To achieve meaningful AI outcomes, the article suggests a practical, data-driven approach. This includes investing in data quality and organization, defining specific, outcome-oriented goals, ensuring executive buy-in, critically evaluating AI consultants, leveraging high-quality data sources, and moving beyond operational applications of AI. The article concludes by emphasizing the importance of focusing on actionable outcomes rather than hype, and the need for businesses to stop paying for consultants to "tell them the time" and start building AI initiatives that actually work.
Key takeaways:
- Quality data is the bedrock of any successful AI initiative, and the lack of it is a major reason why up to 80% of AI initiatives fail.
- Most commercial AI applications focus on operational improvements, missing the opportunity to use AI for higher-value analytics and predictive insights that can guide strategic decisions.
- AI consulting has become a multi-billion-dollar industry, with many organizations investing in it to show they are “doing AI,” rather than to produce real results.
- To achieve meaningful AI outcomes, businesses need to focus on data quality and organization, define specific, outcome-oriented goals, ensure executive buy-in, evaluate consultants critically, leverage high-quality data sources, and move beyond operational applications of AI.