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Agentic will be the word of the year in 2025 . The birth of agentic AI architecture marks a new chapter in human-AI interaction . Generative AI tools are evolving to enable AI agents , which are poised to revolutionise how we engage with AI systems .

In the consumer world , we have seen early agent approaches with virtual assistants , chatbots and navigation apps . In 2025 , a new , more advanced set of agents will emerge . These agents will operate autonomously , communicate in natural language and interact with the world around them , including working in teams of other agents and humans .

They will also be fine-tuned and optimised to perform assigned , specific skills , like coding , code review , infrastructure administration , business planning and cybersecurity .

AI agent systems will feature diverse cognitive , orchestration , and distribution architectures tailored to specific tasks . As complexity grows , multi-agent systems will emerge , requiring the rapid evolution of tech stacks to support agentic systems effectively .

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 .

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 .

John Roese , Global Chief Technology Officer and Chief AI Officer , Dell Technologies

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 .

Source : Dell Technologies

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