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We spoke with Angelo Livanos, Senior Director of Global Support at Lightspeed Commerce, about burning topics in the customer service space right now, like getting stakeholder buy-in for AI, approaches to rolling out the technology, managing change, and keeping a pulse on employee and customer satisfaction.
Since implementing Intercom’s Fin AI Agent, the Lightspeed team has seen impressive results, such as AI resolution rates of up to 65%. Having seen such success with AI Agent, they decided to roll out Fin AI Copilot, which has resulted in their support agents being able to close a whopping 31% more conversations daily.
Let’s see how they’re doing it.
Can you tell us a bit about Lightspeed, your role, and how you became a leader in customer service?
Lightspeed is a one-stop commerce platform that empowers merchants around the world to simplify, scale, and provide exceptional customer experiences. I’m the Senior Director of Global Support for our hospitality business and have been in the customer service space for about 18 years.
I started on the front lines, like many support leaders, and have held various support-oriented roles during that time, particularly in the tech space – telecommunications, cloud services, hosting, and data centers, etc. I have always loved tech and understanding how it all works at a deeper mechanical level. Being able to pair this passion with helping others was something I naturally gravitated towards. Over time I was fortunate enough to transition into leadership roles where I was able to focus on the development of operations, people, tooling, processes, and strategy.
“Customer service has always been a passion for me”
My journey has been very linear, progressing naturally as I gained experience and moved into roles that allowed me to focus on developing others and creating strong contributors within the teams I led. Customer service has always been a passion for me, and I’ve worked to prove that it can be a rewarding long-term career, not always just an intermediary stepping stone to something else.
What were the driving factors behind deciding to adopt AI with Intercom, and did you face any initial implementation challenges?
Lightspeed has always been a very AI-forward company. We had been exploring opportunities to leverage AI technology internally as well as for the benefit of our customers through product enhancements. We also have a great relationship with Intercom so were excited to explore their AI-powered features as soon as they became available to test.
Personally, I feel like It’s a really exciting time to be in the support industry. I’ve been in this space for 18 years and typically, support is saddled with dated and clunky home-grown case management tools. We’re now in an era where Intercom and other platforms are building support-first, amazing tools like Fin AI Agent and AI Copilot that allow us to do our jobs better, further enjoy our work, and deliver better experiences for our customers. That’s one of the great things about Intercom – the speed at which the team ships new features and improvements. The velocity has been awesome. We see that so rarely in the wider SaaS market, whereas with Intercom, every other day you’re like “hey, check out this new thing,” which gets our team excited and constantly adds value for us.
In terms of implementation challenges, the biggest one for us was change management. We were all on board with rolling out AI, but with a contact center of hundreds of agents supporting our customers in multiple regions and languages, we couldn’t just flip a switch overnight.
How did you go about solving that challenge?
We wanted adoption and go-live to be smooth, so we focused on doing plenty of training and enablement, testing, and coordination with teams from across the company – it was very much kind of a ballet to make sure everything lined up and that everyone was equipped to succeed.
1. Getting set up for success with training
Training was the first key focus area. We leveraged Intercom Academy for the foundational training, which provided our team with a solid understanding of the tools. We also have an in-house training team that developed training modules specifically tailored to our own processes and workflows at Lightspeed. We ran these training sessions in the weeks leading up to the rollout to ensure that our team members were well prepared. This was particularly important given the diversity of our operations, which span multiple languages and geographical locations.
2. Providing ongoing support and enablement
After the initial training, we implemented a “hypercare model” post-launch. For several weeks after the go-live date, we had dedicated Slack channels and forums where team members could ask real-time questions or flag any issues they encountered. This allowed us to address concerns quickly and fine-tune configurations as needed, ensuring that the rollout was as smooth as possible.
3. Focusing on clear, frequent communication
When it came to change management, we recognized that the key to success was not just in training but in communication. We made sure that all relevant teams were informed about the upcoming changes well in advance – this included both the support agents and the peripheral teams who might be impacted by the new workflows. We provided adequate notice and clear instructions on what to expect, which helped to minimize any surprises on the day of the launch.
4. Cementing alignment on the vision and goals
We understood that change management in a large, geographically dispersed team required a coordinated effort, so we worked closely with our leadership team to ensure that everyone was aligned on the goals and benefits of the AI implementation. By involving various stakeholders early in the process, we were able to build a consensus and foster a sense of ownership across the organization.
What did the tech rollout process look like?
The tech side was really fascinating for a few reasons:
1. We had the benefit of having a lot of products with their own Intercom workspace, so we were able to A/B test rolling out AI in a very responsible way.
We were privileged in this sense. It allowed us to implement AI in one product and not another to see what the difference was, as well as do isolated testing.
2. We found that the slower rollout model was not the most optimal approach.
I’ll use rolling out Fin AI Agent as the example here. In our slow approach, say we had 30 topics that we know our team usually gets; we picked one to test with Fin, monitored performance, and then gradually exposed it to more and more topics over time. Whereas with other products, we cast a much wider net exposing Fin to more up-front. We actually found that by expanding the dataset early on, we saw better results and were able to gain enough data at scale to actually determine strengths and areas to optimize and improve.
“The challenge with incremental testing and implementation is that you’re getting such little data”
The challenge with incremental testing and implementation is that you’re getting such little data. It’s hard to look at that data on its own and confidently marry it up with all the other metrics you typically report on. If they move up or down by a percentage point, there’s no way to really attribute it to that test because they’re too far removed. But if you apply it to the whole experience and see a material change in anything, you’re able to draw a more solid conclusion and prove value a lot quicker. When we were reporting to more senior stakeholders, showing them those bigger, impactful numbers was definitely effective in showing value and improvements.
What results have you seen since implementing Fin?
One of the biggest changes was our human/AI handling ratio. On day one of rolling it out via our “all out” approach, we were seeing between 35-40% of our conversations not needing a team member. And pretty quickly we were able to fine-tune that to a point where we’re now seeing upwards of 60% AI handling across most of our products.
When you consider that we manage high conversation volumes each month, that creates a very positive impact in our operations. We’re dramatically augmenting where and when our team engages with our customers and that has a knock-on effect on our resourcing requirements and the allocation of where people spend their time. It has freed up our agents to focus on so many other areas of impact for our customers and has given us the space to rethink our career progression paths and the roles within our team.
Outside of that, we also saw results with AI in a number of other areas, like:
- Resolution rate: Fin is currently resolving between 45-65% of our support volume across our workspaces.
- Involvement in and ability to answer queries: Fin is now involved in 99% of our conversations, and is able to provide an answer to 95% of them – even more complex ones.
- Faster handling times: Now that the triage of a new conversation is being done by Fin, human time is being reduced on each case.
- Reduced training time: With Copilot, our training times are being reduced because we’re not needing to do as much long-tail classroom training – they’re able to leverage the AI for that level of help.
- Cost to serve: AI is perfect for those really basic, low-complexity/education questions. It’s so much more effective to use features like Fin to resolve those questions for a much lower cost so our agents can be freed up to handle the complex ones.
- Customer satisfaction: ChatGPT and similar AI tech is now a part of many peoples day-to-day lives. Our customers are responding well to having access to our AI agent and the same friendly team available if needed. Our CSAT has remained stable since our rollout.
There are all of these auxiliary benefits that in isolation are hard to link to a dollar amount, but the roll-up aggregate benefit is quite tangible.
How did your exec team, support agents, and folks from across the company react to the implementation of AI?
Exec team
The tipping point with regard to getting buy-in and a positive response from the executive team was being able to sandbox Fin AI Agent. That allowed us to see – and show – what it’s capable of firsthand. We set up non-customer facing tests and were really impressed with the results, and when we were able to quickly show its effectiveness and impact, there was a lot of excitement internally. So overall, when it got to the stage of putting Fin in front of merchants, we had a good level of support from our leaders.
“We were very open with the business and brought everyone on the journey with us”
I think us being very forthcoming with the results and the data as we were getting it also helped. We were very open with the business and brought everyone on the journey with us. We also made sure that we engaged our internal legal and security teams to ensure compliance.
Support team
There were two distinct cohorts on the global team – one being long-time Intercom users, and the other being agents that had never used Intercom. And Fin AI Agent got a very positive reception from both.
For agents already using Intercom, the impact was primarily on volume – fewer basic queries reached them, leaving space and time to focus on more complex issues. The AI triage allows the team to jump into the meat of the discussion faster.
For agents that were new to Intercom, they were getting big lumps of goodness all at once. Coming from other customer service tools, they were getting an all-new platform with a nicer composer and workspace to work in, plus AI benefits like decreased volumes and more time.
“It’s building a bit of a cult following of people that are saying, ‘This has done some pretty great stuff. How do we tap into this now?’”
One thing that did need to be adapted was the training program for new team members. With AI handling a lot of the basics, new agents weren’t getting exposed to the fundamentals as much and getting harder queries upfront. I wouldn’t say that was a downside, it was just something that we had to adjust our training programs to account for.
Wider company
And outside of just our team, one of the funniest dynamics I’ve observed is how many people now ask me with pure, positive curiosity how Fin works. I’m getting a lot of questions now from non-support parts of the business, like “How does it do that?” and “How can we get access to it?” And it’s building a bit of a cult following of people that are saying, “This has done some pretty great stuff. How do we tap into this now?”
What are you most excited about for the future of customer service in the AI era?
The next big leapfrog change I see happening in the AI space is the analytics component. There are so many opportunities to leverage AI for data analysis that I’m excited to take advantage of. For example, I believe that AI will:
- Reduce the need for reliance on human categorization.
- Add depth to data and insights.
- Allow us to better detect opportunities to improve.
- Allow for analysis at scale in a quicker and easier way.
- Fuel stronger collaboration between customer service and product teams by being able to capture more detailed customer feedback and sentiment at scale.
I think AI is really going to unlock a lot of interesting stuff from a reporting standpoint, so I’m excited for what comes next.
