What CIOs need to create a successful AI team

Artificial intelligence (AI) and robotics will create 7.2 million jobs, more than the number of positions that are forecast to be lost due to increasing automation, according to a report by PwC.

PwC also notes the important economic impact that AI will have for the British economy, predicting that GDP in the UK will increase 10.3% by 2030 as a result of AI implementation.

However, the firm also indicates that for the UK to reap the fruits of AI, businesses and organisations should look at how to recruit the right talent, technology and access to data to make the most of the value offered by this disruptive technology.

CIOs and other IT executives are well aware of the advantages that AI can bring to their businesses and are investigating AI-enabled processes. But as important as the technology itself is building an effective team.

Find a cross-functional team

Effective use of AI demands collaboration among different departmental teams, from IT and security to financial and legal.

Although most organisations tend to set boundaries by putting specific teams in charge of certain projects or competencies, AI requires multidisciplinary groups to come together and offer a holistic solution to pressing problems.

After all, the competencies required for carrying out AI projects include not only data science skills but also product marketing and management, software engineering or user interface design – to name just a few – which makes a cross-functional team essential.

“We hire employees with AI expertise – data science, machine language developers – and pair them with employees who have existing product knowledge and software capabilities,” Rishi Bhargava, co-founder of vendor Demisto, told sister title CIO.com. “This ensures that employees in a pairing learn from each other, improve their skill-sets, and gain a well-rounded expertise with time.”

The three AI musketeers

There are of course some people with technical skills who are indispensable in any AI team and who are the core of any AI project.

AI expert Monte Zweben thinks that a balanced AI team will include three people: a data engineer, a data scientist and a software developer.

While the data engineer can take the information that an organisation collects and turn it into data that can be processed by AI and machine learning systems, the data scientist will test out different algorithms to see which ones perform best, and then adapt them if needed to get worthwhile predictions. The software developer will then collect all that information and incorporate it into actual applications.

In case you feel tempted to save on recruitment and skip some of the above roles, here’s the experience of Chris Brazdziunas, vice president of products at LogRhythm, a security intelligence company, that matches Zweben’s view.

“At first, we attempted to recruit for a single role – a data scientist – who had all of the capabilities we needed. That approach did not work out,” he said. “In our experience, we found that an AI group needs at least three distinct roles: a data engineer to organise the data, a data scientist to investigate the data and a software engineer to implement applications.”

In a perfect world, a team should include elements from the business, practical AI, UI/UX and developers up and down the stack: they should be linked together through a shared problem description and managed by a technical product manager. When this is not possible though, the above three roles should suffice to keep your AI team up and running.

It is important to bear in mind that technical skills are not everything on their own.

Should you hire a Chief AI Officer?

Kristian J Hammond made a strong case in the Harvard Business Review against hiring a Chief AI Officer (CAIO), arguing that innovation initiatives only succeed when there is a solid understanding of actual business problems and goals. For Hammond, the job of the CAIO can rapidly become one of driving technology rather than solving problems.

“This is not to say that you don’t need people who understand AI technologies,” he says. “Of course you do. But understanding the technologies and understanding what they can do for your enterprise strategically are completely different. And hiring a Chief of AI is no substitute for effective communication between the people in your organisation with technical chops and those with strategic savvy.”

However, Forbes takes the completely opposite stand and gives no less than seven reasons of why your company needs a CAIO instead of relying on PhD or people with relevant research experience, making the point that a good CAIO adds perspective to the C-suite and that they are your lifeline into the latest academic research.

At the end of the day most of these decisions depend upon budget, so your decision to hire a CAIO to join your AI-Team will be subject to the financial resources of your organisation.

As for research, if you can’t afford your own, you can always follow what others are doing. After all, not many companies or startups can afford what Google, Apple or Amazon are investing in AI, which amounts to billions of pounds!

How do you keep talent?

As important as recruiting new talent and people with the right skills, is being able to retain it.

Staffing skills is the leading challenge for 54% of CIOs looking to adopt AI, according to Gartner, which has deemed 2018 as the year “AI Democratisation” begins.

“The challenge of creating an AI strategic development plan parallels the staffing challenge, as having AI-savvy workers and executives benefit organisations actively working to set strategy,” according to the Gartner “Predicts 2018: Artificial Intelligence” report.

Dr John Sullivan, who specialises in providing high-business-impact talent management solutions, thinks that the AI talent supply will remain relatively fixed for the near team for two reason: first, because universities still produce a modest number of PhDs each year in the field; and second, because unlike coding and other technology specialties, you can’t really learn AI or machine learning on your own, or even with corporate support. That’s the reason why he predicts an upcoming “war for AI talent”, meaning that small supply of available talent will make recruiting competition particularly challenging.

Many organisations are dealing with the AI talent shortage by forming partnerships with universities and by training and building from within.

“We view the talent shortage in data science similar to how professional sports leagues have worked their way back in recruitment: We use talent at earlier levels,” says Mark Clerkin, a data scientist at venture capital firm High Alpha, in Indianapolis, Ind. “We have relationships with universities and engage in learning projects and doing speaking to have access to talent at that level before [graduates] are placed.”

High Alpha also gives students still in school “real-world experience, and we give them meaningful projects and get to know each other, so it’s kind of a long running interview.” That essentially gives the firm a talent pool to draw from, he says.


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