WhereAs for the “where,” we found that the business areas that traditionally provide the most value to companies tend to be the areas where AI can have the biggest impact. In retail organizations, for example, marketing and sales has often provided significant value. Our research shows that using AI on customer data to personalize promotions, for example, can lead to a 1 to 2 percent increase in incremental sales for brick-and-mortar retailers alone. In advanced manufacturing, where operations often drive the most value, AI can enable forecasting based on underlying causal drivers of demand rather than prior outcomes, improving forecasting accuracy by 10 to 20 percent. This translates into a potential 5 percent reduction in inventory costs and revenue increases of 2 to 3 percent. While applications of AI cover a full range of functional areas, it is in fact in these two cross-cutting ones—supply-chain management/manufacturing and marketing and sales—that we find AI can have the biggest impact, for now, in several industries. Combined, we estimate that these use cases make up more than two-thirds of the entire AI opportunity.
HowWhen it comes to “how” to apply AI—arguably the more difficult question to answer and execute on—we identified several keys to successful implementation. In companies that have scaled analytics and AI, executives have a clear, shared vision. They’ve established a more robust talent model with clearly defined analytics roles and career paths. They’ve prioritized the top ten to 15 decision-making processes in their business in order to identify where applying AI might add the most value. They’ve adopted agile, collaborative environments. And they invest an outsized proportion of resources into the difficult “last mile,” or integrating the output of AI models into frontline workflows, ranging from those of clinicaltrial managers and sales-force managers to procurement officers. While these practices may seem obvious, we found that those successfully scaling AI and analytics were typically at least twice as likely to be engaging in these practices than those struggling to scale. We don’t want to come across as naive cheerleaders. We recognize the difficulty in shifting organizational cultures and practices. And we recognize the other tangible obstacles and limitations to implementing AI. Obtaining data sets that are sufficiently large and comprehensive enough to feed the voracious appetite that deep learning has for multiple forms of training data is a major challenge. So, too, is ensuring ethical AI, including providing data security and privacy protections and preventing human biases from creeping into AI algorithms. Making AI explainable can help in these efforts, although explainability is itself a challenge. However, it’s one that can increasingly be overcome thanks to advances in the technologies themselves (and sometimes by simply sacrificing an inconsequential degree of model accuracy) and one that must be overcome for AI to gain a foothold in some industries such as healthcare and financial services to satisfy regulatory requirements and provide safe, premium care. While businesses must prepare to be both vigilant and dogged as they deploy AI, the scale and beneficial impact of the technology on businesses, consumers, and society make it the responsibility of all organizations to explore the possible with AI. Crossing-the-frontier-collection