Will 2019 be the year of AI? The stakes are considerable: It could contribute up to $15.7 trillion to the global economy by 2030. And organizations are mobilizing: 52% of executives globally say they are planning their investments and use cases, according to our Global Digital IQ Survey.
Last year, PwC offered eight predictions about how AI was likely to develop over the course of 2018, with implications for business, government, and society. The trends we identified — including AI’s true workforce impact, a call for all companies to focus on responsible AI, and emerging threats around cybersecurity — are even more relevant today. One prediction, in particular, that has come true is the increased focus on national competitiveness. It’s worth noting that to date, more than two dozen countries have released national AI strategies or are in the process of formulating them.
But, as we head into 2019, a different approach is needed. We’re not just highlighting what is likely to happen; we call out what business leaders must make happen with AI.
Our big prediction for the coming year? Those companies focusing their efforts in six key areas will be leading the pack this time next year.
1. Structure: Organize for ROI and momentum
If you’re serious about AI, formalize your approach and develop company-wide capabilities so successful (and smaller) projects can be replicated and built into a greater whole. This requires oversight from a diverse team that includes people who have business, IT, and specialized analytics and AI skills and represents all parts of your organization. Its responsibilities should cover business questions, such as what business metrics will AI impact most (e.g., cost savings, better experience, higher revenues), how to identify use cases and how to develop accountability and governance. It should establish and oversee enterprise-wide data policies. And it should determine technology standards, including architecture, tools, techniques, vendor and intellectual property management.
2. Workforce: Teach AI citizens and specialists to work together
Business leaders continue to wonder just what the impact of AI will be on jobs over the longer term. Estimates range widely, including those from PwC’s international jobs automation study, which put the short-term impact at less than 3 percent of jobs lost by 2020, but as high as 30 percent by the mid-2030s. Whatever the ultimate outcome, today the challenge is the AI skills gap: 72 percent of executives in our Global Digital IQ Survey rated their organizations’ AI skills as only slightly developed or not at all developed.
The answer is a workforce strategy that creates three levels of AI-savvy employees — and provides ways for all three to work together successfully.
- Citizen users: As AI spreads, most of a company’s employees will need training to become AI citizen users. They’ll learn how to use the company’s AI-enhanced applications, support good data governance, and get expert help when needed.
- Citizen developers: A more specialized group, perhaps 5 to 10 percent of your workforce, should receive further training to become citizen developers: line-of-business professionals who are power users and can identify use cases and data sets, and work closely with AI specialists to develop new AI applications.
- AI specialists: Finally, a small but crucial group of data engineers and data scientists will do the heavy lifting to create, deploy, and manage AI applications.
3. Trust: Make AI responsible in all its dimensions
Customers, employees, boards, and regulators are all asking the same question: Can we trust AI? How they’ll overcome that challenge depends on whether they’re addressing the five facets of responsible AI:
- Fairness: Are we minimizing bias in our data and AI models? Are we addressing bias when we use AI?
- Interpretability: Can we explain how an AI model makes decisions? Can we ensure those decisions are accurate?
- Robustness and security: Can we rely on an AI system’s performance? Are our AI systems vulnerable to attack?
- Governance: Who is accountable for AI systems? Do we have the proper controls in place?
- System ethics: Do our AI systems comply with regulations? How will they impact our employees and customers?
4. Data: Locate and label to teach the machines
AI answers the big question about data: how to turn it into value. But there’s a big problem: Too few companies are prioritizing data labeling. In the US survey, for example, less than one-third of executives say labeling data is a priority for their business in 2019. For machine learning to detect significant patterns in the present and predict the future, it must be taught. An AI CoE can create and monitor data standards, as well as develop systems and processes that make it easier for employees to create usable, labeled data sets for future use. Alternatively, companies can also adopt unsupervised learning, which does not require labeled data, but is generally more challenging to extract useful patterns.
5. Reinvention: Monetize AI through personalization and higher quality
Right now, the greatest gains from AI are coming from productivity enhancements, as businesses use it to automate processes and help employees make better decisions. But as our Global AI Study found, the majority of AI’s economic impact will come from the consumption side, through higher-quality, more personalized, and more data-driven products and services. AI is also being used to help guide some of these decisions by gamifying strategy.
Companies are also looking at how to find AI resources outside their organizations. Our 2018 Global Digital IQ Survey, for example, found that while just 8 percent of companies were making significant direct investments in AI, another 52 percent would pursue acquisitions or alliances.
6. Convergence: Combine AI with analytics, the IoT, and more
AI’s power grows even greater when it is integrated with other technologies, such as analytics, ERP, and the Internet of Things (IoT). Integrating AI with other technologies will become a focus, as organizations begin to put AI into production, deploying their machine learning models across the enterprise. Organizations that have invested in identifying, aggregating, standardizing, and labeling data — with the data infrastructure and storage to back it up — will be well-placed to reap the benefits of powerful technology combinations to change their businesses.
Ready for what’s next?
In 2019, it’s time to solidify your AI strategy. AI will need its own organizational structure and workforce plans; trustworthy algorithms and the right data to train those algorithms; a plan to reinvent the business to grow revenue and profits with AI; and convergence with other existing and emerging technologies.
For a more detailed discussion, see the full 2019 report here.