TRENDING
To realise AI ' s full potential and the rise of agentic architecture , enterprises must upgrade infrastructure – everything from data centres to AI PCs . This distributed infrastructure optimised for agentic AI can address security , sustainability and capacity considerations by distributing the AI workload across the entire IT infrastructure , cloud , data centre , Edge , and device .
Enterprises are poised to take AI from ideation to scale . Enterprise AI is simply the application of AI technology to a company ’ s most impactful processes in its most important areas to improve the productivity of the organisation .
It requires customers to answer two important questions :
# 1 What problem am I trying to solve ?
Developing a framework to prioritise AI efforts to the most important , impactful areas is critical .
# 2 How do I solve that problem ?
AI solutions implemented as random projects on random tools do not scale . Instead , enterprises must determine the minimum set of AI systems needed to build a reusable and scalable AI foundation . This allows them to solve the first set of critical AI problems , and then leverage that investment to solve all future AI problems .
At Dell , for instance , the priority areas are global supply chain , services capability , sales engine and R & D capacity . Any impact on these areas results in significant ROI over other areas like HR , finance and facilities .
Next , enterprises should look at specific processes in their priority areas . For example , if process analysis uncovers an opportunity not in how salespeople interact with customers , but in how much time they spend gathering content for the customer meeting , which is a clear AI project . GenAI can be used to automate and accelerate content discovery and creation work .
In this case , the ROI is clear : shift sellers ’ time back to customer-facing activities and increase revenue .
To execute prioritised projects , enterprises have multiple off-the-shelf tools from which to choose . So , in 2025 the preferred path is to buy and implement AI tools in their private infrastructure . They can also buy tools that accelerate data modernisation , data meshes , for example , and with the Dell AI Factory advancements over the past year , the infrastructure is now simple to adopt and implement .
In 2025 , we have clear , repeatable approaches for prioritisation and more turnkey and well-defined AI platforms and AI infrastructure options . 2025 is a year when it simply becomes easier to know what to do and how to do it when adopting AI in the enterprise space .
AI and emerging technologies
Key takeaways
• AI solutions implemented as random projects on random tools do not scale .
• Generative AI tools are evolving to enable AI agents , which are poised to revolutionise how we engage with AI systems .
• Enterprise AI is simply the application of AI technology to a company ’ s most impactful processes in its most important areas .
• Enterprises must determine the minimum set of AI systems needed to build a reusable and scalable AI foundation .
• AI ’ s true potential lies in its connections with other emerging technologies .
• AI ’ s impact multiplies when combined with quantum computing , intelligent Edge , Zero Trust , 6G technologies and digital twins .
• We now see the AI PC not just as a client device but part of the end-to-end AI infrastructure .
• With agentic architectures , we expect to shift agents out of the data centre and onto the Edge or to the AI PC .
• Zero Trust architectures are the best path to a more secure world and implementing Zero Trust in brownfield legacy IT is hard .
• We expect customers to adopt Zero Trust by default in new AI factories for optimal security .
AI ' s true potential lies in its connections with other emerging technologies . While AI itself is transformative , its impact multiplies when combined with quantum computing , intelligent Edge , Zero Trust security , 6G technologies and digital twins , to name a few . This fusion creates a dynamic environment ripe for innovation and addressing existing challenges .
For instance , quantum computing in collaboration with AI will significantly impact most industries by providing the computing capability needed to scale AI to domains where classical computing struggles – like complex material science , drug discovery and complex optimisation problems .
AI and telecom are already coming together to transform how cellular networks operate and how fundamental elements of these systems , like spectrum optimisation , work . Even the future of the PC is influenced by AI , as we now see the AI PC not just as a client device but part of the end-to-end AI infrastructure . With agentic architectures , we expect
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