For technologists, AI holds the key to one of the most valuable resources: more time.
According to Thomson Reuters’ 2024 Future of Professionals report, AI is slated to save professionals 12 hours of work per week by 2029. That means over the course of one month, teams can put 48 hours of time toward other tasks and priorities.
Having this extra time is essential, especially for the tech teams at Identity Digital. The company is driven by the aim to “expand and connect the online world through top-level domains,” offering brands a domain extension platform that allows them to connect with customers more closely.
To fulfill this mission, Principal Engineer Pat Ramsey and his peers leverage AI and machine learning in their day-to-day work, which he said has offered many benefits so far, such as reducing time spent on troubleshooting.
And as they continue to embrace these tools, he shared, team members are staying up to date on the latest advancements. This ensures the company keeps up with any changes and delivers the best possible solutions to clients.
Below, Ramsey shares more about how he and his teammates have embraced AI and machine learning, how they keep up with its growth and the impact this has had on the product development process.
How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?
We’re gradually bringing AI into our workflow, using large language models to help with coding questions and some automated test generation. This has cut down on time spent troubleshooting and improved our code quality by catching edge cases earlier. It has also reduced the cognitive load on engineers, letting us focus more on core development tasks.
“We’re gradually bringing AI into our workflow, using large language models to help with coding questions and some automated test generation.”
What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?
A few of us on the team stay up to date on AI and ML advancements and new tools. Some engineers are experimenting with running local LLMs implemented into their integrated development environments for coding assistance, and we regularly update those models using tools like Ollama. This keeps our workflows aligned with the latest capabilities in AI while ensuring our models stay relevant.
Can you share some examples of how AI and ML have directly contributed to enhancing your product line or accelerating time-to-market?
We’re planning to release an improved domain search experience using our AI-driven DomainEngine, which could significantly enhance our ability to recommend relevant domain names to our customers. On the development side, AI helps engineers get answers more quickly, especially when working with unfamiliar languages or frameworks. This has cut down bottlenecks and helped us ship features more quickly.
