Generative AI has captured the imagination of business and IT leaders worldwide. But it also has created a lot of confusion — where to start, how to select use cases, pitfalls to avoid, and so on.

If you need help sifting through the hype of Gen AI to find actionable insights, this article is for you. SoftServe was fortunate to sit with ServiceTrade CTO and co-founder, Brian Smithwick, to get under the hood into his Gen AI journey.

Smithwick talks frankly about his and his colleagues’ first foray into the exciting, but potentially daunting, world of Gen AI.

Like many others, he was convinced Gen AI could make a serious difference to his business and his customers. But Smithwick knew that he would need help to make it a success and avoid pitfalls. His key words of advice to others embarking on their Gen AI course are: “Know what problems you are trying to solve before you begin.”

We think you will enjoy the following conversation as Smithwick explains the journey, outcomes, and lessons learned. If you want to read the full case study, click here.

Preface

Gen AI tools have experienced explosive growth, according to a report by McKinsey, with the share of smaller organizations piloting the technology tripling during 2023.

ServiceTrade, a leading provider of field service management software, is a prime example of this trend. The organization wants to quickly innovate and compete with Gen AI and get a head start on implementing it to provide a better customer experience.

The Gen AI decision took center stage for ServiceTrade last year, as company leaders planned to accelerate growth with the best technology ecosystem. As the organization laid out its roadmap, Smithwick and his team worked with SoftServe on vital Gen AI projects, continuing a strategic collaboration in place since 2022. For Smithwick, a continuation of this dynamic relationship made keen sense as he brought Gen AI into the fold.

Here’s a snapshot of Smithwick and ServiceTrade’s Gen AI journey, which is unique to its business. Although different from the journeys of other clients, this offers valuable insights into the process.

Q&A Interview

There’s no playbook for Gen AI. Given what you know now, is there anything you would have done differently?

I would have tried to understand more precisely what the estimates were of the team's work and what the inputs to that estimate were. We had six weeks of availability, and we got the initial deliverables done in two or three weeks.

But I could have been more prepared to have more scopes of work lined up and ready to go, if I had had a better understanding of what the team was going to be able to do in the time allotted. I'm not sure if we could have fixed that because of the nature of Gen AI projects. It was new to all of us, including the people on my team who had to estimate the work.

We probably could have also better described what success of the proof of concept looked like. We went into it with an “anything is better than nothing” approach, because any results that we get will at least help us know what not to do when we do Gen AI work internally. Literally, any knowledge was good knowledge.

Let’s talk about use cases. It sounds like you already had them lined up ahead of this project. But is there anything you would have done differently to identify those use cases?

I'm not sure if we would have done anything differently. We had two executive sponsors for this effort — me and our vice president of AI — thinking about this a lot. We had been putting together a list of internal and external use cases that we had running for five or six months before we started this engagement. So, I feel like we had a robust list of at least high-level use cases.

The trap you can fall into is seeing Gen AI as this shiny object; there’s a temptation to apply it to everything. But it’s super important to focus on things that are immediate needle movers for your customers — making it fast and easy for the customer. Because if you're not doing something that makes an impact for your customer, then why are you doing it? It's a waste of time.

Remember, for many, AI feels like magic. People have higher expectations for magic. So don’t present something to your user that just doesn't work or is half-baked. If they get something and it works and feels like a win for them, that's a victory. But anything that requires them to do work or change their usage patterns or anything else that requires mental effort feels like a loss.

(Specific details about the use cases ServiceTrade tackled are in the case study here.)

How can other organizations learn from your experience of setting expectations to educate executives about what the outcomes are going to be for a Gen AI project?

First, our executive team fully bought in on AI for our internal operations and our product roadmap. So, the question for us was which workflow improvements would create the best outcomes for our customers for which they will pay. If your Gen AI project gives you the data needed to prove the benefits for the end user workflow are strong enough to be monetized, you’re probably good to go. If you can’t, then you should think about another project.

Second, naming the solutions for which AI didn’t pan out as strong solutions is just as valuable to know, so you’re able to create those solutions in other ways.

How much urgency did you feel implementing Gen AI technology?

We felt tons of urgency. My biggest concern is that we can't ship meaningful capabilities to our customers fast enough, losing a competitive advantage on, at the very least, doing simple Gen AI things like taking a pile of unstructured text and writing a summary of it.

In the meantime, there were some other projects that we started internally slightly before being used by our customers in a closed beta format. I expect we will end up taking the asset summary capability to market in the same way within the next couple of months.

So, there's a lot of urgency to get usage and customer adoption.

Why did you consider using Gen AI for this project? What was the opportunity for you to use Gen AI?

Everybody is — us included — being expected by our customers and by our investors to explore how Gen AI could make our business internally more effective and bring our customers more value.

We have many different use cases in our customers’ businesses of people not having the time or bandwidth to consume all the information they need to do their job effectively. Because there's too much content or they don’t know what to pick out and what to ignore. It all kind of boils down to, “I'm not going to read all this documentation and all this history and all these comments. I can't do that 1,000 times a day.” That's the root of several different use cases that our customers face.

Gen AI is good at taking big piles of content and turning it into five bullet points. This is a no-brainer approach to several of our customers’ problems.

Is there any situation where you could see Gen AI becoming a distraction?

Yes. We were convinced that Gen AI would be just the thing to help us with route optimization. We wasted the better part of a week on something completely not a problem where Gen AI could be a solution.

But it was good that we did that because we quickly learned that Gen AI is great at fluency and bad at precision. So, anything you need precision for, don't do it. Don't even try.

It almost feels like a hammer in search of a nail. You’ve got a hammer; so now you say, “What can I hammer down?”

With distractions, even though you said it was a good learning lesson for you and your team, what would you recommend that other organizations do to avoid those distractions?

You must make space for distraction, particularly with bleeding-edge technology. Because if you don't, then you won't produce any neat, novel innovations. That's the sort of exploring you do and finding the stuff that doesn't work to help you discover the things that do work you didn't expect.

It all comes back to knowing what sort of problem you're trying to solve for the customer and what use case you're going to hit. Start with the problem you're trying to solve and work backwards, such as a text content consolidation problem. That’s a good use of Gen AI. But, if it’s a difficult math problem, that's not a good use of Gen AI.

At what point did you think: “We need to bring someone on board to partner with us to get this done; someone who has more expertise?”

The place where we felt that most acutely was when we needed specific technical knowledge about Gen AI techniques that we didn't have. The landscape is moving so fast that it was literally not possible to keep up by reading documentation — by doing the things you would normally do to understand how to do something.

For example, there’s a technique called retrieval augmented generation, or RAG, that’s common when you have a big set of content and you want AI to answer questions based on, for instance, your knowledge base or your support site. We needed to focus on our content and how to pull pieces out of it to solve the problem. But we didn't know how to do it. And it was hard to get a definitive answer on how to do it.

Having somebody come in with expertise was super valuable. It ended up being valuable for other projects, too. Many techniques we use on our internal use cases are ones we learned from the SoftServe data science team.

Finally, as an executive talking to other executives, what one vital piece of advice could you give them as they get ready to embark on their first of, hopefully, many Gen AI projects?

Know what problem you're trying to solve. That's it. Start with a problem and then work backwards. Find that nail. If I hit a screw with a hammer, the risk of failure is high. Just because it looks like a nail doesn’t mean it’s a wonderful use case.

For us, it wasn't difficult to find good-fitting use cases. We were fortunate in that regard. Not all businesses are like that. And then the temptation to hit a screw with a hammer is higher. But we didn't have that temptation because there were so many great options for us that we didn't have to settle on mediocre options.

We stayed focused on our primary challenge to maximize the effectiveness of our customers’ field technicians.

Conclusion

Learn more about how ServiceTrade overcame its Gen AI challenges to pave the way for continued growth and customer success with SoftServe’s expertise and AWS tools. Read the case study.

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