Key technology trends transforming business in 2025
fevereiro 27, 2025 / Mike Thomson
Short on time? Read the key takeaways:
- Natural language is becoming the primary interface for AI interactions, shifting focus from prompt engineering to intuitive communication
- Small language models and edge computing create practical solutions for secure, efficient AI processing
- Energy-efficient AI offers competitive advantages through optimized resource use and improved sustainability
- Entry-level workers bring essential digital-native skills to building AI-enabled workforces
Advances in enterprise technology continue to accelerate in 2025, creating ripple effects across every sector and industry.
As smart technology adoption increasingly determines competitive advantage, companies must continuously innovate and adapt their processes to create lasting business value—or risk losing market position. Leaders face the dual challenge of capitalizing on emerging opportunities while managing complex challenges that affect operational efficiency and customer relationships alike.
In our comprehensive "Top IT Insights for 2025" report, Unisys has identified eight critical trends that are raising the stakes for business leaders and redefining the business landscape. I'll examine four that demand immediate attention.
AI communication breaks language barriers
We're witnessing a fundamental shift in how humans and AI interact. The focus is moving from teaching humans to craft precise prompts toward AI systems that understand and respond to natural communication. These systems are integrating seamlessly into phones, smart devices, and business applications, responding to voice commands and even predictive cues.
Organizations must now identify specific processes where natural language automation can add value, particularly in customer service, workflows, and data analysis. Success requires infrastructure that can support sophisticated language processing while maintaining strict security standards.
Local AI processing gains traction
Not every AI application requires large language models. Small language models combined with edge computing create practical solutions by processing data closer to its source – on local servers, laptops, and mobile devices. This approach reduces costs, improves response times, and strengthens data privacy.
This shift particularly benefits organizations in regulated industries or those handling sensitive information. It allows them to maintain control over critical data while still leveraging AI capabilities. Local processing also reduces dependency on external cloud providers and optimizes resource allocation.
Smart computing reduces environmental impact
The growth in AI adoption drives increased data center construction, affecting power grids and environmental impact. Forward-thinking organizations now focus on energy-efficient models that support both innovation and sustainability.
This focus creates advantages through two channels: optimizing energy use in AI operations and applying AI to improve efficiency in energy-intensive processes. Technical approaches like model pruning and knowledge distillation reduce computational needs. Strategic data storage and reuse minimize redundant processing.
Digital natives lead AI workplace evolution
A counterintuitive finding emerges when we examine workforce dynamics: entry-level employees, often assumed to be most vulnerable to AI automation, are proving essential to business success. These digital natives bring an inherent understanding of AI tools and creative approaches to workflow integration. Their adaptability and innovation skills make them essential to building an effective AI-enabled workforce.
Organizations should prioritize recruiting entry-level talent with strong digital capabilities while providing opportunities for experienced employees to develop new technical skills. Creating connections between seasoned staff and digital natives through mentorship programs helps build a balanced, future-ready workforce.
Making strategic choices
The technology trends of 2025 require careful consideration and decisive action. Organizations must:
- Build infrastructure supporting natural language AI interactions
- Implement edge computing strategies balancing security and efficiency
- Prioritize sustainable AI practices
- Develop teams combining technical expertise with fresh perspectives
These initiatives demand immediate attention to capture competitive advantages that will extend well beyond 2025.
Learn how Unisys helps organizations implement these emerging technologies effectively. Visit our AI solutions page.
