AI and IoT: Why One Can’t Succeed Without the Other

Eseye

IoT Hardware and Connectivity Specialists

LinkedIn

Artificial intelligence (AI) is grabbing all the headlines. Executives are investing in machine learning, predictive analytics, and generative AI to transform enterprises. But here’s the hard truth: AI without IoT isn’t innovation—it’s wasted investment.

AI is only as powerful as the data it consumes—and that data comes from smart connected devices: sensors on machinery, meters in the field, vehicles in motion, and medical devices monitoring patients. Without secure, dependable IoT connectivity, AI is essentially flying blind.

Julien Bertolini, IoT expert at Volvo Group, puts it straight:

“You can use AI, but only if you have good IoT solutions. The goal of IoT is to get quality data. That’s the foundation to build an AI model.”

Our 2025 State of IoT Report confirms this: 34% of businesses say poor IoT connectivity is holding back their AI and machine learning initiatives.

That means one-third of organizations are investing in AI models only to be held back by fragile IoT infrastructure.

IoT is the fuel that powers AI

Think of IoT as the nervous system feeding sensory data to the AI “brain.” Without that feed, even the smartest brain can’t make effective decisions.

IoT devices around the world transmit measurements—temperature, vibration, location, usage. AI can use this lifeblood of data in a variety of ways to help different industries to:

• Power predictive maintenance and prevent unexpected downtime
• Optimize real-time logistics and supply chains
• Enable timely healthcare interventions

But if connectivity fails or devices drop offline, the AI models become unreliable. Insights become skewed. Decisions are made in the dark leading to missed opportunities, wasted budgets, and disappointing outcomes.

Industry-by-industry: whose AI ambitions are being held back?

Our research surveyed six key industries:

• Agritech
• EV charging / smart grid
• Healthcare / medical
• Manufacturing
• Smart vending
• Supply chain and logistics

The findings reveal that connectivity issues are widespread but vary in severity:

• Manufacturing leads with 39% of firms citing poor connectivity as a barrier to AI and ML.
• In the US, 37% of businesses report connectivity challenges versus 30% in the UK.
• Smart vending reports the lowest at 29%, yet nearly one-third still report AI hindered by IoT.

It’s a unanimous message: AI’s success depends on a strong IoT foundation across all sectors.

Real-world AIoT leadership in action

Volvo Group: Industrial scale, enterprise-grade IoT

Volvo Group is scaling its IoT connectivity across factories, vehicles, and construction assets globally. Its approach seamlessly feeds real-time data into AI systems for predictive maintenance and quality control—demonstrating how AI only delivers when IoT is robust.

Learn more on the IoT Leaders Podcast >

Cedars-Sinai: healthcare reimagined

Cedars‑Sinai Medical Center in Los Angeles launched CS Connect, an AI-powered virtual care platform that automates patient intake and symptom assessments via chatbot, offering 24/7 access to care for over 42,000 patients. Physicians can use AI-generated summaries to focus on meaningful clinical decisions and reduce wait times.

Learn more >

Malawi Clinic: AIoT saves lives

At a rural maternity ward in Lilongwe, Malawi, an AIoT software system monitors foetal vital signs in real time during labour. Over three years, this tech contributed to an 82% reduction in stillbirths and neonatal deaths.

Learn more >

These examples show AIoT’s transformational potential—from manufacturing floors to hospital rooms to human-centered care contexts.

Five strategic steps for Enterprise leaders

If AI is your destination, IoT is the roadmap—and here’s how to get it right:

1. Invest in secure, resilient IoT infrastructure
   Reliability and compliance underpin AI performance.
   
2. Select analytics tools aligned with your sector
   Not all AI platforms are created equal—choose those tailored for manufacturing, healthcare, smart grids or invest resources into creating your own bespoke tools.
   
3. Build in-house AIoT capability
   Develop talent that bridges data science and IoT strategy.
   
4. Train bespoke AI models on your data
   Generic models yield generic results. Use your IoT data to build edge, predictive, and intelligent systems.
   
5. Align with broader goals
   Ensure AIoT initiatives support sustainability, resilience, and regulatory priorities.

The AIoT adoption curve is accelerating

Gartner projects that by 2027, 75% of enterprises will rely on AI-powered analytics—a sharp rise from just 25% today.

Meanwhile, 30% of businesses expect effective IoT deployment to automate significant operations, accelerating efficiency and strategic impact.

The verdict is clear: enterprises that expertly integrate AI and IoT will turbocharge performance. Those that don’t will fall behind.

AI will only deliver its promise when backed by robust, global, and secure IoT structures. With 34% of businesses already hindered by poor connectivity, the window to act is closing.

In this article

• IoT is the fuel that powers AI
• Industry-by-industry: whose being held back?
• Real-world AIoT leadership in action
• Five strategic steps for Enterprise leaders
• The AIoT adoption curve is accelerating