IoT Workforce 2024: Bridging the Skills Gap with AI, ML, and Data Science

Data scientists are frequently described as the heartbeat of IoT initiatives, acting as gatekeepers who unlock actionable insights from data.

Their expertise lies in turning raw data into clear, actionable intelligence—a vital function akin to fueling a car for a smooth journey.

Data scientists are in high demand

According to Gartner, by 2020, over half of major new business processes and systems will incorporate IoT. To deploy these solutions successfully, companies must equip teams with the right skill sets. With a current shortage of data scientists, AI offers a path to automate and mature IoT deployments.

We must broaden training in AI and IoT to close the skills gap. AI can scale projects, freeing human talent for tasks that technology cannot replace, while handling repetitive work.

Studies show that staffing shortages and skill deficits remain the biggest barriers to IoT adoption. Immersat Research Programme found that 33 % of firms would benefit from extra skills, and 47 % feel they lack the right talent altogether. The top three gaps are data security, data science, and technical support.

Simply hiring more data scientists is insufficient. Businesses must grasp how AI and machine learning empower IoT projects and invest in training the next generation.

A more collaborative economy

The prevailing notion that only data scientists can solve IoT challenges is outdated. The goal is to empower every employee to interpret IoT data.

Millennials, accustomed to constant connectivity, are ideal to drive further integration. By equipping them with AI, ML, and deep learning skills, firms can apply analytics to streaming data, derive deeper insights, and make predictive decisions—mirroring a data scientist’s role.

Therefore, we need to focus on implementing more training in tools that can help to automate and enhance roles by:

1. Implement more training – To bridge the current skills gap, we need to offer comprehensive courses in AI, ML, and DL. This enables broader analytics and predictive decision‑making, and upskills the workforce.
2. STEM isn’t the only answer – Relying solely on STEM is limiting. We must also nurture design thinking and business strategy skills, enabling data scientists to shape future models.
3. There is no single required skill – The future workforce operates in a highly connected environment. IoT and digital transformation require a blend of AI, DL, ML, and data analytics; a single skill set is no longer enough.

The workforce of the future must work with AI, DL, ML, and data analytics technologies. Only then can we unlock the true value of our data to drive IoT projects forward. This is why we need to act now.

The author is Anthony Sayers, IoT Evangelist at Software AG.