With most businesses today built on human and physical assets, thinking about how to integrate AI’s cognitive assets is important. This is one of five PwC priorities for making the most of AI in the coming year.
Getting AI technology right is not simple, but it’s actually the easy part. The top AI-related challenges, according to our survey respondents, aren’t moving AI initiatives from pilots to production or managing AI’s convergence with other tech. Instead, the top challenges are business- and people-oriented: measuring its ROI, getting a budget approved, and training current employees to use AI. Indeed, these challenges reveal why some companies may be scaling back company-wide ambitions in 2020. This highlights the need for a sustained commitment to AI from senior leaders.
Measuring and making the case may be difficult because AI usually delivers value indirectly, by helping employees and other technologies work better. It often works best as one of several moving parts in an integrated package. For example, your AI investment may (and should!) help your decision-makers make better choices, improve employee engagement by freeing them from tedious tasks, and speed up your IoT system’s analytics. But traditional metrics may not be able to identify and quantify those values.
That’s why it’s essential to treat AI not as a silver bullet or singular solution, but as part of your broader automation or business strategy. Depending on the business issue at hand, analytics or simpler forms of automation, such as robotic process automation (RPA), might be the best solution. Or there may be bigger strategic efforts in which AI is a great addition, particularly in looking at how to prepare your company’s workforce to be future-ready.
But even if AI’s impact will be incremental at first, as it mostly automates routine tasks, it will soon become more transformative as it disrupts and creates new business models. For example, many firms whose business model is based on their employees and processes (such as an insurance company’s claims model) will need to find ways to first, embed this expertise into AI, and second, build a new business based on how AI can use and expand on this expertise. It’s a challenge on which business leaders need to start working with AI specialists, right now, even if they’re currently just using AI for internal processes.
Revisit your business model. As AI helps automate, assist, and augment your workforce and decision-making, evaluate the consumer value being generated and determine how you want to share, use or invest this new value.
Monetize cognitive assets. As you roll out AI, you should create unique data assets and cognitive assets: AI models that encapsulate your company’s experience and expertise in a specific domain. Your business must be able to capitalize on the insights and outcomes that these new assets offer.
Make your strategy work in AI time. With the rapid changes that AI and other emerging tech are bringing, an annual planning cycle and biannual strategy refresh won’t cut it. Build an AI-driven approach to strategy that is both more dynamic and more resilient to market changes.