Could AI Leave Your Business High and Dry? Expert Insights from IoT Leaders

Mark Troester, VP of Strategy at Progress recently admitted that AI’s rapid adoption in the Internet of Things (IoT) industry may be premature. As a seasoned IoT developer and tools provider, Troester’s perspective is grounded in real‑world deployment challenges.

Freelance IT writer Nick Booth echoes these concerns, noting a growing scepticism about AI’s role in the workplace. He points to the Workforce Futures report by Fuze, which reveals that 40% of workers already experience AI in their company, yet a striking 84% would rather speak to a human than a machine. This gap underscores the enduring value of human expertise.

Why AI Starts Behind Human Intelligence

AI systems are fundamentally limited by the scope of their creators’ knowledge. Even the most advanced models are trained on a narrow slice of human experience, meaning they can only approximate, never replicate, the depth of human cognition. Furthermore, while humans possess a spectrum of learning styles—sensory, visual, active, reflective, sequential, and global—machine learning relies on linear, mathematically driven processes that can’t fully capture this diversity.

Is AI One‑Dimensional?

Machine learning excels at pattern recognition and large‑scale data crunching, but it lacks the contextual nuance that comes from varied learning modalities. While AI can process data faster than humans, it cannot yet anticipate the complex, often unstructured challenges that arise in real‑world IoT deployments.

Many companies, in their haste to automate, have removed essential human touchpoints from their IoT support systems, assuming that every issue can be handled by a machine. Troester cautions that this “jumping the gun” approach can leave businesses vulnerable.

Bridging the Gap: Making AI Accessible

Despite its limitations, AI is becoming more accessible. A surge of smart, connected sensors is generating granular data, while scalable platforms now enable mass‑scale collection, storage, and processing. Troester highlights the automation of the data‑science lifecycle—essentially “applying AI to AI”—as a game‑changer. By automating model training and validation, data scientists can focus on strategic analysis rather than repetitive tasks, increasing the tangible value of AI.

Data from Fuze’s study shows 26% of workers believe AI will have the biggest genuine impact on the workplace, a figure five times higher than blockchain’s projected influence. Meanwhile, only 8% of IT professionals view AI as the most overhyped tech of 2018, and 23% consider virtual reality as the most pretentious.

The study’s most revealing insight is that younger generations are the most sceptical of AI technologies—an encouraging sign that the next wave of talent will demand thoughtful, human‑centric AI solutions.

Author: Nick Booth, freelance IT and communications writer.