Editorial
It seems like everything I read lately is focusing on artificial intelligence (AI). It is an amazing new tool that is available to improve our workplaces. However, AI and the information you receive from using it are dependent on the source of data the technology is using to learn. AI pulls in a variety of information to summarize and provide users with better (or at least, different) and unique answers to questions. In this article, I want to remind leaders and managers about the power of employee intelligence (EI) and ways to use it to supplement your AI and other data analysis efforts to make better decisions.
Why Collect Employee Intelligence for Decision Making?
Employees have day-to-day interactions with customers, vendors and community members. The information in your employees' minds can provide leaders with valuable and unique additional data that will help leaders make better decisions. Of course, the same rules for AI also apply to EI. The data needs to be updated, aggregated and analyzed so that the receiver of the data can use it to improve learning and decision-making.
The same type of intellectual curiosity that sets someone out to learn about AI should make that same individual curious about using EI. It may seem obvious that leaders learn from the knowledge of their employees, but the extent to which employee insights are used for decision making is rarer than you might think.
Today, how do leaders learn from employees? It may be through important but limited one-on-one conversations, engaging in listening sessions, walking around, or through focus groups and surveys. Many of these methods are not done frequently enough to gather ongoing trended data, and the questions used are normally too narrow to be a source of real discovery of unknown issues. Thus, the richness of employee data is often not part of leaders' decision-making.
Employee Intelligence to Drive Your Organization’s Performance
Leaders can use employee intelligence to turn around their organizations, solve specific problems or build for growth. One story comes to mind on this topic. I was walking around a manufacturing plant with the CEO of the organization. He was proud of the high-quality interactions he had with his employees, and as we toured the site, he would point to one person in each department. For each employee, he had a story about the last time he talked to that person, what a great relationship they had and how much information that individual shared with him.
When we were almost done with the tour, he turned to me and stopped. Then he said something like “you know, I didn’t realize until now how many people I don’t hear from.” That is when the big light bulb went on for him. He did not have employee intelligence; he had selected conversation insights. Those are two different things.
He then moved on to developing a strategy for using employee intelligence to solve one of his problems at the plant that focused on quality. We worked together on a three-month experiment.
n month one, we did a survey of all employees in the plant. It was specific and short. We started the survey out by writing up a scenario talking about a recent quality challenge that everyone knew about. Then we asked employees to think about the topic overall and rate on a 0-to-100-point scale where the plant was today. Next, we asked them to explain their answer and then to report one action he/she could personally take in the next 30 days (time is important) to improve quality.
After gathering the employee intelligence on this focused topic, we asked the leader to do a ‘road show’ discussing the topic, results from the survey and ideas that came out of it.
This was phase two. After gathering employee intelligence, he shared the results. Phase three focused on developing action steps based on what employees suggested, which quickly led to significant improvements in quality.
Related Article: How to Train AI on Your Company’s Data
Introducing the DDAR Model
The model I use to describe the overall process I used with this organization and the CEO is called the DDAR model, which stands for data, dialogue, action and results (as seen in the graphic below).
Step one involves collecting data or employee intelligence (EI). Based on numerous projects using this process, I strongly suggest leaders do NOT act on the data until they engage in dialogue with their employees. This is because data plus dialogue leads to not only clarification of the data but motivation of the pool of people to help share data or intelligence in the future.
Then, only after combining data and dialogue, leaders should act. Once they have results from their action taking, they should communicate results and cycle back to obtain more data for clarification and continuation of the work. The reason for ongoing data collection is that trending data is much more powerful than point-in-time data.
In 2024, Add EI to Your AI Efforts
n the project described above with the CEO, we wanted to have an in-depth understanding of what employees were communicating, so we manually coded the comment data. Today, leaders and researchers have the luxury of using multiple types of qualitative data analysis programs and artificial intelligence to sort through data and better understand results.
Having the right data from the right people and analyzing that data in new ways (in this case with AI) may lead to even better, or at least new, insights for leaders’ decision making.
And if you use the DDAR model, one becomes less concerned about the absolute accuracy of the data. Instead, the path of using the data to start (vs. end) a conversation leads to new dialogues and better decision making. Not only are employees able to share deep insights about a topic of interest to you, but they may also contribute added information you had not considered before starting your analysis. Data and dialogue produce higher accuracy and engaged team members who can help take the action needed to drive growth and results.
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Employee intelligence (EI) is high quality data that every organization has but that they do not tap into frequently enough or with adequate focus to make better decisions. In your quest this year to use artificial intelligence, I strongly suggest you also consider making an equivalent effort to tap into your own strategic EI assets.
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