How AI is reshaping Sales and Marketing Alignment?

A future-facing conversation about what happens when AI starts reshaping the whole B2B go-to-market system: targeting, intent, content, seller enablement, pipeline generation, forecasting and follow-up. The episode explores whether AI makes sales and marketing more aligned, or whether it creates more noise, fragmented messaging and over-automated buyer experiences.

Core questions:

If AI can increasingly identify accounts, generate content and automate GTM workflows, what parts of sales and marketing still require human judgement?

Does AI make sales and marketing alignment easier, or does it create new risks around messaging, governance and customer experience?

How should B2B teams combine fit, intent, engagement and pipeline data to know where to focus human effort?

Guests

• Denisse Soker, Sales Enablement and Product Adoption Leader at NetApp

• Kristi de Asis, Director of Demand Generation and ABM at Achievers

Transcript

Joaquin (00:00)

welcome to another episode of B2B Marketing Futures. Today we are discussing how AI is reshaping sales and marketing alignment, a topic that is becoming more urgent as go-to-market teams adopt more tools, generate more content, move faster than ever before. Sales and marketing alignment has always been difficult. Teams often struggle with different definitions of quality, different views of the customer, different priorities, and different ways of measuring success.

But AI adds a new layer to this challenge. On one hand, AI can help teams identify accounts, personalize messaging, generate content, support sales conversations and surface insights faster. But on the other hand, if sales and marketing are not aligned, AI can also amplify the problem. It can create more fragmented messaging, more disconnected workflows, and more inconsistent customer experiences.

Today we'll explore where AI is genuinely helping, where it may be creating new risks, and what skills we require for human judgment, governance and cross functional collaboration. But before we get into that discussion, I'd love for our guests, Christy and Denise, to introduce themselves and share a bit about their role and current focus. So very welcome, Christy.

Then is

Kristi de Asis (01:22)

Yeah, definitely. Thank you so much for having me, Wakeen. So I'm Christy Da Assis, Director of Demand Generation and ABM here at Achievers. We are a B2B SaaS platform in the employee recognition space. And I own the performance side of marketing. So that includes paid media, SEO now AEO, so Answer Engine Optimization, ABM, our website, and of course the team that executes across all of it.

Basically a lot of acronyms. If it sounds like a keyboard smash, it's probably part of my remit. So it's a little bit about me.

Joaquin (01:55)

Thank you so much, Christy. Welcome. And Denise, welcome.

Denisse Soker (01:59)

Thank you. Thank you for having me. so I'm Denise Zoker. I'm in charge of go to market enablement. So my role is basically the goal is to train and educate sellers and provide them with the right message to deliver to the clients, trying to make a cohesive story out of the product that we're promoting. And of course focus on

Product launches and like special occasions which require us to craft specific messages.

Joaquin (02:28)

Awesome. Thank you so much for those introductions. We know that there's a lot of hype around AI go to market, but I'd like to ground this in what is actually happening inside your teams. So I would love to know where is AI already changing the relationship between sales and marketing alignment. Is the biggest impact in account identification, messaging, content creation, pipeline management?

love

to hear how are you using AI and how it's impacting sales and marketing alignment.

Kristi de Asis (03:00)

beyond the specifics of how AI can, augment the processes on the workflows, the place I feel it most is really in the speed of intelligence and the risk that comes with the speed. as an example, we use an ABM platform within Achievers for account intelligence and intent signals. And with

AI baked into those platforms now more than ever. with more sophisticated capabilities, we're getting account prioritization recommendations, scoring changes, trigger alerts, the algorithm gets retrained, right? more frequently. And we get a lot of those recommendations around account prioritization specifically faster than we've ever gotten them before. And that's genuinely useful. But

there's a practical example that I wanna share and a story that I've told Joaquin before, is that, sometime in April we saw MQA accounts spike overnight. So MQAs are what we would consider like these high intent accounts that we want.

sales and marketing to kind of swarm, So it spiked from about 950 accounts to nearly 3,000, literally overnight. someone on my paid team caught it because our paid social audience size also spiked. those same accounts also feed our ad targeting. And we had to go back to our ABM platform and basically say, what happened here?

And it opened up a real conversation about how much we trust the signals that we're acting on. so AI is making it a lot easier to move fast. it won't necessarily make it easier to move accurately, especially when sales and marketing aren't as aligned on certain definitions. and if they're acting on those same signals that we rely on AI on, though.

If those signals are wrong or inflated, you've just amplified the misalignment instead of fixing it. that's I think one risk I've seen play out myself. on a positive note, where I find AI is really helping the relationship is definitely more on the content and enablement side. And I think Denise, you can speak to this really well.

our sales team used to create their own sequences in outreach, even when we gave them marketing approved ones. So it's not out of malice. They just didn't feel like maybe the marketing copy spoke to their buyers' actual language, that they use. And now with AI tools, they can iterate faster, they can personalize faster. we can give them a version, they can adapt it. and AI helps them read through account activity

and helps them tweak it and improve it. And we don't lose months of going back and forth. the feedback loop is a lot shorter in that regard. And I find that that's where AI is really helping that alignment currently within at least my organization.

Denisse Soker (05:44)

there are two sides of the coin, right? Because I mean on one side sales can, accelerate themselves and can kinda do

things alone, but at the same time, marketing is still important and when you come with coercive messages like salesplace and specific, context that the signals are maybe known to sales but they don't always know how to interpret them the same way that marketing does. And I think that, within this context,

What AI does sometimes is that if a marketing and sales are not aligned or are not enough aligned, so it kinda amplifies that and if they are enough aligned, it kinda amplifies that as well. I can tell you especially from a relationship dynamics,

because marketing sales was always a complex relationship and the alignment wasn't just on the tool level, yeah, we have a lot of stuff, a lot of insight, but you had to build trust and

authority because marketing as a whole has always been like people felt like they could do that without the marketers. And now that there are tools that come and are supposedly taking the place of the marketers, then sales might feel that they can do the work themselves. So I think that the work that needs to be done around relationship and around trust between sales and marketing with the context of AI

has become also more important now than ever.

Yeah, I think that within that context, an example I that I can give is that right now, at least from the enterprise perspective,

a lot of times sales has seen marketing as a content creation engine. And from my standpoint right now, there is enough content for sales, one hundred percent. I think an example that I can bring is that from a lot of conversations with sales, we know that there is a lot of customization of content, So we have sales place in place and

when they come to the client. So this client has a very specific problem, very specific context, specific workloads that they're working with, specific data that they want to migrate. So they take from each deck, each sales play, and they craft their own message,

But most of the time it is hard for them to find the right message. And so for example, one thing that we did within our department is help rely on AI for content discovery. And now they can, find it's not just coming to us or going to knowledge bases and filtering through a lot of decks and a lot of content. They can type in I have this case with

this client and I have this problem, and AI can now bring up specific slides in which, talk about the specific problem and they can craft their own presentation, but while remaining consistent with how we want them to deliver the message to the client.

Joaquin (08:40)

and do you have specific tools for doing that? are you doing that on Notebook LM or

Denisse Soker (08:45)

This is a this

is on the works, okay. This is wasn't like one hundred percent delivered, but we're building an

Joaquin (08:51)

Uh-huh.

Denisse Soker (08:51)

agent. So there is like a material pool.

And that it's a little bit also back to the governance, the source of truth. We want them to take from our materials, from what we know works with the clients, with what is aligned with the message that we want to convey. and from that source of truth, they can take out whatever materials they want. And what is great about that, it also helps us kind of manage and make sure that the materials that they're using are up to date for example, we have a project.

that has been alive for two three years and there are new features that are all the time being updated and even from let's example capacity standpoint, a pricing standpoint, a lot of things tend to change. So we don't want sales to use outdated information just because they needed like a specific slide to talk with a specific customer.

this agent has like this source of truth which we make sure that is, relevant and up to date.

Joaquin (09:50)

That's great because you took something that in the past was marketing equals content but in a one to one basis, like hey marketing, give me this new cadence or give me this or give me a new white paper. And now with AI you almost amplified the role of marketing to a knowledge repository where they can pick whatever they want but within the boundaries of

marketing and the company wants to tell. I think that's sounds very clever. And Christy, I'm very curious to also know more about what are the dynamics internally that you have. You mentioned earlier that the engaged accounts tripled and things were moving fast but not necessarily accurately and you can amplify the misalignment. So I'm very keen to know about those processes as well.

And how you put those boundaries if you put the boundaries or you let cells move freely, I don't know.

Kristi de Asis (10:49)

that is a very valid question. And that's actually one of the biggest risks that we're consistently trying to address and think about. mainly because especially moving away from search engine optimization to answer engine optimization, So SEO was about, for example, getting your content to rank on Google so people would click through.

To your site. AEO is about getting your content to be the answer, right? So the thing in an AI assistant or generative search engine pulse when someone asks a question. that click may never happen. now the game is shifted from how do I rank to how do I get cited, referenced, trusted as a source by the AI itself. And the risk that I was referring to that we've been talking about is because.

AI is reducing web traffic, right? Because buyers are getting answers directly from LLMs without ever visiting your site. The traditional intent data that we used to get, which we would feed back to sales too, through whether that's like at least for us, we use it our ABM platform, right? That produces daily reports to sales on like these are your highest intent accounts. That intent data is largely built around, well.

Of course, your first party data, so bargaining activity, sales activity, but also on web behavior. people actually visiting your site. And again, that's not happening as much as it used to, right? So the intent data that we're getting is essentially becoming less reliable, So that means that the signals that we've been using to align with sales around who's in market are degrading.

And if we don't acknowledge that, then we're gonna keep presenting confident looking data to the sales team that increasingly doesn't reflect actual buyer behavior because a lot of research and the evaluation now happens in what we call like the dark funnel, right? And the dark funnel is getting

broader and broader, like bigger. are a lot of that evaluation happens more in the dark, right? In the shadows. because folks are not actively going on your site anymore. I think the paradox that we deal with AI is making our signals noisier at the same time that it's giving us more capability to act on those signals. I think the teams that figure that out are the ones that will have real alignment.

everyone else is just going to have faster misalignment. And it is my take. I think that's the risk that we're constantly trying to address is like, how do we get more reliable intent signals in this environment, in this world? And then how do we build confidence from sales?

in that intent engine that we built in this new world. Right?

Joaquin (13:42)

Yeah, that's of course it has been always tricky, right, in marketing, how you can collect first party data and if you are missing your website which was the main source of first party data, you still tend to start being more creative and maybe analyzing more your ads and how people are interacting with

your funnels and trying to build a story and narrative and testing ideas have you been experimenting with that for example like okay so if people that we are pushing this message, people are interacting positively with these kind of ads, then we should do more of this kind of content, for example.

Kristi de Asis (14:23)

Yes, we've always been a very data-driven, data-centric org. so I'm lucky to be able to operate in that type of culture. so that practice has always been around. But what AI helps accelerate is, before you would have to pull all that data manually, push it into spreadsheets. There's tools now that allow you to ask.

an AI assistant, right? For like, what campaigns are driving our biggest late stage pipeline? And what are the creatives tied to that? What is the messaging that creative's using? So in terms of like how AI is accelerating the speed at which we can get those insights, that's how we're using it currently in our org.

we've gone beyond the experimentation stage in that sense and to like the actual application of how do you actually leverage AI so that you're moving faster on experiments, on tests, right? I have someone on my team

Joaquin (15:19)

Exactly.

Kristi de Asis (15:20)

who manages our conversion rate optimization strategy, and she's built an artifact and Claude that plugs into like our A-B testing tool.

And pulls the data, synthesizes all of the tests that we've run, what are the next tests we could do, and how do we prioritize that based on what makes the biggest impact on our pipeline eventually? And that used to be all manual, tedious work. And now, in a sense, like every person on my team, and they all have their own swim lanes and they're all subject matter experts, they

have their own assistance.

Joaquin (15:53)

Yeah, it's removing the asymmetry of information, right? Because now in the past you needed to ask a paid media manager for the insights of the latest campaign like which creative was the one that was performing better. Now a manager or a director or even CMO or CEO could have access to the information.

directly, right? So you can have conversations, then maybe you can also ask the paid media manager about like how things are performing, like how you perceive things, but they have a lot of information as well. the kind of conversations I think you can raise the bar in terms of implementation and I think that brings us to a bigger question, which is if AI can increase the

suggest accounts, generate messaging, automate follow-up, and read data, etc. What is left for humans to decide? Because alignment is not just about having the same tools, it's about making the right strategic calls, which accounts matter most, which messages are actually credible, when should sales intervene, when does a customer need education rather than a pitch? So

Yeah,

I'd love to hear how both of you think about the role of human judgment in an AEI enabled go to market motion.

Denisse Soker (17:12)

I have to say that from a go-to-market perspective, AI, yes, it is great for bringing the signals, it is great for finding the patterns and to bring them to the table. but I think that at the end of the day,

the human judgment comes from the context because even if AI has the data sometimes it doesn't know how to prioritize it the right way right so things like ICP product market fit strategic decisions I think we're not at that stage yet

And I think that, from a human perspective, you still have to be there at least holding the hand, even if it brings you the insights, even if it brings you the data and it brings you a lot of clarity, right? Clarity is a great value of AI at this point. but we're not on the stage where AI can make higher level, higher touch decisions. Yeah, we can give it some times to manage the bid and that kind of stuff, but

the higher level decisions will still remain with humans, I believe.

Kristi de Asis (18:13)

I would pick it back off what Denise is saying. I would add, aside from context, it's also interpretation and knowing when the data is lying to you in a sense. and I'll follow that with a concrete example. so we have a standing meeting with

SDRs and BDR leadership where we ask for qualitative feedback on lead quality, how is the the MQLs and the MQAs that we're passing over. And we have a similar cadence with the AEs who own the ABM accounts that my ABM team works on. And so what are the reps saying? what's coming up on the calls and

One of the things that I've learned the hard way is a lot of the perception on the sales floor forms through tribe mentality, for lack of a better term. So, not necessarily always actual sample size data. for example, one rep will have a bad week, it they mention it to a few others, they nod along, and suddenly the synthesized feedback that

the sales leadership brings us are getting worse. we don't just take that feedback and spiral into a panic. we try not to. We hear it out and genuinely and then we pull the data. And a few months ago, the feedback that came in is that we were seeing more what we consider internally as like below the line or like non-ICP titles coming through. we actually pulled the year over year breakdown and

Yes, there was in fact an increase in absolute volume of titles that don't necessarily fit like our typical ICP, but there was also an increase in the absolute volume of folks that do, right? And proportionally everything was the same.

I think the composition hadn't actually changed. It was just the volume of both, right? Like the non-ICP and the ICP titles. so that conversation could have easily turned into like a three-month, marketing leads are garbage narrative. but instead it had it got resolved in one meeting because we have a standing meeting with our sales team where they're able to surface those types of

concerns. an AI system, in a sense, and this is where I'm gonna tie it back to the question, is an AI system could quickly flag the volume change and surface that as a signal. What it can't do is understand the perception on the sales floor and that perception doesn't actually match the composition data and what the volume is showing us. And

The of relationship risk of not addressing that perception quickly is real, even when you are technically Even when sales is right in saying that the volumes increase. But if you look at proportionally year over year, the volume's the same. I think that's a good example of the human judgment layer that AI won't be able to do. It's not going to be able to push back. it's not going to know when to listen and

It's not always gonna know when the story the data is telling is incomplete. could have probably phrased that better and more concisely, but I just wanted to show that parallel of how we would address it with the sales team, Like in

Joaquin (21:22)

course,

Kristi de Asis (21:22)

real time on the floor, but AI is not gonna be able to have that.

critical judgment and thinking to be able to understand when to poke holes in the claims that are being made. I think AI still has that confirmation bias.

Denisse Soker (21:39)

while you were talking, something popped out of my memory and I think it's said context, you said interpretation, and what came up was intuition, human intuition. And I have a specific example to bring here. when I just came to NetApp when I had was more on the pipeline creation demand generation side, I came and I had to basically I was part of a go to market team for an install based product.

I just came, it was early, I said, okay, we have installed this data of a lot of customers. let's do something like quick and dirty, let's build some sort of sequence, some sort of nurturing campaign. And it was something that it was quick and dirty, short term,

But it was a very, very slice and dice from a messaging perspective and very layered build. so it was something that I set up pretty quick. the campaign ran through about two, three months. And then at the end of it, we kind of saw the results. There wasn't a lot of engagement, there wasn't a lot of results out of that. We didn't see okay, maybe someone was opening the email, was seeing the ad, but no hardcore engagement, right?

So I put it aside. and about, after a year of being within the role, I don't know why that campaign kind of came back to my mind. And as we were measuring attributed the ARR, like influence ARR. we were creating a new scoring and we were working on that. so when we kind of

pull that campaign back in. I said it's interesting to see, what happened with that. it figured out that campaign didn't have an immediate influence, but within a course of time it actually became clear that it kick started a lot of the sales cycle and overall,

funn engagement that clients had over the year. and it was amazing to see because, if we just looked it as it is, just one time, something that didn't succeed, we put it aside, didn't look at back at it. And if I hadn't didn't have the hunch that, this was something worthwhile maybe getting back to, then we would miss it. And then

Definitely, we brought it back and we doubled down on that as an initiator of other processes and other

Kristi de Asis (23:52)

Yeah. It definitely won't always have like the same historical context, internal knowledge and

understanding of even your seasonality. maybe we'll get there eventually, once everyone becomes expert prompt engineers and can write

prompts but I think it's gonna take time. There's still a bit of a learning curve for humans and for

Machine.

Joaquin (24:15)

Yeah,

and if we ever want AI to perform like a human, we need to give full context of things, right? We can't just give one data set and expect AI to perform like a human because you really need a context of how things are going on outside of the company, inside of the company, in the past, in like everything, right? So yeah, it takes time to build those

Systems and yeah, once you build the system, something new appears, right? And probably a new AI, a new model or something will appear and whatever you are writing will sound like AI because you're not humans, in my opinion, we are always ahead of how things are happening, right? Because we have way more context than any any system that we could create. But yeah, of course the goal is to create something that looks like human, right?

But that's really, really hard. And in the past I think alignment might have meant shared pipeline, agreed MQL definitions, common dashboards or regular feedback calls between sales and marketing. I think today we need more alignment around data, the prompts, the workflows, messaging, the customer signals. So it's way more data oriented in my opinion.

Denisse Soker (25:34)

Yeah, there was one thing that I wanted to add to the conversation and that is kind of a challenge that we have. I think it's from a compliance slash governance standing point, because

One of the challenges that I face in my role is that I work with salespeople, but when it comes to feedback loops, which AI is supposed to really support here, we have a challenge because when sales come to me and they say this is something, that happened that was brought up in a customer call, it's already too late to act upon that.

However, from an AI adoption perspective, those conversations are, what's being sensitive client information. And we're not at the stage in which AI adoption can be incorporated there. And so I think at one side is one of our biggest challenges that AI could solve at the same time from

compliance and governance perspective we're not there yet. and so I don't know how to fill the gap, but if I could do that I think it would be like a great solution to my job.

Joaquin (26:38)

okay, we've covered a lot today, like how AI is changing the relationship between sales and marketing from account selection, messaging, enablement, governance. I would love if you could share one key takeaway that you learned today or that made you think that

Yeah, you should do something different to improve sales and marketing alignment.

Kristi de Asis (27:03)

what does AI mean in the context of sales marketing alignment?

The good alignment in the AI era, and you talked about it, Joaquin, saying, before alignment between marketing and sales meant like shared definitions around MQLs, MQAs. shared understanding of data and the context that we feed AR, for example. And so I would actually argue that.

Good alignment in the AI era looks a lot like good alignment always did. Right. So I think the fundamentals still really matter, having those shared definitions for example in the context of ABM and even actually demand gen, like what does a qualified account look like? Right. having shared account lists between ABM and sales, having a recurring forum where sales can give feedback and input.

Where marketing and sales are looking at the same data and making decisions together, right? Not marketing percenting to sales, but both actually working together in unison. I think that's one of the main takeaways here is even with AI, it shouldn't change how we work together and the foundations that we build for that relationship.

what I think AI does is it adds the ability to do what we've done at a pace and scale that may not have been possible before, right? for example, specifically with an ABM, we can now run account level personalization for 200 accounts with the same effort it may have used to take to do it for 20, right? We can monitor intent signals across thousands of accounts and get alerted to.

meaningful changes, within minutes, within hours, and not necessarily have to wait for individuals to surface it. But the ritual that I would put at the center of it all is still the human feedback loop. And there's three words that definitely stuck with me in this conversation. It's,

Where does the human judgment matter? It's in context, interpretation, and I love what you said, Denise, about intuition. Like so that context, interpretation, and intuition.

Denisse Soker (29:17)

And I have to say, as a marketeer that has gone a little bit to the other side and has exposed to the complexity of enterprise sales and understanding that the sales organization is also very, very complex for example, as a marketeer I was,

always aware up to the opportunity level and then to the end of the deal, the ARR and the pipeline. And now I know that from the opportunity to the actual money, it's a journey. It's a a big journey. It's called find the opportunity, it's going doing the POC, it's accompanying the client and tailoring

The VPOC to the sneeze. And then another layer that sometimes gets missed a lot within the marketing context is the customer success side, which are also the influence revenue and should be also considered part of the sales organization. And that journey is a long one and a complex one. And understanding that as a marketeer, I think that if I were today to kind of go back.

and being in charge of bringing a pipeline, it would be a lot easier for me to understand the sales organization and the context in which this funnel is being managed or led to.

Joaquin (30:29)

Yeah, one one hundred percent. I think in my case what really stands out is that AI doesn't remove the need for alignment, it raises the bar for alignment. If teams can move faster, if you can create more content, if you can automate more workflows, you need even more clarity on who they are targeting, what they are saying and how they define success. And

Well I want to say thank you so much, Christy and Denise for joining us today. It's been a pleasure having you on B2B Marketing Futures. Until next time, thank you so much.