Hello, and welcome back to PRIMO Tech-a-Break.
As each year draws to a close, the technology sector closely watches trend-reports from leading research firms such as Gartner and Forrester to predict emerging innovations. Yet for the upcoming year 2026, signals from these reports all point in the same direction: the era of AI exploration and experimentation is ending, and we are shifting into the phase of strategic implementation.
Forrester describes this phenomenon interestingly as “AI trades its tiara for a hard hat,” indicating that AI is no longer just a dazzling innovation—it is a serious force that must “Get to work” in business.
As someone working in software engineering, I’d like to summarise four key directions organisations must prepare for:
1. The arrival of Multiagent Systems
- Trend: We are moving beyond one-to-one AI usage (for example, feeding prompts to ChatGPT) toward an era where we can command an “ecosystem of AI agents” working together to achieve complex goals.
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Technical perspective: Instead of issuing step-by-step commands, you give a single objective—such as “analyze competitors and plan a new product launch”—and a Multiagent System automatically decomposes the task and assigns specialist agents (e.g., a data-analysis agent, a content-creation agent) to collaborate.
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Research support: Gartner identifies “Multiagent Systems (MAS)” as one of the major trends set to revolutionise automation processes; Forrester predicts organisations will begin creating “Agentlakes” as a central management hub for these agents.
2. Focus on ROI and AI Security
- Trend: 2026 is the year organisations must seriously evaluate business outcomes from AI investments. Budgets formerly used for “experimentation” will face tighter scrutiny and shift toward “scaling” usage.
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Technical perspective: This challenge will drive investments in measurement tools (AI Observability) and—critically—into security.
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Research support: Forrester forecasts many CIOs will struggle with AI projects lacking governance. Gartner notes the trend of “AI Security Platforms” as essential, to protect against emerging risks such as prompt injection, data poisoning, or model-leaks of sensitive data.
3. Demand for high-level infrastructure (AI Supercomputing & Energy)
- Trend: As AI models become more complex, they demand unprecedented compute resources and energy. AI workloads will become among the heaviest in data centres.
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Technical perspective: Legacy server architectures will no longer suffice. Organisations must redesign entire data-centre infrastructures.
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Research support: Gartner identifies the “AI Super Computing Platform” trend, combining CPUs, GPUs and next-gen processors such as neuromorphic chips. Many analysts agree that a looming “tech energy crisis” will force the industry to seriously seek alternative power sources.
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4. Physical AI & Confidential Computing
- Trend: AI is about to move fully out of the digital realm into the physical world—and at the same time must handle the most sensitive data.
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Technical perspective: We will see AI integrated with robots, drones and autonomous vehicles (what Gartner calls “Physical AI”) alongside growth of “Confidential Computing”—tech that creates trusted execution environments where even the service provider (e.g., a cloud provider) cannot access data during processing. This becomes essential for handling health or financial data in the AI era.
Conclusion
In summary: 2026 will be the time when the role of software engineering is emphasised again—not merely piloting innovations, but building solid, secure and measurable foundations so that AI can be deployed across business sectors sustainably.