Artificial intelligence projects are a top priority for many companies, but there are plenty of potential pitfalls for the unwary.
Despite years of hype (and plenty of worries) about the all-conquering power of Artificial Intelligence (AI), there still remains a significant gap between the promise of AI and its reality for business.
Tech firms have pitched AI’s capabilities for years, but for most organizations, the benefits of AI remain elusive.
It’s hard to gauge the proportion of businesses that are effectively using artificial intelligence today, and to what extent. Adoption rates shown in recent reports fall anywhere between 20% and 30%, with adoption typically loosely defined as “implementing AI in some form”.
A survey led by KPMG among 30 of the Global 500 companies found that although 30% of respondents reported using AI for a selective range of functions, only 17% of the companies were deploying the technology “at scale” within the enterprise.
But what all the reports point to is that businesses’ interest in AI is growing. According to research firm Gartner, the number of companies implementing AI-related technologies in the past four years has grown by 270%.
“It’s not an issue of awareness, I can assure you,” says Johan Aurik, partner at global strategic consulting firm Kearney, told ZDNet. “When you talk to executives, it’s evident that they all go to all the AI conferences, they all read about it, they are all aware of what the technology can do.”
“Everyone talks about it, but no-one’s actually done it,” Aurik said.
The promise of AI is certainly tempting. The much greater growth that the technology can accomplish has been extensively endorsed by experts, to the point that it would be difficult for any executive to remain unaware of the hype.
One application of AI that’s already well documented is the use of machine learning for marketing and sales, which analysts estimate could generate up to $2.6 trillion in value worldwide. Using AI, businesses can gain much better insight into customer behavior to design personalized offers. Brick-and-mortar shops’ sales could increase by up to 2% as a result.
Kellogg Company, the American food manufacturer responsible for your morning Coco Pops and midnight Pringles, is pioneering the use of AI to gain further insights into behavioral science.
Together with Qualcomm, Kellogg has developed eye-tracking technology embedded in virtual reality (VR) headset, which picks up on customers’ behaviors when they are shopping. As users browse a simulated store, the device gathers data about the products that attract their attention or where they gaze the longest data that are then fed to machine-learning algorithms to understand what triggers a buying decision.
Stephen Donajgrodzki, director of behavioral science at Kellogg, explained at a recent conference in London that AI helps his team understand exactly why people act; and once the behavioral mechanisms of customers are crystal clear, it becomes possible to influence their buying decision.
“AI and data are really useful for understanding what we should be looking at, hypothesizing on where we should create change, and then evaluate that change,” said Donajgrodzki. “We are 400 times more likely to change someone’s behavior if we understand the specifics of that person’s behavior.”
Case in point: Kellogg tested the new VR headset for a pilot trial when the company launched a new Pop-Tarts Bites product, with conclusive results. The algorithm determined that, contrary to popular belief, the best place to display the new product in the shop was on lower shelves a recommendation that led to an 18% increase in sales during the trial.
“These systems are helping us reach specific targets more easily,” said Donajgrodzki. “As soon as you have a better overview of the overall situation, you get a much better idea of how to change individual behaviors.”
It’s not only about managing human actions. In data-heavy industries like manufacturing, AI’s ability to process and analyze huge quantities of information also holds the promise of better efficiency all the way through the supply chain.
Take the oil and gas industry, where information pours in every day about pipelines, offshore assets, reservoirs, fields and wells, and so on. Much can be gained from a technology that can accurately process and interpret data in real-time; for example, anomalies or breakdowns can be detected faster and enable predictive maintenance.
Chevron, in fact, is the latest energy giant to have made the news after announcing a new initiative together with Microsoft, to build a cloud-based platform leveraging data analytics to monitor and optimize field performance.
Despite the evidence that AI brings growth, Kearney’s Aurik stressed that such examples are far from being the norm across industries. “It does differ across sectors, with energy and medical companies increasingly adopting AI,” he conceded, “but those are exceptions. The bulk of established companies are still running on Excel. Most industries’ financial services, consumer goods are very limited in their deployment.”
Fear is commonly designated as the main reason that businesses are reluctant to take up AI, with the narrative that ‘robots will take our jobs’ understandably putting off CEOs and their workforces. Yet polls tend to show that the ‘fully automated labor’ alarm bell is both unjustified and losing traction. A recent report found that up to 87% of organizations are planning to increase or maintain employee numbers after the adoption of automation.
Another issue is the question of definitions. AI can cover a very broad range of technologies, ranging from the data-crunching of machine learning right up to cutting-edge work on artificial general intelligence a.k.a. machines that think like humans. That’s a pretty broad area of technology. When you throw in a bunch of tech companies and start-ups keen to make an impact by rebadging their efforts as ‘AI’ to attract zeitgeist-hunting customers, then the true state of AI usage becomes even harder to measure.
Another major barrier to the take up of AI is the lack of skills, which over half of the business leaders name as the top challenge to adoption.
Mark Esposito, professor of business and economics at Hult International Business School as well as Harvard University, explained that bridging the gap between hype and reality is indeed giving those businesses a hard time. “There was a first trend, where organizations thought of AI as a proof of concept,” Esposito told ZDNet. “The problem now is that a number of them can’t convert their idea into code and algorithms, and implement them alongside their existing infrastructures.”
Among the selection of Global 500 companies surveyed by KPMG, the five businesses with the most advanced AI capabilities have, on average, 375 full-time employees working on the technology. They are spending about $75 million each on AI talent, and expect the bill to grow over the next three years.
Needless to say, this level of investment in new resources is beyond the reach of most organizations.
But even when they do secure the appropriate funds and workforce, businesses still seem to be stumbling their way through the deployment of AI. When it comes to applying the technology to real-world use-cases, organizations lack a clear strategy and ultimately, are incapable of making the most of the AI opportunity.
“Businesses tend to think of AI as a single, off-the-shelf technology,” said Esposito. “It’s not. The only AI that works is bespoke AI. The magic of technology happens when the objective is very narrowly defined.”
“You can’t engage with it by saying you want to digitize or improve efficiencies that’s not narrow enough. There is still a lack of clarity in what companies are trying to do with the tool.”
For Kearney’s Aurik, too, most organizations’ first mistake is to misplace their expectations of AI. Cost reduction? Efficiency? That’s too easy, according to the consultant. “Sure, you can make that business case easily,” he said, “but it’s a missed opportunity. The value of AI is not in transactional or efficiency initiatives. It’s in extending or growing new business lines.”
In other words, think bigger and some entrepreneurial-spirited business leaders are starting to. James Lee is one of them. After 20 years working as a professional lawyer, Lee realized how AI could fit into the legal sector, as a way to streamline the so-called ‘early discovery’ phase of a proceeding the time-consuming, meticulous task of going through a complaint to gather the information needed to draft the first response.
Lee found out that he could train IBM’s Watson model with thousands of lawsuit complaints and responses to it, to quickly pinpoint the entities and relationships that would be crucial to writing an early-phase draft. What would take a human worker up to eight hours of labor, he said, could be done in a couple of minutes thanks to the new technology.
“At first, I was just trying to figure out where the pain points were,” Lee told ZDNet. “And then I discovered this unbelievable opportunity. It’s a game-changer: apply that kind of efficiency to a whole portfolio, and the gains can be huge.”
Lee founded his own company, LegalMation, to start selling the technology to corporate legal departments and law firms, aimed at using AI to fix a specific problem that he had spotted.
For Aurik, the lack of imagination is effectively what is currently holding back AI. “The irony is that, for AI to contribute more, we need humans to come on board,” he said. “The technology is there, but now we need humans to use it in a creative way.”
It’s all well and good to call for creativity and imagination, but that doesn’t resolve the question of how to get started with AI in a way that businesses can actually engage with. When it comes to giving practical advice to CEOs, however, both Aurik and Esposito agree: think big, yes but start small.
While Esposito suggested conducting pilots in small environments, Aurik recommended finding a specific business case where there’s money to be made “I don’t care what it is, make it a merger, a new market, anything” before slowly gaining momentum and “working your way up”, step by step.
Beginning with a contained use case will not only ensure that value is successfully generated down the line. As Esposito pointed out, it’s also the only way to incorporate good business practice from the earliest stages.
“Businesses are even more reluctant to adopt AI when they can foresee issues with transparency, compliance, privacy, security, and so on,” he explained. “If you start with a pilot, with all these ethical requirements in mind, and then scale it, it becomes business practice.”
Ethical AI, responsible technology, unbiased algorithms: there is mounting concern in the technology industry with ensuring that innovations don’t spiral out of control. In this context, it’s easy to see why many businesses may be reluctant to deploy an algorithm that could grow into an irresponsibly powerful and opaque tool.
In a survey, Gartner found that concerns with data scope or quality are the third biggest challenge to adopting AI identified by respondents because unreliable data is certain to generate bias and undermine trust.
“People are worried about privacy and confidentiality, and it doesn’t help with the desire to engage with the technology,” Aurik admitted. “That’s why getting the strategy right from the very start is crucial. It’s where all those tech giants got it wrong, and why there is so much backlash now.”
Whether it’s through money, feature updates, or even through the creation of pretty unsuccessful AI ethics committees, some corporations are fixing the ethical mess they have created after the damage has been done. And researchers have noted that although such companies are now pushing their efforts in the field of ethics, the notion of responsible tech ultimately challenges “the core logic” of their business model.
But it doesn’t have to go this way and if companies incorporate ethics in their strategy early enough, it won’t. “It’s not easy at all,” said Esposito, “but if you start with a global project, you can be certain that there will be loopholes. So you have to scale it down.”
So, to AI or not to AI? In fact, it doesn’t look like businesses are going to have much of a choice. Aurik stressed that it’s in no-one’s interest to miss out on the revolution that algorithms are about to bring. “If you don’t embrace it, you’ll be out of business,” he said.
Aurik predicts that it will be another few decades before the technology is fully deployed. But, at least at the enterprise level, it seems that another ‘AI winter’ is not on the cards. Time to spring into action.
Source: ZDNetRelated posts: