to the human eye. In addition to the prescribed metrics that a monitoring or logging application may spit out, AI can integrate data from a multitude of sources like user activities, network traffic, and even external threat intelligence feeds, thereby offering a more holistic view of an organization’s network environment. Ultimately, it’s not just the data but the rich, informative insights derived from it that drive transformative change and strategic decision-making.

The Personal Trainer

Many elite, professional athletes have personal trainers, nutritional advisors, and positional coaches that constantly work with them to incrementally improve their game regardless of how much they have achieved. They do this to become the best they can be. For IT operational teams, generative AI has the potential to analyze, optimize, and suggest improvements to IT configurations to ensure that IT infrastructure operates according to best practices. Think of it as a team of experts at your disposal.

Most enterprises are already equipped with established tools such as SIEMs, monitoring applications and predictive failure tools to gain visibility and understanding in their operational environments. But are they getting the most out of those technologies? Elevating your observability capabilities to the next level is key. This is where AI steps in, acting akin to a personal trainer. It helps extract maximum value from both tools and personnel, enhancing performance and efficiency. Leveraging AI optimizes the investment in these resources, ensuring they deliver their best, much like a trainer brings out the peak potential in an athlete. Examples include automated configuration analysis in which AI quickly scans through large sets of configuration files and data to identify inconsistencies, errors, or security vulnerabilities that might otherwise go unnoticed. This particularly benefits hybrid or multi-cloud infrastructures. AI can also perform dynamic reconfiguration, for instances in which configurations need to change in response to real-time data. It can also recommend configuration adjustments in line with evolving best practice guidelines or achieve specific metrics, such as improved performance, decreased latency, or efficient resource utilization. AI generated configuration templates can also serve as starting points for further customization.

A Predictor of Change

Change is inevitable, and because IT operational environments are so complex and have so many moving parts, even the most subtle change can open your organization to the risk of inadvertently breaking something. Imagine the advantage if you could foresee the outcomes of each change before rolling them out in a live environment.

One way to do this is with a digital twin. Generative AI can quickly model real-world IT intricacies to ensure that an assigned digital twin continuously mirrors its physical counterpart. Large manufacturers such as Boeing have utilized digital twin versions of aircraft and parts for years to test rapid iterations and change optimizations. Hyperscalers synchronize digital twins with their production environments to facilitate predictive analyses, allowing organizations to foresee potential issues, such as traffic impacts or software update risks. Additionally, AI can generate optimization strategies, test them on the twin, and predict outcomes of various scenarios. That may be anything from network breaches to hardware failures. Use of generative AI can unravel the uncertainties of change.

Possible Concerns about AI

While AI has proved a game-changer in many industries, for some organizations, it still represents uncharted territory, often perceived as a formidable venture. Embarking on AI implementation is not just about deploying cutting-edge technology. It also involves a comprehensive evaluation and integration process. Before diving in, it’s crucial to assess the quality of your existing data and ensure that AI solutions can seamlessly integrate with your current infrastructure and systems. Given the significant investment required for AI implementation, establishing a clear path to a substantial return on investment is vital.

Moreover, the expertise required to develop and manage AI systems is highly specialized. With a limited pool of AI experts available in the job market, recruiting and retaining the right talent can be a significant challenge for many organizations.

The task of collecting and curating IT operational data from an array of sources – servers, applications, devices, and networks – is a complex undertaking. It’s not just about managing the sheer volume and diversity of data but also about breaking down data silos to achieve a comprehensive overview of your IT environment. Additionally, it’s imperative to avoid treating AI as a mysterious “black box.” There must be clarity in how AI systems derive insights and conclusions.

This requirement underscores the necessity for complete transparency in AI-driven decision-making. It is essential that the outcomes of AI models are not only accurate but also explainable. Traceability is key, ensuring that every step in the AI decision-making process can be tracked, validated, and understood. This transparency is critical for maintaining accountability, trust, and compliance, particularly in environments where AI-driven decisions have significant implications.

You Still Need Human Experts

AI shouldn’t be seen as a solution for every problem. The payoff is greatest for your more complex tasks. It’s crucial to have advisors proficient in both AI and IT operations to guide you on where to best utilize AI for optimal returns and remember that some operations inherently require a hands-on approach. AI operational environments are quite diverse, which is why there is more than one AI solution out there. Otherwise, platforms like AWS would have been universally adopted years ago. At Evolving Solutions, our dedicated teams offer seasoned expertise to recommend the most fitting AI solution tailored to your unique needs. We’re committed to ensuring you harness its full potential for maximum ROI. Yes, it is best to change before you must, and Evolving Solutions can ensure that any change regarding AI and your IT operations is for the better.


Michael Downs

Chief Technology Officer

Michael is the Chief Technology Officer at Evolving Solutions and joined the company in 2014. Connect with Michael on LinkedIn here.

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