And such labeling is the foundation that AI needs in order to succeed. Survey respondents identified integrating AI and analytics systems as the top AI-related data priority for 2019.īut less than a third of the participants identified labeling data as a business priority for the coming year. “With interpretability, for example, you want to strike the right balance between performance, cost, a use case’s criticality, and the extent of human expertise involved.”
“As they continually test and monitor controls, these teams will have to consider appropriate trade-offs,” the report says.
Fairness: Are we minimizing bias in our data and AI models?.“How they’ll overcome that challenge depends on whether they’re addressing all facets of responsible AI,” the report says. Surveyed executives cited ensuring that AI systems are trustworthy as their top AI challenge for 2019. “Forging partnerships with colleges or apprenticeships is a good place to start.” “Upskilling can create citizen users and developers, but you’ll likely need to hire highly trained programmers and data scientists,” PwC counsels. In PwC’s survey, 31% of executives said they are worried about an inability to meet demand for AI skills over the next five years.
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They’ll learn how to use AI applications, support data governance, and get expert help when needed.Ībout 5% to 10% of the workforce should receive further training to become citizen developers: power users who can identify use cases and data sets and work with AI specialists to develop new applications.įinally, a small set of data engineers and data scientists will “do the heavy lifting” to create, deploy, and manage the applications. The answer is creating three levels of AI-savvy employees - “citizen users,” “citizen developers,” and “data scientists” - and providing ways for all three to work together successfully, according to the report.Īs AI spreads, most employees will need training to become citizen users. They could accidentally apply the wrong algorithms, with unintended results.”
It also should determine technology standards, PwC says, including architecture, tools, techniques, vendor and intellectual property management, and just how intelligent AI systems need to be.Īs the report notes, even user-friendly AI is complex: “Even with basic training, business people may not fully understand different AI algorithm’s parameters and performance levels. This group should determine how to identify AI use cases, develop accountability and governance, and establish enterprise-wide data policies. Rather, a company’s AI exploration and implementation should be overseen by a diverse team that includes people with business, IT, and specialized AI skills, representing all parts of the organization. That is, AI initiatives should begin neither with AI specialists nor with business leaders. Once that’s done, that programming can be relatively easily modified to speed up data extraction in other areas, including customer service, marketing, tax, and supply chain management.Īlso, PwC counsels, make sure to set the right AI foundation. “If you successfully apply them in one area of your business, you can usually use them in others,” says PwC.įor example, by automatically extracting information from invoices, even those that aren’t fully standardized, AI systems can automate the process to reduce costs and processing time.
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