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Closing the AI skills gap

A growing demand for AI technology but a shortage of work can be solved by infusing AI into a business

Artificial intelligence (AI) is expected to change the world. AI can help automate decisions and processes, predict and shape future outcomes and optimise employees’ time to focus on higher value work. The problem is, advances in AI are being slowed by a global shortage of workers with skills and experience in areas such as deep learning, natural language processing and robotic process automation. The talent pool for such disciplines is limited and therefore in high demand– so much so that some companies encounter inter-departmental jostling for software programmers and IT developers who have the prized expertise.

Advances in AI are being slowed by a global shortage of workers with skills and experience in areas such as deep learning, natural language processing and robotic process automation.

That demand goes far beyond the IT department. According to the research and advisory company Gartner, the strongest demand for talent with AI skills over the past four years has come not from IT department but from other business units – marketing, sales, customer service, finance, and research and development.

Augmenting human intelligence

People are essential to the success of any digital transformation project. AI can automate much of the mundane administrative work and repetitive tasks that employees used to spend hours on – enabling them to focus on more creative, analytical and strategic work that can give their organisation a competitive edge.

Organisations can start by identifying those mundane tasks that can be performed by AI − such as scanning thousands of insurance claims for possible fraud, summarising the morning’s news stories or even sharing price recommendations to customers of an investment company. That can leave employees free to do the more stimulating work that adds value to their company.

However, even big corporations with large IT departments may struggle to find staff with the requisite AI skills to start using the technology

This is where various technologies can oil the wheels of AI. For example, AutoAI, a capability within IBM Watson Studio, can help companies get started with AI by automating the laborious parts of a project, including preparing data and developing AI models.

For projects that are heavy on data science, IBM can provide customers with a data science team to work with the client to design and pilot an AI project.

Deutsche Lufthansa, Germany’s largest airline, recognised early on that with the right data and AI strategy, it could improve customer services, empower employees and improve operational efficiency.

The airline has worked with IBM and its cloud computing services to move from AI proof-of-concepts to scaling data science projects across the organisation. Lufthansa built a computer platform enabling its data scientists to experiment and test AI projects before rolling them out across the company.

Augment and retrain

AI can be a game changer for organisations, but skills shortages and the complexity of data models and AI technologies can stall progress.

Technology can do much of the heavy lifting in the early stages of an AI project.

Fortunately, when implemented correctly, technology can do much of the heavy lifting in the early stages of an AI project. By using machine learning to automate data tasks, such as creating and testing a data model, it can cut experimentation time from weeks, to hours, or even minutes.

Organisations can use IBM’s AutoAI to automate the development of a data model, measure the accuracy and pick the best algorithms.

Businesses may have a shortage of workers with AI skills, and as a result, need external support. IBM’s Data Science and AI Elite team of nearly 100 data scientists helps organisations plug tech skill shortages and overcome the challenges of AI adoption.

In the past three years, the team has completed projects in 50 countries, helping clients in work as diverse as reducing litter on beaches, cutting a telecom company’s operating costs by 15 per cent and proactively mitigating bias in a company’s hiring process.

Given that there has been much speculation that AI will replace many human workers and cause widespread job losses over the coming decades, it’s important that organisations reassure workers about the AI they use.

After all, new technologies usually create new jobs. Also, the main value of AI is to augment human work and reduce drudgery, rather than make employees redundant.

There is no quick and easy solution to the AI skills gap, but by following a three-pronged approach – training staff in data and AI skills, infusing AI technologies to automate mundane tasks, freeing up human workers to upskill and using AI to streamline hiring – companies will be doing much to close the talent shortage.

Daniel de la Fuente, Vice President, Data and AI at IBM EMEA, discusses how to demystify AI and how to overcome Artificial Intelligence challenges through the application of IBM's AI Ladder.

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