5 Most In-Demand Enterprise AI Skills for 2024

AI Magazine considers some of the most sought-after AI skills that businesses are keen to capitalise on, in order to upskill their workers so that organisations can thrive
With AI expected to continue revolutionising the digital landscape, AI Magazine considers some the most-desired skills needed to better harness AI

With AI technologies set to continue growing in the global business landscape, companies are now seeking out workforces with top-level technology skills to actualise AI progress.

As a new and growing technology, generative AI (Gen AI) can lead to increased enterprise efficiencies, reduced costs and accelerated innovation cycles for many businesses. Businesses therefore are continuing to shift their strategic focus towards greater AI investments, in order to stay ahead in a digitally evolving world.

With this in mind, we consider some of the most sought-after AI skills that businesses are keen to capitalise on, in order to upskill their workers so that organisations can thrive.

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5. IT project management

IT project management is cited as a top-needed skill for businesses, as businesses can harness AI to quickly process vast amounts of data. As a result, the technology can empower project managers to make evidence-based decisions with greater accuracy.

Likewise, using AI to analyse historical data can also lead to the technology identifying patterns and predicting potential risks and outcomes for a project. It also allows human employees to be free from more mundane and repetitive tasks, making them available to handle more complex tasks.

4. Model tuning

Model tuning is the process of identifying and establishing the parameters for machine learning and deep learning models. This is where pre-trained models can be customised to perform specific tasks or behaviours.

Fine-tuning these parameters allows teams to improve the performance and efficiency of their AI models. This skill is vital when it comes to Gen AI, as it can improve the quality of customer-facing products and services.

3. Skills-based job requirements 

In recent years, some employers have been increasingly focusing on workers skills, over college degrees. Training or upskilling workers with an in-depth technical knowledge of AI models, data and security, AI leaders will be able to be more effective and mitigate negative impacts on their workforces.

This will also ensure that business operations align better with company objectives, allowing them to better harness AI within their company.

Businesses are also increasingly prioritising soft skills, according to a recent Hexaware report, in order to drive more informed strategic decisions.

2. Ethical considerations

When developing AI, workers will need to be aware of the social and ethical implications of the technology. With new regulations, such as the EU AI Act and the new US-based restrictions, coming into play in 2024, businesses will be increasingly required to share their data on AI safety and development.

Businesses that factor in algorithmic bias, cybersecurity concerns and have clear frameworks will ensure that their AI technology builds greater trust within the company and for its customers.

In fact, The British Standards Institution (BSI) has suggested that closing the AI confidence gap and building AI trust is crucial to harnessing the benefits of the technology for humanity worldwide.

1. Gen AI modelling

Gen AI models are vital for businesses that harness AI for its customers, as they can remember previous interactions. This enables the results to be more coherent and relevant for the user.

Organisations implementing this technology, including the likes of KPMG and Bumble for example, require engineers to implement and train these models in a variety of ways. This results in companies being able to enhance productivity and create faster outcomes for their customers.

This skill also involves programming, which makes it easier for AI developers to implement and deploy various generative models. There are a wide range of programming languages that can be used in a business context, with Python currently being the most popular. 


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