The future of enterprise technology is taking shape rapidly, and the start of 2025 is only accelerating the impact it will have on all businesses, no matter the industry. IT and business leaders face incredible opportunities alongside complex challenges that will reshape everything from daily operations to client value.
As technology redefines how we work, organizations are in a race to innovate to tap into new possibilities and drive sustainable growth, or risk falling behind. To succeed in this dynamic landscape, leaders must focus on the following trends shaping the tech landscape.
Natural Language: Default for AI-Human Interaction
Over the past several years, AI has become a major focus for businesses and in everyday life. We went from the onset of ChatGPT to learning how to engineer prompts and are now coming to terms with the privacy and governance considerations needed to use the technology safely.
AI will continue to become more intuitive and accessible, transitioning from basic web interfaces to seamless, natural interactions. AI will integrate into all aspects of our lives, enabling us to communicate with machines as naturally as we do humans. Further, we will see AI integrated into devices like phones and smart home systems, responding to voice commands, gestures and predictive cues.
This marks a reversal of the current approach to AI, a shift from teaching humans to prompt AI toward teaching it to understand humans. Today, prompt engineering plays a central role in making AI systems deliver optimal results. However, prompt engineering will become obsolete as natural language processing improves, and AI becomes more intuitive.
To stay ahead of this shift, organizations should identify key processes unique to their business models that could benefit from natural language automation Expanding AI use beyond traditional interfaces — incorporating voice, gesture and predictive features — will be crucial to staying ahead. Leaders should prioritize intuitive user experiences that make AI tools easier for all users to navigate. Finally, as AI’s capabilities grow, ensure that your infrastructure and security measures are robust enough to handle the demands of natural language processing at scale.
Small Language Models and Edge Computing
Due to connectivity, privacy and security concerns, not all AI applications can rely on large language models (LLMs). Small language models paired with edge computing can process data closer to the source — like on local servers, laptops and mobile devices — reducing LLM token usage, improving latency and addressing privacy challenges.
This hybrid approach enables organizations to process sensitive data locally, resulting in faster, more secure AI applications. Organizations can achieve more reliable AI-driven insights by using localized models that rely on curated data while optimizing resource use and managing operational costs. It mainly benefits organizations operating in regulated environments or those handling confidential information. Edge deployment also helps organizations control their AI operations, reducing reliance on external cloud providers.
Organizations can use this hybrid approach by evaluating where moving computing power to the edge can improve data confidentiality, security and cost-efficiency. For example, consider areas where sensitive or regulated data can be processed locally, minimizing the need to transmit information over less secure channels. Partnering with edge computing providers will allow organizations to expand their AI capabilities while keeping sensitive operations closer to home.
Energy-Efficient AI as a Competitive Advantage
As AI is incorporated into everyday life, it drives the construction of energy-intensive data centers, straining global power grids and raising environmental concerns. Consequently, the focus is now on developing energy-efficient models to balance innovation with sustainability.
Practicing sustainable AI can be a differentiator in two ways: optimizing the use of energy resources in the training and operation of AI, and applying AI to energy-intensive processes and applications.
Business leaders should consider implementing efficiency techniques such as model pruning, quantization, and knowledge distillation to reduce computational complexity and resource usage. Additionally, the focus should be on reusing datasets and optimizing data storage to avoid redundant data processing and reduce energy consumption. Partnering with cloud providers and hardware manufacturers who prioritize energy-efficient AI solutions is another step toward sustainability.
Entry-Level Workers and an AI Workforce
One temptation with generative AI technology is to assume it can do the work of entry-level workers. However, even with these advances, these workers remain essential to the future of business. While AI can automate many repetitive tasks, entry-level employees often possess a deeper understanding of generative AI tools and how to integrate them effectively into workflows. These workers are digital natives who are highly adaptable, innovative, and capable of handling AI technologies, making them valuable contributors to an AI-enabled workforce.
To leverage the potential of an AI-enabled workforce, organizations should prioritize hiring entry-level talent who bring valuable digital-native skills and a deep understanding of generative AI tools. Retaining these workers is key to nurturing future leaders who can harness AI’s capabilities for long-term success. Additionally, organizations should provide targeted training programs to help experienced employees adapt to AI advancements and integrate new technologies into their roles.
In an era of rapid technological advancement, businesses need to stay nimble while planning ahead. The trends shaping enterprise technology offer both challenges and opportunities. Taking action today will position organizations for sustained innovation and competitive advantage in 2025 and beyond.