There was a time when artificial intelligence (AI) had a menacing ring, but things have moved on. Chat GPT and generative AI apps are creating new roles or making some skills and careers obsolete, depending on who you listen to. Meanwhile, we see its benefits every day, like finding efficiencies and automating repetitive tasks.
AI could soon be essential to most businesses. But it’s not always clear how to use it.
Kaspersky’s new podcast, Insight Story unpacks emerging tech trends with global specialists and businesses successfully using the tech. In Episode 5, I speak with AI expert Dr. Romesh Ranawana (Sri Lanka,) customer loyalty consultancy Truth CEO Amanda Cromhout (South Africa) and Fabio Assolini, Head of Research in Latin America for Kaspersky’s Global Research and Analysis Team. We examine how business should approach AI and machine learning to make the most of the opportunity while staying secure.
Understanding what’s meant by AI
Romesh says we normally mean something more specific when we talk about AI. “All technology – be it Alexa, your car or washing machine – uses algorithms. An algorithm outputs something when you input something. For problems too big or hard to solve with software, a particular type of AI – machine learning – offers a new way to build algorithms, instead of a programmer writing lines of code.”
And this kind of AI is everywhere. “We hear much about how it’s changing e-commerce and video streaming, but nearly every industry uses AI – from healthcare to retail to factories. Many of the biggest brands, like Amazon and AirBnB, owe their success to it.”
AI in customer loyalty
Customer loyalty programs have long used AI to target customers with more relevant, personalized offers. Amanda’s company Truth specializes in these. “Data is fundamental to customer loyalty programs. Everything we know about a consumer – whether the information they give or gained through transactions – has proven power.”
UK grocery outlet Tesco’s Clubcard was one of the first schemes of its kind, with some 20 million members today. Amanda says their approach to data was groundbreaking: “Every quarter, Tesco sent out thank you vouchers with a small discount on your next shop. The vouchers were relevant to what you bought, and no set was the same.”
“It’s a value exchange,” says Amanda. Customers give their data because of what they get out of it. “Sometimes companies ask members or customers for data less overtly. Financial institutions may offer a reward to answer a questionnaire, creating a profile of your financial needs and propensity to borrow. They’re paying so they can get a higher response rate to sales. If done well, it’s win-win.”
Recent research, Global Data Privacy: What the Consumer Really Thinks 2022, found discounts and freebies were in the top three reasons consumers share data with brands.
Amanda also thinks some companies confuse using data well with hard-sell. “They’re not thinking through the value proposition. The younger generation especially knows their data’s worth. They’re asking, ‘What am I going to get for that?'”
Keys to success with AI
Most companies investing in AI aren’t getting immediate results. MIT research found only three in 10 companies investing in AI are getting a return on their investment.
Who succeeds depends on how they approach it, Romesh says.
Dr. Romesh Ranawana, AI expert
Companies leading with AI have continuous feedback loops – the AI operates, people give feedback and the AI adapts. They’ve understood it’s not something you set running and leave.
Dr. Romesh Ranawana, AI expert
He thinks why you’re doing it matters too – it should be about changing how the company does business, not simply making savings. “If you look at AI as a way to automate what you do now, you’ll get reduced costs but not much benefit. You need to mesh AI and people in your organization to generate value, so they’re learning from each other.”
Amanda adds, “Many companies want to flick a switch and, in two hours, make it happen. They don’t understand it has to be a strategic driver for the business.”
Romesh and Amanda both highlight good-quality training data as a critical success factor.
Romesh says, “Customers and their behaviors change – what you train today may not be relevant in a week’s time. AI systems must be continuously trained on real-time, up-to-date data. Companies need data collected continuously kept in a single space – we call it ‘the data lake.'”
Amanda Cromhout, CEO, Truth customer loyalty consultancy
The quality of the output is only as good as the quality of the data. If you don’t have good data, fabulous software can’t necessarily make sense of it.
Amanda Cromhout, CEO, Truth customer loyalty consultancy
Amanda also believes the C-suite must support an AI strategy, even lead it. “It doesn’t start with marketing executives wanting to do this – it starts at the top, with the CEO and the board.” But they shouldn’t expect too much, too soon. “The leaders of any company must understand it’s an ultramarathon, not a sprint.”
Romesh notes it’s becoming possible for more people to work with AI. “AI systems are still complicated. It takes knowledge, data engineering and software skill. Over the last few years, more tools have come out – low-code or no-code tools – making AI more accessible to more people.”
How AI is changing cybercrime
AI didn’t need to be more accessible for cybercriminals to use it, which has implications for business. In research with Kaspersky’s Global Research and Analysis Team, Fabio Assolini has seen several applications of AI in cybercrime. “AI is helping cybercriminals bypass new authentication methods. Some financial institutions and banking apps use biometric authentication, where the user must look to the right, to the left and smile. We’ve seen cybercriminals using AI tools to do this with only a victim’s photo.”
How businesses should respond isn’t straightforward – they need to identify more fraudsters while not misidentifying too many customers. “You can fine-tune authentication methods to better identify fraud attempts, but you may get many false positives. It’s a balance,” says Fabio.
Fabio recommends businesses ensure AI training data is ethically gathered and complies with privacy regulation in countries they distribute to. “Some free apps for adding special effects to pictures collect and sell data sets for training AI. There’s a privacy issue, especially in Europe with GDPR, but sometimes those collecting the data are in parts of the world without privacy restrictions.”
To comply with GDPR, AI systems that use personal data must be developed, trained and deployed with a clear objective determined at the design stage. It must be legitimate and compatible with the organization’s goals.
AI’s future in business
Amanda stresses the need for brands to understand what their data really means about every human behind it. “Data scientist Dr. Shorful Islam says, at the end of every data point is a human being we’re trying to persuade to do something differently, or trying to make their engagement with a brand more pleasurable, or whatever the desired outcome. So you’ve got to think about behavior more like human beings, rather than robots.”
Romesh sees AI leading to powerful change in consumer behavior. “We’re buying things we’d never have bought before because companies are pushing them to us. Amazon reports 35 percent of its revenue comes from recommendations.” And those features will only get cleverer. “Systems will become more powerful because they’ll have more data feeding in.”
Listen to the full Insight Story audio series on Podbean or your usual podcast provider.
