Getting to the next stage requires a three-pronged approach: establishing trust as an operating principle, ensuring data-centric execution, and cultivating the leadership to successfully scale AI.
Trust as a scalable, high-stakes bet
Trusted indicators mean that users can actually rely on the answers they are getting from the AI ​​system. This is important for applications like developing marketing copy and deploying customer service chatbots, but it’s absolutely essential for high-stakes scenarios.
Whatever the use case, building trust will require doubling the quality of the data. First and foremost, inferential conclusions must be made on reliable grounds. This fact informs one of Partridge’s go-to mantras: “Bad data equals bad data.”
Reichenbach provides a real-world example of what happens when data quality degrades. The rise of unreliable AI-infused content, including deception, disrupts workflows and forces employees to spend significant time fact-checking. “When things go wrong, trust is lost, productivity isn’t gained, and the outcome we’re looking for isn’t achieved,” he says.
On the other hand, when trust is properly engineered into the innovation system, efficiency and productivity can increase. Delegate troubleshooting configuration to the network operations team. With a reliable inference engine, this unit gets a reliable copilot that can provide faster, more accurate, custom-tailored recommendations. “They didn’t have that 24/7 team member before.”
A shift in data-centric thinking and the rise of the AI ​​factory
In the first AI wave, companies rushed to hire data scientists and many saw sophisticated, trillion-parameter models as the primary goal. But today, as organizations move to turn early pilots into real, measurable results, the focus has shifted to data engineering and architecture.
“In the last five years, what has become more meaningful is breaking down data silos, accessing data streams, and increasingly unlocking unlocked value,” says Reichenbach. This is an evolution alongside the rise of the AI ​​factory. It is an ever-evolving production line where data flows through pipelines and feedback to continuously generate intelligence.
This shift reflects an evolution from model-centric to data-centric thinking, and with it comes a new set of strategic considerations. “It comes down to two things: How much of the intelligence—the model itself—is really yours? And how much of the input—data—from your customers, operations, or the market, is uniquely yours?” Reichenbach says.