Corporate executives seem to be experiencing an acute reality check heading into 2020 when it comes to plans for implementing artificial intelligence solutions, judging by one study.
Only 4% of 1,062 respondents to a PricewaterhouseCoopers survey said their companies plan to deploy AI “enterprise-wide” next year. In PwC’s similar survey released last year, five times as many participants (20%) said they were expecting to deploy AI at scale in 2019.
“Plan to deploy enterprise-wide” was among five answer options for 2020 AI plans from which participants could choose. The others were “investigating use” (42%), “pilots within discrete areas” (23%), “already implemented in multiple areas” (18%), and “plan to deploy in multiple areas” (13%).
What happened? Isn’t AI supposed to be a colossal value generator in the coming years? PwC’s own research claims that AI will generate about $16 trillion of incremental value by 2030.
The primary reason for the present retrenchment, according to PwC, is that companies are realizing they need to focus on getting a handle on AI fundamentals before enlarging AI projects.
Companies are also becoming aware that they’ve placed insufficient emphasis on cleansing and labeling data that AI solutions would use, says PwC, which warned about that problem in its 2018 survey report.
PwC created a list of five AI-related priorities that companies should consider for 2020, opining that those that do so will be better positioned for a transformative payoff in the years ahead.
1. Get on Board with Boring AI
Much of the excitement next year will come from incremental productivity gains for in-house processes, PwC predicted, acknowledging that these “may sound mundane.”
For example, 44% of survey participants cited “operate more efficiently” and 42% cited “increase productivity” among the top three benefits they’re expecting from AI investments in 2020.
“Companies can see remarkable savings from (for example) using AI to extract information from tax forms, bills of lading, invoices, and other documents that typically require long and tedious hours of human work,” PwC said in the report.
Managing risk, fraud, and cybersecurity, supporting decision-making, and gathering forward-looking intelligence (cited as top AI capabilities by 38%, 31%, and 30% of respondents, respectively) are “great examples” of how AI can augment complex processes.
“This kind of ‘practical AI’ — ranging from chatbots to recommendation engines and advanced modeling methods for business processes and better decision-making — will become more widespread,” PwC wrote.
2. Rethink Upskilling
According to PwC, the “old kind of upskilling” — offering learning opportunities focused on a siloed technology — is not enough to get employees or the company ready for AI at scale.
Beyond training courses, companies should provide immediate opportunities for people to apply what they’ve learned, so that knowledge turns into real-world skills that improve performance. This “creates a digital, AI-ready mindset that focuses on lifelong learning and cross-functional ways of working and problem-solving,” PwC said.
Companies also need to pursue cross-skilling, or giving specialists in one area (such as data science) enough basic skills in another (such as the business) so they can speak the other’s language.
“Such cross-skilling is critical not just for collaborating on AI-related challenges, but also for deciding which problems AI can solve,” the report says. “Your teams should by ‘multilingual,’ integrating multiple tech and non-tech skills. That helps non-tech employees come up with tech solutions and tech employees come up with business solutions.”
3. Lead on Risk and Responsible AI
Ominous headlines about AI’s dark side seem not to have fazed corporate executives: 85% of those surveyed said their companies are taking sufficient measures to protect against AI’s risks.
“However, this finding suggests an underappreciation for the true challenges and effort needed to responsibly capitalize on AI,” PwC said.
Only about a third of respondents have fully tackled risks related to data, AI models, outputs, and reporting, according to the report. “Considering the growing public concern over issues such as bias in algorithms or facial recognition tools, and AI-powered ‘deepfakes,’ that’s not good enough.”
Further, with AI “increasingly present (and often invisible) in everyday business processes and vendor-supplied solutions,” rigorous AI risk management is increasingly critical, the report warns.
Encouragingly, most survey respondents said they have company-wide AI governance, whether through a new and specialized AI center of excellence (18%), an organization-wide AI leader (16%), outside providers (16%) or an existing automation group (15%).
4. Operationalize AI — Integrated and At Scale
“AI doesn’t do its best work when it’s isolated from other technologies, or when it’s siloed in a lone function or business line,” PwC wrote. “First of all, AI needs data, and as it gets more quality data from more sources, it gains power.”
Also, some of AI’s most valuable uses come when it works 24/7 as part of broader operational systems, such as marketing or finance. “AI leaders are therefore operationalizing AI, across multiple functions and business units, in full integration with broader automation initiatives and/or data analytics.
PwC admonishes companies that AI development is very different from software development and requires a different mindset, approach, and tools. “Whereas software development is based on rules of coding, AI model development requires a ‘test and learn’ approach, in which the algorithms are continually learning and the data is being refined,” the report says.
5. Reinvent Your Business Model
While getting AI technology right is not simple, it’s actually the “easy part,” PwC says. The top challenges aren’t moving AI initiatives from pilots to production or managing AI’s convergence with other tech.
Instead, the top challenges are business- and people-oriented: measuring AI’s ROI, getting a budget approved, and training employees to use it.
“These challenges reveal why some companies may be scaling back company-wide ambitions in 2020,” the report says.
Measuring and making the case may be difficult because AI usually delivers value indirectly, by helping employees and other technologies work better.
“It’s essential to treat AI not as a silver bullet or singular solution, but as part of your broader automation or business strategy,” PwC wrote.
“Depending on the business issue at hand, analytics or simpler forms of automation, such as robotic process automation, might be the best solution. Or there may be bigger strategic efforts in which AI is a great addition, particularly in looking at how to prepare your company’s workforce to be future-ready.”