AI Support Agents - Build vs. Buy?
An evergreen question :-)
Recently, several prospective clients have asked for my thoughts on building versus buying AI support agents. My typical response is, "You probably shouldn't listen to anyone who is trying to sell it! However, I'm happy to share some objective thoughts that might help :-)"
Companies considering building their own AI support agents generally fall into two categories:
Group A: Companies that have been working on AI support agents for over six months with a significant investment in personnel (at least 5-10 people) and have generally made substantial progress.
Group B: Companies that want to start building soon (or have just begun 1-2 months ago) or have invested in 2-3 people for the project.
Perhaps surprisingly, it's usually the Group A that wants to work with us, even though they've already made good progress. They've had enough time to realize how challenging it is to do it well and that, over time, there's no strategic advantage to building it in-house as tools become readily available to solve the problem out of the box. As an analogy, I don't think anyone would try to replicate Intercom, Zendesk, or Salesforce in-house these days (it’s quite obvious in hindsight). It’s much better to focus on efforts which are specific to your company instead.
For Group B, I usually offer the following advice based on the most frequent mistakes I’ve seen to help them succeed:
Treat it as a strategic priority and allocate a team of at least 5-10 people full-time to building it. Don't treat it as a side project!
Progress will come as a step change. It takes only 1-2 months to get something that works 70-80% of the time (which isn't useful in production). It then takes 9-12 more months to achieve something that works 90% of the time. Depending on your quality standards, you might not see live results for a long time.
Give your team enough space to create that step change. You can't expect gradual progress every month. Also there is no general blueprint for high quality results that your team can just copy. Some failed attempts will inevitably happen.
While it's tempting to think of this as another platform-building project, you need more than just engineering expertise. You need someone who deeply understands customer support (a domain expert), is willing to read thousands of conversations to improve and test their work, can instruct LLMs and create sophisticated chains of LLM calls. Let's call this person an "unfussy unicorn AI engineer". You can't approach this project with a pure engineering or classical machine learning mindset, as LLMs behave very differently.
Finally, set your team an informed target. To give you a benchmark: the best tools out there can achieve 80% handling time reduction for customer ops work like customer conversations plus back-office processes that underpin those conversations. Make sure you have a plan on how to get there, otherwise you might spend a year building something trivial and delay the crucial business impact.
If you're considering building an AI support agent in-house, please reach out. I'm happy to offer a free 30-minute advice session.