Integrating AI Into Daily Work: Q&A with Sarah Mogin

Earlier this month, Technology Director Sarah Mogin spoke to Built In about the ways AI is becoming part of her personal process — and what excites her about the impact of AI at Work & Co, both internally and for our clients. Read on to learn about her view on the opportunities and challenges companies across all sectors have ahead of them.

How is your company embracing AI in its operations and making it an integral part of your company culture? 

I’d say we’ve always embraced AI, but our ability to embed it into our work and processes — and see real impact — has totally transformed. Because Work & Co is a design and technology company focused on creating new, innovative interfaces — like chatbots, apps, websites & ecommerce platforms — we’ve been building on the foundations of AI for some time.

In the past year, the rise of generative AI has expanded knowledge, tools and access well beyond just our engineering team. Diving in and experimenting has become part of our culture across all our teams. I think our decentralized approach to knowledge building has been really rewarding for our employees. Employees share work in our AI Show & Tell series and trade resources in our #club-ai on Slack.

We’re also integrating AI in more daily work. Like a lot of engineers, I was initially wary to adopt tools like Github’s Copilot. Can AI really improve the quality or speed of my code? For me, the answer is sometimes. I use Copilot as an autocomplete to save a little time, but it really comes in handy when using languages I’m less familiar with. Now I don’t need to pause frequently to look things up; Copilot helps me without leaving my text editor.

In what ways does your company leverage its resources and expertise to implement AI in different areas of your work? What specific AI technologies or applications excite you the most?

We view generative AI through the lens of building and enhancing digital products, and we’re engaged in AI projects across retail, healthcare and travel. We regularly share experiments and client work on Work & Co’s dedicated AI Hub.

One theme we hear from clients is an interest in deploying generative AI within their organizations first, while increasing investment in responsible building of generative AI tools and features that meet customer expectations. These include: conversational interfaces, AI-powered booking and content generation and summarization.

We’ve also been tapping into AI to accelerate internal processes, automating repetitive setup tasks like scaffolding new design and development projects. The potential to impact backend code generation has me particularly excited. I’ve been working on a tool that Work & Co is releasing soon called CodeSail.

This proprietary software helps launch products faster while promoting code quality through a variety of mechanisms. It gives development teams that manage many complex data sources a way to rapidly implement the code to integrate with, transform and expose that data. CodeSail generates code tailored to fit into a human-led development cycle.

What are the benefits of incorporating AI into company work? Are there any challenges or considerations that need to be addressed when implementing AI?

Using Natural Language Processing, AI helps us derive intent from an unstructured input, like a message from a user or an image. With the intent, we can hand off to deterministic systems not powered by AI, to do things like book a flight or add an item to the shopping cart. And, of course, AI frees up humans to work on the coolest, most challenging tasks.

A big temptation is to think about AI as the solution, then try to reverse-engineer a use case where you can apply it, rather than starting with a problem. What are you trying to streamline internally? What new features do you want to offer users? Think of the question and then decide if AI is the answer.

Remember that AI isn’t a magic bullet, and it doesn’t always know best. I think AI works best when we provide clear parameters and expectations.

Lastly: trust, but verify. One example is code generation. As developers, we often ask AI to write or update code for us: “Write a function that does X.” “Refactor this file.” Although most AI models will return something that looks like code in your language of choice, they aren’t compiling or running the code to check for errors. You have to do that on your end.

This piece is excerpted from an original story on Built In titled "Good Data Makes Good AI: How 2 Tech Leaders are Successfully Integrating AI into Daily Work". Read more on Built In's site.