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

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>> Well, thank you everyone for joining.

I'm hoping that you guys had a great

time so far at this conference and you

guys have a lot of takeaways back, you

know, to your company.

Um, I don't know if any of you guys saw

the uh AGI pills downstairs.

Yeah, well, I just took some. So, if I

say any phrases about that's the uh

the

the gun What's that What's that 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.

All 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 sale the

sales have been done.

And uh

just a little background about me.

As Moda said, I started in sales

operations uh

and sales and then I moved to sales uh

to the machine learning side about the

last 7 years at Grainger. And I really

wanted to do that to be able to learn

how to

uh

have prescriptive analysis and

prescriptive uh prediction so I can help

the business better to make decisions

and to understand what's the next step

next best step.

So, uh

6 months ago, I had an opportunity to

move back into sales operations

uh because I really wanted to use all

the skills that I'd been learning from

machine learning as well as from sales

operations in general.

So, what's the general problem? The

general problem is that traditional

go-to-market does not scale.

There's a few um

facets to this.

The first facet is that usually teams on

the back office side, uh they're either

doing work in Excel or sheets at worst,

um building analysis each week, multiple

hours a week, and as they take on

multiple projects, it gets exponentially

long with how many of those analysis

that they're doing.

Uh 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. They'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

I 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 um one call and

talking to a current customer in the

next call, or um talking at an adoption

conversation the call after that, they

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.

Um the second one what is like 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

adoption call, how he would handle a

customer satisfaction issue, and

between also the

a new salesperson or someone that's just

ramping up. So, ideally like everyone

working at the same operational level,

so you have consistency in execution,

consistency in messaging of how to

assess a

a customer's problems and how your

product can help fit that portfolio. So,

with these two problems with manual work

as well as

uh

salespeople not having enough

information and having the gap of having

to get all the information they need

and gather that as well as not be able

to execute the same level, it really

creates an inefficiency in the

go-to-market organization.

And so, for the last 6 months or so

since I've

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 some of those

findings with you.

So, the framework that I'm proposing

here, which I think is is

it's going to really be effective in as

we flesh this out in the future is a

three-pillar approach.

The first pillar approach is how can we

scale analysis and the ability of

operations team to meet the data needs

of executives, leadership,

as well as be able to build applications

using business context. How can they

take things that would take 2 hours to

do down to 5 minutes? Um back in well,

not back in January of this year,

I after I joined I joined the company,

I had built these skills and I'd 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.

The second pillar is how can 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?

And 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 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.

Uh this is a huge part of what I've seen

we've done at Cloudflare, and I'll share

a little bit what that looks like.

So, as it relates to scaling the

analytical capability, the back-office

operations,

what we have done as we have built

role-specific skill files, which have

the context of the

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 a

lot of solutions. And then the

non-technical people would be more

individuals who are closer to the

business with the sales people 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.

And

one example here is in those skill

files, we also,

through testing, we've included the

types of questions that the business

would ask of the data. In this case,

looking at

closed 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.

And so, overall, this allows the teams

to be able to embed all of the logic

into the skill files and get answers uh

fairly quickly. So, I've seen users who

do not know any SQL, and the essentially

their request in the past we 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. Also, 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

table to build these applications rather

quickly. So, 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

on 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, and you can 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 some 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 standard 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 data 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

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

embedding 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 up front. This

from my experience, this handles uh 80

or plus percent of the requests is just

getting information about the

performance of the teams and how they're

doing. You 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-agent workflow

where we first get the data, and then we

do a first pass draft on the data

calling our MCPs,

um and then this 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 just

craft the message and highlight the

risks and opportunities

um equally. And with every run, we have

observability into each of the LLM

calls, so we can see what is passed and

what is the response that is going on

there.

And so, this architecture we tested for

about 2-3 months and, you know, looking

every single run to see what is going

wrong. And this is the the model that we

had set up that is really working for

us. And we really hope to expand this

beyond just what I've shown you for

multiple teams, but also down to the

customer level like I was just talking

to you about.

The 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 on Cloudflare where the

go-to-market team can come in here. It

spins up their own compute and their own

persistent environment using Cloudflare

workers as well as durable objects,

which is basically a storage

sort of like S3.

And so, the sales people can come in

here and get the data they need it when

they need it.

So, some use cases that these teams are

using it for is doing a forecast brief,

building QBR decks,

building a purchase deck on what the

customer that they're onboarding has

purchased, doing account planning,

general queries of the data,

as well as renewal preparation. How are

they going to look at what the customer

has used and

either upsell them or figure out how

they can get them adopting their product

more.

So, just a little bit more into that

Cloudflare S setup that I just showed

you. The AI agent workspace is where

that screen I was I was showing you. And

through the the three-part

piece of the skills in the lower left,

which is our like expert

level information,

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're able to execute the

jobs that they need to do when they need

to do it.

And so, a little more information about

the skill repository, uh we have a

central 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.

And so, just a few more uh 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 their um you can see the

agent is responding by looking into the

MCP and starting to pull the data

together.

And related to the QBR deck, uh here's a

slide of generating a custom slide deck

for a customer call.

And so, this has 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.

And 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

optimizing the use of Agentyc 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.

The 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 so that there's no and also uh

so they're aligning with source of truth

on performance.

Um 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 meet the

needs of the go-to-market team um is not

met.

So, some findings um and the future.

So,

uh the first thing is skill curation is

the basis for all of this agentic

workforce. If you're able to embed the

knowledge of the business into the skill

files as well as the skills uh uh for

the an analyst to be able to build uh

answer questions or build applications

as well as the skills that I showed you

in the Cloudflare OS,

you're really able to uh give them the

ability to use the agentic systems in a

more predictable and deterministic way

so that they can execute um evenly

across the board.

The 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 it actually useful? What are

some issues that they're having? And how

can you make this uh work more

efficiently? And 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-serviceability. 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

teams as well as allowing them to be

able to get the information that they

need to do their job.

And some some things um that I see going

into 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. We 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

getting meeting notes from those calls,

which you have to set up that across the

board. So, there's some system side

thing that we have to work on there. The

second thing is harder problems

around quoting and approvals and

updating the CRM itself. Uh we use

Salesforce and we're just in the midst

of

building the connections and the ability

for us to update Salesforce with these

Agenty systems. And I see that being um

set up in a way that I set up with that

automated analysis where you have

workflows uh to just make sure that

everything is getting um done right. And

the second thing is we're sort of

reached the Cambrian stage of using

Agenty 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 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 Agenty system so

that the source of truth in all the

systems are aligning.

All right. Well, thank you for joining

this talk and I appreciate you,

um you know, coming here. Hope you have

a great conference.

>> [applause]

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