Based on an interview between Hélène Gey, Axceta’s CMO and Olivier Bloch, IoT advisor and host of The IoT show
Technology is on the cusp of a significant transformation, driven by the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT). While AI continues to evolve rapidly, IoT has reached a level of maturity that allows for seamless integration of intelligent systems. These two technologies are unlocking new opportunities across industries, from manufacturing and healthcare to agriculture and energy.
This article kicks off Axceta’s Connected Futures series, exploring the trends, challenges, and opportunities shaping the AI-IoT landscape in 2025. We begin with an overview of why these technologies are transformative, their interconnection, and the pivotal shifts expected in the coming year.
The Next Big Shift: AI and IoT Convergence
The hype surrounding generative AI has dominated headlines, but experts emphasize that this is only a fraction of what AI can accomplish. AI’s true power lies in its ability to process vast amounts of data, optimize processes, and enhance efficiency in ways far beyond content creation. As AI advances, we see a renewed focus on predictive analytics, safety applications, and automation—all critical for industries leveraging IoT.
IoT, on the other hand, is no longer just about connecting devices. It has evolved into a robust ecosystem enabling real-time sensing, decision-making, and automated responses. When AI and IoT are combined—often referred to as AIoT or edge AI—devices become more autonomous, capable of running sophisticated models directly on the edge rather than relying solely on cloud computing. This shift is expected to accelerate in 2025, bringing AI-driven decision-making closer to the point of data generation.
The Rise of Edge AI: Smarter, Faster, and More Efficient
Edge AI represents one of the most significant breakthroughs in AI-IoT integration. Traditional AI models require vast computing power, typically housed in cloud data centers. However, running AI on constrained edge devices—such as industrial sensors, autonomous vehicles, and wearable health devices—requires optimized models that can function with limited resources.
Significant advancements are making this possible:
- Model Optimization: AI models are being refined to run efficiently on smaller devices without sacrificing accuracy.
- IoT Maturity: Secure connectivity and over-the-air (OTA) updates now enable remote monitoring and model updates at the edge.
- Cloud-to-Edge Expansion: Technologies like Kubernetes and Docker extend cloud capabilities to edge devices, enabling better deployment and management of AI workloads.
By running AI at the edge, businesses can achieve lower latency, enhanced security, and more efficient operations, whether in autonomous vehicles, smart factories, or connected healthcare systems.
5G and IoT: A Game Changer
The rapid expansion of 5G is another driving force behind IoT’s evolution. With ultra-low latency and enhanced connectivity, 5G enables IoT devices to communicate faster and more reliably than ever. This has significant implications for:
- Real-time monitoring and analytics in industries like healthcare and manufacturing.
- Autonomous systems, where vehicles, drones, and robots require instant data exchange.
- Scalability, as 5G allows for many more connected devices without overloading networks.
For businesses, 5G means more data, faster insights, and more excellent reliability, which are key factors in unlocking the full potential of AIoT.
Adoption and Challenges: Overcoming the Barriers
Despite its promise, AI and IoT adoption is not without hurdles. Key challenges include:
- Technical Complexity: Developing AI-driven IoT applications requires expertise in embedded systems, AI model training, and specialized programming languages like C++.
- Security Concerns: As AI models are deployed on edge devices, protecting intellectual property and ensuring data security becomes critical.
- Regulatory Compliance: As AI usage expands, industries must navigate evolving regulations on data privacy, safety, and AI ethics.
While these challenges can slow down implementation, they also create opportunities for innovation in security, compliance, and AI tooling.
Autonomous Systems: AI and IoT in Action
One of the most compelling applications of AIoT is autonomous systems, which are already transforming industries like mining, agriculture, and energy.
- Mining: AI-driven autonomous machines are reducing human exposure to hazardous conditions, improving efficiency while enhancing worker safety.
- Agriculture: With labour shortages, AI-powered tractors and drones optimize farming processes, increasing yields with less human intervention.
- Energy: Smart grids leverage AI to manage electricity distribution efficiently, reducing costs and minimizing environmental impact.
The rise of these systems signals a future where AI and IoT work together to automate complex processes, improving both safety and efficiency.
Where to Start: Industry Recommendations
For companies looking to integrate AI and IoT, it’s essential to take a strategic approach:
- Define the Problem First: Rather than adopting AI for the sake of innovation, businesses should start by identifying specific challenges AIoT can solve.
- Leverage Existing Data: Many organizations already collect valuable data,and evaluating AI solutions on this data can reveal immediate benefits before investing in new infrastructure.
- Find the Right Partners: AIoT implementation requires expertise in both hardware and software. Collaborating with technology partners can accelerate adoption and reduce risks.
Looking Ahead: The Future of AIoT
As AI and IoT continue to evolve, 2025 is poised to be a pivotal year. The increasing maturity of IoT, breakthroughs in edge AI, and the rollout of 5G will drive greater adoption across industries. However, businesses must stay ahead of regulatory changes, security risks, and implementation challenges to harness the full potential of these technologies.
In the coming articles of the Connected Futures series, we will explore specific industry use cases, best practices, and the emerging opportunities AIoT presents. The future is connected, and it’s arriving faster than ever.
