How AI Agents Let GTM Teams Scale — Justin Joyce, Cloudflare

AGI pills, and a route into sales ops via machine learning

00:12Well, thank you everyone for joining. I'm hoping that you've guys had a great time so far at this conference and you guys have a lot of takeaways uh back, you know, to your company. Um, I don't know if any of you guys saw the uh AGI pills downstairs.

00:21Yeah. Well, I just took some. So, if I say any phrases about that's the uh the the gun, what's that? What's what's the phrase? That's the uh burning gun or the smoking gun or I start hallucinating in general. Well, please snap me back. That's probably just the AGI pills.

00:41All right, so without further ado, let's get started. Uh my name is Justin Joyce. I'm a principal sales operations and strategy manager at Cloudflare. Um and I work with the go to market team as part of the revenue operations organization specifically on the teams that uh produce leads for the sales teams as well as the customer experience team which works on uh the customer experience after the seal the sales has been done and uh just a little background about me as Mada said I started in sales

01:15operations uh and sales and then I moved to sales to the machine learning side about the last seven years at Granger and I really wanted to do that to be able to learn how to uh have prescriptive analysis and prescriptive predictions so I can help the business better to make decisions and to understand what's the next step next best step.

01:38So uh six months ago I had an opportunity to move back into sales operations uh because I really wanted to use all the skills that I had been learning from machine learning as well as from sales operations in general. So what's the general problem?

01:57The general problem is that tra traditional go to market does not scale. There's a few u facets to this. The first facet is that usually teams on the back office side uh they're either doing work in

Why traditional go to market does not scale

02:13Excel or sheets at worst um building analysis each week, multiple hours a week and as they take on multiple projects that gets exponentially long with how many of those analysis that they're doing. At best they are producing dashboards um providing information to the uh leadership and executive team um which you know meets the needs of most teams um but not all of them and that needs meets that needs uh means that in general not all the requirements of the go to market teams are met.

02:44They're not able to really provide all the information that the teams need when um they need it. The second problem which is more on the go to market side with the sales and uh the sales teams and the other teams that

The context gap and the expert gap

03:03I mentioned that I support is they have two gaps. Essentially the first gap is the context gap. Meaning when a salesperson is uh talking to a prospect uh one call and talking to a current customer in the next call or um talking adoption conversation the call after that.

03:19They have to constantly switch contexts and they have to gather information for those specific calls which is good. They really need to get that information to have those calls and understand how to approach the situation but they have to do all that work in between.

03:36Um the second one what is like to what I like to call is the expert gap which is the gap between how your expert salesperson or expert go to market um sales individual how he would approach a situation how he would talk to a prospect how he would uh work on an adoption call how he would handle a customer satisfaction issue and the um between also the a new salesperson or someone that's just ramping up.

03:59So ideally like everyone working at the same operational level. So you have consistency and execution, consistency in messaging of how to assess a uh a customer's problems and how your product can help fit that portfolio. So with these two problems with manual uh work as well as uh salespeople not having enough information and having the gap of having to get all the information they need um and gather that as well as not being able to execute the same level, it really creates an inefficiency in the go to organization.

04:37And so for the last six months or so uh since I've uh joined Cloudflare, I've been really focusing on how can I make this operation efficient from back to front and there's a lot of great things that we've been doing at our company in general that's helped me enable that and I really want to share um some of those findings with you.

Three pillars

04:56So the framework that um I'm proposing here which I think is is is going to really be effective in as we uh flesh this out in the future is a three-pillar approach. The first pillar approach is how can we scale analysis and the uh ability of operations team to meet the data needs of executives leadership um as well as be able to build applications using business context.

05:20How can they take things that would take two hours to do down to five minutes? Um back in uh well not back in January of this year um I after I join I joined the company um I had built these skills and I started asking questions of the data directly and I was able to get answers immediately while doing other things and I saw the huge power of how if we can scale the analysis and the operations of the teams we can actually focus on the second part of my job which is strategy.

05:50The second pillar is how can uh is to scale insight. There's story in the data and how can we provide that to the team. How can we provide that team at you know the weekly level at the different levels of management? How can we provide that information that insight that story to every customer that the sales teams are talking to.

06:11And the third one which is arguably the biggest one is uh to provide self-service capabilities to the go to market team. When these sales uh individuals when they're talking to a customer, how can they get the expert level information that they need to interact with that customer and to best assess, you know, how they should approach the situation, how to handle rejections, how to upsell them, and how to handle customer satisfaction issues.

06:39uh this is a huge part of what I've seen we've done at Cloudflare and I'll share in a little bit what that looks like. So as it relates to scaling the analytical capability the back office operations what we've done is we've built RO specific uh skill files which

Skill files that let non SQL users query the data

06:56have the context of this um business information tying it to the data. This is for both technical and non-technical users. technical users you could say the ones who are building SQL and being able to data engineer um a lot of solutions and then the nontechnical people will be more uh uh individuals are closer to the business with the salespeople uh who may not know how to write SQL and so we have skill files that they're able to use to ask questions of the data to get answers fairly quickly while doing other tasks

07:28and uh an example here is in those scale files we also um through testing we've included the types of questions that the business would ask of the data. In this case, uh looking at uh close date changes and opportunities as well as uh changes in the amount of the opportunities so that we can answer essentially 80% or or more of the questions where the other 20% might be more uh complex strategic questions.

07:54And so overall, this allows the teams to be able to embed all of the logic into these skill files and get answers uh fairly quickly. So I've seen users who do not know any SQL and essentially their requests in the past be bottleneck to someone who knows data and can write SQL for complex queries be able to just ask questions of the data and get answers and this is very useful also for what I show later on on the third pillar is for building skills for these go to market teams so that they um can and ask questions of their data and and get answers.

08:27Also uh in our team we've used these same skill files to build multiple applications uh when usually you know that is uh done in IT and bottlenecked in those areas we're able to use the semantic information about the business um knowledge as well as the columns to be able to build these applications rather quickly.

08:44So this allows us to free up our time so that we can focus on strategy and enablement. All right, for the second pillar, uh what I mentioned earlier, there's a story in the data and they really shouldn't have to search for it. Uh what I'm showing you here is synthetic data

There is a story in the data; stop making them search

09:06on the right. Uh we have a weekly summary that goes out which highlights how the business is doing, how they're pacing to their goals and then highlighting um trends, uh standouts as well as watches. So this uh we provide this information to the business so they can as you just like you can open your phone now uh on Gemini if you have it you could see your notes for the day or the things that you need to do giving that same level of information to the go to market team so they can just go along their day and if they do need to look at

09:37some of the reports or dashboards they can to drill in um but we bring the story to them and I'll pull this together why I think this is really important um you know of course there's a place for dashboards and standardize information but there's different level of adoption of the KPI metrics at any given company you're going to have people who are love dashboards people are never going to look at them so I think you really need to have a way to scaffold that across um the business so how do we do this automated analysis so a big part of this is simplifying the

10:07data so that the AI agents can actually um analyze the data in a very consistent and clean way here what we do is we transform the data by the uh the dimension of time also slice of the logical part of the business which is manager theater and finally the metric here we have data uh that is wide you could also go um from wide to long uh our trend information that I showed you um uh that data is long and then we do some pre-processing on that data um to then highlight trends so the the uh

10:40embedding of the logic of how you would filter this data to even analyze it um as well as the logical um aggregations the business want to see is all engineered upfront. This from my experience this handles uh 80 plus% of the requests is just getting information about the performance of the teams and how they're doing.

10:55You can always go down to the raw data but this last uh pillar which I'll go over in a minute um allows them to do that to be able to orchestrate this uh effectively and be able to rely on it. We have a multi- aent workflow where we first get the data and then we do a first pass draft on the data calling our MCPs.

11:10Um and

Drafter, reviewer, tone agent

11:18then the we have a second reviewer agent who checks the veracity of the data. And then we have a third um agent which is a tone agent who using a multi-shot prompt um is able to craft the message and highlight the risks and opportunities um equally.

11:28And with every run we have observability into each of the LM calls so we can see uh what is passed and what is uh the response that um is going on there. And so u this architecture we tested for about 2 three months and you know looking at every single run to see what uh is going wrong.

11:44And this is the the model that we had set up that is really working for us. And we really hope to expand this beyond uh just what I've shown you for uh multiple teams but also down to the customer level like I was um just talking to you about.

12:01The third part is the self-service model. And what I'm showing you here is our internal tool called Cloudflare OS which is an agentic workspace that is running

Cloudflare OS: a self service agentic workspace

12:13on Cloudflare where uh the go to market team can come in here. It spins up um their own compute and their own um persistent environment using Cloudflare workers as well as durable objects which is basically a storage um sort of like S3 and so the sales people can come in here and get the data they need it when they need it.

12:36So some use cases that these teams are using it for is doing a forecast brief, building QBR decks, uh building a purchase deck on what the customer that they're onboarding has purchased, doing account planning, general queries of the data, um as well as renewal preparation, how are they going to look at what the customers used and um either upsell them or figure out how they can get them adopting their product more.

13:01So um just a little bit more into that Cloudflare s uh setup that I just showed you. Um the AI agent workspace is what that screen as I was showing you and through the the three-part uh piece of the skills in the lower left which is our like expert uh level information um as well as the MCP connection and the AI gateway they're able to have conversations in this agentic workspace to pull data they need and using expert level skills which is curated um so that they are able to execute the jobs that they need to do when they need to do it.

13:36And so a little more information about the skill repository. Uh we have a central uh alias where skills are presented uh to uh the central team curated by the uh go to market team as well as by operations team and they're reviewed so we can make sure that we're not having a proliferation of skills and we have an expert level knowledge skill at every level so that they can really get all the information they need for how to approach uh any customer situation.

14:04And so just a few more um images here of uh them using it. Here uh we have them building a prescriptive plan for their daily work. They're asking a question. It's they're um you can see the agent is responding by looking into the MCP and starting to pull the data together.

14:20And related to the QBR deck, uh here's a slide of generating a custom slide deck for a customer call. And so this is really I think unlocked the ability of the go to market teams to be able to really have all their information uh serviced to them.

14:39And so bring it together with the three pillars that I talked about. Um if you really don't have all these I think you have issues with serving the go to market needs uh in terms of uh using uh optimizing the use of agentic uh systems. With self-service, you allow them to be able to pull data when they need it for whatever situation they need it with the expert level information.

14:59The the second is by pushing the uh the insights to the business, you're able to surface the generalized standardized information of how these teams are doing and also um yeah so that there's no and als uh so they're aligning with source of truth on performance.

15:14Um and then the the third one is the scaling of the analytical team for them to be able to answer queries and build applications for the teams which really unlocks a lot um because the opportunity cost of that team being overloaded and being able to help is that the me the needs of the go to market team um is not met.

15:34So some findings um in the future. So uh the first thing is skill curation is the basis for all of this agentic workforce.

What worked: curation, feedback loops, layering

15:43If you're able to embed the knowledge of the business into these skill files as well as the skills uh uh for an anal an analyst to be able to build uh answer questions or build applications as well as those skills that I showed you in the cloudflow OS you're really able to uh give them the ability to use agentic systems in a more predictable and deterministic way so that they can execute um evenly across the board.

16:00The second thing is through this whole process, the feedback loop is very important just like uh a company would try to sell a product externally and get feedback with these internal teams. Uh the feedback loop is very important to be able to see is is what you're building is actually useful what are some issues that they're having and how can you make this uh work more efficiently.

16:25And the third thing is the layering of those uh three pillars that I talked about being able to answer questions where the team comes to you. some of the go to market team that's how they like to interface with the operations team is to be able to ask questions um and then the pushing of information and then self-service ability through that you're able to uh interweave all the needs of the team to be able to be met by this uh agentic run uh team so through all these uh different pillars that I talked about we've really been able to 2x our efficiency and be able to serve the

17:03teams as well as allowing them to be able to get the information that they need to do their And so some things um that I see going

Next: CRM writes, and reining in the explosion

17:11into the future, number one is a deeper integration with uh our systems that we work in. So for example, those QBR decks and um renewal call skills. Uh how can we set up meetings for the go to market team and embed those artifacts in those meetings so they don't have to actually pull them.

17:25We can allow them to that self-service portal to be more ad hoc in what they need. But that requires some information or some security setup and how can we do that and then also another example is uh getting meeting notes from those calls which you have to set up that across the board.

17:41So there's some system side thing that we have to work on there. The second thing is harder problems uh around quoting and approvals and updating the CRM um itself. uh we use Salesforce and we're just in the midst of um building the connections and the ability for us to update Salesforce with these Ajento systems and I see that being um set up in a way that I set up with that automated analysis where you have multi- aent workflows uh to just make sure that everything is getting um done right.

18:13And the second thing is we're sort of reached the Cambrian stage of using agentic systems which means there's an explosion of excitement and skills and finding out ways to solve anything with AI. But I see as we uh get to this uh fuller integration and standardization, we're going to uh want to come back and and and not really limit but just figure out a really strategic approach for allowing each team to use the Agentic system so that the source of truth and all the systems are aligning.

18:47All right. Well, thank you for joining uh this talk and I appreciate you um you know coming here. Hope you have a great conference.