Inside Gainsight’s Roadmap for the Next Era of Customer Success

[Speaker 1]
Hi everyone, I'm Liam McGillivran, Regional Vice President for Customer Success and Solution Consulting here at Gainsight and I think we're going to take the next 40 minutes or so just to walk you through the new features that are coming out and we'll try and label these so that you can see what's available now, what's in beta and what's future looking from a roadmap perspective. I think either side of this is, is there anything beta that you like the look of, feel free to speak to your CSM and see if you can get on to the waiting list for that as we start to open it up. So in terms of the product teams in the room, I see Rakesh in the back, I've got to go through a safe harbour statement which is essentially, you can read the wording yourself, that these are timelines that we are planning and the features that we're looking to release but there may be some changes that come out.

So take them, take them as guidance but that's what the plan is right now so they may be subject to change. I think in terms of when we're going into the platform itself, one of the things we've recognised is that the CS operating model is changing and when we think about that, we want to just set the scene of why we're building what we're building. So in terms of the customer success, the four changes that we see are first around the agents which are detecting, they're analysing and they're also able to take action now, which is something that we didn't used to have in the past.

The second thing is that context is king. Context is what's going to make AI super reliable, how you get the right answer first time rather than having AI guess the end result when it doesn't have the full context behind and we've all been there with those hallucinations in the past. And then the third one is that intelligence follows the work wherever it happens.

And so when we think about that, it's not just living in the tool that you're working in all the time, it's some of that will happen in Salesforce, some of that will happen in Slack, some of that will happen in Gainsight and actually how do we start to link all of those together because the workflow needs to continue. And then the fourth is that outcomes matter more than the activity. So when we think about how we're measured by CS in the past, it's been things around calls and meetings and EBRs and as AI is starting to take on some of those responsibilities and actions for the repeatable work, how do we start to think about that in the new model?

And that's where we need to focus on things around expansion and adoption and actually kind of the risks and renewals that the CS will work on. So I think where most of us are today with AI is that we've rolled the dice on how we use AI inside our business. So when we think about that, we've run some pilots, we've tried some new things and some of those have worked and some of them haven't.

And the reason why most of them haven't worked is because the context wasn't available or context wasn't all connected for the outcome that we wanted to achieve. And so when we think about AI going forward, it's how do we make sure we have all of this connected data in one place so that we're able to make the right decisions? And working with a client earlier, they talked about how they've got their Cortex system, which is kind of bringing all this together so that they know that when they ask the right question, they can get the right answer for their business.

I think, where do we play from here? We wanted to start with a clear point of view. And so our vision really hangs around the understand, decide and act framework that we've put in place.

So understand means every conversation that happens inside your business, every relationship, every usage signal, it's all together in one picture. It's not just the data in CRM, but it's also that data that lives in phone calls, in your inbox, as well as obviously the systems of records that you have in place. Decide means actually turning that picture into evidence-backed risk expansion, hand over notes based on that core context.

So your team should always be able to see how the AI has got to the decision point that it's in. And then the last piece is the action. So actually getting it done.

And so that can be through your CSMs, or it can actually be an agent delivering that action on your behalf. It doesn't have to be human-led activity. But actually making sure that we're choosing the right action approach, depending on what we're trying to deliver.

If it's relationship-based, we want that to be a human delivery. If it's process-based, we can get that straight through from an agent. It doesn't matter who's delivering that content.

And so in terms of what we're looking at, we're going for three moves that are going to make this real for us. And so the first one is the one platform. And you've heard this earlier today with Erica.

This is really about staircase intelligence that we have, actually now native inside customer success. So those signals that your team are already looking at, risks, adoption, expansion, relationship health with different stakeholders, rather than it being a separate product, that's unified into one data set and into one model. The second is the deeper intelligence.

How do we get more of those customer conversations into the overall platform? Not everything is going to happen on a recorded Zoom call, for example, or it's not going to be on an email. It could be a phone call that you make directly to a client.

So how do we make sure we capture all of them? And also, it might just be some notes that your team are logging. And then the third is headless by design.

Again, we mentioned this earlier. This is simply meaning that our intelligence and our actions are available across any of the platforms that you want to work in. So whether you're doing some of those things in Salesforce, if you're doing things in Claude, if you're doing it inside your own LLM that you're building internally, we want to make sure that's available and you can take that wherever your teams are going to live.

I think the key thing with headless by design, when we think about this, is this still needs to respect all of your organizational rules around data visibility and in terms of what data those individuals inside the business can see. So for example, a CSM can see their portfolio, maybe got a different geography that we don't want to see, and in the geography of my organization's book of business. So it's going to respect all of them and even down to the granular field level position, field level permissions that we've got.

And so the one platform, as I mentioned, bringing staircase into CS so that the signals that exist across the customer, they're going to be layered inside all of your other data points you've got in place. And the first way that we want to show this is this feature that's now live today. So account intelligence that's native with inside CS.

It's kind of the clearest example of what one platform means for us. And so what this actually does is it takes all of those unstructured calls, meetings, all of that data, and it starts to present them into AI created fields. So rather than having to go and build a rule that tries to look through those AI signals and then generate you some, generate your populator field that you've created, we're going to create these out of the box.

So you're going to have a list of when was the last time we actually had a meeting with that client? What was the stakeholder engagement like? What's the different scores?

They're already pre-built. And when they keep adding those different AI fields for you, just again, to stop the admins having to go and build it off the data that already exists, we'll start to bring them forward. And the next one that's already live is how we actually think about the relationship tracking.

So every successful CS account and program that you have in place should be built around multi-threading and stakeholder engagement. So we all know that if you've got one person you speak to and that's the only person you speak to the account, your whole relationship is at risk based on their sentiment and based on their tenure inside that organization. So this is a great way to understand who are the different stakeholders that we have that we work with a client and understanding what was the last time we had an interaction with them?

What was the last sentiment we've got from their calls or meeting transcripts? Did they even attend the call or not? It will even pick up things like did they have their video on or not, depending on what that context looked like.

So as soon as we start to see, hey, this person has stopped attending these meetings or calls, we can start to highlight that to the team. And so it's a way of not only mapping who are the people we're talking to, but what also is the relationship strength that we have with each of those individuals by the execs inside our organization. In terms of the next thing that's coming through is this is for customers that are large with multiple products.

So what we recognize there is that actually, as you have multiple products in an organization, they may be surfaced by or serviced by different members of the team. It might be you have different CSMs, you might have product adoption consultants, you might have different support team that helps manage them. So how do we start to give you a blended view of all of those different conversations that are happening so that a CSM has the full context without some of that nuance getting dropped because it's being handled by a different product team?

So the way this now works is that with Inside Staircase, you can go through and configure the different products you have and the product descriptions for them. And as the system is looking through the communications and content, it will automatically start tagging that entry with the products that it's regarding. And so the good thing about this is if any one of you guys are using relationships with Inside Gainsight as your kind of go-to-market model with accounts and then the different product lines, it will automatically assign a relevant communication sentiment to those different product layers.

But this is also going to not be dependent on relationships for those that don't use that because that can be slightly more complex than you need sometimes. But actually, you'll be able to still go into there and configure that and get that information sentiment out without measuring your products at each individual level. And then the next one that's already live, and I think Erica kind of brought some of this up with Slackbot, is how we're bringing together all of our MCP and everything available.

If any of you guys are already Staircase and CS customers, one of the things you'd recognize is you've got Ask Staircase in the Staircase product, all the Staircase views, and you've also got Copilot inside Gainsight. And those two didn't talk together behind the scenes. So now you can go to one place, for example, go into Copilot with Inside CS, and this will actually allow you to ask that question and it will go and search all of the connected Gainsight services and be able to synchronize all of that together for you.

So the best thing about this is that you don't have to get different answers. It's got the full context behind the scene. And then the last piece that's kind of just about to be released when we think about AI is AI health scoring.

So similar to how we've created AI fields where we're automatically going to populate that data based on without you creating everything from scratch, we're also introducing the AI measures directly into the health score. So when we think about sentiment score shifting, that will automatically be updated, whether it's based on engagement rates, whether it's based on the number of people because those meetings are coming through. Maybe it's based on the exact sentiment that's coming through or the exact engagement on an account.

All of that data is going to be populated directly into the health scores. And you can just drag and drop them into your normal framework. The benefit here is that that then goes alongside your kind of more holistic measures of usage, support to get data, etc.

But you're not having to build all of these scratch from the data points below. And then I mentioned around the sources for the activity for things like staircase and the analytics. So at the moment, if anyone is using AI follow up or staircase, they'll know that it's based on emails, meetings, call transcripts and things like support to get threads.

But we recognise that clients are logging notes in meetings, they're logging in different places directly in Gainsight timeline, for example. And we wanted that to be a direct source of data to also inform any of the sentiments going on in the account. So we're releasing the ingest meetings.

And for the admins out there, don't worry, that's not going to be everything that gets logged in timeline is going to sink into sentiment. You've got configuration there to be able to decide what meeting type, what conditions is going to be then brought through directly into the team. One that I think most CSM teams will be excited for is AI success plans.

So this is closed beta. So please do if you do want to join this, go in and ask your CSM if you can join that plan, that beta plan. And so what actually happens in here is if you think about success plans traditionally, they have to be created by a team.

Normally we'll go through an exercise of someone creating them and then they'll go stale. They don't get updated as often as you want or they get updated very quickly. It doesn't get all of the context.

So with AI success plans, the benefit here is if you've got things like call recording connected and that account is now one, the system can automatically generate a success plan based on the previous conversations that have happened with the sales or the onboarding teams. So it's going to create that skeleton view for you. And then CSM can then decide, actually, do they accept those things?

So they've got full control. And then what will happen is as a client stays with you during the adopting phase, as the conversations happen with the CSMs, as those transcripts, as emails happen, it's actually going to be looking through them and seeing, is any of this data related to a success plan that I have open on that account? If it is open on the account, it will then go and actually make a suggestion to say, we can close this item on the success plan now because it's been achieved based on the EBR that we've just had or the call we've just had.

It again can suggest future actions for the success plan. Again, all within the CSM's control. It's not going to create all of this without the CSM's permission, but essentially it's prompting them and suggesting them to update the success plan based on the latest changes.

I'm going to pause. I was going to do Q&A at the end, but I'm conscious a lot of you might want to go to Fabric. So if there's any questions on any of the first pieces, feel free to shout them out or you can go into the app and there's a slider in there.

You can put them in and we can kind of pick them up as we go along. I'm just going to pause and see if there's any questions on this piece before we get into the next phase. Go for it.

Yeah, so I'll double check. I'll come back to you. Most of the stuff on here that we've seen should be available in your standard contracts.

I mean, the only thing on there is obviously we mentioned around things like relationships as a concept within Gainsight. That is a paid for extra. That's the multi-product.

But as I said, that's already available now. If you have relationships, you can buy relationships and activate it. But we are going to remove that as a condition in the future state.

We just need a new date. I need to get a date for that one.

[Speaker 5]
Quick question on the success plan.

[Speaker 1]
Oh, sorry, there's a mic, there's a mic, who wants it?

[Speaker 5]
Sure, question on the AI success plan. How will it prompt CSMs to accept a change? So, for example, closing a CTA or making a change to the plan?

[Speaker 1]
Yeah, so there's already the notification sensor inside the platform. So we'll surface them as available now. But the main thing for us is that actually when we've got things like your productivity tools connected, like Slack, and we've talked about going headless, it'll be able to prompt them just in whatever productivity tools you want.

So we'll make them available through the headless integrations that we have, but also through notification hub or prompting on calls to action.

[Speaker 5]
Very cool, thank you.

[Speaker 1]
No problem. Just getting Julia's steps in.

[Speaker 4]
Hello, my name is Habiba, I'm from Workiva. I've already asked this in the Slido, so you can take it off from there. But for account intelligence, does every meeting that we do at Workiva, we log that in Gainsight via Gong?

However, we have lots of customers who use different meeting links. They use Teams, they use Google, they use Zoom. Does that need to be manually logged by the CSM or are there integrations into these different meeting links?

[Speaker 1]
Yes, so Gainsight itself has the integrations. So, like you mentioned Gong, you've got Zoom, we've got Teams. The key thing there is that you need to initiate the recording.

So as long as we can get the transcript through and that's been completed, connected to AI follow-up, we'll be able to ingest that and process it, regardless if it's your main call or not. So my advice is if you've got them, make sure you've got connected. In Gainsight, for example, we have clients that want to use Teams versus Zoom.

So we kind of have to be available for all of those. So we can connect all of them to Gainsight. And then anything that gets recorded, as long as we're there in attendance, that will flow through and match to the account.

So the next thing I want to go into is the deeper intelligence. So kind of putting AI inside the platform only matters if we can see the full picture of the customer. So we're going deeper into those customer conversations, understanding more of what customers are saying and making sure we understand it as fast as possible.

So in terms of hearing more of what the customers are saying, we recognize that in your super high enterprise accounts, there's a chance that your CSMs are going to be having much more kind of, I'm going to phone my customer because I speak to them on their mobile, versus having a Zoom call, for example, that's set up. And so that's where some of those real conversations actually happen. Again, much more on the higher touch side, but it is an area of a blind spot right now that we aren't capturing unless you're hitting record on that conversation through your Teams or your Zoom.

And so actually we can connect this now to the mobile through with your call provider. And the benefit here is that every one of those phone calls, you can now capture and put through into the Gainsight platform as a full transcript without relying on Zoom or Teams or anything like that. And then typically the main one we're looking for is that any call over two minutes, we're going to apply a different level of analysis because two minute call might just be, hey, is everything okay?

It's kind of a quick check-in. It's a touch point, but it's actually not that valuable necessarily to the overall health of the account. Whereas as it starts to go over the two minute piece, we can start to then bring that data through and analyze it and we can apply the different insights and AI that we want to it.

So for anyone that has used the Ask Staircase piece for inside the platform, this is where you can ask against, regardless in one account, you can ask it against your whole book of business. So this is where you might want to say kind of which accounts changed the most this week? What's the latest theme around this topic that I'm seeing in my customer base?

Where's my next risk with inside my total book of business? And we've actually rebuilt this whole Ask Staircase piece. So it kind of takes the question and then it applies a full co-work type model to it.

So you can start to understand, it goes and gathers the information it needs across all of those different data sources. And then it writes the answer as it goes. So you're able to actually watch it as it starts to find new insights, build out across all of those different data sources rather than just looking through the local data model it had before just around emails, for example.

So the benefit here is you can actually have a full conversation with it rather than just a single question and getting the answer. You can keep refining and asking based on the answer it gets you back. Again, to get you deeper into that intelligence.

And then in terms of the next sneak peek, so this, you'll see more of this tomorrow in the day two keynote. But one of the areas when you think about the different agents that we talk about that exist in the platform, one of those is the risk agents. So that explains to you kind of what accounts are at risk, what the next action we recommend you take and why have we decided there's a risk inside the account.

We also have the expansion analyst, which is looking through our clients mentioning different products that they haven't purchased. Is there any insights in there that can help contribute towards things like NRR? That's already available right now.

And also we've got the handover analyst, which is helping your teams as you transition accounts from different lifecycle stages or to different CSMs. How do you make sure you give them a really brief, an overview of what's going on? The next one that we're bringing is our adoption agent. And so the adoption agent actually scans the CSMs book of business for usage data, support ticket data, success plans, calls to action.

And it recommends adoption goals based on those products. So the key thing here is this could be about driving new feature adoption as they get released, or it's actually taking the whole account and trying to improve the overall adoption of the overall product. The key thing here is it's going to make recommendations.

So it's going to be able to say, hey, here is an action I think you should take, but it's still on a CSM to be able to approve and decline what happens there. So again, we're leaving them in control, but we're just surfacing this insight faster without the admins having to go through and build all the different rules around percentage drops, what features are most important. The system will automatically work that out based on by your product line.

And then the third piece we mentioned the pillar as around headless by design. So one of the first things we mentioned about was we talked about MCP a lot in the last year. And so I don't think we need to really go into how that works, but essentially connecting GainSight to all of the other systems that you wanted to talk to.

But the reason why this is really important for us is that it actually brings GainSight directly into the tools you use on a regular basis without having to go through lots of different tabs, which is kind of the barrier to adoption from our perspective. And so you're able to automate, you can already turn this on right now. So again, one of the first things you'll start to see is like you can connect this to your actual Microsoft Copilot.

If that's what you use as your AI tool, you can connect it to Slack. So actually you can experience the whole of the GainSight insights available with inside that one productivity tool. And as Erica had on stage earlier, you can have that.

So you've got your Salesforce kept into that. So you can surface all of these insights and layouts and views from directly with inside those common tools that you're using already. And again, this one is all available now.

If you do want to, there is a QR code on there. So you can actually take, you can scan that if anyone wants to, and you'd be able to actually go through all of the different use cases. One of the things we've done with AI, if you haven't seen already, if you go to the GainSight website, we also have produced all of our skills.

So any skills that we've created internally or for all clients as an exchange, they're all available on the GainSight portal for you to add to Claude or Gemini or whatever your LLM is of choice. We mentioned as well, kind of bringing those, bringing those MCPs to life with different use cases. So it's not just about Claude and ChatGPT and Gemini.

For a lot of other people inside your organization, Salesforce is typically where they're going to live most of their day. So if you think about your sellers, renewal managers, maybe support agents using Service Cloud, they are going to be there's a new AI assistant across that called AgentForce. And so this assistant can now answer not only whatever lives in Salesforce, but also it will connect to GainSight and go and gather those insights from there as well.

So this is really opening that up. So if a seller, for example, wants to ask anything I should know before renewal about this call or anything I want to know before I want to do an expansion, it's going to be able to look across all of the Salesforce ecosystem and automatically connect to GainSight and gather all the information from there to go and surface that. So again, how do we start to break down these silos so that we can get this right across the organization?

And in terms of building these out, it's also thinking about if I haven't got access to certain tools like ChatGPT or I'm getting concerns around token usage when it comes to things around Claude tokens, for example. One of the things we've released recently is the Agent Studio. So this is coming.

It's in closed beta right now. Again, you can go through and ask this. But the benefit with Agent Studio is GainSight actually takes on that burden with inside its own platform.

So you can go and say, hey, I want to visualize this data in a certain way. GainSight will go and not only provide you an answer for that, it will actually allow you to actually see that visualization directly within the platform. It could be that you want to create a certain agent to run a task for you on a regular basis.

Again, you can go and put that through Agent Studio and the prompt and it will go and build that and run it inside the platform. So the benefit here is that you're not having to use a separate tool to go and do all of this. It's baked into the product.

Those assets become reusable. So if you've created a visualization because you want to view renewals in a slightly different way than the GainSight dashboards have right now, and you don't want to go through and build it manually, you can just put it straight through Agent Studio, visualize that, and then make that available across the organization for your teams. So it's great to get inside that workflow and you manage it all with inside the platform.

Again, this is closed beta. So please, again, speak to your CSM if you want access to this. So I think we've covered quite a lot of ground in terms of those three areas.

So we mentioned that the intelligence layer that's built into CS, thinking about a deeper understanding of what the customer is saying, that different context, and also then the intelligence reaching the tools that your teams are using on a regular basis. As I mentioned, some of these are in beta. Some are still to go ahead, but make sure you go and speak directly to your CSMs if there's anything that's of interest.

So I want to try and get into more of how do we make this practical? So where do you actually go and start? So I think as you start to think about, I'm going to go on a journey, the first thing you'll probably do is look on a map or just to start to plan that route.

And so what we want to be able to do is actually give you a roadmap of how you would start to build from this. And we'll share these slides with you at the end. So from the bottom, you need to start with the basic low lift features that instantly add value to your teams.

So some of you guys have maybe been using these for a little while. So the cheat sheet, for example, the right with AI to make communication creation a little bit faster. We've got things like the AI follow up.

How do we remove the burden of someone having to click through into Gmail or BCC and email to get it into the platform? All of that kind of overhead has now been taken away. Those out of the box AI insights are going to be available in there.

Again, the reason for this is these are just turn on features. You don't need to do a load of work to connect these to lots of other different systems. And then in the middle, we want to think about the more AI forward features.

So that's when we start to think about building on the foundations that you already have, you've already got from those out of the box. So this is where you think about how do I integrate this with building co-pilot? How do I think about my AI scorecards?

Now I'm confident with that data. I've got my understanding of how that is analyzing the different risks, the expansions and things like that. How do I start to bring that into a health score, which is going to be a fundamental change for your business?

That's bringing together both the holistic measures that we mentioned before, support tickets, escalations, usage type data, along with that sentiment that's automatically being generated without the teams having to manually go and do it. And then at the top layer, we want to think about those AI native workflows. So this is where MCP, AgentForce, MCP comes in when we're thinking about the agent studio that we're building.

How do I start to build agents that are going to do some of this work for me, for my teams, and actually control that from a central place? Again, the reason why that's at the top is because it needs to have good context to be able to perform at a good pace for you to trust it. So this is why we want to start building this up.

So we have as much context as possible for you to get the most successful outcome when it gets to the agent side of things. So the main thing there is you'll be able to progress through with this and our teams will help you from a CS perspective. But as you kind of go up the chain here, it's going to depend more on your capacity as a team.

You may need your Salesforce teams to help you with AgentForce, for example. So again, these are things that you can start today that should be very low level of effort, all the way to how do we start to transform your business? So in terms of the three things that we want to think about when it gets to the AI and just how to manage this inside your business, is AI needs to clearly align with your business objectives.

Think about what is the outcome that you're trying to achieve? Is it a retention outcome? Is it an expansion outcome?

Is it a better risk model than you've got right now? And then that's how you can start to think about what's the right capability that I need to then go and build to go and achieve that. The second one is there is no one path to AI success.

So you've seen the range today. There's features you can switch on immediately and get value from. There's going to be features that are going to take you a week or a month or two months to actually go and build out.

And it's just making sure that you've got the data and the context available and you've got the capacity within a team to start building these through. And then the third is thinking about kind of how your teams work. So this is where there's already lots of other things in motion.

And so how do we start to think about making the right choice in the right area? If it's a retention play we want to go for, how do we do that for CS? But also how do then we think about that across the whole of the business line?

So when we think about renewals managers or expansion sellers, how do we make sure they get access to that context when we think about it's not just CS that will be touching these clients and maybe having some of these conversations. That is pretty much the run through at a high level of the different areas of the platform. Again, keynote tomorrow.

We will focus on a couple of these. But again, please, we'll share with you at the end what's closed beta and what you can sign up for and also when the different dates are coming available. So there are a couple of Q&As.

I think, Julia.

[Speaker 2]
I can read them out if you want to. Thank you so much, Liam, for all the insights. Let's start.

We've got a few minutes. So do we have a timeline for when the AI custom success plans will be available for everyone?

[Speaker 1]
Yeah, I mean, at the moment they're in, it's in beta. So I would say normally it would be in beta for around a quarter. So I would expect in our term by the end of Q4, so Q1.

So that may be worst case January time for general availability. Come up, Rakesh, if you want. Grab the mic.

Rakesh is in our product team. So he manages all of the AI features. So best person to answer from a timeline perspective.

[Speaker 3]
So there are two aspects that we're hoping to learn in the beta. The first one is mainly the UX of notifications to CSMs showing the draft what converts CSMs to update success plans. Like this is a statement.

Success plans always die. Like the minute it's created, nobody updates it. So how do we make it auto-updating?

What's the right UX? What are the kind of things that should get auto-updated? And the second part is currently we start with ROI type success plans and then we add more types.

These are the two things that we're looking at and one quarter for post-beta, Q1.

[Speaker 1]
Thank you. So yeah, Q1 for us December. Our Q1 starts in February.

So I know that's different things for different people. So think about like end of December, you'll start to see more announcements, but anything that's going into beta now, you should be able to ask. And as long as we've got capacity in that beta plan, we should be able to get you in there.

I was going to answer the second one. So I mean, core recording is a really difficult one and it really varies depending on your industry. It varies depending on your geography.

US is a lot easier to do some of these things. Finland or some of my team here, it's not so easy to do these things. So we have to look at this in our case by case basis.

Ultimately, we can provide the technology. You have to make the decision within your business of how you want to apply the correct rules based on the jurisdiction you work in. But the main thing there, that isn't going to be a barrier.

That channel isn't going to be a barrier going forward. If you're able to adopt it based on your business, that's your choice, but it's not going to be something that's a limitation from the product.

[Speaker 2]
I can read out the second one just so that's recorded and people know exactly what questions. How do we find out about future betas? I'm only just finding out about these now.

And it would have been really good to be proactively notified about them so I could get involved sooner.

[Speaker 1]
Yeah. So I think we do a regular product roadmap session. So it's a webinar.

If you're not signed up for the marketing, please make sure you do go and sign up for that. You can register for those alerts. We do them typically like once a quarter.

We'll go through the future roadmap. That's where we actually announced, okay, what's going into beta, what's not. And then you can go and outreach to your CSM if it's something you want to be part of.

So if you're not attending those regular updates, that is the best place for you to go and find it. There's also the Gainsight community. So communities.gainsight.com.

You're able to go onto there. There's a product update section and that will actually have all of the kind of high-level feature releases that we're doing. And again, it will advertise if it's closed beta, if it's open beta, if it's generally available.

And again, if you see something on there, please outreach to your CSM and ask them to put you on the list.

[Speaker 2]
Perfect. Some of these features live inside Gainsight and others work from tools like Claude or ChatGBT through MCP. How should I think about which to use and where do you see most teams working in a year?

[Speaker 1]
Yeah, I think the key thing here is we want you to work wherever you get the most productivity, right? We don't want to be a barrier to say you have to log into the Gainsight UI and click around and go and use that. If that's not what you use, that's not where you will work in your common day.

I think the big thing, the reason why we're making this available inside Gainsight with the things like Agent Studio isn't just about new capability. It's also about, we're seeing a lot more clients now talking about token spends and how that's crept up past the point. They're actually starting to restrict the use of LLMs directly for their teams.

So actually, this is a way that you can really start to manage that cost a lot better because essentially it becomes part of your Gainsight package. And again, you can go through and build that. It's also easier to roll out because it will be available for anyone you've given that license to with inside the platform as part of their full license.

So again, it removes the barrier of saying, hey, we have to go and get a load of Anthropic licenses or a load of chat GPT licenses, open AI licenses for our customer base. If they've got Gainsight and you've enabled this for them, they will have access to all the data behind the scenes. And also it's safe.

You've already gone through data privacy and security hosting and everything with Gainsight. So actually, it's already vetted from that side of things too.

[Speaker 2]
Okay, a couple more questions we can squeeze in. So a lot of the new features pull insights from emails, calls, meetings. How do you keep that useful instead of overwhelming?

So a CSM isn't buried in alerts.

[Speaker 1]
Yeah, I think this is a good one. I think this probably stems from everyone who's had Gainsight has probably gone through call to action fatigue, right? So it's a case of like, hey, we've got this insight.

If it happens immediately, we want someone to take action. And then CSMs are the 100 CTAs that are open and they've been open for three months and they're buried in there. So we don't wanna go through that model.

The benefit with this is that the system can detect it. It doesn't have to act on it immediately. It can analyze it for a period of time.

It can verify if it's really a trend or not. And it also has access to wider context. If anyone's been used to like usage signals in the past, it's pretty been like, has usage dropped?

Is it dropping week on week, month on month? And then I'm gonna go and trigger an alert. But it's really only looking at that one single source of data, which is usage.

The benefit here is that the AI now can start to look across, okay, we've spotted a usage trend that's declining. It can now go and have a look at previous transcriptions or notes or anything else inside the platform and understand, okay, they've just been a major riff in that organization. For example, the number of licenses that they used to have is now gonna drop.

And actually that's gonna be an indication of declining usage for the client. So as much as the pattern is correct, the action isn't required because it's got the context to understand why that's happening. But it still will surface that in things like a health score, but it's not just gonna create a call to action for the team because it's not just looking at that single data point.

But again, we are gonna be conscious that we don't just wanna flood the teams with a load of activity.

[Speaker 2]
That makes sense. And second question is, can we personalize the pre-built features for our company? Where does the context layer sit and gain site for these features?

[Speaker 1]
Yeah, so everything we build, we want you to have control on. So for example, when I mentioned around the product concepts with inside the platform, you can go in there, you can say, hey, here my product names are X, Y, and Z. You can also go through and configure a description for each of those products, which is the context the system is using to decide when it's applying the rules to the relevant piece of data coming through.

So a bit like if you are familiar with your own like chat, because you're Claude, you can go into the personalized section and you can enter the bio by yourself, your communication style, all of those types of things. We're exposing that same type of detail and context setting with inside the product because each of your businesses are different. Each of the ways that you wanna think about the decisions that you made are gonna be different.

So you've got full control of being able to do that with inside gain site.

[Speaker 2]
And then the last question that we have here on the list, what are the fundamental pieces of data we need to have synced and working in order to use multi-product accounts such as relationship data?

[Speaker 1]
Yeah, so I think when on these, when you think about the relationship side of things, if you're firstly, when you've configured it, we need to make sure we set the product names in the context. If you've got relationships, the other thing that I would say is critical is having the contact synced at the right level. So you've obviously got accounts, contacts with inside there, but you've also got relationship based contacts.

So how do we make sure rather than dump everyone at the account level, we have the people that we work with at the product level defined as contacts with inside the product level that will help with the matching so that we know exactly when we speak to Rakesh, it's because we're talking about, we're typically talking about staircase. So it's not gonna be the only defining rule, but it is also gonna be part of our rule chain that helps define, we tag the right area of the products with the right communication data.

[Speaker 2]
Perfect. And that basically wraps us up with all the questions. Thank you Liam so much for all of the insights.

Of course, any questions to Liam, you can find him. I wanted to also announce quickly before we wrap up the session and I'll give you some time back. At 6 p.m. we can see you at Fabric. We will, you can network, socialize a little bit more. We've organized something. So thank you so much for being here.

I know it's been a long day. So again, any questions? Hopefully we'll see you also tomorrow back.

We haven't shooed you away. So yeah, that's a wrap, I guess. Thank you so much.

[Speaker 1]
Thank you.