Can AI help foster cohesive community in an organization? LiiRN thinks so.
Creating a healthy work environment that scales is something of a holy grail for all growing companies. As internal networks become more dispersed and organizational structures grow more complex, it becomes easier for communication disconnects to occur. How can companies continue to cultivate a shared vision and culture, and give employees a chance to define and improve both? LiiRN CEO George Swisher thinks the answer is AI-driven.
Swisher founded LiiRN, a people-centric, AI-powered transformation software, in 2018. The AI platform has a two-fold purpose: to help leaders make decisions based on employee feedback, and then allow employees to participate in enacting those decisions. The LiiRN platform collects customized survey data on leadership performance and company priorities. The AI synthesizes upward feedback, converts it into leadership performance ratings, and identifies quantitative and qualitative trends and findings to inform decision-making. The platform also invites self-nominated change-agents to shape and drive forward company-wide initiatives.
In an interview with Swisher, he shared how AI can drive rather than reduce personal connection, and help business leaders to listen to and lean on their people.
What problem are you solving with LiiRN?
LiiRN aims to help companies drive change through people versus processes. Many leaders working to design strategy end up working with small populations of people, doing surveys or doing stakeholder interviews. But trying to drive a huge change with the input of a small group of people is a disservice to both the firm and the company. People are fearful of change when they don’t understand it. So a few years ago I thought, what if I had the ability as an individual consultant to work with all hundred thousand employees in real time? The impact would be tremendous.
And so the idea was to launch a software that could do that, that could physically touch people as if it was someone you knew and who understood the big program that was going on out there and help the employee relate. When you drive change from the bottom up instead of from the top down, you avoid the education and awareness gaps that come with large scale change.
Companies can use our technology as kind of a middleware between the leadership and staff, to find the gaps between what leadership thinks and what the people on the ground are actually seeing and thinking. Our voting feature makes people feel like they’re part of the decision-making process. If you can do that for a company, say, that’s 100,000 employees, you’re able to help 100,000 employees feel like they’re contributing to a decision that the leadership is making. You get people who are more empowered, and I think that’s a big emotional feature of how you activate people. It automates some of the change management processes and helps leadership make decisions and investments that their company believes in. With ongoing feedback collection, you can create a dynamic feedback loop, to continually shape the change journey.
What are some of the most common pain points the leaders you work with encounter?
New leadership teams are sometimes nervous to listen to data and to draw conclusions if it can be interpreted in multiple different ways. It’s one of the reasons that we have moved to partnering with consulting firms with expertise in software-based data analysis. We use the data to quantify how many people activate and why. Typically, we see north of 30% of the total population raising their hand to be on a work stream in a specific change management area.
If you have lower adoption, we use the data we collect to understand why. We track when people opt out or say “I don’t understand what you’re asking and talking about.” This feedback surfaces whether the real issue is understanding and awareness, versus the willingness of people to participate. Alternatively, the data can also show if people think the initiative is misguided or has implementation risk. Leaders gain transparency through the software’s data analysis.
It sounds like you’ve found ways for AI to create more human interactions. What are the limitations to leaning on AI? In what ways can AI tools be anti-social, and how do you mitigate those risks?
If you’re going to trust the output of our system, you have to know it’s based on the right input. Potential biases to data come in so many different forms. Ideally, if we look at, for example, who is in the sample population that you’re getting information from, we’d account for any skewing as we analyze it. We have limited control, of which population, the stakeholder at the enterprises chooses to invite into our software. So if they choose to only involve the US population and use that information to influence the way they make decisions for their Asia-based population, for example, that clearly creates a lot of challenges, given the cultural differences. We work to screen out and limit bias with some of our onboarding screens and some of the setup and training that we do. We promote as much as we possibly can an approach of widening the sample size, to make sure that you’re involving as large a population as possible that is as diverse as possible. But there’s definitely limitations to it. It’s hard to solve it when you’re collecting what others choose to input.
Also, if there is a high concentration of a certain demographic in a company, we can’t control for who they’ve hired. So if they’re only getting information from a specific group of people that’s the majority of their population, it clearly sways the input that we’re getting and the resulting outcomes. So for us, I think we’re trying to maintain a middle ground where we highlight who companies are asking for input from and how it impacts the output.
We’re focused on making our data inputs more comprehensive by integrating with more internal systems in our upcoming work. HR systems can provide added layers of data, like performance management data and learning data; systems like NetSuite provide more business performance data. The more that we can integrate, the more our machines can learn, and the more we can build better cases for the viability of the decision we’re recommending.
Change management in the context of technology often raises the specter of worker displacement. How can technology-based change management tools like yours help us prepare for an unknown future of work?
What I learned personally moving from a tech-enabled service businesses working with big enterprises to being a full software company is that technology isn’t replacing us. There is a fear of tech advancing too fast. But I think the bigger question is how do we reskill and retrain ourselves? And how will we hold the enterprises of the world responsible for managing change? Even if there are people who will be losing jobs, which is never a good thing, we have the opportunity to say, “Well how do we rethink what workers are doing and what new skills they need to adapt? And how can we help them do that?” Yes, we’ve introduced self checkout into the grocery store. But if we’re going to replace those people, what are the skills they have that we can still benefit from? They may be really great at customer service and customer success — can you retrain them to help people shopping inside the store, to create a personalized experience? Flipping the way that you look at it can help people understand the opportunity. Then we all advance. But a lot of companies don’t think that way when they’re developing or implementing automation technology.
It’s a large number within consumer retail and manufacturing — upwards of 70% of some of the largest companies and employers in the world — whose jobs will be automated away in the next 10 years. The magnitude of that is scary. Unless you retrain people to think about it as an opportunity and change the way that they’re actively pursuing alternatives, we’re going to have problems. Being a coder isn’t the answer for everyone.
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