- Businesses are increasing their investment in AI in the wake of COVID-19.
- Three-quarters of businesses are not breaking even when it comes to their investments.
- To capitalise on their AI projects, companies should be aligning with five key trends.
Despite being a tough year for many, companies are accelerating their approach to artificial intelligence (AI). A quarter of the organisations that participated in the latest AI survey from PwC US reported widespread adoption of AI, jumping from 18 percent last year. Another 54 percent are heading there fast, and they’re no longer just laying the foundations. Those investing are reaping rewards from AI right now, in part because it has proven a highly effective response to the challenges brought by the COVID-19 crisis. In fact, most of the companies that have fully embraced AI are reporting major benefits.
As we’ve done for the last four years, we’ve made key predictions informed by the survey of more than 1,000 executives (including over 200 CEOs) at US companies that are using AI. Together, these insights should help your company navigate the top AI trends defining 2021 and beyond.
1. No uncertainty here: AI investments will increase
This trend is crystal clear: businesses are ramping up their AI investments. Fifty-two percent of survey respondents have accelerated their AI approach in the wake of the COVID-19 crisis, and the results will be felt for years to come. These ‘accelerating’ companies cite their top changes as new use cases for AI (40 percent) and increased AI investments (also 40 percent). Of all the participants in our survey, 86 percent 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 decisions and improved customer experiences. In fact, the companies in our survey that have rolled out AI enterprise-wide are more optimistic about growth, despite COVID-19: 25 percent expect to increase revenue, compared with 18 percent for all companies. The future payoff is even greater and could give early adopters an edge that competitors may never be able to overtake.
Yet when considering not just the benefits, but the costs, 76 percent of organisations 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.
2. Your strategic ally: Faster and better decisions, thanks to AI
The fastest way to get ROI on AI is to use its advanced automation capabilities to improve efficiency and productivity. Understandably enough 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 survey respondents have increased investments in AI for workforce planning. Forty-eight percent are ramping up investments in AI for simulation modeling and supply chain resilience. Forty-three percent are upping investments in AI for scenario planning, 42 percent 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. COVID-19 has accelerated this more advanced use of AI, and it has provided a major payoff during the pandemic — one 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, while continually monitoring and improving its own performance. This dynamic sense, think, act approach to strategy, which AI makes possible, is within reach today.
3. From risk awareness to risk action: Responsible AI’s time is now
The good news? Companies are aware of the risks of AI. 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 percent) was responsible AI tools to improve privacy, explainability, bias detection and governance. But only about a third report plans to actually 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 defences against cyber threats. And in the case of explainability, companies have 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 understand 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 super-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.
4. Beyond upskilling: New talent strategies will emerge
Upskilling is necessary but it’s not nearly enough — and there is growing consensus about what really might be ‘enough.’ One long-term impact of AI is likely to be net job growth — but these new jobs will be different from the old ones.
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 data (invoices) and output (payments) 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, learning — like scientists. With time, this approach will have to guide not just your AI and technology teams, but your entire workforce. Your company can get there, but it has to act now.
5. The model is never done: The AI reorganisation accelerates
The top choices for 2021 AI and analytics priorities all — inevitably — have one thing in common: They cross the entire organisation. 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 are going to have to reorganise so that you can collaborate as needed.
AI reorganisation 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 organisation. When AI models are constantly improving themselves, your decisions can constantly improve too. Your company will need to be ready to pivot quickly, not on a yearly planning cycle, but few organisational flow charts are currently set up for that kind of speed.
This organisational transformation may sound like a tall order, but it needs to happen. One sign of this is that the easiest AI and analytics application — automating routine tasks — has fallen down the priority list. This year only 25 percent cited it as a top priority going forward. In last year’s survey, 35 percent 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 reorganisation is inevitable.
AI is hard. Too many AI investments end up as “pretty shiny objects” that don’t pay off. Most companies have yet to adapt talent strategies, organisational structures, business strategies, development methodologies and risk mitigation for a world that moves at AI speed
There’s work to be done, but the reward can be enormous: concrete benefits today and the foundation for success tomorrow.
For recommendations on how to achieve each of the above predictions, visit PwC US’ full AI Predictions 2021 report.