Turning pilots into production Figure 6: Achievers excel at turning AI pilots into production Achievers have largely moved beyond the AI investment “tipping point,” going from experimenting with new AI in isolation to applying AI at scale to solve critical business problems (Figure 6). Achievers are 25% more likely to scale AI pilots across the enterprise compared with Experimenters. Take product development as an example: Procter & Gamble (P&G) uses “explainable AI” algorithms to harness its proprietary data and formulation models and recommend product improvements. If, for example, the company wants to increase the foam in its dishwashing liquid without changing the price, in-house software developers can now ask the explainable AI to recommend a replacement ingredient. If, however, that new ingredient modifies the liquid’s color, developers can then instruct the AI to search for a new ingredient—and so on and so forth. P&G also relies on AI to generate product formulations that are more likely to perform as expected, resulting in less physical testing of new products. The payoff is lower product-development costs, as well as the ability to better Source: Accenture Research tailor products for specific markets and launch them faster. Note: Score 0-100, ranging from 0 = AI use case not started, 50 = AI use in early stage, 100 = having AI programs in place for full productization. The chart shows the average scores for AI use cases of different functions, between Achievers and other firms. Those differences are statistically significant after controlling for industry, geography and company size; see Appendix for more details. The art of AI maturity—Advancing from practice to performance 15