It’s easy to feel overwhelmed by the speed of new technologies coming to the market, particularly in the AI space.
Rather than considering it a lost cause to stay on top of these changes, Level Green Landscaping, based in Upper Marlboro, Maryland, opted to create an internal AI council that allows their team the freedom to explore the different AI software available and test their viability for various aspects of the business.
“We also created it to ensure information sharing across the company,” says Marion Delano, director of technology & marketing for Level Green Landscaping. “Before the council was created, we had an instance where an account manager had found a really novel application for ChatGPT to help them stay on top of customer communication. This approach was siloed; it wasn’t shared out to other branches. By having team members across all branches and all positions on the council, it ensures there is more information sharing.”
Creating the Council
Level Green created their council in the fall of 2025 after seeing the rapid changes in the AI space and seeing it had the possibility to help people in positions across the company. Prior to this, they’d conducted some AI training courses for their business developers and account managers, but nothing was formalized.
“We formed the council to ensure we had a company-wide body that could build buy-in on new use cases, provide feedback, and test solutions in different areas,” Delano says.
To help with company-wide buy-in, Delano says they selected a cross-section of team members to represent all the roles in their organization, including an account manager, operations manager, business developer, branch manager, regional manager, accountant and himself.
“We also made sure people were from different branches, so each member represents a unique branch, again ensuring we have a broad cross-section of the company represented,” Delano says.
Delano says they specifically chose individuals who were more tech-savvy and open to trying new technologies.
“We did this for two reasons,” Delano says. “First, they may have more familiarity with AI coming in, so they have more to offer in terms of suggested use cases from the get-go, though some had no experience with it at all. Second, we’re planning on using the council members as the test cases for new applications. It’s important in that role to at least have a willingness to try these things and a desire to test them.”
Keys to An Effective Experiment Team
When reviewing the various technologies, Level Green’s top priority is ensuring the tools are user-friendly.
“Although there are many remarkable AI solutions out there, it can still be difficult to understand their functions and make effective use of them,” Delano says. “The data generated by these tools may be challenging to interpret in practical settings, and the implementation of AI solutions often demands advanced technical expertise that exceeds the typical skill set of our management team.”
Another feature they look for in their preferred AI applications is company ownership and control. Delano explains it is essential they maintain ownership rights over all the materials created by employees using AI tools.
“For this reason, we require the use of business accounts for all large language models, ensuring that intellectual property remains protected when employees leave the organization,” Delano says. “Given the significant investment of time and resources in developing these tools, it is essential that they remain assets of the company.”
Delano says they have funds allocated to subscriptions for tools like ChatGPT and Claude, as well as an AI solution designed to integrate with their ERP software, enabling them to extract real-time insights from their business data.
He says while they have a budget in place for experimentation, they will also evaluate new opportunities as they come up throughout the year on a case-by-case basis.
“If we believe something is truly worthy of investment or could have a significant positive impact on our operations or profitability, we will consider trying it out and investing in it even if it wasn’t in the original budget,” Delano says.
Level Green also makes a point to give their team enough time to thoroughly test the technology. Delano says it typically takes a year before their users can be sure that a new technology can be implemented as efficiently and effectively as possible. He says this was the case when they rolled out their robotic mowers as well as their site mapping and auditing software.
“Had we set an earlier decision timeline, we might have ended both technology trials prematurely,” Delano says. “Patience allowed us to realize positive ROI from each. The key takeaway is to expedite the learning curve as much as possible in order to reach that inflection point sooner.”
Determining ROI and When to Roll Out
When it comes to what Level Green looks for to give a certain technology the green light, they have a flexible and long-term approach with ROI.
“We understand the payoff periods for some of these technologies can be long, so a positive ROI from the get-go cannot be expected,” Delano says. “However, we are typically looking for at least a break-even return within a year or two.”
He says because commercial landscape maintenance operates on such thin margins, they cannot implement technology that erodes it, but they are willing to experiment with technology that may have a long-term positive impact and is relatively neutral in the short term.
“This is true for us right now with robotic mowers,” Delano says. “The ROI returns are relatively minimal, but we believe the institutional knowledge we are gaining now is important. As autonomous mowers become more advanced and can be used on more properties, we expect ROI to increase significantly.”
Beyond ROI, Level Green also looks for behavioral signals that indicate a technology is gaining traction and providing real value.
“One of the strongest signals is when we hear managers saying it makes their job easier or saves them time,” Delano says. “If they’re actively incorporating it into their daily workflows, that tells us something.”
Another sign they watch for is if managers outside their pilot team begin asking about the tool, as this can mean the technology is providing value and others are interested in using it as well. If customers notice improvements such as better communication, faster response times or higher quality work, then the technology is providing the impact the company is looking for.
Level Green’s leadership team, which includes senior managers from all departments, handles final approval of rolling out a new technology.
“The leadership team will strongly consider the feedback of the AI Council as well as my feedback as our director of technology,” Delano says. “They will want to see the ROI calculation before considering the other qualitative factors that influence the company. Typically, the decision is made by evaluating these elements together and calculating the overall net impact.”
Once a wider rollout has been approved, Delano says they will conduct formal training for AI tools.
“We believe it’s critical we immerse our team in the capabilities and changing landscape of AI so they are familiar with what it can do and comfortable with trying it out themselves,” Delano says. “With that in mind, our aim is to hold AI training sessions every quarter for all employees at the manager level.”
Advice for Others
If you want to create your own technology experiment team, Delano encourages selecting employees from different parts of the organization to provide a well-rounded perspective on both the advantages and challenges the company may encounter in implementing technology.
He adds that it’s best to stick to one dedicated pilot crew, as rotating between crews can hamper your ability to tweak and experiment with small changes.
“There is a learning curve to understanding new technology and truly harnessing the benefits of it,” Delano says. “We typically see it takes months, if not years, to fully capture the total potential value.”
Once formed, this group needs to meet consistently, particularly in the initial stages of the team’s formation.
“A regular cadence underscores the team’s importance and reinforces the value placed on members’ time and focus,” he says.
It’s also important to have a dedicated budget for this team’s initiatives and recognize their participation, as this allows them to purchase and test additional technology.
“Such rewards acknowledge the team’s efforts and provide them with direct opportunities to experiment with technologies that support and advance our AI initiatives,” Delano says.
Delano says proper training and patience are critical to effectively rolling out AI solutions.
“Working with council members on AI initiatives and projects has shown that you need to give people time to learn and get their hands around the technology,” Delano says. “Introducing new technology requires us to prioritize training, so people fully grasp what it is and how to use these tools effectively. When talking about adopting AI in an organization, putting training at the heart of every discussion is essential.”
