Tech trends to watch in 2025: The rise of agentic AI and beyond
The past year has witnessed an explosion in the capabilities and adoption of artificial intelligence, impacting nearly every facet of our digital lives. This transformative technology will continue its rapid evolution, shaping the key tech trends of 2025 and beyond. From AI agents that anticipate our needs to humanoids joining the workforce, the coming year promises to be a landmark period of innovation and adaptation.
Here are some of the key trends to keep an eye on:
1. Agentic AI takes center stage
In 2025, AI will transition from a mere tool to a proactive collaborator. Unlike today's static AI systems, agentic AI will continuously learn from user interactions, integrate contextual information (such as account history, user behavior patterns, and network environments), and make decisions with minimal human intervention.
Imagine AI customer service that anticipates customer needs or network management AI that autonomously resolves potential issues. These AI agents will also collaborate with each other, forming complex workflows to automate advanced business functions like supply chain management.
This shift will bring significant gains in efficiency and personalization for businesses but also necessitates robust ethical guidelines and governance to ensure fairness, transparency, and intellectual property protection.
2. Humanoids enter the workforce
2025 will likely mark the first instances of humans and AI-powered humanoids working side-by-side. This integration will require companies to rethink workplace dynamics and the underlying technology. Robust, low-latency connectivity will be crucial for humanoids to process and analyze data in real-time.
Cybersecurity will also become paramount, not only to protect data but also to safeguard humanoids from hacking and malicious modifications. Managing transparency in this hybrid work environment, where humans and machines collaborate, will be essential.
While this collaboration offers exciting opportunities for scaling operations, it will also likely fuel concerns about job displacement, requiring strong leadership to balance AI's power with the essential human touch.
3. AI implementation reality check for many businesses
Despite significant investments in AI, many companies will face challenges in implementing AI solutions effectively. Cisco’s AI Readiness Index reveals that only 13% of companies are truly ready to leverage AI's full potential. In 2025, organizations will grapple with securing sufficient compute power (only 21% currently have enough GPUs), upskilling their teams, and modernizing their infrastructure.
Managing and organizing data, both structured and unstructured, will become a critical focus. Leadership teams will likely need to adjust expectations and timelines as they navigate the complexities of AI implementation, realizing that significant gains will be achieved progressively over time.
4. AI-powered cybersecurity threats and defense
The rise of AI brings new cybersecurity risks, such as prompt injection attacks, which only 30% of companies feel prepared to handle. Advancements in quantum computing will also challenge traditional encryption methods, pushing organizations to adopt quantum-resistant protocols. The increasing interconnectedness of digital ecosystems will exacerbate the risk of social engineering and supply chain attacks.
In response, organizations will increasingly integrate AI into their security strategies, augmenting human capabilities to fortify network defenses and policy enforcement.
5. New solutions to counter AI sustainability challenges
The significant energy consumption of AI and its impact on carbon emissions is a growing concern. By 2025, the energy used by AI data centers could match the annual consumption of a country the size of the Netherlands.
Businesses will increasingly seek partners who can help them deploy technology while meeting their sustainability goals.
AI itself will also play a role in unlocking energy efficiencies through "energy networking," combining software-defined networking with DC microgrids to optimize power usage and distribution.
