AI Has a Foothold in Business, Now for the Next Steps
Everyone's talking about AI, but many companies have trouble getting their efforts off the ground. In a recent survey and brief, EY pinpoints the challenges and opportunities.
AI is seeping into different industries, slowly remolding the global competitive landscape. However, most business leaders still don't know how machine intelligence will impact their businesses.
EY recently published a brief, which focuses the current state of AI. We interviewed Nigel Duffy, EY Global Innovation AI leader who co-authored the document with Chris Mazzei, EY Global Innovation Technologies Leader and Global Chief Analytics Officer.
The brief frames the current state of AI well: "Most organizations aren't exploiting the potential of AI; they are just at the beginnings of their AI journeys. What should be holding companies back is a lack of talent, but it's actually a lack of understanding of what's possible – particularly at the top of large enterprises."
Addressing the C-Suite disconnect
It's often hard to imagine the impact new technologies will have on a business. Granted, AI is not new; however, due to recent research and developments, it's finally at a point where more organizations are either using it in production or experimenting with it.
Some say AI is at an inflection point, namely, at the beginning stages of exponential "hockey stick" growth. If that's true, the latecomers may find themselves blind-sided by competitors, simply because they didn't think about and learn first-hand how AI would affect their own companies.
According to Duffy, part of the confusion stems from the fact that AI is a broad set of technologies as opposed to a single, coherent capability. Given the complexity of the landscape (machine learning, computer vision, natural language processing, deep learning, neural networks, etc.), it's not surprising that business leaders don't have a clear understanding of how it will transform their businesses.
Also, the hype about AI is skewed. When new technologies hit the scene, evangelists and the media tend to focus on the opportunities and disregard the potential challenges. These skewed views fuel silver-bullet belief systems when silver bullets do not actually exist. It takes hands-on experience, including successes and failures, to truly understand the potential and limitations of a technology as applied to a specific business.
"It goes without saying that AI has the potential to completely transform business. I recently spoke on a panel about this topic at Fortune Global Forum in China, and everyone there, from prime ministers to chairmen of Fortune 500 firms, discussed the transformational potential of AI," said Duffy. "There is a broad understanding that it is going to be transformational, but the challenge is that it requires work and investment to develop the strategy [and] vision to realize that potential."
Many organizations are in the early stages of AI adoption, so they have not yet invested sufficient time and money in the process. In order to bridge this gap, leaders need to start gaining experience now, developing initial use cases or proofs of concept. Duffy recommends investing in a big-picture strategy, and developing a vision for how this could transform a firm or sector.
AI is more than a technology
AI is a piece of the digital transformation puzzle. As with all things related to digital transformation, technology is only part of the picture. The most effective strategies focus on business problem-solving.
"I believe companies will have the most impact with a business-first, value-led approach," said Duffy. "The best way to approach AI is to focus on how to add value to a business beyond just cost efficiencies. Businesses must think now about AI from a strategic perspective and ask themselves how much more value they can deliver through more intelligent use of AI."
Nigel Duffy, EY
Of course, there are some barriers to adoption that are technology-related. As is typical in the early adoption stages of a technology, the initial tools tend to be targeted at a narrow, technical audience that is capable of using them. However, as the technology matures, easier-to-use tools follow and abstract the some of the complexity. Usually those tools are aimed at "power users."
Finally, becomes easy enough for the masses to take advantage of, such as analytics dashboards in the enterprise. Already, AI is built into and will be embedded in many kinds of devices and software, to the point where it is transparent to the user. For example, one does not have to be an AI expert to use Amazon Echo.
"AI will create winners and losers in every industry," said Duffy. "AI is here today and can provide significant value now. Can you really afford to be slower to adopt it than your competitors?"
Business leaders and organizations should get familiar with AI technology now, because it will make it easier to determine where AI can be used as an effective problem-solving solution, Duffy said.
Business leaders and technologists need to work together. EY does this internally to meld cultures and disrupt traditional ways of thinking.
Set reasonable ROI expectations
In the AI brief, Duffy and Mazzei say, "Many early projects will have low ROI and a limited impact." So, at what point, then, should businesses invest in AI? On one hand, the early adopters gain insight and experience that those sitting on the sidelines miss. On the other hand, those who are later to the game have the luxury of using more mature toolsets and learning from others' mistakes.
"Early adoption doesn’t necessarily have a low ROI. [To clarify,] the early adopters are often focused on the technology rather than the business problem – this can lead to low ROI," Duffy said. "However, [early adoption] does lead to invaluable learning."
Early technology-led projects may also have low ROI because they are (and should be) as much about learning as about value. Rather than limiting the scope to only technology-led projects; businesses should identify projects based on their business value and have them led by business stakeholders.
"Because of the transformational potential of AI, if you wait and your competitors don’t, you will be at a disadvantage. AI will differentiate between winners and losers, and the pace at which that is happening is only accelerating," said Duffy. "Most people are early in their AI journey and the actual investment can be small relative to the potential. It’s a smart decision to make a relatively small investment to start."
Overconfidence can be dangerous
The immense interest in AI is creating career opportunities and with it overstatements about qualifications. Duffy said it's important to get the right talent.
"The Dunning–Kruger Effect is of significant concern in this space, that is, people can be unskilled and unaware of it," said Duffy. "The field has grown so rapidly that there are many people who can solve technical problems, but they have a lack of deep experience."
EY conducted a survey of 200 senior AI professionals, 56% of which said that a lack of talent is the greatest barrier to implementation within business operations. If companies don't have competent AI professionals, they face three big risks that are easily preventable if business leaders think about them in advance and do something about them proactively.
The first is testing. It’s much easier to get AI testing wrong compared to other technologies. Getting it right requires a certain amount of sophistication, as there are many subtle statistical issues. According to Duffy, AI testing requires talent deep expertise, working with this type of technology.
[Maybe it's time for your organization to make a real investment and commitment to AI. Read more here.]
The second challenge is that machine learning can amplify bias, which was another one of the key takeaways from the recent EY AI survey. Forty-one percent of the survey participants said they see the gender diversity of existing AI talent influencing machine biases. Researchers need to be especially mindful of bias, specifically, racial, gender or other cultural biases. To proactively avoid those types of bias, organizations will need to ensure that they're hiring from a diverse talent pool when hiring AI talent.
Finally, AI tools are making automated decisions, quickly. A sophisticated monitoring system needs to be in place to ensure that anomalies are caught quickly, Duffy said.
How to ask the right questions
Business leaders who lack experience with AI may wonder how it's possible to know whether they're asking the right questions in the first place.
"Some of this is about building up experience over time, which reflects back to my point about how it’s better to be an early adopter. You start asking the questions, seeing the answers, and seeing the outcomes that lead to asking better questions,"' said Duffy. "By starting soon, leaders can get experience in determining how AI can have the most meaningful impact on their business."
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Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio
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