Catalyst or Casualty? Surviving the Change Reaction

[Speaker 1]
having the same context that the AI and the agents can leverage, and with one focus, the customer value, showcasing that in every single step that there is. And this is an example of a bigger scale transformation that we are actually undergoing just right now. Now, a few examples about what we've actually built already.

Starting with this one, support, the obvious one, the most repetitive one, and usually having a lot of historical information, data about previous tickets, something that should be fairly easy to automate. And we have built a lot. We have built with N8n, we're using Atlassian robo-agents to do ticket triaging and categorization.

We have Claude to do logging and handovers. And we're also using something called Quora. It's an internally built platform that we are really excited about.

If you want to know more about that, happy to tell more about that. And one key here is that none of these solutions have been built from top up or they've been given by someone above, but they've been built in good coordination with our AI enablement team. And Dylan Bolton here, could you show your hand?

This guy. Good, thank you. So if you want to know more about these things, talk to that guy.

But anyway, everything is built with the team and for the team. And you can see some estimates of saved hours. Dylan can tell more about that too.

But the key thing is that the usage, adoption, and ROI, all is tracked centrally. And if someone is not using, or if nobody's using a tool, it will be deleted or revamped and reviewed periodically. The next one is something that our CSMs are doing quite a lot, verified outcomes.

And at Relics, we have a lot of data from our customers, retailers, and manufacturers. And we have the luxury of actually calculating and easily monetizing the ROI that the customer is getting from our solution. But it's really data analysis heavy.

There is a lot of data. Previously, it might have taken 40 to 80 hours even work to create a one verified outcome out of our customers. What we did was we created a cloud skill or cloud plugin for our CSMs. Something that starts with intaking the customer KPIs and context as an input. Then it does a bit of a validation that the data is good. We have a good period against which we are comparing baseline. And no weak data gets through that.

Then we are analyzing the KPI movement. Are they going up? Are they going down?

If they are going down, we're still surfacing those because CSMs need to be aware that the customer's KPIs might not be tracking positively. We need to know that because then we can act upon it. Then the system goes and monetizes this, calculates the ROI, calculates the payback.

And finally, what it delivers. It delivers customer ready deck that you can go with and start the discussion with the customer that are you seeing the same numbers. It creates a one slider for our sales motion to be used as a reference.

It creates an Excel audit file that every single number is tracked and we can check those. And the latest one that we, I don't know if we've added it already, but we are also pushing that verified outcome into Gainsight CS timeline because we want to find those verified outcomes. We don't want to keep them in our SharePoint or wherever.

Days or weeks have become hours with this one. A huge saving. But something that one of our CSM pointed out and they were right.

If we, with this solution, we might end up showcasing our customers the numbers that we don't necessarily understand. That is correct. But we have a strict rule that while AI or Python in this everything, it does the math, but CSM or that human needs to own the claim.

They still need to be responsible of those numbers when they are bringing that to their customers. With R&D, as I told you, they've gone through major changes over the course of a year or so. What we came up with and how do we bring them closer to the customer?

We're using our beloved community. So with community, we can, and with the community MCP, we can create trend analysis. We can surface topics of the product manager's area.

What the customers are talking, what do they have on top of their mind if they have issues. And the community provides the kind of first contact point if the product manager wants to know more about it. So they go to the community and start a conversation.

And here's a good example of one of our R&D leads going into the community. There's been some discussion about whether to do this and that. He posts two options or actually three options.

And then a customer says that I would lean towards option two. And then Tero here says that, okay, that's a good idea. Let's do that.

And obviously they deliver through AI a lot faster than before. Now, this is again a good example of how R&D can be closer to our customer. Customer is a friend that you should be building with the customer, not for them.

And I think this was mentioned at the keynote once again. So really, this is the key mantra for R&D today. Now, the age old question, build versus buy.

And to kind of showcase a good example of this, and buy. And I wanted to take this example to showcase what happens if the platforms do not open up. Community platform opening up.

So our PSA system, it's not been, let's say, the best one that there is. PSA, by the way, professional services automation. So what our professional services teams used.

So we have created around it a workload estimate tool for our projects that connects our CRM and PSA tool. And the team actually built a mini game inside of the tool just for fun of it, because it's easy, and to drive adoption. I've played already like 100 times that little flappy game that there is.

It's really fun. Now, StarMap is our resourcing tool to do the resourcing before the project starts. And our PSA system never supported this one.

So we built it. It took a couple of months to build. Compass did about two months too.

And the final one is time tracking. And this is something that we delivered around three or four weeks with our team. And our people, they have hated the current PSA system and the time tracking.

Nobody loves that. So we created our own that connects your Outlook and pulls some information that what have you been working on and makes it easier. And the fourth one is a bit of a personal one.

So over the last four or five years that I've had this current position, I've been always really sort of frustrated that I don't have a good visibility into the projects that we have internally happening at Trellix. I've tried Jira. I've tried Asana.

I've tried Notion. And nothing worked. A couple of weeks ago, one of our AI PMO team member posted something that was close to what I've been dreaming of.

I contacted him, gave the specs. And just last week, he delivered the first prototype and working version of it. And we are actually starting to test it hopefully this week after we're out of here.

Again, four years of frustration delivered in a couple of weeks and less than 20 hours of work. And this is the name of the game. What happens if the platforms do not open up?

We're going to build around it. And this is also my invitation to Gaineside that opening up that platform even more so that us customers can build on top of it. We really want to do that ourselves and we're sort of itching to get hands off with the latest and greatest things.

And the final one, something that I've already referred a few times, is the customer memory. And I've explained that we need to have all the context in order for the AI to work accurately. And everything depends on that the teams have a common knowledge of what is happening within the customer.

And in center of everything, we are using Gaineside CES and staircase to consolidate all of the information into one place. And then again, using the MCPs and other means to surface that information. We're bringing in a lot of information and data from different sources.

So we're not using multiple different MCPs, but trying to consolidate everything into one. Even the voice of the customer from the community. Staircase AI handles our emails and transcripts and some of the incident ticket, categorizing them and doing some sentiment analysis on top of it.

And this is the sensitive part when we are tapping into people's emails. That we need to be really careful with. The next hurdle will be to share this insight, so that we don't reveal any of the sensitive or confidential data objects like opportunities or emails.

We want to keep those within the accounting, but we want to share the higher context, for instance, with the support. If they are working on trying to figure out an issue with some, well, some issue, they might leverage that information. Let's say, if you've been using cheat sheet, that level of information, higher level of abstraction information that there is.

The goal is simple. One place for all of our customer insights and something that the whole team can leverage more. And for all of this to work, the foundation is the new gold, how I put it there, the transcripts.

Who would have known, by the way? A year ago, we were still here scribbling down our notes or in our customer meetings. Now we're fighting for the different or getting our hands to the transcript for us to do the meeting summaries.

This is really, really interesting development that we've gone through within the last 12 months. But here is the more uncomfortable part. That how can we use these transcripts without making the customer feel uncomfortable of sharing, sharing their insights, sharing their worries, et cetera.

Because if the customer feels that everything that they say is kept, shared, analyzed, you name it, categorized, they will start using any trusted advisor approach with our customers. The most valuable thing in our line of work is that the customers can share freely. And they are not afraid of being, or we bring those things back.

And I want to close now this presentation with a discussion that I had with a colleague of mine the other day. We were having lunch and we were talking AI, as we all do. We were talking about asking the next question and we were talking about transcripts.

And then he said something that really, really, really struck me. A good relationship is as much about forgetting as remembering. And that really hit me.

Because not only I've spent a better part of my session here telling you about AI that never forgets customer memory, transcripts are the new gold. But he was right. Between us people, forgetting is actually a feature.

That we let go, we know when to let some things go when it's said during a bad day. Forgetting is how we forgive. And that's my answer to the question that was in the previous slide.

Customers can share freely with people that they can trust. And overall, this transformation that we are doing, it's people first. We need to also think customers as people.

And I think for both Gainsight and Relics, we have kind of a common idea there in the background, purpose and mission and values. I think Gainsight is something that being the living proof that you can win in business while being a human first. Did I get it right?

Yes. Whereas at Relics, it is customer is a friend. And none of these things, neither one of these slogans you can hand over to a model.

So the key is to understand what to hand over to AI and what to keep ourselves. And my argument, let the AI take the admin and we take the friendship and relationship. That's it.

[Speaker 2]
And in the spirit of forgetting, maybe you forget that I can't pronounce your last name.

[Speaker 1]
It's very easy. Idan Haimer. Idan Haimer.

[Speaker 2]
So I really, really, of the many things you said, I think what struck me is, yes, building all that context, building all that, you know, mindset and way of building a habit in the organization while still be that human-like argumenting and challenging, what actually are we doing with that information and how are we enabling each team to build a piece of that puzzle to help with the overall focus of customer value? All right, we're going to jump into question.

We're going to have probably a bit less than 10 minutes. Thank you so much, team. Right.

We have the most voted one. Can you share a verified CSM example of an AI driving measurable revenue impact? For example, revenue retained, expansion generated through AI enabled QBRs.

How do you attribute the impact to AI? We have loads of questions. I'll let you tackle it.

[Speaker 1]
Oh, thanks. Gee, thanks. To be honest, this is like having really a KPI that we have seen moving.

I think it's still about the efficiency. Are we being faster? Yes.

Do we produce better quality? But does it show in retention, for instance? Too early to tell, to be honest.

I honestly believe that for only the last six months we've had some tools to tackle this, even though we are talking about starting to use AI 12 months ago. It was still early player playing around with the tools and not really a progress. I think for support we can already see that trajectory, hopefully soon also with CSMs. As said in my presentation, it is a slower progress for our post-implementation teams. It's not going to happen just like that. There are bigger topics there.

[Speaker 2]
There's a lot of productivity and anecdotal good feedback that I think a lot of the teams, especially that I'm also working with, are gathering, but the revenue or business impact will come and it's where in the market we probably need to challenge ourselves and keep discussing what's the way that you found helpful and what kind of metrics can we move and how. Imagine you go back 12 months. What advice would you have given yourself when you were starting or bringing the teams into the AI journey?

[Speaker 1]
Access is not adoption. That's the first thing. I wish that we would have done that a bit sooner.

Then again, the MCPs came about six months ago, so that was the breakthrough when we started to get access to the data. Then it made life much easier, but to have those sessions that how do you really use AI, we would have done those earlier. Then again, you never know what Anthropic will release next month, so it is a bit of a black box.

Throughout the spring, it was like, okay, that idea went out of the window and towards the next one when co-work was presented, for instance. All of that, it's all about reimagining the work again and again and again when something new pops up.

[Speaker 2]
Of everything you've measured, and maybe we can phrase it also you've implemented so far and that you've just exposed, what was the hardest to prove that was actually working and how did you crack it? Since you did have to gather a lot of teams and probably a lot of resources and investments, so how did you manage to prove the values you were going?

[Speaker 1]
Good question. I think Dylan might be actually a better guy to answer this from the support side, but I think for many, it was still the adoption and the resistance to start using the tools. There was always a reason that, no, it doesn't work with my customers or it doesn't work with my data.

There is always this basic human resistance with every single new tool. You can think of AI as a new tool and you need to do the transformation and all that change management. That has been one of the hardest parts and it's getting even harder when we are designing the processes once again.

I don't know if that answered the question, but still.

[Speaker 2]
The implementation of your new AI solutions, has that reduced your post-sales team headcount or has it enabled more customer interaction and value delivery or both?

[Speaker 1]
Obviously, the book of business will grow within our CSM, so we do not recruit as much, even though we still see that 30% growth in our overall ARR and subscription revenue. That has balanced it quite a bit. The recruitment rate has stopped, as you can imagine, from being 160 10 years ago to 2,400 today-ish.

It's been quite a lot of recruitment, but not anymore. I think that's something that we are also seeing quite a lot in the news that there are unfortunately quite a lot of layoffs.

[Speaker 3]
All right. We have two maybe last ones. How do you tackle AI pushback?

[Speaker 1]
With patience and showcasing how the system works. No, yeah. It's hand-helding.

It's showcasing. It's really going almost team by team, showcasing what the system does. I did last week six or seven enablement sessions for the Staircase AI with each country team.

In our session, it was the first one and we will book the follow-ups later on. It's just being there, hands-on showcasing what is there, what is the value, why are we doing this for Staircase AI. At the moment, it's still about the customer memory, that it survives if there are handoffs and that we can start building the AI that really works and knows the context and design the work.

[Speaker 2]
The last one, I think it's very interesting because it's connected to this one. What about the learnings? You're doing these sessions, right?

But also, what's maybe a more programmatic or systematic way of not just uncracking how AI is working, but how do you enable it and talk the narrative on the learnings back to the organisation?

[Speaker 1]
Yeah, obviously, we want to do or we have planned at least to do a lot of, again, enablement sessions, but it is also a bit of a wild, wild west at the moment. What I mentioned there, the one platform called Cortex, that is a platform where you can use all of the data that we have at Relics about ourselves, but in the future, also about the customers. On top of that, you can build whatever tool you wish to build.

It's a white-coding app also that we built for our internal purposes. It's been online for a couple of months and it's been still developed and there are almost 500 different apps there today that people have created. For instance, we created an app for following up our NPS survey and the results and insights that there are.

Someone even built a tycoon game inside of that platform. You can build whatever you want. You can imagine, out of a sudden, we have 400 new apps, and everyone's doing this and that, maybe some overlapping here and there.

It's bloody difficult today. This is something that we've been discussing also with Dylan here, that we're in a place where we still want the people to explore, go nuts with the AI, but we need to start also consolidating and centrally push the actual apps that and the best practices and the workflows, reimagining the work that the people should be using. For instance, for staircase, one of the CSM, one of Chris's CSM, sent me a message that could you share some best practices, how to utilize it in day-to-day operations and day in the life of CSM.

That's the next thing that we need to start gathering and sharing those globally, those best practices. It's a bit of a balancing act between going nuts and here's your tools to use. Proven tools, perhaps.

[Speaker 2]
I think we're out of time. I know we have more questions, so I would invite, we have some brilliant minds from the Relics teams around, so I would encourage you to connect and maybe just get some answers directly from them. Once again, thanks so much for coming and for telling us about your journey with AI.

Thanks again. Let's give it up for Timo. And thank you as well for participating.

There's half an hour break and then at a quarter to four, all the sessions start again. Thank you.