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Having spent nearly four decades in the tech industry, Avishai Sharlin has seen it all. Yet he believes that the next ten years will be the most disruptive yet. Now Division President at Amdocs Technology, he’s spending his time preparing for what tomorrow’s AI technologies have in store.
Avishai sees a future where artificial general intelligence (AGI) may be achieved within the decade, a change that could “totally and dramatically change the world”. In this new era, Avishai doesn’t just see AI automation enhancing systems, but becoming essential to them. “Future complex systems will be too challenging to manage manually,” he predicts, leading to AI becoming a “mandatory layer across all IT functions”.
Even today, we can see the beginnings of this AI revolution. “Generative AI is already influencing multiple domains,” Avishai explains, pointing to its ability to manage hybrid infrastructures. In particular, he mentions how satellite networks have become “increasingly integrated with traditional infrastructure,” and the potential AI has to “bridge these hybrid networks” and improve performance.
Avishai also sees a future where “AI adoption will drive pressure to reduce the IT workforce,” with the growing potential of AI agents to “operate autonomously, optimising network performance based on user preferences”. While full autonomy may still be on the horizon, he’s confident that tangible benefits will rapidly emerge.
But not all technological shifts will bring opportunity. “Quantum computers”, Avishai warns, will “threaten the security of applications using traditional encryption”. To stay ahead, organisations must “refactor applications to post-quantum cryptographic methods,” a task that will require extensive software updates, manual releases and careful consideration.
So where does this uncertain future leave IT professionals? Funnily enough, that was our very first question…
The next decade will reshape IT and the entire IT workforce. We will see transformation across economics, how people work and make a living, as AI will be all around us. This technology will develop capacities that surpass humans. It may be that artificial general intelligence will be accomplished in 10 years – when this happens, it will totally and dramatically change the entire world, including computer science and how infrastructure is being managed.
AI-driven automation will become standard practice, as future complex systems will be too challenging to manage manually, especially in a multi-cloud era, where operations span the cloud, edge, hybrid environments and beyond.
The IT workforce will increasingly rely on GenAI to interact with and manage systems. AI will become a mandatory layer across all IT functions—from AIOps and AI-driven security operations to troubleshooting, system failure investigation, recovery and ongoing management. Data management will be a key issue, as AI and GenAI depend on clean, high-quality datasets from reliable sources to be processed by foundation models and ML algorithms. As data becomes the foundation of the future, IT will play a crucial role in ensuring data availability and effective management.
A major challenge for IT professionals will be navigating significant disruptions. Over the next decade, AI adoption will drive pressure to reduce the IT workforce. However, AI and GenAI will act as key enablers, allowing a leaner IT team to manage complex systems more efficiently, with fewer errors and stronger security.
AI and post-quantum computing are two transformative technologies reshaping IT.
Generative AI is already influencing multiple domains, from AI-enhanced networking to advanced analytics. One key application is real-time network optimisation. For example, as satellite networks become increasingly integrated with traditional infrastructure, Gen AI can bridge these hybrid systems, managing network traffic to ensure reliable connectivity. Looking ahead, AI agents will evolve to operate autonomously, optimising network performance based on user preferences. While this level of automation is still a few years away, the rapid pace of AI advancements means tangible benefits will emerge within the decade.
Post-quantum computing, meanwhile, presents a different kind of challenge. As quantum computers threaten the security of applications using traditional encryption algorithms, organisations must refactor applications to post-quantum cryptographic methods. This is especially important to mitigate the threat from nation-state actors with access to quantum technology.
The vast majority of IT systems rely on traditional encryption that will be made redundant by quantum computers. This will require extensive software updates, manual releases and careful consideration of backwards compatibility. Given the complexity of modern IT environments, full adoption will take time. Forward-thinking organisations are already developing post-quantum skills and mapping out upgrade strategies to stay ahead of emerging threats.
Hybrid cloud and multi-cloud strategies will become the standard as enterprises seek to leverage edge computing for real-time data processing with the scalability and flexibility of public cloud.
The reduced latency provided by edge computing is critical for industries such as telecommunications, media and IoT manufacturing, where seamless, high-speed connectivity underpins essential operations. Edge capabilities will also fuel innovations like autonomous vehicles and smart buildings, which rely on ultra-low-latency responses to function effectively.
As AI adoption continues to expand, public cloud platforms will provide the processing power and storage needed to train AI models efficiently. Enterprises will be able to handle datasets without prohibitive infrastructure costs. By leveraging cloud resources, businesses can scale AI initiatives dynamically, making advanced AI capabilities more accessible without heavy upfront investments.
To maximise performance and resilience, enterprises will increasingly diversify their cloud providers, moving beyond reliance on a single hyperscaler. Multi-cloud strategies will enable organisations to tap into specialised capabilities, optimise workloads and enhance operational flexibility. As cloud networks evolve—from enterprise-wide infrastructure to application-specific ecosystems—self-service capabilities will expand, empowering both technical and business teams to drive innovation with greater agility and collaboration.
IT infrastructure is set to become increasingly decentralised, driven by emerging technologies such as IoT, AI and edge computing.
According to IDC, investments in edge computing are projected to reach $350 billion by 2027, as businesses prioritise real-time processing by running workloads closer to data sources. Beyond reducing latency, edge computing enhances security by allowing organisations to retain full control over their data through localised storage.
The shift toward decentralised computing will accelerate with the continued adoption of Internet of Things (IoT) technologies. IoT devices enable real-time data collection from remote locations, such as factories or hospitals in remote locations, where immediate processing is crucial for operational efficiency. To support critical functions and deliver more responsive, location-based services, enterprises need computing capabilities at the edge.
Additionally, the rise of private and sovereign AI will further drive decentralisation, as organisations seek to run AI models on their own infrastructure rather than relying on public cloud services. This approach allows for greater customisation, security and compliance with regulatory requirements. Technologies like NVIDIA’s AI Factory are enabling businesses and governments to deploy AI within their own data centres, maintaining greater control over sensitive information. AI at the edge will also unlock new innovations, particularly in sectors like healthcare, telecommunications and autonomous systems, where ultra-fast response times are essential.
