More thoughtful, collaborative, and sustainable: 4 tech predictions for 2025

As we enter 2025, it’s clear that artificial intelligence (AI) will continue to redefine the way we work, innovate, and address global challenges. However, this evolution won’t follow the sensationalist narratives of AI replacing humans or solving all our problems overnight.

Instead, we will see the maturation of AI as a collaborator and a reckoning with its environmental impact. At the same time, we’re going to see hyper-personalized, proactive, and preventative solutions powered by edge computing, and the rise of visual AI in business applications.

These innovations will improve operational efficiency and also redefine user experiences across industries. They’ll also demand a nuanced and forward-thinking approach to how we integrate AI into our businesses and lives.

Prediction 1: AI will establish itself as a co-pilot, but true automation remains on the horizon

For years, the dialogue around AI has been filled with speculation about its potential to fully automate complex workflows and decision-making. In reality, 2025 will be the year of AI collaboration. AI will firmly establish itself as a co-pilot—an intelligent assistant deeply embedded in workflows to augment human capabilities rather than replace them.

AI’s greatest strength lies in its ability to process vast amounts of data, identify patterns, and generate actionable insights. These capabilities will become indispensable in decision-making processes, but human oversight and intervention will remain vital, particularly in nuanced and high-stakes scenarios. This shift will move the industry beyond the hype of full automation and toward a more practical vision of human-AI partnerships.

The benefits of this collaborative approach are immense. For example, AI provides real-time insights to help customer support teams resolve issues faster, while humans make the final judgment calls. This partnership combines the best of AI’s efficiency and precision with the irreplaceable value of human intuition.

Advice for organizations

Businesses should recalibrate their AI strategies to focus on enhancing human capabilities. This involves designing workflows that harness AI’s strengths in areas like data processing and predictive analytics while keeping humans in the loop for critical decisions.

For now, full automation will remain out of reach due to both technological constraints and regulatory considerations, so it’s important not to overpromise or misdirect resources. Instead, invest in training programs that equip employees to collaborate effectively with AI tools.

These programs should emphasize building synergies between human expertise and AI’s computational power, ensuring teams can leverage AI to its fullest potential without losing sight of the human touch.

Read more: The power of AI: How IT leaders are harnessing AI for business growth

Prediction 2: AI’s environmental impact will shift from footnote to headline

In recent years, much of the conversation around AI ethics has centered on fairness, bias, and responsible usage. While these remain critical issues, 2025 will bring another pressing concern to the forefront: AI’s environmental footprint. The energy consumption of large AI models and data centers has long been an overlooked aspect of the technology’s adoption, but this is rapidly changing.

As AI scales, its energy demands grow exponentially. Training large language models or deploying complex AI systems consumes vast amounts of electricity, often powered by fossil fuels. Despite the promise of AI in tackling environmental challenges—from optimizing supply chains to advancing climate modelling—its own contribution to global carbon emissions can no longer be ignored.

To date, major AI providers have focused on showcasing the capabilities of their systems, leaving discussions about power consumption and sustainability in the shadows. By 2025, this will no longer be tenable. Stakeholders across industries, from regulators to consumers, will demand transparency and accountability around AI’s environmental costs.

Advice for organizations

Businesses should begin factoring the environmental impact of AI into their decision-making processes. Start by prioritizing energy-efficient AI solutions, favoring smaller models that consume less power where they can deliver similar outcomes. In cases where larger models are necessary, look for ways to offset their environmental costs, such as by leveraging renewable energy sources or optimizing data center operations.

Transparency will be key to success. Companies that openly report the energy usage and environmental impact of their AI deployments will build trust with stakeholders and set themselves apart as leaders in sustainable innovation. Finding the balance between AI capability and environmental responsibility will define the most forward-thinking organizations in 2025.

Prediction 3: AI at the edge will enable hyper-personalized, proactive and preventative solutions

The transition from reactive to proactive and preventative solutions for troubleshooting device issues is a major leap forward for AI, made possible by the convergence of edge computing and advanced language models.

By embedding AI capabilities directly into edge devices, organizations can analyze support-session data in real-time, delivering highly contextual and personalized experiences. This shift will enable businesses to proactively remediate issues rather than addressing them after they occur.

Current remote connectivity solutions focus on addressing issues as they arise. However, the integration of edge AI and in-session data analysis is paving the way for a more advanced and proactive approach. Real-time data processing at the edge will provide actionable insights without relying on cloud servers, enabling faster responses while maintaining user privacy through local processing.

Edge AI’s ability to combine localized, high-speed computation with user-specific insights will drive a new era of hyper-personalization. Imagine software that not only identifies a likely issue before it occurs but also adapts to individual user contexts—anticipating needs, providing tailored guidance, and enhancing the overall user experience.

Advice for organizations

Organizations should begin mapping their in-session data opportunities and developing strategies for edge AI deployment. Success will require understanding the unique contexts of different users and use cases, as well as investing in edge computing infrastructure that can support AI workloads. Companies should focus on building preventative capabilities that leverage real-time insights while maintaining user privacy through local processing. 

Prediction 4: Business AI will evolve from text-only to visual experiences