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Productivity gains and efficiency improvements top the list of artificial intelligence (AI) benefits, the AI priorities 2020 study from PricewaterhouseCoopers (PwC) has reported.
But out of the 1,062 senior executives who took part in PwC’s annual AI study, two-thirds did not put good data governance practices as a top priority for their organisations.
While the respondents rated data aggregation (45%), integration with analytics (45%) and using AI in internet of things (IoT) applications (43%) as their top three AI prerogatives for 2020, only a third put taking a comprehensive lifecycle approach to data as one of their top three priorities. Just 36% had the need to ensure that data meets regulatory requirements in their top three AI concerns.
According to PwC, the results show that AI leaders are operationalising artificial intelligence across multiple functions and business units, integrating fully with broader automation initiatives and data analytics.
In the report, PwC stated: “To solve these and other challenges as you make AI operational, it is critical to realise that while there are a number of ways that the software delivery life-cycle can inform AI development, it has many important differences and requires additional tools and a change in approach and mindset.”
However, although data is the key to operationalising AI, the study found that it is low on executives’ priority list. Only one-third of respondents said labelling data is a 2020 priority, and 13% view it as a key challenge.
“If you’re currently focused on bringing AI to a single function or process, it’s essential to begin cultivating secure, quality data from throughout (and outside) the organisation. Likewise, you need to build the skills and the enterprise-wide governance to use that data responsibly,” the report stated.
The study found that business leaders are also unfazed by the negative reports on AI bias. PwC’s research found that 85% of the executives actively working with artificial intelligence said their companies are taking sufficient measures to protect against AI’s risks.
However, PwC believes this finding may suggest an under-appreciation for the true level of effort needed to responsibly capitalise on AI. As the survey responses on data governance found, there’s still a long way to go for many business leaders working on AI initiatives, implementing controls around decisions or data.
Only around one-third of respondents have fully tackled risks related to data, while 35% are still exploring how to address errors in the outputs of AI-based decision-making.
PwC warned that rigorous risk management becomes more important with AI increasingly present – and often not immediately visible to users – in everyday business processes and in supplier-supplied systems.
The PwC report stated: “In our survey, the leading area that executives are working on is making AI interpretable and explainable. Half of them are taking steps around explainability for those building and operating the system, while a similar proportion are focused on explainability for those affected by the system.
“We also see companies beginning to realise that addressing larger issues around data and tech ethics requires collaboration with customers, industry peers, regulators and tech companies,” it said.
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