Sales and Marketing Alignment When AI Sits in Both Seats
Sales and marketing alignment has been B2B's favorite unsolved problem for twenty years. Now AI just raised the stakes: SDRs run AI-powered outbound, marketing generates pipeline with agents, and nobody's sure who owns what anymore. We discuss what SLAs look like in 2027, why the MQL is finally dying as the currency between teams, and how the best revenue organizations are redesigning the handoff when software, not people, does most of the passing. If your pipeline meetings still turn into finger-pointing sessions, start here.
Guests
Melissa McCann-Tilton, President & Chief Revenue Officer at Criteria
Gabriella Fishkind, Head of Revenue Development at Ceros
Eswar Sai Prathap Singavarapu, GTM Systems Engineer at Fivetran
Transcript
Joaquin D (00:00)
welcome to another episode of B2B Marketing Futures. Today we are focusing on sales and marketing alignment at the moment when AI sits in both seats. Sales teams are running AI-powered outbound, and marketing is generating pipeline with agents. And it's not always clear who owns what anymore. In this episode, we'll explore whether AI is actually bringing the two teams closer together.
Or just helping each one automate its own silo faster. We'll look at what the handoff between marketing and sales should look like when the MQL is no longer the currency between teams, what both sides need to agree on in the data before AI can really help, and where human judgment still has to take over. Before we jump
into the conversation. I'd love for our guests to introduce themselves and share a bit about their background and current focus. So maybe Ishwar, would you like to start and give a sentence on yourself?
Eswar Singavarapu (00:59)
Sure. Hi all my name is Ishwar. I'm a senior go-to-market engineer at FiveTran. I've started my career in analytics and data space, but then I've moved into owning systems process with data being the foundation as well. I think of go to market engineering or GTM systems as a product where the users are sales reps and marketers and I
tend to like take the feedback from the customers and improve the product. And yeah, that's how I look at GTM engineering.
Joaquin D (01:27)
Thanks so much. Great introduction. Melissa, welcome.
Melissa McCann-Tilton (01:30)
Thanks. I'm Melissa McCann Tilden. I'm the president and CRO at Criteria. my career is really stretched across sales and marketing most of the time. I actually started in marketing and jumped to sales because I needed a metric. And in those days, marketing didn't have as many reliable metrics. And to really have a seat at the table, I needed an ARR number. I needed revenue. And
That's worked out well for me, but I think there's a lot of lessons to be learned and hopefully we'll have some discussion about those two disciplines sitting under one roof and Ishwar. It's hard to find people with that data background, so I'm super interested to hear what you have to say about some of this.
Joaquin D (02:10)
that's great, great introduction. Thank you so much, Melissa. And Gabriela, welcome.
Gabriella Fishkind (02:15)
Yeah. Thanks. I'm Gabriella Fishkind. I am the VP of Go to Market Strategy at Saros. And I actually started my career at Saros. I've been here almost 10 years now on our Outbound team and have been lucky enough to been be with this company as we've grown from a much smaller startup to the company we are today. And a lot of my last few years has focused in as AI has come up.
in the space on how we bring AI internally into our teams, how we use that across sales and marketing, where we find cohesive patterns and moments of when what has been like, downfalls maybe over the last few years of where we've brought it in maybe too quickly or in the wrong way. And that's where I've been spending a lot of my time to date.
Joaquin D (02:56)
Well,
thank you all. Great introductions. let's start with the big picture. Because both teams now have AI for research, targeting, content, outreach and prioritization, right? So is AI actually bringing sales and marketing closer together or is it simply helping its team automate its own silo faster?
Melissa McCann-Tilton (03:18)
I see places where there is cohesiveness, but I think all of us have gone through this experience in the last year or so where you're just trying to figure out what it can actually do, right? you're like, can it do this thing or or not? Is it a myth?
Like we tried an AI SDR. That was a myth. It did not work. I mean it was a lovely idea that didn't function. and we were finding that's definitely a place where humans need to be at the wheel. So I think there's just this sort of feeling around going, what can it do? And then the other observation I have is what can a rep consume?
at ESWAR I'd be interested in your experience with this. I've got some really smart RevOps people that can come up with some really good conceptual stuff that my sales reps cannot do anything with. It's just like that that's something over there. I don't understand that. I don't understand how to implement it. I can't connect with it. So I think there's just a real feeling about right now that's going on.
Eswar Singavarapu (04:17)
Yeah, in my perspective, I think AI doesn't itself fix the alignment between the two teams, but it enhances whatever foundation we have and like if there is disagreement between the definitions, I think the disagreement grows larger because we are generating content faster, we are doing marketing faster and sales faster too. And I think
I would think of AI more like assisting the existing marketing and sales teams. how do we surface this signals? How do we surface what marketing has done to sales teams? And how do we surface the past engagement? And like how do we give a quick summary for sales rep to act on and maybe we can come up with the next best action for the sales teams using all the past history and engagement.
Gabriella Fishkind (04:57)
Happy to build on this a bit. And Melissa, we definitely share similar experiences, I think, in what we've tested and what's worked and hasn't worked and where we've brought the team together. similarly on outbound, we've been building agents for the past few years. We've gone out of house, we've built in-house, and the range of success. It's just been failure, a little success, failure, a little more success. And so far it's been fairly siloed. I do think AI has the power to bring
both marketing and sales together, I think where we've struggled is maybe just because we can do something doesn't mean we always should. But AI gives you the power to do so much so quickly. And now we're in this phase of like we all have to question ourselves, especially at Sarah's of okay, just because we can do those 20 things, we can send sales those 20 notifications in all these different ways. Like what's the most cohesive experience? Where do we actually get the message we want across? And then what's gonna win?
And I think the other thing we've learned, and I keep trying to stress across both teams, is not being afraid to say like this thing isn't working, even though we all put a lot of bets here. Like I struggle with it where I'm like, hey, to my CEO, this actually isn't working. we've been doing this for six months. We're not AI like an AI S TR. and we've actually had some great success recently, but for a while it was like, hey, we're not seeing much do we want to go a different route? So also making sure
across both teams, just because we're being told, hey, use the thing, that we're not afraid to say the thing actually isn't working or it's not making our teams like more efficient or productive.
Joaquin D (06:19)
Yeah,
the AI SDR I think is a particular use case because AI can do so many amazing things, right? But writing as humans I found is one of the few areas where AI is not performing as maybe other areas like doing calculations or things like that. But writing is probably one thing that you still need humans in the wheel. is that
Why it didn't work or was for something else?
Gabriella Fishkind (06:46)
Wells, I'm very curious to hear what you all have tried as well. On our end, so we trialed a handful of out-of-the-box AI SDRs, like ones that came from other platforms that we signed on to and gave them a big like six-month trial. We didn't see much success there. We're also a really outbound heavy company, so we've had a good handle. We had 45 SDRs at one point. we're around, I think, 10 or so right now. So we've found a happy medium between AI efficiency and what the humans on our team can do, which is
Their efficiency from like four or five years ago is incredible and AI has given them that time. But when it came to actually using AISDRs, the success we've seen is building in-house. And we've really honed into LinkedIn, coupled with the team still doing a ton of like call work. Email is a tough one. we've trained our agent to write really strong emails, but I just think email actually maybe is a bit dead, knock on wood. But where we've seen a lot of success with AISDRs on LinkedIn, it's
A lot less volume, but the touches are more personal and have a better success rate. And with that, I think what we've had to learn is you constantly have to be coaching the model. We are doing coaching every single week and giving across four or five people a ton of feedback to the model that then RevOps goes and takes and re-jiggers. So we're constantly making it better. But most, I am curious, like from your end, what you guys have done and not seen success with. Cause I think we probably share a lot in that we saw a lot of like,
lack of success for a long time.
Joaquin D (08:07)
And Gabriela, who owns that model? Is it marketing, is it sales or both
Gabriella Fishkind (08:11)
Sales and RevOps own it together, but marketing is actually one of our biggest partners and giving a lot of the feedback with the sales lead. we also sell to marketers. So for us it's important for marketing to think through the lens of like what would a marketer want to hear? But marketing's one of the coaches to our AI to come in and say, Hey, sales, I know you guys think this works, but here's a ton of feedback from us. Like, why don't we marry the two? And that's been a really productive process we've had for the last four or five months.
Melissa McCann-Tilton (08:35)
That's
really good. I'm a big believer in sitting your SDR team and your marketing team, which is how I have mine structured. And yeah, because you get into this finger pointing thing when SDRs sit in sales, like well, if the marketing content was better, then I would be successful. and then, it just sort of sort of goes back and forth. So I really like sitting my S Dr team with marketing. I also like to sit
sales and marketing under the same tent and then goal marketing for pipeline production. we tried just like you, Gabby, we tried the out of the box SDRs. And I think maybe one day they'll be more successful. It's just a little too early days, right? It was just like it was one of those things where it was almost too delicious to not try it. You're like, this could be really good. sounds
so good. And then it tasted bad.
it didn't work. I also think there's this dynamic where the buying process is changing for people right now. And we are really observing how people are shopping, wanting to make purchase decisions, the shift out of Google paid, how do you interact with the LLMs? What are people's tolerance levels for AI being part of the process and where do they get offended?
Because we're finding people like buyers will actually get actively like miffed that you stuck AI into a part of the process. They notice, they kind of roll their eyes at you. So really watching for where those moments are. So what we've found is using AI sort of behind the SDR is the right way to do it. So you're still having the conversations, they're still hitting the phones. There's some really nice tools out there that help them do that really efficiently.
That are AI enabled that help it's like the research part is super efficient all of a sudden. Things that like I would have SDRs sitting there researching for hours. We'd have a research day where you'd go research all your territory accounts. That stuff's happening in minutes now instead of hours. So it's just far more efficient. it's far less obnoxious for them to get ready to hit the phones.
And then as they hit the phones, they can use those tools to just make that process move faster and be more informed. And I think that's where we found the success is in enabling the person, not trying to like replace the person.
Gabriella Fishkind (10:50)
You hit the nail on the head for where we've seen the same thing. Yeah.
Melissa McCann-Tilton (10:53)
good, good. It's fascinating. Like it in LinkedIn, same way. Like LinkedIn seems to be the place where we in the business world all connect, but like people still want to buy from a person. Right? Like
Gabriella Fishkind (11:05)
Yeah, yeah.
Melissa McCann-Tilton (11:06)
I'm happy to do my research in an LLM, but I still wanna talk to a person if I'm gonna make a purchase decision of a certain amount.
Gabriella Fishkind (11:14)
Agreed. My one of my biggest frustrations as a, buyer who's always looking at vendors and I've talked to our marketing team about this when we're setting up our PLG flow and the website and request a demo is make sure there's not too many layers of AI and make sure they can get to a human quickly because a lot of buyers will just want to get to a human at the end of the day.
Eswar Singavarapu (11:29)
So I think we've had very similar situations where like we want AI to enhance the work and improve the productivity of our SDRs and AMs and marketers, but we don't want AI to replace it. Even our SGS had a lot of pushback when we try to like
like use AI a lot. They're like, no, my customers don't want this. My customers don't want a lot more AI generated content. We want like real human interactions. And as I I can relate to that because me being on the consumer side if someone if if there's an AI email that comes to me, I'm like, no, not doing this. I want I want to talk to a real human. Like this is all automated. I I just want someone that's putting in the effort to reach out to me. And yeah, I think that makes a real difference. So I think like you said Melissa, like synthesizing
the insight and like surfacing them and research and surfacing that to the SDR has become really faster now and AI is really enhancing that. so I think that's where that's where AI can do really well.
Joaquin D (12:27)
Mm-hmm.
And Ishwar, at at the beginning you mentioned that one great way to to bring teams together was surfacing what we do. What do you mean by that? And if if you could explain it, whatever if it's in the AI SDR example or any other example, like why it's so important to surface what you do for the rest of the of the team and your counterparts?
Eswar Singavarapu (12:52)
Yeah, I think so something we are experimenting with right now is like using AI to summarize what has been the engagement history so far. So there are three things to it. Like
what has been done and why do we think this is the right fit and why do we think it's a right fit now. so getting that first party and third party information for signals, stitching that together with the engagement history and surfacing that to the reps gives them confidence okay, this seems to be the right motion now. and
The surface shouldn't be like a totally new surface where reps don't natively use in their day-to-day workflow. It has to be part of their day-to-day workflow. It it shouldn't be like a one-off dashboard where they have to go to and it can't be like an additional step in the process. It has to sit where reps use their process and systems today.
Joaquin D (13:36)
Yeah, particularly I found really helpful the use of cloud code and connecting with all the tools I use with an MCP and surfacing the way I work with all those tools that I used to work in the past on my own and managing mate dot com or whatever manually. And now I can ask Cloud Code via an MCP to show what I'm doing to the rest of my team, and I do this
As you mentioned, not as a one off things, but with a daily email, for example, for me and my team, and I see all the outbound with all the tools and how the tools are connected and if anything broken and some things that in the past were really hard to do and you needed to be a developer to build a system like that. Now you can do it with a prompt and that's really helpful.
Eswar Singavarapu (14:25)
Yeah. One thing
one thing from my side I've seen is like we had really good data foundations with structured data, but there's a lot of unstructured data that we can now use with the help of AI. We could really synthesize like our
call recordings and we can really synthesize our meeting notes and really make sense out of it and quickly summarize what we have seen from unstructured data, including third party like data from signals to everything even we're also looking at hiring signals today to see what technologies are these companies hiring for or these accounts hiring for so that can that can give us some really good insight into what might they look at.
Joaquin D (15:00)
That's a really interesting point. And let's get practical and talk about the handoff, because traditionally this has been a source of conflict between sales and marketing, right? so I would love to hear how now AI can bring together all those different signals that you were mentioning, Ishware, like intent data, engagement, CRM activity.
Buying
group data, previous conversations, and explain to sales why an account matters and what they should do next. Gaby, you work around ICP intent signals and deciding when an account is generally ready for sales. so maybe you you can share your
Gabriella Fishkind (15:39)
Yeah.
Joaquin D (15:40)
perspective.
Gabriella Fishkind (15:40)
Yeah, absolutely. I will caveat this by saying we can definitely do this better than we're doing today, and that's not a knock on our marketing team at all. I think we just in reflection, I was even thinking leading up to this call, there's just more we could be doing to bring those signals together better. But I'll share a bit of what we've done today. So we had a bit of a revamp last year of our ICP and the different signals. And right now we've actually built
Most of that with HubSpot's AIs. We were a HubSpot company and brought a lot of those signals together into one centralized forum and scoring tier again with tons of agents built in the background. And they're pretty incredible. we sell to marketers, we sell to designers. It's a bit of a niche market what we're doing. And we have agents looking for such niche signaling information that really tells us for us a company is ready to buy.
At the same time, we've built an intent model that sits outside of HubSpot that's a bit of a separate signal that goes even deeper into like niche moments that if companies are doing these things, it's likely they're looking for something like Saros. And right now, those two signals don't speak to each other, which I think is actually the moment. It may not involve bark, like we might have gotten to a far enough spot where we don't need marketing to help put these two signals together. But I think now we could actually say, 'cause the feedback we hear across both teams is we have these signals like.
Do they speak to each other and they don't right now? So if there's a way we can bring our scoring intent data over here in HubSpot with the signal we kind of built homegrown over here and marry them so our team really has one source of truth, I think that will be a new iteration. But to your original question, I think there's so much power behind where AI can lead you to have like the strongest intent data possible. If anything, our team is screaming, we want more of it. So we've, sat down with marketing and our RevOps team are
Good market engineer. He comes from a marketing background and he has incredible ideas of like where we can further marry the data, the intent data, where we can look for more intent data. We're very big on just looking at different intent tools, smaller, newer tools on the market to see like, is there intent we're missing that we can bring in? So I think for us it's just like an ever, a constant on cycle of like what's the best intent data? Do we have it? Is there better data? Are we using the best tools? And how do we marry those to make sure marketing's flowing everything they can over to sales?
Melissa McCann-Tilton (17:50)
it's interesting, like you're so outbound, right? You're a very outbound business. When when I first came into Criteria, it was 96% inbound, which is a problem right now, right? Like you can't run a business that way anymore, right?
Gabriella Fishkind (18:03)
Or the opposite. Yeah.
Melissa McCann-Tilton (18:06)
so one of the things that the second business I've come into that was like that pay scale, it was another company that I helped.
place with Francisco partners and it was the same. It was like a 98% inbound business. So you had this strange experience where sales reps were like, well back in the pay scale days, we were in the office, sales reps were like sitting at their desk and I'm like, what are you doing? They're like, I'm waiting for a lead to come in.
Like, you're just sitting there waiting for a lead to come in. And we were kind of doing the same thing at criteria. So starting to layer in outbound has been an interesting experience as AIs come on the scene. And, Joaquin, you're saying like a handoff. This feels more like a dance to me now.
Because one of the things that is super clear in the data. One, if you hand somebody a territory and you're not doing what Gabby's saying with looking for intent data, you're just screaming into the wind. Like the rep's gonna get very frustrated very quickly because they don't have any idea who wants to buy. They're not gonna get response. And there's so many ways to find that right now. That like you're just really failing your front line if you're if you're not providing that. So I think I think that's
really important, but also the interplay between like what is outbound, because what we're finding is as people are getting more into emotion of intent based outbound, we get more inbound. And they they just relate to each other.
more deeply than I've ever seen them relate to each other before. So you have to keep marketing in the conversation with sales to continue to nurture the territory itself. And that's the way that I think about it. You're nurturing the territory, you're not necessarily nurturing a lead.
Joaquin D (19:43)
So are you thinking of outbound almost like a brand awareness kind of thing? So the more outbound you do, the more inbound you get because people are familiar with your brand or something like that?
Melissa McCann-Tilton (19:55)
Not really brand awareness. We don't have giant budgets for brand. I don't know who has giant budgets for brand anymore. I don't I haven't met these people. if you got a giant marketing budget, congratulations. I just don't I don't find it's all that prevalent in today's world. it's more what I think what Gabby's talking about, where you're enabling your sales team and your SDRs to really know who is active in their territory.
where they're looking, what the signals are, so that you know who to reach out to. that outbounding motion becomes intentional as opposed to just sending out a bunch of email blasts and seeing who can reply to you. And I think when you meet people where they're shopping, where they're doing the research and they're, maybe they're expressing a business problem. They're not even shopping yet. That's where I think that marketing and sales dance together is really important right now.
Eswar Singavarapu (20:44)
Yeah, I think like Gabby and Melissa mentioned, like in the past, like we could have gotten away with like just marketing, just delivering an MQL, but today reps would not like that. I think they would want more context, more okay, what is the intent for this account? What is the intent for this customer? Like why do we think it's the right fit now? And what should be the next best action? Should I just go ahead and pitch? Or is there something that
we could do before. So it should be a a collaboration between marketing and sales. And I think AI can help with that in a bit, like trying to summarize the intent information and some of that. But
the real conversations and the decision making has to be the persons doing the engagement. And Gabby to what you mentioned, like I think one thing I've been really focused on and I've advocated for a lot is having a good customer 360. You should have like a good way to stitch all your data together. Like without that foundation, I think a lot of these AI initiatives fail. I think we need to have a strong data foundation where your first party data ties in really well
your third party data as well, like all the engagement how it is. Like we might have like engagement which don't really tie into a specific contact or an account. How do we tie that back into a specific account or a contact and like how do we stitch them all well together? So I've spent a good part of my career trying to figure that out.
Joaquin D (22:01)
Yeah, and does better AI require sales and marketing to agree on the data first, Eshwar? that's what you need to put a system like that in place?
Eswar Singavarapu (22:12)
Yes, absolutely. Like I
I've seen things like that in the past where marketing has their own definition of an MQL and an opportunity, whereas like sales has their own definition and the most of the time is gone trying to discuss okay, why is my account volume different to your account volume? do we have the same definition of an account even like what granularity or grain of an account are we looking at? Are we looking at a country level or are we looking at a global level? Are we looking at like a parent account? Like what hierarchy level are we looking at? Things like that. And
What do we call an MQL? What is the entry and exit definitions for each stage? So I think these definitions need to be like very strongly aligned, and I think not just on paper, but they should be coded into the systems that marketing and sales regularly use. So the systems themselves are the system of record for these definitions.
Gabriella Fishkind (23:03)
I can definitely build on that. It's been a real time issue that we've been struggling with as AI has become so prominent and helpful in how we look at our data and how we surface, intent and leads the team and look at everything in our CRM. And I think the biggest thing for us is we actually have a lot of bad data, just due to years of, you know, it not being the biggest priority. And now in the last four or five years, I think folks realize like, whoa, your CRM data and everything you have in there is super important. and will help you close deals and
Both sales and marketing on our team right now are struggling with a lot of the data is bad. So when something gets surfaced, can we really trust that? When we try to do certain nurture campaigns and outreach on marketing and sales, it's should we go through this manually because we know maybe a third of the data actually can't be trusted? And that's that's an interesting one because AI can get us super far. But if the data is wrong from the start, before it even enters something we're doing with AI, that puts
both sales and marketing in a really tough boat to trust the data and trust the output from AI. So I think that's like a real time kind of struggle we're dealing with at Saros on how to, address that and fix that so we can let AI do what it needs to do.
Melissa McCann-Tilton (24:05)
It's fascinating. when Eshwar, you said you had a really good data foundation. I almost jumped in and said that's the first time I've ever heard anybody say that in my life.
Gabriella Fishkind (24:14)
Same.
Eswar Singavarapu (24:15)
I mean we try to have a good data foundation. So at every role I've been at, the one thing I advocated were for is having a customer three sixty. Align on the definitions first, try to code it into the into the system and then start from there. without that, I think a lot of these processes are haywire.
Melissa McCann-Tilton (24:30)
Yeah, I don't disagree with you. I mean like we're a twenty year old company.
And I would say, like, we paid for Salesforce for a while, but I'm not really sure we were using it kind of thing. we do have data structure challenges, is the same, Gabby, as we're trying to like mobilize the data. There's a lot of times where we have to comb through it and see if the data actually makes sense. Most of the time you can spot check it, but there's some times where you just find weird stuff going on, which would cause you to take an action that would almost be embarrassing.
So you kind of have to catch it, which I think is a lot of pressure on RevOps teams. It also creates this environment with sales reps and with account managers who are trying to sell where they're suspicious, right? They're like, well
I don't know that you're gonna give me this list and this set of actions and like, Am I gonna embarrass myself? So there I think I really think there's a lot of bringing the sales reps along with the process, helping reassure them that we've really looked at it, we've done the right things. I think there's another interesting angle here that I think many of us are experiencing, but I'm not sure properly articulating yet. And that's the executive perspective on the data. Like your founders.
Your CEOs, your people and your executive suites that have looked at the data in a certain kind of way for the past two decades. The data is not the same and it doesn't mean the same thing. So I think frequently there's this moment of panic that starts to happen where they had ratios running in their mind around MQLs and SQLs, and this is how my business has worked for a long time. And the business doesn't work that way anymore.
And they have to be re-educated. And I might argue that we're, we take a minute to get through our heads. Like we, you know, you've seen seen something a certain way for a long time. You want that warm blanket of feeling like it's that way. So I think that can create real chaos for a RevOps team and for a sales team when you haven't reframed the metric, not just from a data foundational perspective, but from an internal comms perspective.
Joaquin D (26:31)
Alright,
so we we've covered a lot of ground today from whether AI is closing the gap between sales and marketing alignment or widening it to the hand of the data underneath it and where people still make the difference, right? And before we wrap up, one last question for each of you. If you were rebuilding sales and marketing alignment today, knowing
what AI can do now, what would you design differently? Which processes, roles, metrics or systems would you keep, remove or completely rethink?
Gabriella Fishkind (27:07)
I'm happy to to kick this one off. I think there's a laundry list I could give you and not in a bad way. I just think the opportunity's incredible and everything looks so different today, but you can't also remove everything you've built and done and say, let's change everything today. but honestly, like I think I'd go back to the start. I mean, for us, like I said, data is a huge issue. Like could we have done so much differently the past five years and ten years even and
Just to get our data to an amazing place so then the AI can work so seamlessly. I think our intent and our understanding of, the market we sell into would be so much different if we had that piece sorted out from the start. Melissa, something you hit on. We right now marketing sales doesn't sit under the same team at Sarah's. We work hand in hand. I think the relationship has really been great, especially the last year, year and a half. And
At the same time, it would be great to have those teams, I think, sitting under the same hood and then really thinking just everything really cohesively on like how do we apply AI in the best way that makes the most sense all the way through the funnel, to surface the most insights. I think now we are going back to try and patch up moments where we want this to happen together. But I wish we could just go back and have done this from the start so
that the customer journey on our end and how we look at everyone and what the touch points are would just be that much more seamless. But now it's kind of like taking AI and going back and saying, what have we done and where can we now insert AI to fix or change things?
Melissa McCann-Tilton (28:25)
really good.
Eswar Singavarapu (28:25)
I think
Melissa McCann-Tilton (28:26)
it's interesting that sort of the data foundation. I think there's also just like a role foundation. And I work with a lot of IO psychologists. We're we're like constantly studying how literally how work should work, like not just in sales, but how work should work. And I think there's this moment we're going through where we we kind of need to bring our HR departments in and like
talk about job design and the type of people we're hiring because one of the things we're finding is people who were really successful be in the before times. I'll call it the before times.
Eswar Singavarapu (28:59)
Okay.
Melissa McCann-Tilton (28:59)
They're frustrated in a post-AI sort of era. They, they're just not comfortable in it. There's things about it that bother them. But we were hiring for a different profile.
And I think the first thing I would do is look at sort of the job description and the types of people, the personality traits, the cognitive skills that we're hiring for as we approach go to market. Because I think if I reflect on who I hired before, I was looking for people who had skills, who could had used Salesforce before, who had used Marketo before, who had, these things were like you needed to be deep into systems to be successful.
now you have this the single pane of glass thing where I can talk to anything pretty easily without a deep set of system knowledge. I need people who are creative and adaptive and learn. I think the biggest thing that I'm curious about is how embedded marketing and sales could get together.
I remember like way back, if you go way, way back, there were these field teams. Like this was, before maybe it was before the internet. I've been around a while. But we'd be in these pods in the field working together with a field marketer and a set of salespeople. And you learned so much when you were embedded like that together. And then you'd find something that worked and you could sort of push it out to other teams.
Now that was slow learning, right? Because you're knocking on doors and like that's how you're collecting your data and it's not very centralized. But sort of imagine a world where you had that embedded, that's the kind of way out to think about, but you had that embedded structure and you could do that fast learning pattern and then throw that out against an entire team of people. That could be fascinating right now.
Eswar Singavarapu (30:37)
Yeah. and talking from an operations perspective, I think one thing that definitely needs change in my perspective is that we shouldn't focus too much on manual data capture. Data capture needs to be automated. We can't
force like marketers or sales to capture data into the systems. It has to be automated in my opinion. And in the past we didn't have a lot of emphasis on unstructured data, which is why I think we missed a lot of gold in that data and there's a lot of good information in that data. There's lot of like conversations that happen between sales and the customers and like okay, I think there's a lot of value in having that information and like synthesizing that. And with AI I think we can do a lot of that better now. So
all the unstructured data needs to be captured into the systems and it shouldn't be a manual process.
Joaquin D (31:21)
Yeah, and Melissa, I think what you describe is very well documented and very well researched in the innovation theory that even if you are not knocking doors anymore like it you used to in the past, but working on the same project that is different to the day to day operation, or going to an event or to a conference with someone from another team and you spend time and you talk about things, it's a great
way to collaborate and share knowledge that otherwise is really really hard to share in a day-to-day. just wanted to mention that. well Melissa, Gabi, Ishwar, thank you so much for a great conversation. We know that AI won't align sales and marketing for you, but it will amplify whatever alignment you already have. And the guest details are in the
Show notes if you enjoyed this episode, please follow B2 Marketing Futures and share it with your team. Until next time. Thank you so much.
