Navigation for a surprising pandemic side effects: AI whip

Amid the numerous trade disruptions caused by covid-19, here’s a largely overlooked one: artificial intelligence (AI) whipping.

When the pandemic began to sweep the world last year, companies have reached out to every tool at their disposal – including AI – to solve challenges and serve customers safely and effectively. In a 2021 KPMG survey of U.S. business leaders conducted between Jan. 3 and 16, half of respondents said their organization has accelerated its use of AI in response to covid-19 — including 72% of industrial producers, 57% of technology companies and 53% of retailers.

Most are happy with the results. Eighty-five percent of those surveyed agreed that AI was helpful to their organization during the pandemic, and a majority say it provides even more value than anticipated. More broadly, almost everyone says that a wider use of artificial intelligence would make their organization run more effectively. In fact, 85% want their organization to accelerate AI adoption.

However, the feeling is not entirely positive. Even as they try to weigh gas, 44% of executives think their industry is moving faster on AI than it should. More surprisingly, 74% support the use of AI to help businesses remain more hype-than-reality in key industries since our September 2019 AI survey. Both in financial services and in the retail sectors , for example, 75% of executives now feel that AI is overlapping, by 42% and 64%, respectively.

How do you square these views seemingly opposite to what KPMG calls AI whiplash? Based on our work helping organizations apply AI, we see some explanations on the hype. One is the simplicity of technology innovation, which has allowed for misperceptions of what can and cannot be done, how much it takes to achieve enterprise-scale results, and what mistakes are possible while organizations experiment with AI without the right foundation.

Although 79% of respondents say that AI is at least moderately functional in their organization, only 43% say that it is fully functional at scale. It’s always common to find people who think of AI as something to acquire — like a new piece of machinery — to provide immediate results. And while they may have experienced some success with AI – often small tests of concept – many organizations have learned that scaling them to the enterprise level can be more challenging. Requires access to clean and well-organized data; a robust data storage infrastructure; subject matter experts to help create labeled training data; sophisticated computer skills; and buy from business.

Of course, it’s not even a deal to believe that AI defenders have been able to exaggerate their potential from time to time or underestimate the effort required to realize their full value.

As for why leaders are conflicting as to the speed of AI adoption, let’s look at the basic human nature at play. For starters, it’s always easier to believe that the grass is greener on the other side. We also suspect that many people worry that their industry is going too fast especially because their own organization is not responding at that speed. If they have experienced early stage strains with AI – especially last year, when the world witnessed AI-enabled implementations such as the record development of covid-19 vaccines – it may have been easy to help those fears.

We see another factor leading to mixed feelings about the potential of AI – the absence of an established legal and regulatory framework to guide its use. Many business leaders do not have a clear vision of what their organization does to govern artificial intelligence, or what new government regulations might follow. Understandably, they are concerned about the associated risks, including the development of use cases today that regulators may squash tomorrow.

This uncertainty helps to explain yet another seemingly contradictory finding from our investigation. While business leaders generally take a skeptical view of government regulation, 87% say the government should play a role in regulating AI technology.

Transcending from AI whiplash

While every organization will need its game book to recover from the AI ​​blow and optimize its investment in technology, a complete plan must contain five components:

  • A strategic investment in data. Data is the raw material of AI and the connective tissue of a digital organization. The organization needs clean, machine-digestible data labeled to form AI models, with the help of subject matter experts. They need a data storage infrastructure that transcends the functional silos in the enterprise and can transmit data quickly and reliably. Once the models are distributed, a strategy and approach to collecting the data is needed to tune them and form them continuously.
  • The right talent. Computer scientists with expertise in AI are very demanding and difficult to find, but crucial to understanding the AI ​​landscape and guidance strategy. Organizations unable to build a full team of scientists internally will need external partners who can fill the gaps and help them solve the ever-expanding range of vendors and AI offerings.
  • A long-term AI strategy driven by the company. Organizations get the most out of AI by thinking about finding solutions to problems, not by buying technology and looking for ways to use it. They leave the company, not the IT department, to lead the agenda. When investments in AI linked to a business-driven strategy go wrong, they become opportunities to fail fast and learn, not fast and burn. But even when companies iterate quickly, they need to do so in line with an AI strategy in the long run, because the biggest advantages are realized in the long run.
  • Culture and upskilling employees. Few AI agendas take strength without buying from the workforce and a culture invested in AI success. Gaining employees ’commitment requires providing them with at least a rudimentary understanding of technology and data, and an even deeper understanding of how they and the company will benefit. Also important is the improvement of the workforce, especially where the AI ​​will assume or complement its existing responsibilities. Embracing a data-based mindset and instilling a deeper AI literacy into an organization’s DNA will help them scale and succeed.
  • A commitment to the ethical and impartial use of AI. AI holds great promise but also the potential to harm you if organizations use it in ways that customers don’t like or that discriminate against certain segments of the population. Each organization should develop an AI ethics policy with clear guidelines on how the technology will be implemented. This policy should send measurements and be part of the DevOps process to check for problems and imbalances in the data, measure and quantify unforeseen bias in machine learning algorithms, track the origin of the data, and identify those that form algorithms. . Organizations should continuously monitor models for bias and drift, and ensure accountability of model decisions are in place.

What comes next

Managers ’goals for investments in AI for the next two years will vary by industry. Health executives say their focus will be on telemedicine, robotic activity, and patient care delivery. In life sciences, they say they seek to roll out AI to identify new income opportunities, reduce administrative costs and analyze patient data. And government executives say their focus will be on improving process automation and analytical capabilities, and on contract management and other obligations.

Expected results also vary by industry. Retail executives anticipate the greatest impact in the areas of customer intelligence, inventory management and customer service chatbots. Industrial manufacturers see it in product design, development and engineering; maintenance operations; and production activities. And financial services companies expect to improve fraud detection and prevention, risk management and process automation.

In the long run, KPMG sees AI playing a vital role in reducing fraud, waste and abuse, and in helping companies refine their sales, marketing and customer service operations. Ultimately, we believe that AI will help address fundamental human challenges in diverse sectors such as disease identification and treatment, agriculture and global hunger, and climate change.

It’s an event worth working on. We believe that government and industry have roles to play in achieving this – in working together to formulate rules that foster the ethical evolution of AI without stifling innovation and momentum already underway.

Read more at KPMG “Prospecting in an AI World” Report.

This content was produced by KPMG. It was not written by the editor of the MIT Technology Review.

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