When executives talk about AI, they’re naming many of the same anxieties I was hearing 15 years ago. This is good news for executives. Because many of the questions are the same, the answers are, too.
1. What if there’s a data breach?
One common mistake from the public cloud era was waiting to create a security strategy. Companies were blindsided by data breaches because they thought they could put off security planning until the last minute.
In the AI era, companies must think about security, compliance, and governance from the get-go. Gartner recommends that organizations apply AI trust, risk, and security management (dubbed “AI TRiSM”) frameworks before and while adopting the technology, not after. These frameworks should encompass AI “governance, trustworthiness, fairness, reliability, robustness, efficacy, and privacy,” according to Gartner. Organizations that work to operationalize AI TRiSM will deploy models that work better, drive business goals, and increase customer trust, while organizations that don’t will face negative outcomes such as breaches, privacy failures, and reputational loss.
2. What if the cost spirals out of control?
When businesses moved toward public cloud adoption, expenses quickly got out of hand. Businesses rarely developed a unified enterprise architecture for managing cloud storage. As a result, individual departments didn’t reliably communicate who was doing what. Confused, businesses spent money on multiple vendors that were basically offering the same services, and various teams developed redundant workflows and technologies to interface with these vendors.
To avoid making the same mistakes with AI, it’s important to build a centralized enterprise architecture: a platform through which everyone can see what processes are in place, what is being automated, what data is being generated and collected, and what roadblocks employees are encountering. Creating a centralized architecture reduces the amount of duplicate work that teams end up doing, and it makes it easier to communicate about strategies and goals.
3. Do we have the organizational culture to make it work?
At the beginning of the public cloud era, executives didn’t make any changes to organizational culture prior to adopting the new technology. That’s understandable. It had never been done before. But the status quo was not up to the challenge. Public cloud technology is fundamentally about sharing resources and tools. Employees were not ready to think about projects that way.
What executives should have done—and what they should do with AI—is foster a culture that emphasizes three things: communication, collaboration, and agility. To make AI work without breaking the bank, employees must talk openly about failures, work together to iterate, and pivot when things go wrong. Above all, they should feel comfortable sharing ideas and resources across teams.
While AI is an innovative technology set to radically change the way businesses operate, putting it to work quickly and with minimal missteps does not mean having to reinvent the wheel. Past experiences with tech offer a guide for executives looking to the future.