From Data to Insights and Action: The Very Human Challenges of AI Transformation

A few years ago, the idea of ​​collecting a million data points per day during any process was unfathomable to most organizations. Now, with the advent of powerful acquisition methods and affordable storage options, we are flooded with data. The challenge is to sift insights from this deluge and then transform them into actions that transform processes and organizations.

This is where AI can help. No matter the industry, AI’s unprecedented ability to analyze and identify patterns in data promises to radically change the way organizations operate, such as making sales calls more productive, reducing waste in factories, and saving lives in high-risk industries. But to truly transform AI, we need to understand people better than technology.

As cognitive scientists, we’ve seen AI transformation happen in three stages: collecting data, finding insights, and taking action. These last two stages require a deep understanding of what drives human behavior: the fears, motivations, biases, cognitive limitations, and other brain processes that cause people to behave the way they do. AI can identify patterns in data, but to draw insights from them and then design effective organizational change initiatives, it requires understanding people.

Using AI to save lives

Let’s look at the three-step AI transformation process with a real-world example. Dr. Teodor Grantcharov, a professor of surgery at Stanford University, wanted to use AI as a tool to analyze and hopefully reduce surgical errors in the operating room. Although estimates vary widely, studies suggest that between 44,000 and 250,000 patients die in the U.S. each year due to medical errors. About a quarter of those deaths are due to preventable errors in the operating room, the study estimates.

For 20 years, Grantcharov has been developing a “black box operating room” that analyzes everything that happens during a surgical procedure. He drew inspiration from the flight data, or “black box” recorders used in airplanes. Since 1957, when the U.S. Civil Aeronautics Board mandated flight data recorders in all passenger planes, the instruments have helped explain the causes of accidents and disasters. Black box recorders have saved countless lives by changing pilot training, airline equipment and regulatory standards.

The OR black box was developed with a similar goal in mind: identifying and taking action to mitigate avoidable errors. In recent years, advances in artificial intelligence have allowed Grantcharov’s team to overcome the former data analysis bottleneck. The insights gained have significantly improved individual and team performance and increased compliance with standard operating procedures. These changes have reduced morbidity, mortality and costs in operating rooms where the black box has been implemented, Grantcharov says.

Step 1: Data collection

The first step in the AI ​​transformation is collecting data, which is currently the easiest step. So far, Grantcharov has placed the platform in about 20 operating rooms across the United States. Using a variety of sensors, the operating room’s black box captured up to 1 million data points per day per facility. This included audiovisual data of surgical procedures, electronic medical records, and input from surgical devices. The data also included biometric readings from the surgical team, such as heart-rate variability as a reflection of stress levels, and brain activity measured by wireless EEG.

The data contained a wealth of information, but, as Grantcharov put it, “Data is useless if we can’t turn it into information that doctors can use to change their behavior.”

Step 2: Finding Insights

Identifying patterns in data is an area where AI is particularly helpful. “The human brain can’t constantly monitor all these data points and look for patterns and hidden connections,” Grantcharov notes. “This is where modern AI methodologies can really enable us to turn data into insights and then into action.”

But understanding humans is also important here. AI can correlate operating room accidents with certain events, but without a working hypothesis, all of that is just noise. For example, Grantcharov’s team hypothesized that stress could affect a surgeon’s performance by affecting their cognitive processing and decision-making. So they designed an experiment to collect physiological data from surgeons, and the AI ​​was able to link that data to operating room accidents. The finding: Stressed surgeons were 66% more likely to make a mistake.

Grantcharov also noticed that events like a door opening, a phone ringing, or someone talking about yesterday’s football game—in other words, distractions—caused the most catastrophic errors. Finding this information required understanding the brain’s finite cognitive capacity.

Drawing other conclusions required understanding team dynamics. The researchers observed that teams that communicated poorly and lacked psychological safety—the belief that they could speak up and raise concerns when necessary—had poorer outcomes, regardless of the surgeon’s technical skill level. “One of the most dangerous operating rooms is a quiet room where no one is talking or communicating,” Grantcharov says.

While you might assume that the surgeon’s skill is the most important factor in determining success, nontechnical attributes of the surgical team, such as how they worked together or whether they felt safe expressing concerns, had the strongest impact on patient outcomes. “It all comes down to culture,” Grantcharov says.

Step 3: Taking Action

As AI has helped uncover the biggest sources of errors in the operating room, hospitals and surgery centers could, at least in theory, begin to implement new procedures to prevent errors. But first, they need to understand how behavior change happens. To effectively change the culture of an entire organization, it’s necessary to establish priorities, habits, and systems that

Priorities are the tasks or activities that are considered most important to the organization, and it is important to communicate these priorities so that everyone knows where to focus their time and attention. In this case, the priority is clear: improving patient outcomes by avoiding avoidable errors in the operating room.

Habits are behaviors that are performed automatically with little conscious thought. For example, talking about concerns rather than remaining silent can become a habit with training and practice.

At last, Systems are procedures or policies that make a desired behavior easiest to perform. For example, to reduce distraction and preserve cognitive abilities, hospitals could implement a new policy that limits irrelevant discussions during critical steps of a surgical procedure.

Beyond priorities, habits, and systems, AI transformation requires everyone in the organization to adopt a growth mindset—the belief that failures are opportunities to improve, rather than threats to one’s position or status. Grantcharov says that at first, many surgical teams were wary of the black box in the operating room, worried about making them look bad or exposing them to litigation. But gradually, their mindsets changed.

“When we realize that we can’t improve without objective measures of our performance, it really opens up a world of growth mindset and continuous improvement,” he says. Hospitals that have embraced this change have seen huge gains, not only in quality and safety but also in efficiency and productivity, he says.

Outside the operating room

Not every industry has as much at stake in terms of human lives as healthcare. But regardless of the sector, AI can analyze data and lead us to valuable insights that drive action, from improving a specific process to changing an entire culture. But simply pointing AI at a data set will reveal little without a hypothesis worth testing.

For example, in a meeting environment, AI-powered devices could collect audio and visual data (anonymously and ethically) and, with the help of human insights, detect patterns that may not be obvious: Are there quiet people in the room who have great ideas but are constantly being shouted down by others? Is anyone showing signs of excessive anxiety or stress? Do people tend to look down during a video call, perhaps distracted by their devices?

In this way, AI can help leaders first recognize the obstacles that impede productive meetings and then find ways to overcome them, for example by increasing psychological safety or reducing distractions.

Whether in the operating room or the boardroom, AI can help unlock the potential in your organization. But ironically, the more technology plays a central role in our lives, the more we need to understand how people interact with and process the world.

Dr. David Rock, who coined the term neuroleadership, is co-founder and CEO of the NeuroLeadership Institute (NLI).

Laura Cassiday is a managing content editor at the NeuroLeadership Institute.