Data Difficulties and Organizational Lack of Preparedness
“In the majority of organizations that I deal with, there are always a few pockets of very good, clean data,” says Michael Schrage, research fellow at the Massachusetts Institute of Technology Sloan School of Management (MIT Sloan). “But very few organizations have the quality of labeling, lineage, and governance that allows them to immediately take their data to reliably train machine learning algorithms—or to fine-tune a ChatGPT or a Llama.”
Your insights are only as good as your data. Indeed, 67% of CEOs identify potential errors as a top risk of AI and ML. But data integrity isn’t the only issue holding business leaders back. And nearly half (49%) of CEOs say their organization is unprepared to adopt AI and ML, with businesses lacking some or all of the tools, skills, and knowledge needed to integrate these technologies.
Looking Back to Understand the Road Ahead
When it comes to capitalizing on innovation, speed is of the essence—and that’s certainly true with AI. “The people who sit on the sidelines are missing all that learning time that those who are building their AIs now are benefiting from,” Ajay Agrawal, professor at Rotman School of Management, University of Toronto, shares in the report. “The faster you get in, the faster your AI starts to learn.”
We’d all be wise to remember the early days of the internet—many of the brands that made early attempts at leveraging the new technology are still with us today. The brands that didn’t, or that waited so long they never had a chance of catching up to their first-mover competitors, are no longer around to remind us of what happens (or doesn’t) to companies that wait too long.
For more C-suite insights on AI and first-mover advantage, download “C-Suite Global AI Indicator Report: AI Is the Ultimate Level-Up.”