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Four Rules For Integrating AI Into Your Business

It is difficult to predict how AI will reshape different industries but what’s clear is that the most open, flexible and forward-facing companies have the best chance of adapting to the new landscape. Here are four rules for getting started.

four rules for AI successArtificial intelligence (AI) might sound like something that belongs in a science fiction movie but in reality, it’s fast-becoming the most exciting new technology in the world right now. And while AI is often discussed in the context of high-tech industries like robotics and cloud communications, it’s increasingly having a huge impact across the entire business landscape in industries as diverse as pharmaceuticals, law and education.

Crucially, AI won’t just lead to the development of new products. It will enable entirely new methods of strategic-planning and business administration on a scale similar to the changes brought about by the rise of the personal computer in the 1980s.

Few can deny that AI is driving a new age of business innovation. Indeed, a recent study by PwC’s found the potential contribution of AI to the global economy could be as much as $15.7 trillion by 2030.

As someone with over 30 years’ experience working in AI, I believe that successful AI disruption comes from a willingness to embrace change. It is difficult to predict how AI will reshape different industries but what’s clear is that the most open, flexible and forward-facing companies have the best chance of adapting to the new landscape. Here are four rules for getting started.

Don’t become an AI addict

AI is an evolving technology. It is neither perfect nor beyond criticism, but it’s already proven sufficiently promising. Take robotic process automation (RPA) for instance. RPA is often equated with AI, but it actually refers specifically to the tools capable of carrying out repetitive or rules-based activities independent of human oversight. By automating simple, repetitive processes such as basic data entry, companies could save thousands of hours and millions of dollars every year.

Unsurprisingly, forward-thinking industry leaders are very enthused by RPA’s potential to generate savings and boost productivity because it means that highly complex procedures can be carried out quickly, accurately and with total compliance. Forbes has even gone as far as to call RPA a ‘gateway drug to digital transformation’ on account of its potential to add immediate value to companies and drastically change the manner in which they operate.

However, processes shouldn’t be automated either for the sake of it or because the prospect of savings are too attractive. Before signing off on automation ask yourself three crucial questions. First, is every stakeholder on board with the change?

Second, do you have the right people in place to make automation work? Crucially, this means hiring competent managers with a sound understanding of business strategy as well as PhD data scientists. Don’t be afraid to hire people without decades of experience in the industry. Genuinely groundbreaking advances are being made every day and young, talented individuals might be better placed to adapt to what is likely to be a constantly shifting landscape than their more experienced peers.

Finally, you should ask if anything is being lost in the transition from human workers to AI. This is particularly pertinent in customer-facing roles where even the most sophisticated AI are a long way off being capable of answering hyper-specific service queries let alone being able to offer empathy when the situation demands. Remember, just because it’s possible to automate a process, it does not mean that you should.

Consider a restructure

While the usefulness of the technology is not in doubt, the extent to which it can be seamlessly integrated into the operations of any particular organisation is a far more complex question. Here, business leaders have to be careful to ensure a clear chain of command so that people know who has ultimate responsibility for any work carried out by ‘digital robots’.

Just as chief innovation officers have become boardroom fixtures, expect more companies to employ a Chief AI officer (CAIO) to oversee everything from the hiring of new data scientists to ensuring that ethical and regulatory obligations are adhered to.

Some larger corporations might benefit from having an entire department devoted to AI while others would do better with a CAIO placed in every department to ensure that the entire business is working towards the entire organization’s AI objectives.

Take steps to eliminate bias

Perhaps the most important business decision that arises as AI grows in significance is the nature of the personnel tasked with developing new AI-enabled software. As someone who works in the AI industry, I know that the key to being a successful company is the ability to adapt to new challenges as they arise, and no challenge is greater when it comes to AI tools than that posed by bias.

This is because AI systems are at risk of inadvertently replicating the biases of their creators. If development teams share the same, male, perspective then the tools they create will at best, be limited and at worst, extend gender discrimination into the digital arena.

This flaw was painfully exposed at one of the biggest technology companies in the world after Amazon employed an experimental AI-driven hiring tool in order to efficiently review resumes. However, Amazon found that the hiring algorithm was systematically discriminating against female candidates, partially because they didn’t use forthright verbs like ‘captured’ and ‘executed’ as frequently as male applicants. Essentially, the algorithm began to privilege male traits based on the resemblance resumes from men bore to the resumes of previously successful applicants who were overwhelmingly male. The system even went as far as to penalize applicants for attending an all-women’s college or for including the word “women’s” as in “women’s chess club captain” in their resume.

However, the problem of gender bias isn’t confined to pre-existing issues contained within historical data. All-male developer teams are liable to inadvertently create ineffective AI on account of their failure to pre-empt how their algorithms might produce discriminatory outcomes. It goes without saying that to prevent an issue from having dire consequences you have to be aware it exists to begin with.

The best way to prevent this type of bias from infecting AI technology is to involve people from underrepresented backgrounds at every stage of the development cycle. Diverse and representative development teams are better able to pre-empt problems that otherwise wouldn’t be evident until the system had begun discriminating against people in the real world. Therefore, having gender balanced teams is a necessary step in tackling inadvertent biases.

Embrace the new

For any business leader, the road ahead is bound to be uncertain. However, this is also an exciting time in which technological and strategic innovations brought about by AI will create new opportunities, provided your organisation is well placed to take them.

Importantly, the challenge presented by AI isn’t something you can simply throw money at. It has to be managed sensitively with particular attention paid to the personnel you recruit to manage and facilitate the transition. Moreover, it’s important that entrepreneurs and industry leaders don’t neglect the very human skills and instincts that are required to succeed in business. While new technologies are always providing new ways to save time and money, AI should be seen as a tool that can be deployed a bigger objective, not a solution in of itself.  Ultimately, AI has so much to offer to businesses across all industries but first, industry leaders and entrepreneurs have to stop fearing change and start embracing it.

Read more: What’s Missing in Silicon Valley Models for Digital Transformation Programs


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