Eight AI tips for business leaders

An organisation’s first foray into the world of artificial intelligence can be a daunting task, so data scientists and tech experts share some vital advice to help those on their journey to full AI deployment

Don’t ignore AI – whatever you do

The most important thing for executives is to just start engaging with AI today – tomorrow is not good enough,” warns Shamus Rae, partner and head of digital disruption at KPMG UK. “Business leaders need to understand the capabilities of these new technologies and put in place an AI strategy that includes some clear self-challenge. This ensures that they don’t get stuck in ‘play’ mode and fail to make any tangible change to their operations or business model.” Mr Rae predicts that, due to the common misconception of AI, “we will see some major missteps by household names failing to adapt fast enough”.

Work out what specific challenge you’re trying to solve

“Think hard about what problem in your business you want to solve, not with artificial intelligence but with data,” advises Kim Nilsson, founder and chief executive of data science hub Pivigo. “The solution always needs to come from that intersection of where your business challenges overlap with available data sets. Only there will significant value lie in data science and AI.” Antony Bourne, industries president of global enterprise software company IFS, agrees. “Before you initiate any project, you must figure out your ‘why’,” he says. “What exactly do you want to improve and enhance? The more targeted your objectives, the more competitive and transformative your results.”

Target low-hanging fruit

Clare Barclay, chief operating officer of Microsoft UK, advises business leaders to aim at small targets, or minor improvements – the so-called “low-hanging fruit” – when it comes to AI. “If you start thinking of the really big things, you do nothing,” she says. “Ask yourself: ‘What is the problem I am trying to fix?’” Dr Will Venters, assistant professor of information systems and innovation at the London School of Economics’ Department of Management, adds: “Starting with process improvement – perhaps through robotic process automation or simpler and proven AI algorithms – is more likely to be more profitable and successful than trying to push the envelope.”

Understanding data is critical to business success

David Gonzalez, head of big data for Vodafone Business, believes employees at every level should analyse data to maximise the potential of AI technologies. “A chief data officer and trained data scientists are very important to organisations looking to implement AI solutions,” he starts. “The integral role that data plays in powering businesses to succeed in today’s economy means that understanding data even at a basic level really has to be everyone’s job. The entire C-suite has to provide support, ensuring that their area is primed to harvest robust data and that stakeholders understand the value of using it in conjunction with analytics and AI.”

Decide whether to build or buy AI solutions

John Abel, Oracle’s vice principle of cloud and innovation in the UK and Ireland, says: “The next logical question is: ‘How do I implement?’” He continues: “This essentially boils down to either buying a pre-built AI application, or building your own. If you choose the former, your implementation time will likely be shorter, costs lower and maintenance easier. You won’t have to employ data scientists or pay for development platforms and architectural components to learn, buy, maintain and integrate. Buying a ready-to-go AI application provides the lowest barrier to entry, near-immediate benefits and they are often bundled with third-party data sources.”

Don’t forget about ethics

“A major issue with AI is trust,” posits Mr Abel. “The machine provides an output, but how can the user trust it made the right decision, or is recommending the correct action? To establish trust, a machine-learning algorithm needs to show its working and what data was important for the machine to make a particular output.” Ms Barclay of Microsoft UK urges executives to “establish a clear set of ethics, commitments and values around the use of AI. Not only will this ensure ethically grounded innovation, but it can support your bottom line, too. Microsoft research shows companies that consider what AI ‘should’ do have been shown to outperform those that don’t by 9 per cent.”

Take advantage of AI democratisation

In a bid to help overcome the barrier to AI adoption because of a huge skill shortage, the introduction of pre-built algorithms and open-source machine-learning libraries is helping non-experts grapple with organisations’ data, and deliver insights through AI. Mark Skilton, professor of practice in information systems and management at Warwick Business School, heralds a newfound accessibility for AI solutions – the so-called democratisation of AI. He lauds the “astonishing” amount of, often free, online machine-learning and AI training on offer from Coursera, Udemy, as well as Stanford and Harvard universities, and many others.

Preach to the converted to accelerate workplace AI adoption

According to Maximising the AI Opportunity, Microsoft’s study published in October, some 67 per cent of the 1,000 executives and 59 per cent of employees surveyed are open to experimenting with AI – however, almost all of them will require training and development. For AI to stand the best chance of success in your organisation it is critical to seek out those workers who are most excited by its potential and nurture that interest. “By involving employees, you’re culturally engaging with them around the things that are going to change, and you’re equipping them with new skills,” suggests Ms Barclay.