7 best practices for implementing data-driven technologies, like AI and machine learning

According to a new Forrester report, "The Tech Executive's Primer On Data Science, Machine Learning, And AI, a lack of understanding is hampering the ability of business leaders to effectively deploy data science, machine learning and artificial intelligence projects to solve business problems. "All executives need to make strategic decisions about how and where to leverage these technologies, but few leaders have experience with them, so misconceptions abound, causing poor outcomes, wasted resources and resistance to future initiatives," the report said. The report defines data science as extracting meaning from data; machine learning as applying algorithms to data to train machine learning models; and artificial intelligence as an umbrella term for machine learning and automation methods used in new ways. To successfully deploy these technologies requires business and technology acumen and executive leadership, the report said. While technical experts can be hired, finding business executives who understand these complex, cutting-edge technologies is much harder. If it looks like you think AI "should" look, it's probably not. As smart as AI technologies like personal assistants or grammar-checkers appear, real-world AI does not exhibit anywhere near the intelligence and autonomy portrayed in the movies. "The actual advantages and disadvantages of ML and AI technologies vary so dramatically from popular perceptions that if an idea, proposed solution, or vendor offering looks like something a layperson would expect, it will be doomed to fail, is overly hyped or will have to rely on a person hiding behind a curtain," the report said. DSMLAI shouldn't start with just the end in mind or with what the AI and ML technologies can do. You have to meet in the middle. "Start purely with the business value and you'll choose use cases that play to AI's weaknesses and miss its strengths (think fully autonomous vehicles).