Despite a tough year for many, US companies are accelerating plans to implement artificial intelligence (AI). A quarter of the companies participating in our latest AI survey report widespread adoption of AI, up from 18% last year. Another 54% are heading there fast. And they’ve moved way beyond just laying the foundation. Many are reaping rewards from AI right now, in part because it proved to be a highly effective response to the challenges brought about by the COVID-19 crisis. In fact, most of the companies that have fully embraced AI already report seeing major benefits.
Too many AI investments end up as “pretty shiny objects” that don’t pay off. Most companies have yet to adapt talent strategies, organizational structures, business strategies, development methodologies and risk mitigation for a world that moves at AI speed.
So there’s work to be done, but the reward can be concrete benefits today and the foundation for success tomorrow. As we’ve done for the last four years, we’ve made key predictions informed by our survey of more than 1,000 executives (including over 200 CEOs) at US companies that are using or considering AI. Together, these insights should help your company navigate the top AI trends it will face in 2021 and beyond.
This trend is crystal clear. US companies are ramping up their AI investments. Fifty-two percent of our survey respondents have accelerated their AI adoption plans in the wake of the COVID-19 crisis. The results will be felt for years to come. These “accelerating” companies cite their top changes as new use cases for AI (40%) and increased AI investments (also 40%). Of all the participants in our survey, 86% say that AI will be a “mainstream technology” at their company in 2021.
AI is paying off in concrete ways, with benefits ranging from revenue growth to better decision-making and improved customer experience. In fact, the companies in our survey that have rolled out AI enterprise-wide are more optimistic about growth despite the pandemic: 25% expect to increase revenue, compared with 18% for all companies.
The future payoff is even greater and could give early adopters an edge that lagging competitors may never be able to overtake. AI leaders are building a virtuous cycle, sometimes called a flywheel: AI leads to better products, increased productivity and superior customer experiences. That leads to more customers who share more data. More data leads to smarter AI algorithms, creating still better products and experiences that attract more customers who share more data, producing even smarter AI. For evidence that this flywheel exists and is turning fast, consider how those companies that have fully embraced AI are already seeing far more benefits than those still seeking to get their AI up to speed.
Yet attaining this virtuous cycle isn’t easy. When considering not just the benefits but the costs, 76% of organizations are barely breaking even on their AI investments. Breaking even isn’t necessarily bad for an investment that could be the foundation of your company’s future. But it’s possible to invest smarter, for better returns right now and long into the future.
The fastest way to get return on investment (ROI) is to use AI’s advanced automation capabilities to improve efficiency and productivity. Understandably enough — who doesn’t like fast ROI? — that’s the top goal for AI strategies.
But increased innovation and revenue growth are also rising in importance, and that requires making AI an ally in strategic decisions. Fifty-eight percent of our survey respondents have increased investments in AI for workforce planning, 48% are ramping up investments for simulation modeling and supply chain resilience, 43% are upping investments in AI for scenario planning and 42% for demand projection.
Together, these investments can make AI a strategic ally, closing the gap between idea and execution to drive faster and better decisions. The COVID-19 crisis prompted many businesses to accelerate this more advanced use of AI, and it has provided a major payoff during the pandemic — a payoff that should continue long into the future. With the right data and models, AI can sense coming changes in your markets and risks to your supply chain. It can think through options for your investments, workforce and go-to-market strategies. It can help you decide and act, all while continually monitoring and improving its own performance. This dynamic sense, think, act approach to strategy, which AI makes possible, is within reach today.
Make strategy a game.
AI can game out different scenarios for your company, modeling future conditions and their likely impact. It can also assess different responses (whether in workforce, supply chains or go-to-market) that are likely to work. The results will be data-driven strategic decisions, even in highly uncertain situations.
With AI constantly ingesting data and delivering strategic forecasts and models, you need to be a strategic executor. Rethink and fine-tune your strategy continuously — not just once a year.
The good news about AI’s risks? Companies are aware of them. The bad news? Most are not actually mitigating them. When we asked our survey respondents for their top-three priorities for AI applications in 2021, the top choice (picked by 50%) was responsible AI tools to improve privacy, explainability, bias detection and governance. But when it comes to action, only about a third reported plans to make AI more explainable, improve its governance, reduce its bias, monitor its model performance, ensure its compliance with privacy regulations, develop and report on AI controls, and improve its defenses against cyber threats. In the case of explainability, companies have even taken a step back compared to our 2020 survey.
Responsible AI is the only way to mitigate AI risks. When you use AI to support business-critical decisions based on sensitive data, you need to be sure that you know what AI is doing and why. Is it making accurate, bias-aware decisions? Is it violating anyone’s privacy? Can you govern and monitor this powerful technology?
AI’s data, technology and talent tend to be highly distributed across different functions and multiple third parties. You have to keep an eye on AI (and its data) from the beginning of model design through development, deployment and ongoing adjustments — because AI keeps learning and changing itself. Adding to the challenge: AI is a complex technology that many executives, including risk officers and even IT experts, don’t yet fully understand.
If your company is using AI, you need to make it responsible — right now.
Assess your risks and establish a plan to test and monitor.
Take a close look at how AI affects your financial, operational and reputational risks wherever you (or your partners) are using it. Update controls around its use accordingly, making sure they cover every stage of the AI life cycle — to support trust in your AI program.
Since AI keeps learning and changing itself, your governance has to function at AI speed. Your responsible AI toolkit must be always-on, always monitoring model performance, potential for bias and new sources of risk — and always adapting.
Upskilling is necessary, but it’s not nearly enough to match the demands of an AI-centered workplace. Net job growth is predicted to be a long-term impact of AI, but these jobs will be different from the ones that have existed in the past. Business leaders need to reevaluate exactly what they’ll need from this future workforce.
Many new jobs will affect your tech teams, and team members will need to adapt by learning new ways of working and thinking. AI model development is very different from software development. Software is usually rules-based and typically follows unchanging rules to turn data (such as invoices) into output (payments). An AI model, on the other hand, is constantly changing and works with probabilities, not certainties. It might look at both data and output to continuously adapt to new vendors and new invoice formats, and then adjust its own rules to predict the probable size of future invoices.
Ever-changing, continuously learning AI means that agile software development, with its linear, iterative approach and rigid handoffs, won’t work. Instead, AI teams have to be constantly testing, experimenting and learning — like scientists. With time, this approach will have to guide not just your AI and technology teams, but also your entire workforce. Your company can get there, but it has to act now.
Hire for the hottest jobs of the year.
If your company is building its own AI, it needs machine learning and model ops engineers with skills that cross software engineering and data science. ML engineers help integrate, scale and deploy models. Model ops engineers monitor and improve post-deployment model performance and stability.
Democratize with care.
You should certainly democratize AI to reduce rote work at all levels and increase innovation with plug-and-play AI tools. But to make AI democratization work, you’ll need to offer appropriate training and governance. You’ll also want to limit democratization for more sophisticated and risky models and use cases to data scientists and data engineers.
The top choices for 2021 AI and analytics priorities all — inevitably — have one thing in common: They cross the entire organization. That’s because AI does too. Unless your company is already effectively sharing data, subject matter expertise, governance and AI models across teams and functions, you’re going to have to reorganize so that you can collaborate as needed.
AI reorganization goes beyond breaking down silos. It also requires a cultural shift so that everyone’s decisions become more based on data — and the simulations and forecasts that AI produces from that data. It also requires integrating machines that think and learn — and teach themselves to learn even better — into your organization. When AI models are constantly improving themselves, your decisions can constantly improve as well. Your company will need to be ready to pivot quickly, not on a yearly planning cycle, but few organizational flow charts are currently set up for that kind of speed.
This organizational transformation may sound like a tall order, but it needs to happen. Our survey results show this is the case, because the easiest AI and analytics application — automating routine tasks — is no longer a top priority for many businesses. This year only 25% cited it as a top priority going forward. In last year’s survey, 35% did. This drop is certainly not because automating routine tasks isn’t a highly profitable use of AI. It is. But many companies have already advanced well beyond that point and their current priorities are more strategic uses of AI, for which reorganization is inevitable.
Bring the three A’s together.
When AI, analytics and automation are part of a unified effort — either through a centralized hub or centralized governance — you increase your ability to monetize data, build a data-driven culture and reduce risk along the way.
Pick the platform.
Give AI the technical foundation it needs, including a platform architecture suited to your unique data sources, business processes and use cases. Some businesses will want to build this platform in-house. Others will find it more cost-effective to rely on third-party providers.
PwC’s annual AI Predictions survey, now in its fourth year, explores the activities and attitudes of US business and technology executives who are involved in their organization’s AI strategies. Among this year’s 1,032 survey respondents, 71% have C-suite titles and 25% are from companies with revenues of $5 billion and up. They represent industrial products (20%), consumer markets (20%), financial services (18%), tech, media and telecommunications (17%), health industries (17%), and energy, utilities and mining (8%). The survey was conducted by PwC Research, PwC’s global Center of Excellence for market research and insight, in October 2020.