Hello everyone!

Back in the ‘90s, as part of my postgraduate research on extreme parallel computing, I was captivated by the potential of artificial intelligence, particularly the integration of multiple neural networks to achieve higher-level outcomes. This fascination led me down several paths, some of which ended in dead-ends, leaving my thesis in tatters!

Fast forward to today, and Fusion, where I work, has made its mark in IT Asset Management (ITAM). We've managed significant projects, including the UK's smart metering programme, overseeing 54 million devices. Alongside, our strides in AI have been noteworthy. Our R&D team rolled out Talos in 2016 and has since been incorporating advanced AI technologies, such as large language models and transformers for over 3 years.

Now, as AI and IT Asset Management begin to intersect, we're uniquely positioned to delve into this synergy. The published research on this combined topic is sparse, but I have sifted through available resources and combined them with our experiences to shape this blog series. My goal is to introduce the basics, explore practical applications, peek into future possibilities, and share best practices (and feed my hunger for neural networks!)

The Growing Complexity in ITAM and the Role of Generative AI

IT Asset Management has always been a cornerstone for organisations, ensuring that technology assets are efficiently utilised and managed. But the landscape is shifting. With the convergence of IT and Operational Technology (OT), and the proliferation of Internet of Things (IoT) devices, the scope of ITAM is broadening. It's no longer just about tracking servers or software licenses; it's about managing a vast, interconnected ecosystem of digital and physical assets.

Why Traditional ITAM Tools Fall Short: 

As the IT environment becomes more intricate, traditional ITAM tools, which were once the gold standard, are struggling to keep up. They're not equipped to handle the nuances of a blended IT-OT landscape or the sheer volume of data from IoT devices. This is where many organisations find themselves at a crossroads, aware of the challenges but unsure of the way forward.

Enter Generative AI: 

Unlike conventional AI models that analyse and react, Generative AI creates. In the context of ITAM, imagine a system that can generate a holistic view of an organisation's assets, both IT and OT, and even predict future challenges or needs. It's like having a bird's eye view, but with the added ability to foresee potential hurdles.

The Promise of GANs: 

A standout in the Generative AI space is GANs (Generative Adversarial Networks).

GANs are machine learning models designed to produce new data. They function by having two neural networks, the generator and the discriminator, compete in a game-like scenario. The generator creates data resembling the training set, while the discriminator differentiates between real and generated data. As they train together, both improve, leading the generator to produce increasingly realistic data.

GANs, traditionally known for generating diverse data types like images, text, and music and having applications from creating synthetic data for training other models to crafting realistic images for virtual reality, are now making strides in IT Asset Management (ITAM). Within ITAM, these AI systems delve deep into asset data, identify patterns, and simulate various IT scenarios. Whether predicting the lifecycle of an IoT device or pinpointing potential security vulnerabilities in an OT system, GANs provide unparalleled insight and foresight.

To fully appreciate the impact of GANs, it's essential to first understand the foundational principles of IT Asset Management.

Understanding IT Asset Management (ITAM)

IT Asset Management (ITAM) focuses on the effective management of an organisation's technology assets, from hardware like servers to software licenses and mobile devices. The main goals are to account for these assets, deploy them efficiently, maintain them, optimise costs, and ensure regulatory compliance.

In the digital era, IT assets are integral to most organisational operations. Effective ITAM offers:

Cost Savings: Through smart purchasing decisions, volume licensing, and timely software renewals.

Compliance: Ensuring adherence to software licenses and regulations, thus avoiding penalties.

Operational Efficiency: A clear view of assets leads to fewer disruptions and a streamlined tech experience.

Despite its clear benefits, ITAM isn't without its challenges:

Visibility: Hybrid IT setups, cloud platforms, and remote work can obscure a full view of assets.

Complexity: Managing a diverse range of assets, each with its own lifecycle and terms, can be daunting.

Technological Evolution: The rapid pace of tech advancements means assets can become outdated quickly.

Navigating ITAM can sometimes feel complex, but with capabilities like Generative AI, it can be transformed from a challenging task into a strategic asset. With this understanding of ITAM's intricacies, let's explore how the integration of AI is revolutionising this domain.

The Intersection of AI and ITAM

As IT environments expanded in both complexity and scale, the limitations of traditional ITAM tools became evident. Yet, with the evolution of Machine Learning and Generative AI algorithms—capable of processing vast data, discerning patterns, and forecasting future trends—AI has seamlessly integrated into ITAM. It addresses the escalating challenges of visibility, intricacy, and swift technological shifts. Rather than just responding to IT asset challenges, organisations are now harnessing AI to proactively manage and refine their IT infrastructures.

This integration of AI into ITAM has opened many opportunities:

Automation: Manual tracking and management of assets, especially in large organisations, is a herculean task. AI can automate these processes, ensuring real-time updates and reducing human errors.

Predictive Insights: Instead of just telling you where your assets are, AI can predict where you might face challenges, whether it's an upcoming license renewal, potential hardware failure, or areas where resources are underutilised.

Enhanced Decision-making: With AI-driven insights, IT leaders can make more informed decisions, be it procurement strategies, deployment of resources, or compliance measures.

Real-world Examples: 

The marriage of AI and ITAM isn't just theoretical; it's already yielding tangible benefits:

·         Microsoft is using AI to automate the process of discovering and reconciling licenses across its organisation. This has helped Microsoft to identify $1.3 billion in unused or unlicensed software.

·         Honeywell is using AI to automate the process of asset discovery and classification. This has helped the company to improve visibility into its IT assets and reduce the risk of security breaches.

·         IBM is using AI to forecast future license needs for its software products. This has helped IBM to avoid over- or under-purchasing licenses worth $200 million.

·         SAP is using AI to optimise license usage for its software products. This has helped SAP to reduce the number of licenses that it is using by 10%, which has saved the company millions of dollars.

These examples underscore the transformative potential of integrating AI into ITAM, turning challenges into opportunities, and driving efficiency and innovation.

The Need for AI in ITAM

The world of ITAM is expanding, and it's not just due to the intricacies within IT. The gradual merging of IT and OT is a reality many organisations are grappling with. As businesses attempt to bring OT under IT's umbrella, they're finding that the landscape is more complex than anticipated. The rise of IoT – devices, sensors, and other 'smart' technologies – is a significant part of this equation.

Generative AI offers a way forward:

Holistic Oversight: As IT and OT begin to overlap, especially with IoT devices bridging the gap, generative AI can provide a comprehensive view of the entire asset landscape. This ensures that everything, from servers to sensors, is accounted for.

Adaptive Maintenance: The beauty of generative AI lies in its adaptability. Whether it's predicting the needs of a software update or an IoT device's maintenance cycle, it offers insights that span the IT-OT spectrum.

Security in a Merged World: The merging of IT and OT not only broadens the scope of potential vulnerabilities but also presents a larger playground for attackers. Historically, IT and OT systems operated in isolation, but as they increasingly intertwine, vulnerabilities in one can be exploited to compromise the other. This interconnectedness doesn't just elevate the risk of breaches but also amplifies the potential consequences. OT systems, pivotal in controlling tangible processes like manufacturing or power generation, when compromised, can lead to significant financial setbacks or even tangible damage. However, AI emerges as a beacon of hope in this scenario. It can proactively identify, assess, and prioritise security threats, offering a robust defence mechanism. By continuously monitoring OT systems for anomalies and effectively filtering out malicious traffic, AI ensures a fortified security posture in this converging landscape.

The Evolution of IT Infrastructure and the Rise of IoT: 

Modern businesses aren't just dealing with traditional IT assets. The proliferation of IoT devices, coupled with the integration of OT systems, paints a picture of a rich, interconnected, but challenging landscape. As the boundaries between IT and OT blur, thanks in part to IoT, the task of asset management becomes more intricate.

While many businesses are still finding their footing in this converged world, the role of AI is clear. It offers a way to navigate the complexities, ensuring that every asset, IT and OT, is effectively managed, optimised, and secured.

Having explored the current landscape and the transformative potential of AI in ITAM, let's wrap up our discussion and look ahead to what's next.

Conclusion

In our journey through the evolving landscape of IT Asset Management, we've touched upon the pressing challenges businesses face, especially with the convergence of IT and OT, and the rise of IoT. The complexities of modern IT infrastructures underscore the need for advanced tools and strategies. Enter Generative AI – a promising solution that not only addresses these challenges but also offers a way to proactively manage and optimise assets. Through its capabilities, from simulating IT environments to predicting potential challenges, Generative AI stands as a beacon for the future of ITAM.

Keyvan Shirnia

Chief Strategy Officer