Without a clear plan for data, even the boldest efforts risk stalling in pilots, according to Jason Mills, senior vice president of solutions engineering at Cloudera Inc. Enterprises are now defining AI strategy around hybrid models that allow workloads to move between on-premises systems and the cloud. This gives companies control over cost while maintaining an agility to expand, according to Mills. “What we’re talking about is the difference between on-prem and cloud deployments of machine learning or AI models,” he said. “For some of our largest customers, we’re even looking at ways to burst into the cloud so that they can have hyper-agile solutions within their environment.” The harder step is turning pilots into production. Many organizations remain trapped in “pilotitis,” unable to advance because their AI strategy cannot make good on promises of efficiency or security, according to Mills. “If an AI model is not highly accurate to the questions or use cases that you’re trying to address, you will stay in pilot mode,” he said. “If that AI model costs more than you actually forecasted, then again, you will stay in pilot mode. And that’s part of the understanding: how customers need to pay attention to and rely on platforms like Cloudera to provide them with those capabilities to govern the entire AI ecosystem.”