---
title: "Is Your GTM Operations Stack AI Ready? Three Practitioners on What It Actually Takes"
id: "45924"
type: "resources"
slug: "gtm-operations-ai-ready-webinar"
published_at: "2026-06-24T17:13:52+00:00"
modified_at: "2026-06-24T17:34:17+00:00"
url: "https://www.leandata.com/resources/gtm-operations-ai-ready-webinar/"
markdown_url: "https://www.leandata.com/resources/gtm-operations-ai-ready-webinar.md"
excerpt: "Ops leaders from Drata, Korn Ferry, and Checkmarx share how they moved AI from experiment to execution inside their GTM operations — and what made it stick."
taxonomy_topic:
  - "AI"
  - "Intelligent Go-to-Market Orchestration"
  - "Lead Management"
  - "Lead Routing"
  - "Operations"
  - "Routing"
  - "Tech Stack"
taxonomy_role:
  - "Operations"
taxonomy_content_type:
  - "Video"
  - "Webinar"
---

Video

# Is Your GTM Operations Stack AI Ready? Three Practitioners on What It Actually Takes

AIOperationsVideo

Getting AI into production across your go-to-market motion is harder than the headlines suggest. In this on-demand session, LeanData CMO Jim Bell hosts marketing operations leaders from Drata, Korn Ferry, and Checkmarx for an unfiltered conversation on what it takes to move AI from experiment to execution.

If you lead revenue operations, marketing operations, or GTM strategy at a mid-market or enterprise company, this session surfaces the real blockers — and the practical steps these teams took to get past them.

### What You’ll Learn

- Why fewer tools in your MarTech stack does not mean less complexity — and how leading ops teams design for it instead.
- Why data quality is the deciding factor in whether your AI deployments create leverage or accelerate the wrong outcomes.
- How to use a risk-based framework to identify which workflows are ready for AI and which ones are not.
- What it looks like to build LeanData as the orchestration layer connecting AI agents, human reps, and downstream systems.
- How enterprise GTM teams are structuring AI governance to move fast without losing control or auditability.

## Speakers

**Jim Bell** — Chief Marketing Officer, LeanData Jim leads LeanData’s go-to-market strategy and is a frequent voice on GTM orchestration, revenue operations, and AI transformation across the B2B marketing community.

**Kacee Court** — Head of Marketing Operations, Drata Kacee oversees marketing operations at Drata, an AI trust management platform with 8,000+ customers, where she has led the company’s transition to an AI-augmented GTM motion.

**Jessi White** — Senior Director of Marketing Operations, Korn Ferry Jessi manages marketing operations across a complex, multi-region GTM environment at Korn Ferry, a global organizational consulting and executive search firm.

**Brooke Bartos** — Marketing Operations Lead, Checkmarx Brooke leads marketing operations at Checkmarx, an agentic application security company, where she manages both new business and cross-sell workflows.

## Watch the Full Session

Kacee, Jessi, and Brooke go deeper on the decisions, tradeoffs, and lessons they have accumulated building AI-augmented GTM operations at scale.

Click to Open

**Jim Bell** (00:07)  
 Hello and welcome everyone to “Are Your GTM Operations AI Ready?” My name is Jim Bell. I’m the Chief Marketing Officer at LeanData. Today’s session is built on a study that we did along with the folks at LXA — the B2B State of Martech and Revenue Operations report — which covered over 200 senior leaders across seven different countries, predominantly mid-market and enterprise level companies with at least 2,500 employees.

Before we jump into the survey, I want to quickly introduce and welcome our panel. I’m going to let them introduce themselves.

**Kacee Court** (01:36)  
 I’m Kacee. I am the Head of Marketing Operations at Drata. Drata is an AI trust management platform, and we help our customers automate governance, risk, compliance, and assurance. The company is only five years old, but we’ve already grown to over 8,000 customers across the globe and 600 people who make that happen.

**Jessi White** (02:06)  
 I’m Jessi White. I’m the Senior Director of Marketing Operations at Korn Ferry. We’re a global organizational consulting and executive search firm that helps organizations solve challenges across talent, workforce strategy, and performance. From a marketing ops perspective, that means we’re supporting a really wide range of client journeys that span business units, regions, and stakeholders. It’s my team’s job to create the operational foundation that helps all of that work together.

**Brooke Bartos** (02:45)  
 Hi everyone. I am Brooke Bartos. I lead the marketing operations team at Checkmarx. We are an agentic app sec solution in the cybersecurity space, and we operate globally, though I am based here in the Chicago area.

**Jim Bell** (03:06)  
 Getting back to the report — I’m going to go through four themes that came out of it.

The first is around companies consolidating in size, but not in complexity. We found there were 37 tools in the average MarTech stack, down from 62 from the prior year. 51% named integration complexity as their top barrier. Even though they have fewer tools, integration is still one of the biggest challenges people are facing. And 21% are still running 50 or more tools in their rev ops stack.

Jessi, I know Korn Ferry runs a pretty complex, multilayered GTM process across different regions. What makes your environment difficult, and how do these results land with you?

**Jessi White** (04:44)  
 I think what makes our environment challenging isn’t necessarily the number of systems or tools. It’s really the number of relationships that we’re managing. A single client could interact with multiple Korn Ferry solutions over the course of their relationship with us, sometimes in parallel. There are different teams involved, different ownership models, different regions that may participate in and out of the journey. But our clients don’t really think about those internal distinctions — they expect a consistent experience from the outside.

What we’ve really learned is that we need to manage this at scale, and manual effort alone can’t help us do that. We’ve focused on creating clarity and consistency across our different lines of business and regions so that we have clear business rules that make decisions happen consistently regardless of who is involved and when. Complexity isn’t something you need to eliminate — you just need to plan for it and design for it. If your foundation is strong, you can continue to grow without creating a snag every time a new variable is introduced.

**Brooke Bartos** (06:26)  
 I think Jessi nailed it. Complexity is about how you plan for it. Any of us who have worked in marketing operations know that vendors always say they can integrate with everything — but that’s oftentimes not really the case. For a lot of organizations, things like data warehouses and orchestration platforms that let you really tap into that data and have a real source of truth — instead of “this is a source of truth for this team, and this is a source of truth for that team” — have been the key to keeping information manageable. And with that, the key to starting to unlock AI capabilities that before would have been a “choose your own adventure.”

**Kacee Court** (08:10)  
 Going back to the comment about having a strong foundation — Drata is only five years old, and we’ve had to do a lot of building a plane and flying it too. We have a big focus on fixing that foundation right now. What adds complexity for us is that we have no shortage of tools. We’ve got a lot of data and integrations, and another thing we’re up against is time.

We’ve got a lot of enrichment tools and have had to build out a strategy around what we’re pulling from them, from where and when — and that doesn’t happen overnight. When we were setting up our systems, Salesforce namely, we weren’t expecting to grow this fast. A lot of what we’re doing now is ripping off band-aids and finding the skeletons in the closet and actually having to address them.

Trying to figure out what’s accurate, what’s reliable, and what we can actually trust before we put it into an AI system is a big focus. We’re also in the security and compliance space, so we can’t integrate every tool even if it does integrate with everything. And the AI landscape is ever-changing. At Drata, we’re very much early adopters — but trying to develop an AI strategy while also keeping the broader organization focused with an understanding of the purpose can be a challenge in itself.

**Jim Bell** (10:09)  
 You’re leading us into our next topic. The second finding was this idea of an execution gap — execution is not keeping up, and process is the thing that’s not working. Process was the lowest scoring maturity pillar in the report and the slowest advancing.

37% of respondents said they were unsure that every lead reached the right person in time — not something you want to tell your sales leaders. 26% said enforcing SLAs on lead routing was a challenge, and 17% are triaging by hand and not enforcing SLAs.

Kacee, how did you go from routing that was kind of broken to something much more airtight?

**Kacee Court** (11:36)  
 Two and a half years ago, we migrated to LeanData because our previous routing provider was consistently breaking. When we totaled it up, we were looking at over $2 million in pipeline that was missed. That became a forcing function to migrate.

Fast forward to now — our story is much larger than just routing inbound leads or enforcing SLAs, which is where we started. LeanData has actually evolved with us, especially in this new world of AI. It’s really becoming the backbone of the orchestration layer that we’re building with our AI systems. We built out an AI system for down-market efficiency, and LeanData was getting the perfectly packaged account to the SDR team. We now actually have LeanData handling part of the human process — being the handoff between compounded signals and passing it off to our AI agent, which has significantly increased speed to lead.

Past the operations piece of that, it’s really been about the full customer journey for us. LeanData has given us the structure to organize what that journey should look like, which clarifies what the process should be and the roles and responsibilities between teams. Whether someone is a new prospect or a customer, we can deliver a consistent experience to them.

**Jessi White** (13:33)  
 By moving to LeanData about two and a half years ago, we drove down our speed to lead time from about fifteen minutes to around five minutes. And we’re doing a lot of transformation in those five minutes — waterfall enrichment, normalization, all of the different rules we have across business units and regions.

The time savings is important because the faster a lead is followed up on, the better the conversion rate. But what it really did was improve the confidence that our sales partners have in the leads that marketing delivers. Historically, there was a notion that the leads marketing gives us aren’t of high quality. Delivering leads in a quick and consistent manner really helped drive confidence in the sales team. We saw better outcomes, better SLA adherence, and it gave us some trust in the process with that team.

**Brooke Bartos** (15:47)  
 Our organization has multiple product lines — new business, but also a lot of cross-sell and upsell. Prior to bringing on LeanData, there was a lot of manual handoffs. Somebody was responsible for looking at every new hand-raiser that came in and saying, “you go here, and you go here.” We knew people were going where they should, but it wasn’t fast.

By having in place the logic of who goes where and when and who’s responsible, with clear lines of ownership, we also have clarity around SLAs. Are people getting followed up on? What’s getting missed? Is somebody on vacation and something is sitting too long? Nobody wants to be the lead that filled out a form and doesn’t hear anything for two weeks. There are three vendors who already reached out within a few days that are now two weeks ahead of you in the process. You lost that deal before that conversation ever happened.

Having that clear transparency — being able to say “this went here because of this” — lets us know whether we have the right things in place, the right team members, and whether we’re actioning things fast enough.

**Jim Bell** (17:37)  
 I hear so many stories about where trust gets broken when someone asks “why did this get routed to this person?” and doesn’t get a good answer. That really underlies the whole sales and marketing relationship.

With Rockwell Automation, they were at about 10% of leads getting matched correctly and moved up north of 85%. At Uber for Business, about 10% of MQLs were not getting assigned correctly — they got that down to under 1%. SLA compliance went from 40% to 85%, and they had great results in terms of deal velocity after that.

Let’s move on to theme number three — AI. Pretty much everyone is under some form of mandate to transform with AI. The AI conviction is near universal, but deployment — people are at very different stages. 78% call AI agents a meaningful part of their go-to-market technology. 17% are embedding AI across different areas, but just 2% call it central to their process. 46% are leading with the easier, earlier use cases around AI for content creation.

As something becomes autonomous, being able to trust that to run different processes becomes a lot scarier. Kacee, you’ve admitted you guys are pretty forward on this. How do you think through where to draw a hard line?

**Kacee Court** (21:43)  
 Transparently, it all kind of feels like experimentation and iteration these days. We’ve launched quite a few things, but it’s not like we’ve launched something and it’s been 100% perfect. Lots of iteration happening.

The way I think through how we draw a hard line in the sand is through a risk assessment lens. People are doing content generation, research and prospecting, maybe some campaign-level AI SDR plays. Those are experiments we’re comfortable running broadly because the cost of a bad output is low. But when we start to scale and incorporate AI SDRs and broader AI systems, it inherently brings on more risk. It also forces much larger conversations with SDR leadership that go well beyond just the technology itself.

If an AI SDR is handling some of the middle-of-funnel outreach, what is a human SDR now accountable for? Should there be an AI SDR quota for humans to be accountable for? How do we help the team see this as leverage rather than a threat? Because it’s a very real thing — people are getting nervous. Those organizational and cultural questions have to happen and be a conversation. They’re just as large as the operational ones.

**Brooke Bartos** (23:56)  
 A lot of our AI so far has been focused on both the sales and marketing side. SDRs actually sit within our marketing organization, so we’re very close to that hand-off process because we are one team. That’s been a great opportunity to lean into AI — for account research, looking at what an account does, who we’re talking to, what they’ve done, and building that out.

We also have Piper. We’re leaning into areas where we can augment SDRs with an AI partner to help do some of that pre-qualification. When an individual gets handed over to an SDR, they’re truly ready — a qualified lead ready to have a conversation. The SDR can then move past early pre-qualification or basic question-answering that would come with a scored MQL that wasn’t ready to buy.

We’re also using AI for analytics — helping analyze where things may be breaking down or where teams can do better, so we’re applying resources in the right place.

**Brooke Bartos** (25:52)  
 We’ve talked about actually giving our AI SDR a similar KPI set to what our human SDRs have — where it has goals and KPIs just like other areas of the team.

**Kacee Court** (26:07)  
 I’ll add to that. The people who are asking those questions are actually leaning into AI, and they have a different framework — “how do we scale the business and leverage our people for the most important touchpoints?”

**Jim Bell** (26:31)  
 An AI SDR is getting paid basically in credits rather than a salary. It’s still going to cost something, and at some point those credits are probably going to become more expensive. So the question is, are we doing this efficiently and how do we measure it?

Jessi, at the enterprise scale that you all have — how do you decide something is ready to be in a mission-critical process?

**Jessi White** (27:08)  
 Not to harp on the trust piece, but I really think that’s key. As we get closer to the revenue and the client experience, we’re really thoughtful about where the risk is in AI usage. We’re concerned about making sure we have the right governance, visibility, and control — because we all get the question of why something happened.

With that said, we do have some really positive examples of AI creating value today. We pressure-tested and monitored and refined, and we introduced an equivalent to Piper. We use Sixth Sense AI email for AI-enabled follow-up on leads that are past SLA and somehow slipped through the cracks. Our AI SDR was able to re-engage those leads that were not followed up on, and we’ve generated a lot of what would have been lost pipeline from that.

Another area — Korn Ferry doesn’t have a traditional SDR function. We had a lead management team sitting between the marketing team delivering leads and the business that works the leads. We regularly get hundreds, if not thousands, of form fills from people looking for a job, career coaching, or pitching themselves for an open role. We are piloting the AI inference node with LeanData to see if it can help us determine what is a commercial versus non-commercial request, based on open text fields, before we deliver those leads to the business.

**Jim Bell** (29:41)  
 For those unfamiliar — the AI inference node is the ability that LeanData provides to plug a node into a routing graph to call out to your own LLM. For example, somebody fills out a text box, and you can use AI to infer: is this a job seeker? A customer support case? A legitimate new potential customer? Then send that back into the deterministic system — the graph — to make sure the right thing happens from there.

This is the same reason Anthropic, OpenAI, and other leading AI companies use LeanData as part of their tech stack. That stuff has to work. The revenue engine depends on it. There’s a ton of complexity in round-robins, market segmentation, and revenue plans — you need all those things to happen the way you designed them. It has to be observable. It can’t be a black box.

Let’s move on to theme four — governance. In the study, 50% of people felt confident they can govern AI safely at scale. 82% agreed that foundations must come before scaling AI. And 42% cite poor data quality as a barrier to ops maturity.

I heard a great quote at the Gartner conference: with AI, it’s not just garbage in, garbage out. It’s garbage in, highly confident garbage out — that can actually poison your go-to-market process. The fact that what comes out thinks it knows what it’s doing, and then spreads itself throughout your process, is a cautionary tale.

Brooke, how do you think about governance since security is already in your company’s DNA?

**Brooke Bartos** (34:14)  
 You mentioned garbage in, garbage out — but AI is kind of like throwing a can of gasoline in a dumpster fire if you’re not careful. We’ve all heard the horror stories of teams that have accidentally deleted their entire CRMs by AI by mistake.

In our organization, we have a marketing operations team, a business applications team, and a revenue operations team. The three teams work very closely together because all our work is interdependent. Anything done by one team has the potential to impact the rest of the business.

Aggressive testing in sandbox environments — by all teams, not just the team making a change — is prioritized. Even something simple like using AI to standardize job title and function affects reports, processes, and workflows across teams. If we’re going to ask prospects to trust us with the code of the tools they’re developing to sell to other organizations, we have to show up right from the beginning.

**Jessi White** (37:04)  
 I’ve been spending a lot of time with our legal teams. For us, governance is typically initiated by IT and legal at an enterprise level. Any new AI technology — whether a new vendor or an existing vendor with new AI capabilities — has to go through those teams to establish any security risks.

But it’s really a team sport. IT can assess the security risks, but they don’t necessarily understand the day-to-day business processes like we do, and they don’t know the client journeys and the interconnected technologies that are one, two, three connections away from where we’re initiating a new AI capability. We have a shared accountability and an open-door policy. Brooke’s point about testing — not just the group that owns the initiative but everyone involved — is important because it’s easy to get blinders on and only test for the expected outcome.

**Kacee Court** (39:28)  
 It’s inherent to Drata. We are a trust management platform and we have a standard to uphold. It’s something that is top of mind every day — working with legal, security, and IT, assessing risk, and making sure we’re tracking the agents we’re deploying across all these different systems.

AI is like everybody’s playground, so having some controls and guardrails to make sure things are working properly and we’re following all compliance rules is critical. There’s also an added layer with GDPR and EMEA. And going back to customer experience — we want our customers and prospects to have a positive experience. Emailing somebody who unsubscribed ten weeks ago is not a positive experience.

**Jim Bell** (40:42)  
 That last story reminds me of a mini AI horror story I heard at a conference last week. A company set their scoring model based on web page visits, and the model started generating MQLs from people who were unsubscribing from their emails. These people were getting MQL’d — and it was not working the way they intended.

**Jim Bell** (41:34)  
 I want to share a summary of what we’re hearing consistently from our customers. Everyone is transforming with AI — it has become a requirement. And the assumption is you’re going to do that without more budget, because AI should make you more productive.

The pendulum is swinging from the playground phase to AI needing to produce. That means dealing with more autonomous use cases. What we hear very consistently is: first, the data foundation is super important — data has to be clean and trusted so an agent can act on it autonomously. Second, business process clarity — what’s supposed to actually happen has to be really clear. Third, shared customer context — it has to work across different teams. You need to know, for example, that you shouldn’t be trying to upsell a customer when there’s a severity-one support case open.

Jessi, what guidance would you give to people trying to move forward with AI in their go-to-market process?

**Jessi White** (44:54)  
 Start with an honest assessment of the fundamentals. Take a good hard look at your data quality — if it’s not there, hard stop. Look at your routing processes, what consistency already exists, and identify where people are still relying on manual interventions. Then really look at where AI can create leverage.

It’s not always going to be the most impressive or flashy use cases, but those that help improve outcomes and reduce friction will ultimately get organizations going farther faster. AI is going to amplify what you have, whether it’s good or bad. Be really thoughtful about where you’re starting before throwing it on the fire.

**Kacee Court** (46:11)  
 Echoing that — but I think about it as organizing the work first and grouping it. A lot of people say “start with the foundation,” and that’s true, but sometimes you don’t know how large your foundation is or how narrow it is. If you can group the work and the processes that power it, you can start to assess what your foundation actually is.

My team now owns the entirety of the prospecting tech stack and the data enrichment behind it — but we don’t own all of the data across the tech stack. Making sure we can focus in on those areas and build strong partnerships with our data team and with security and legal is key.

**Jim Bell** (47:03)  
 So what you’re saying is — rather than thinking of it as fixing the data foundation in its entirety, focus on the specific process or task, figure out what data that agent needs within that process, and focus there?

**Kacee Court** (47:23)  
 Exactly. Because you don’t want to bite off more than you can chew. It’s really easy as an ops person to spiral into rabbit holes. Organize the work, put the people around that work, and then you can start to build out what you need to go fix. It keeps people focused.

**Brooke Bartos** (48:03)  
 Starting small is truly the key. The number of data fixes we’ve found and implemented as a result of starting small in testing — catching those early before they’re scaled — has unlocked a lot of capabilities across all of the teams.

Think back to when we were first starting to do account research. You can ask AI to go research an account, but when you start to put together a brief with actual guidelines — here’s what I want to know about this person, how does this company talk about their own customers, how can we use that information to personalize aligning to our ABM strategy — creating those guardrails and starting with something that is low risk lets you start to build skills and confidence to experiment into more complex things. You’ve learned how to prompt effectively, and you’re building two motions at the same time.

**Jim Bell** (50:30)  
 What we’re seeing is that every AI use case needs to be framed around: when is this agent going to get triggered? What is the signal or data change or human that’s going to start that process? What data does it need to operate on? What is its objective? And what does it need to pass back to the next action — whether that’s a system action, another agent, or a human?

One question from the audience: what systems do you have in place to control the types of AI being used by marketers and sales teams independently? Do you have AI councils, or is it still the Wild West?

**Jessi White** (51:43)  
 We have an AI working group, primarily set by our IT, security, and privacy teams. Any new AI use cases have to go through that team and through a really rigorous risk assessment — evaluating brand risks and other potential opportunities for things to go wrong. The teams do shut those down from time to time.

The hard part — but also the good part — about being a large organization is that nobody has access to individual API keys. It is really confined by this AI working group, and we have a strong AI transformation team helping to guide the business. When you want to move fast, it does make it hard — a lot of hoops to jump through.

**Kacee Court** (52:54)  
 Similar — we have a very rigorous procurement process. Anything coming through is flagged to our AI team, and if it has anything to do with go-to-market, I’m usually involved with legal and security as well. We used to be a little bit of the Wild West with people wanting to get tools all the time, so we had to put some process around that.

**Brooke Bartos** (53:34)  
 Similar — we have a data governance council within our organization and a very rigorous review process, especially being in cybersecurity. I think gone are the days of “bring your own tech stack.” The Wild West days have passed.

**Jim Bell** (53:52)  
 It reminds me that people in your roles — those who understand the business process, the data layer, the rules, what’s supposed to happen, how marketing operates, how things get handed off to SDRs to sales to customer success — are in a great seat to move up and evolve into AI GTM roles. That understanding can’t be left to people just experimenting. Revenue is at stake and there are a lot of teams involved.

We’re seeing more and more people evolve into either being on those AI councils, leading them, or taking on a completely dedicated GTM AI role.

With that, I’m so grateful for the three of you. This was really outstanding — the context you were able to give people and bring to life the data from the report. I welcome everyone on the webinar to download the report, and you’re welcome to reach out to me on LinkedIn if you have questions.

Thank you all for joining us today, and thanks again to this great panel for sharing all your wisdom and experience.

## Frequently Asked Questions

### How did Korn Ferry cut speed to lead from 15 minutes to 5 minutes — and what did that change?

By moving to LeanData about two and a half years ago, Korn Ferry reduced lead response time from roughly 15 minutes to around five — while running waterfall enrichment, normalization, and multi-region routing rules in that window. The time savings improved conversion rates, but the more significant shift was organizational. Sales teams gained confidence in the leads marketing delivered, which improved SLA adherence and strengthened the relationship between both teams.

### How did Drata use LeanData as the orchestration layer for its AI motion?

Two and a half years ago, broken routing was costing Drata more than $2 million in missed pipeline. Today, LeanData sits at the center of a more sophisticated motion: packaging AI-generated account signals and routing them to the right destination — a human SDR, an AI SDR, or a downstream system — with visibility maintained throughout. Kacee Court describes LeanData as the structure that made the rest of the AI strategy possible to build.

### What governance structures are these teams using to manage AI across GTM?

All three panelists have moved away from open experimentation toward formal oversight. Korn Ferry routes every new AI use case through an AI working group led by IT, security, and privacy teams. Drata flags any AI procurement touching go-to-market to legal and security for review. Checkmarx operates a data governance council with a structured approval process. Each team treats governance not as a slowdown but as the condition that makes it possible to scale AI with confidence.