Technology leaders are increasingly looking to AI for transformative solutions. But the tools are only one piece of the puzzle. To get the full picture, leaders need to position their people and their businesses to make the most of these new tools.
This was a major theme from a webinar that Volaris Group hosted in September 2025. The webinar featured insights from two of our leaders who are working with AI in their day-to-day work:
- Jeff Chow, VP of Integration and Strategy at Volaris Group, who leads a team of experts that make up our AI Center of Excellence, and who helps our businesses address challenges that they face post-acquisition
- Glyn Trott, CEO of agentOS Proptech Group, a leading provider of property management software solutions across the UK that has made a push to become an “AI-first” company
Did you miss the webinar? Watch the replay and review key takeaways below.
1. To capture value from AI, invest time in rethinking workflows
A common misperception about AI is that it is a magic solution for businesses. Jeff Chow pointed to examples of AI products that claim to increase productivity: “There’s a lot of hype in the market right now.”
Having worked with many businesses within the Volaris ecosystem to integrate AI into their operations, he has learned that it is unrealistic to expect that simply buying a solution can immediately create impact and value within the business.
“A handful of our businesses are going through this journey right now,” he shared. “What we’re really seeing is that it does take a lot of time to set up foundations and change workflows before you can start moving a lot faster.”
He advocates for businesses to invest time, resources, and talent in experimenting with the new AI tools and fully integrating AI into workflows. For example, he worked with one business that put a full stop to their ongoing development – stopping a sprint cycle so that they could thoroughly reconsider their workflows and rethink how to change their processes so they were “AI-first.” This team was able to use their break to evaluate AI’s potential to act as a thought partner with planning, and as a builder with hands-on coding and testing. Ultimately, the pause allowed them to accelerate progress and finish their product roadmap earlier than they expected.
2. Prioritize AI adoption and learning acceleration for employees.
As a business leader making a push to become an “AI-first” company, Trott acknowledges that employees may have concerns about job security and changes created by AI. Team members may worry about how their roles are impacted or struggle to see the value that AI can bring.
“I still have people in the company with lots of concerns, like: ‘If I identify areas that can be automated or where AI can help, I could be putting myself out of a job,’” he reveals, and as a leader, he empathizes with their concerns. He advocates for meeting people where they are and realizing that everybody’s learning journey with AI will look different, with some employees needing more support.
Everyone at agentOS is encouraged to invest a set amount of time each quarter learning about and using AI. To increase company-wide AI literacy, agentOS has introduced OKRs (Objectives and Key Results) and KPIs (Key Performance Indicators) to measure progress for each employee.
He encourages his employees to become more adaptable by thinking of their careers as “T-shaped” – one where they develop a deep, specialized area of expertise (represented by the vertical bar in the letter “T”), in addition to a broader range of knowledge and skills across disciplines that can be enhanced by AI (“represented by the horizontal bar in the “T”).
For critical tasks that involve AI, Chow urges keeping a human in the loop for safety and oversight. This best practice ensures employees understand that human expertise is still essential while helping them feel secure about the role that AI plays.
Finally, Trott points out that there are many use cases of generative AI for “builder” tasks like coding and content creation, but there are ways to get “non-builder” employees involved. For example, he values the contributions of employees who can identify inefficiencies that can be eased with AI. He says that even if those employees aren’t personally building with AI, “that contribution is really valuable, because it’s hunting out those use cases that we can quickly turn around.”
Chow adds that a key opportunity with AI is to move employees away from repetitive, low-value tasks and free their time to allow a deeper focus on high-value work. “There are certain things that AI does incredibly well, but there are certain things it can’t do. AI can’t go and visit customers,” he says, speaking to examples of work where human expertise and empathy are irreplaceable.
The goal with AI is that by automating routine operations, employees can focus on work that matters more to the business, such as building stronger relationships with customers, understanding their pain points, and solving meaningful business problems.
To accelerate employee learning, Chow noted that the Volaris community has been crucial for helping businesses succeed with AI. Through the Volaris network and learning events, our leaders have been able to learn and share what has worked or not worked during their AI experimentation. “I don’t think you can actually capitalize on this opportunity [as easily] if you don’t really have this community of peers to learn from.”
His team at the Volaris AI Center of Excellence actively supports employees with four structured learning programs:
- AI developer training programs – Upskilling development teams in AI-first practices
- Productivity accelerators – Hackathons where employees experiment, learn rapidly, build use cases, and build teams of AI champions
- Secondments – Temporary assignments that enable employees to gain AI project experience
- Functional working groups – Regularly organized meetings where leaders across businesses can discuss and brainstorm applied AI use cases
3. AI is reframing the customer relationship.
As customers are making sense of AI, businesses have an opportunity to engage in new ways with customers, says Trott. His team at agentOS is finding that they can build trust with clients who are feeling unsure about their AI strategies.
“Any disruption we’re facing, our customers are also likely facing,” said Trott.
He talked about how agentOS has leveraged its own journey of AI adoption to become a thought leader for their customers. After working through their AI strategy with Volaris, they have been able to help customers navigate the process of adopting new technologies and even share strategies, learnings, and best practices about AI.
The panelists talked about how AI is raising customer expectations, such as customers increasingly wanting AI-enabled product development and feature releases.
“They’re going to expect that we can build things faster and we can provide very similar agentic engines in our products that a lot of the big competitors do,” said Chow.
Businesses also have the opportunity to introduce increased customization for customers. Trott talked about his company putting AI agents to work that can tailor software to the unique workflows of users, which helps make the product more indispensable to the customer, thus increasing customer loyalty.
4. Business leaders need to set goals about how to measure the success of their AI experimentation and investment.
The conversation moved toward how businesses can make smart investments in AI.
Businesses can use a practical framework to identify where AI will have the best return on investment, said Chow, who identified key criteria to think about:
- Feasibility: Is there a problem that can clearly be solved? Will end users adopt the change?
- Value: How frequently is the problem happening?
- Measurability: Is there data to measure the impact of solving the problem?
- Approach: Is this the most appropriate approach to solve the problem?
Both panelists spoke about the risks of not enough AI experimentation. Chow explained why Volaris pushes businesses to jump in when experimenting with AI – because the cost of inaction is too high. “There is a snowball effect if you don’t do any experimenting to try and understand what is possible.” He warned of businesses falling too far and not being able to catch up to early adopters of technology if they don’t start now.
Trott agreed: “Every time you’re bringing AI into your business and your product, you get the benefit, a bit like daily interest on your money,” he said of the efficiencies gained and the capacity built within team members. “The difference is what I call the AI compound effect.”
He continued: “The real challenge, if you are slow to adopt AI in your own business, is you’re losing out on that compound effect… You truly don’t know what you can do [with AI] until you start to do it.”
Trott gave an example of how agentOS started its AI adoption journey. Teams within the company spend the first hour of each day discussing AI uses before they get caught up in the tasks of the day. The practice started with the IT team and has extended to senior management.
Panelists also spoke about how to avoid unwise investments in AI. Specifically, Volaris encourages experimentation with goals and time limits.
“You might say you’re going to experiment for a month with AI, and if you can find impact, value, and feasibility, double down there,” Chow advises. “If you can’t find that, you need to stop and pull the plug quickly, or else you’re just wasting money.”
Trott agreed and added: “Experimentation is good, but what I would add is to have a really solid goal,” emphasizing the need to be focused with AI experimentation to avoid going down rabbit holes.
Adding value by partnering with AI
Near the end of the webinar, attendees had the chance to ask questions. They covered topics including data security, safety, ROI, and avoiding hallucinations — topics which have also been discussed at several of our employee-only Volaris learning events.
In summary, our discussion sought to drive home the point that success with AI requires more than simply adopting new tools. Becoming an AI-first company is about making a cultural shift toward continuous learning and experimentation. Becoming AI-first is also about upskilling people and fostering a community where lessons and best practices can be shared openly. AI also introduces new ways to engage customers while changing their expectations of product releases. Finally, AI requires business leaders to be thoughtful about how to measure investment in new tools and processes.
For a full look at the webinar, watch the replay.
