Shift & Thrive: CEO Insights on Driving Change

S&T_Kristina McMillan
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[00:00:00] In today's business world, change is the only constant, and mastering transformation is the ultimate key to success. Welcome to Shift and Thrive. I'm your host, Natalie Nathanson.

Each week we'll bring you conversations with CEOs who delve into how they successfully drove critical change in their organization. This show is sponsored by Magnitude Consulting, bringing you the thinking power of a growth consult. And the getting it done, power of a full service B2B marketing agency.

Natalie Nathanson: Welcome to another special edition episode on Shift and Thrive as part of our Go-to-market spotlight series. If you're a CEO, or business leader wondering how your Go-to-market strategy needs to evolve right now to meet today's market realities, this episode is for you and I am super excited to introduce today's guest.

She's a hands-on Go-to-market strategist with over 20 years of experience working with early and [00:01:00] growth stage startups. Earlier in her career, she served as a VP of research at Topo, later acquired by Gartner, where she authored over a hundred reports and analyzed the fastest growing SaaS companies.

She's also an entrepreneur herself, including having co-founded a climate tech startup. Today. She's in venture capital and advises founders and CEOs to help them strengthen their Go-to-market strategy and incorporate AI to help drive scalable growth outcomes. She is the entrepreneur in residence at Scale Venture Partners.

Christina McMillan. Welcome to the show.

Kristina McMillan: Thank you so much for having me, Natalie.

It's

wonderful

to be here.

Natalie Nathanson: I'm really looking forward to the conversation and have Alon of topics that I wanna dive in with you today. But really wanted to start, uh, talking about Go-to-market transformation in a world impacted by ai. And I'll share that, You know, from my perspective, uh, most, You know, tech CEOs and founders that I know are certainly already thinking or working seriously about how AI impacts their business overall.

And often with product leading the charge not always quite as [00:02:00] much on the Go-to-market side. Uh, so, You know, I think it'd be really helpful to give listeners a bit of a insight into what kinds of transformations are taking place. And so given your vantage point, is there a specific example of a, of a recent like AI driven Go-to-market transformation you could share?

Kristina McMillan: Sure. And I always, I've always considered myself very fortunate to sit in the seat

that I

sit in because I get to see Alon of different organizations and how they're growing and changing. When I was younger in my consulting days, I used to call it job popping securely. 'cause I got to see Alon of different things.

So one story that I thought was really. Impressive, um, that I've heard in the last six months. Um, it is a, not a portfolio company of scale, but another organization. And what's interesting is AI was not the thing that started the transformation, but AI supported the transformation. So this particular organization, um, had been growing pretty well up to about a $15 million SaaS company.

Um, and then all of a sudden everything just started tanking around them. They dropped to around [00:03:00] 30 million in revenue per year. And like it was, it was sliding, downhill in the wrong direction. Um, they brought in some fresh, um, folks on the executive team to try and literally turn it around. They had a two year mandate, let's turn this around, see if we can, uh, become a market leader, and if we can get to a hundred million, like we have to like.

Hey, Mary, this is it. Like let's try and see if we can do this. And what I think was so interesting is that team, what's old, is new again. And that team went back to some really, really

important basics

from a Go-to-market standpoint. I'll call it thought leadership 2.0. And so I've been in and around watching Alon of different markets, um, come and go, Alon of different vendor categories come and go.

And we all know kind of thought leadership can

be very helpful

to that, to an organization finding their way. Um, establishing a brand that is then trusted, incredible,

and growing

from there. Um, but that's changed. Thought leadership used to be Alon about our organization having an opinion. This particular, um, company decided that they [00:04:00] were gonna.

Put

the people forward,

they're gonna take their executive team each in their different positions. The head of marketing, the head of sales, head of product, the CEO themselves, like all of them in their different vantage points.

Um,

I think only one of them was a founder, but they said, we all have a perspective

on how

what we do as a business affects people, uh, affects our target prospects.

And so they

set out to do a very specific personal branding oriented thought leadership, which is why I kind of call it thought Leadership 2.0. Um, and what was really magical about it is they coordinated it as a team. They used AI to be able to execute. So obviously creating Alon of

content across Alon

of different individuals was really challenging at first, um, but they used AI to do that.

And were able to create

consistency, create

accountability, and literally it was

things like the

CMO would be like, all right, CEO, all right, head of product. Have you guys posted on LinkedIn today? Like very, very basic things, but it created an incredible moment.

Momentum for

the organization. And they, [00:05:00] within uh, 18 months, they had hit, um, they were back on track and at the end of

two years

they hit a hundred million.

I mean, it's astonishing. We all wanna grow that

fast anyway,

but to have grown and then contracted and then be able to stretch beyond that is just, it was incredible. So, uh, I guess my, my main message there is for everyone listening, even with ai, as much as we will talk about it, let's not forget that there's a combination of old things are now possible, again with ai.

Um, so let's not forget some of those old basics.

Natalie Nathanson: I love that message and, uh, I love so much of what you just shared. There's really two areas that, uh, really jump out at me. Um, I'll talk about foundations, uh, in a moment as you, as you mentioned it, but I think, You know, that concept of, uh, branding and when a company is struggling with, especially a startup, kind of struggling with lead gen or needing kind of more pipeline as quickly as humanly possible, um, the temptation is lead gen and where can we put in more, You know, dollars in right?

Things like paid [00:06:00] ads and more sales dialing and things like that. And your example is a perfect one for why, unfortunately, the quick fixes are not often the most fruitful ones. Um, and the increasing and kind of growing role of, of brand.

Kristina McMillan: Yeah. And I think, You know, it's worth saying particularly with that 'cause Alon of folks are questioning their old

methods, right? The

way we used to build pipeline is not working the way we used to. Try, try and, You know, um, uh, engage prospects early is not working. So how do we do things differently? But sometimes doing differently is not, um. Something completely new. It is actually revisiting old things. So we are seeing people revisiting handwritten notes and veto campaigns from like the early two thousands.

And some of that stuff is a, it's easier to execute now because of all the additional resources that ai, um, uh, supports, but also because those things were, um, one-to-one and [00:07:00] Slightly targeted and personalized in a way that is standing out amongst the digital noise that we have today. So we can dig in more to this.

Natalie Nathanson: For sure. And You know, we're often talking to our clients about like the same kind of thing, the old tactics through new techniques. Right. And I think you can use, whether it's AI or automation or some combination of that, um, to be able to get Alon more done, make it easier for these thought leaders and SMEs to not start with a blank page, but use these kinda tools the right way and move, uh, move through that.

The other area you referenced was kinda some of these foundations, which I think is another area I'd be interested in, in your view, but we're often talking about a greater importance of getting the foundations right and thinking of like ideal client profile and your messaging and unique positioning and all of those things, which are very tempting, I think to skip over.

Um, but I would say like more important than ever. And I'm curious, like, do you see this, what are the areas that you [00:08:00] talk about when you think about like

the key foundations to get.

Kristina McMillan: Yeah, I

think in the early wave of AI adoption

and Go-to-market, we

all sort of thought like, phew, all that hard preparation we did, we're not gonna have

to do anymore. The

reality is, is it's even more important. Um, I think folks who are adopting AI are realizing faster

than ever that

they need to, if they didn't have a good, um,

ideal customer

profile, they need to go define one because AI is not going to be able to pick for them. It

does not

Have the taste and judgment to be able to pick the direction.

It can just make a bunch of recommendations. And so teams that were weak on some of those foundations before, um, are I've seen them try to scale without it and have

to sort of

reset, restart, redefine, and clarify those things before we can then push forward. The same goes for process. Um, You know, I'm often talking to folks who are, um, trying to incorporate it. I either on an individual level, a team level, or org wide. And what usually [00:09:00] happens is

they say

like, well, I tried it and I didn't really think it was that great.

And

I hate to say it, but we usually revisit like what you put in and what you get out

of it.

So yes, it can help save Alon of

time and

we'll talk about examples

of, of

how people are doing that. But you, you can't skip over some of those foundational sex steps, if you haven't done the thinking.

Um, AI is not going to be able to clarify the thinking for you. It can offer. Brainstorming help. It can give you ideas, but like at some point you have to clarify all of that, uh, for you to get out what you need. So foundations are more important than ever in that equation.

Natalie Nathanson: Thank you for, for underscoring that with such a great insight. Uh, I wanted to ask like, who in your view is, needs to be involved in that strategic thinking? And I had, uh, gram Va Adriana episode recently and we were talking Alon about kind of the different, uh, voices and kinda roles that need to be in the room.

Um, and I'd be curious to hear from you, like what are those most critical ones, [00:10:00] especially given kind of your purview of kind of the. I guess A and b uh, stage, uh, funded startups.

Kristina McMillan: Yeah, I mean, I, when you're early stage, I mean, I think the, the benefit of that is you've got, um, a much smaller team. And so your ability to be nimble and to do

things across

lines, um, You know, sales and marketing lines, let's say, um, is, is,

is

heightened and. I think it's essential to take advantage of that.

So in the example for, um, that I shared the transformation story, one thing that I thought was really cool is the CMO was driving Alon of the, um, onus around we need to do this type of thought leadership. But what was really beautiful is how they were orchestrating it across all of the lines. So in that case, Go-to-market strategy specifically around this thought leadership.

Um, they were going around and helping each thought leader figure out their unique point of view so that they can then generate their content and meet their posts, et cetera. And they had accountability metrics across all of them. But then what [00:11:00] happened was they were also enabling their reps to be able to take those posts and traffic 'em on their behalf.

So if we look at back in circa 2012 where social selling was all the rage, and we wanted every rep to be an expert, right? We wanted every rep to have their social selling brand and persona. The reality is, is that's not credible to

the prospect,

like your average rep. Unless they have 20 years of experience in the industry and can talk about that, they're not the right person to

be that expert,

but they absolutely can be the one to share the expertise from credible places.

And so. Coordinating that, those posting efforts with then trafficking them with the team. And that doesn't have to just be reps. It could be the entire marketing team is trafficking them on their LinkedIn, we're trafficking it on the company page and then doing things like, um, marketing is then organizing events and the reps are want to invite accounts so the reps go to the executive who might know and say, can you invite this account and include me?

And so this triangulation of all these activities that are based [00:12:00] around this thought leadership just led to some of the most beautiful orchestration of Go-to-market motions that I've seen in a while. And so my message would be is like, consider doing things across lines that might not seem scalable, but gets you to what the results that you need.

Once you figure those out on that, that sort of more smaller unscalable level, then you can figure out how to scale it from there. And AI can help with Alon of those things.

Natalie Nathanson: Yeah, that's music to my ears. And I like the point about like, moving across the typical lines. I think it does require a shift in mindset from, You know, marketing owns this, sales owns this, nobody else needs to get involved. Right. To really showing the benefit when you do have kind of the right thought leaders, SMEs, product leads, et cetera, working kind of across the Go-to-market efforts.

I would love to dig in, uh, a bit deeper and maybe more specifically around, uh, AI and how you see it showing up in Go-to-market. Can you talk about kinda some of the ways you're, you're seeing it in kinda in your purview.[00:13:00]

Kristina McMillan: Yeah. So, and I'll maybe, um, create a little structure to, to this. So I'm seeing Alon of use cases at what I'll call the individual level. Like I think that

the floor

for, um, good is raising, like right now everyone's in a transition where they're learning how to leverage ai and we're. Alon of the conversation around how to leverage agents, which to me are things that are more department and organizational level.

There's still Alon folks can do on the individual level to level up their own game, to level up

their team's game, et

cetera. And so I'll give an example first on maybe the individual level. So obviously in sales we're seeing things like using AI for meeting prep. It's like.

Take a

look at everything I've done and give me a

quick summary so

that I'm knowledgeable when

I go in

as if I had been talking to only this account.

I'm really knowledgeable about what's happening. Um, help me do things like prepare for and follow up. So I, I, as a rep have really, really good hygiene. Those are some like very basic execution things. [00:14:00] On the marketing side, of course, we see things like content generation. I, um, in the research that we've done on Go-to-market AI adoption marketers have been the fastest and most furious adopters of ai.

And it's because one of the biggest things that marketing has to do, which is pump out content, was really well aligned to what AI did. Right out of the gate. Um, it might not have been the best writer, but it sure as heck got you to that

first draft

faster, which then allows you to edit faster, which then allows you to get stuff out the door faster.

And so I think anything that can be done on that individual level is something that everybody should be looking at. What can I do? What are the, the, the repetitive tasks that I'm trying to do that can be done bigger, faster, stronger, smarter with ai? So, um, another example that I really like is, uh, one of our portfolio

companies took, took

that, um, to what I would say is almost like a really cool extreme.

Um, they were trying to reach out to, uh, CEOs in their prospect base, and they were like, we we're just not getting any [00:15:00] response. And so they said, what if we just tried to offer a ton of value? A little bit, um, account based

1 0 1,

but what if we could create custom market research reports for their business, um, on the challenges facing their industry and just some ideas related to what we do on how their particular business could navigate those incredibly value, almost a consulting like deliverable.

But I'm sure everyone listening would be like, Ooh, that must take Alon of time

and effort.

They used AI to do it, and they could create 20 page, 20 to 30 page market research reports. In minutes using deep research, the earliest iteration of deep research in in chat GPT, then they would package those and they would, they would, instead of having it sent from a rep, they would have it sent from their CEO to the other CEO and they would use that to just get that first conversation.

It was wildly successful and they credit their success to this microsegmentation. It was one report for each account,

and

normally we might think, if, You know, pre [00:16:00] ai, we'd never be able to create that type of content with that depth of richness and doing all of that type of research. It's people pay thousands of dollars for market research reports like that, but now we can offer that value directly ourselves.

Now. Theoretically the prospect could go and do that themselves, but

they don't

have time. And so it, what I find really, really interesting is we can now delight them by doing their work for them as a value add in our go-to-market motion. And, um, that's just been so

promising. So

on that individual level, there's still a ton of value as we move up into the more department and or team and department level.

Let's say we're seeing things in like helping, um, workflows of the team

move faster. It doesn't

mean the entire thing is agentic and autonomous, but things like, again, I'll use content as an example. If, if we are trying to, um, uh, support a meaningful social media presence, we've got Alon of assets we have to create and, um, we could use, You know, chat GPT and do those ourselves.

But [00:17:00] now we're seeing things like Canva who is helping you take one campaign and create many different formats of assets that might support it could be a month's worth of content in a matter of a couple of hours. Um. It's simply so powerful that I would, it's just you can't ignore what it could do for you and your team.

And so, um, as we continue to go up beyond just individual and team level, You know, those are very focused

on productivity.

Like how do we do more, um, in less time and how do we create those outputs? As we continue to move up the scale of, um, use cases, we're starting to look at things like how

do we

improve the quality of what we're doing?

So some of those, um, later adoption

use cases look

at things like,

how can we

use AI to analyze the behaviors that we've had that we've been doing and make better decisions? How can we go back and look at past deals to identify signals that we weren't aware of that are indications of good deals that will eventually close?

So those are just some examples of, of [00:18:00] the cool things that I'm seeing people do.

Natalie Nathanson: I think those are great. And I like how you kind of took us through almost like a maturity model of, You know, these different types of examples, which I think is a really helpful mental model for, You know, other leaders to be thinking about. I'm interested to hear, You know, one of the, the first example that you gave, um, around kind of the individual like salesperson use.

Uh, maybe this is more of a philosophical question for you, but do you feel like organizations can get to kind of a department level shift or some of these higher levels without like every individual in that department, let's maybe stay with the sales team, uh, using it.

Kristina McMillan: it's a great question. I mean, I've, so there's Alon of concern that productivity improvements, um, it's like all this magical time saved, um, is, is the goal of ai, but then what are people doing with that time saved? And what I think is really, really interesting is, um, to answer your question specifically.

At some point, everyone in our organization needs to [00:19:00] have basic AI knowledge. I equate it to, You know, when I was in school, it was back when, um, they taught us how to type right and everyone had to learn that basic, um,

fundamental

skills of business. AI is gonna be one of those on the individual level.

You will need to figure out how to apply it to your individual role to make you better in that individual role. That said, when we're applying it to, um, roles that duplicate in organizations like a sales team, it doesn't mean that I want each person to repeat those things individually. Can we put some of that

stuff behind

the scenes?

So we, we have um, uh, one organization that, um, presented at our go-to-market AI summit. And what was really cool is they had hundreds

of sales

reps and they said, You know, we're gonna set out to save the sales reps, uh, 10 hours per rep per week. So could we do lots of little ai, You know, um, efficiency projects to add up to 10 hours per rep per week.

And of course [00:20:00] they, they want their reps to be able to, You know, use AI and all that. But they just said, let's just take that off the table for right now. Could we create the space for them completely behind the scenes? So things like, um, before they would have to go in and click in five different places to look at the account history and prepare for a meeting.

Could we actually look at their calendar, see what meetings are on their calendar, and then have AI automatically pull synopsis and email them to them in the morning so that all the rep has to do is open their email

and

they did things like

this.

Delighted their reps. Like it was, they said it was astonishing.

Like we thought for sure the reps would be like, I want it somewhere in Salesforce, but their reps were not even using some of the platforms that they had used as their foundational systems because their, they could just go to their email and get what they needed. Um, that then allowed them the space in the organization to try more things.

And I think last I checked, um, they were up to like 8.7 hours on average per rep, per [00:21:00] week with those types of things. So yes, the entire, uh, workforce will need to step up, but what we don't need to do is have everybody figuring out how to set up their own agents, how to create their own custom gpt, how to get perfect at prompting just yet, there's Alon of improvement we can make for them, and then scale behind the

scenes.

Natalie Nathanson: I think that's a, a great, uh, framework and I think the reality is, You know, some individuals just based on personality or right, how their mind works, et cetera, will naturally like latch on more easily and intuitively to coming up with their own kind of new workflows and things like. That whereas others benefit from that happening at kind of the, the department level and then using what is brought, just like any kind of sales enablement function.

Hey, this is Natalie, your Shift and Thrive host. After chatting with lots of CEOs, one thing is crystal clear. Leveling up your company means having a killer Go-to-market strategy. That's what my crew at Magnitude Consulting does every day. If you're trying [00:22:00] to step up your marketing game, whether it's strategizing, accelerating your pipeline, expanding into new markets, or getting into AI and automation, let's talk.

No pitch, no pressure. Just good conversation. Visit shift and thrive podcast.com/natalie to schedule a time. Can't wait to connect.

Natalie Nathanson: Uh, I'm curious to ask from kind of an organizational structure standpoint, if there are shifts you're seeing or advising, uh, founders around kind of staffing or, or structuring either sales or marketing teams or both.

Kristina McMillan: Yeah. Uh, definitely 100%. Um, the biggest change that I'm seeing is that, um, we need to bring, because we don't have our entire organization stepped up and it's

like default

now that everyone knows.

um, Best practices around ai, we need to bring in folks that know. And so, um, for example, You

know, we're

seeing Alon of, um, rev ops

teams start to

bring on Go-to-market engineers. Um, and that's because [00:23:00] knowing AI and knowing the models and interacting with the models can't be a side of desk for anyone. So it can't be like, I have my main job and then I sort of do ai.

Things are just changing too fast. Um,

and.

Getting somebody in there to dig in and really understand it

is required.

So there originally there was thinking that this person needed to be like a seasoned director who's also technical. That's not the case. They're bringing on very early career folks who are just tech wonky and like to get in there and like get their hands dirty on like, oh, I use base 44 and created a website in 10 minutes.

Or, oh, I did just use

quad cowork

and figured out

how to

do blah, blah, blah. Like people who really enjoy that. And so there will be the, the people who need to understand

what's possible

with AI and then apply that to the subject matter areas we need

to improve.

And so we're seeing the partnership of that technical person with a subject matter person creating kind of that ai, um, uh, nerve center, if you will, in an organization.[00:24:00]

For organizations that don't have rev ops or aren't necessarily, um, that for far along to have that type of function. We're seeing CMOs do it where it's like, I want my own internal, um, AI person who's gonna be able to help me figure out workflows, help me set all of that up, um,

and

and get it dialed. So, uh, individual organization, like department organizations, they're creating centers of excellence as well, but having somebody who's in charge of staying up to date on the models, figuring out how to make everything work is really important because that brings us to the second point,

which is

organizations are now setting up an AgTech workforce, which means just as I have a

a people org

chart, I'm starting to have

an agent org

chart and we need somebody to manage those agents because as my organization starts to love it, um, as things go wrong, that's gonna be problematic so that those reps that love now getting those, those, um.

Uh, short summaries to their email every morning to prepare for their calls. All of a sudden one day they don't get them and they're like,[00:25:00]

uh, I'm not

prepared for my, like, what are you doing? Like, and they get upset about it. And so they need somebody who can then troubleshoot

that because

as models change, integrations are changing.

All of these things, things break left and right. And so we are definitely seeing orgs change to add in that capability to be able to build those, those agentic workforces themselves. That said, I think we are entering a period where more folks are gonna be buying, um, point tools because not everybody can afford the, the technical debt of setting up your own build infrastructure.

Um, so on an individual level, having it

to make the

individual successful is great. But when we get to more the ent, that's where I think, and especially if you're in a regulated industry or things

like that,

going to some of those vendors who are compliant and whatever

they

need to be compliant, know how to handle things like PII know how to deal with certain regulatory requirements can be very beneficial.

Natalie Nathanson: That is super helpful and I wanna take the kind of CEO and founder view for a moment and ask, You know, what [00:26:00] patterns are you seeing when it comes to like when founders are either helping or hurting kind of their organization's charter, like specifically within the Go-to-market arena.

Kristina McMillan: Um, most of what I've seen is helping, I think where it starts to hurt is when we try to, so I mentioned there's like the individual level, then the team department level, and then organizational.

It's, it's very hard to skip doing things on the individual level and then think you're gonna be successful on the agent organization wide level.

And so.

Those that try to

skip ahead

and overcomplicate and like dive full in on like rebuilding the cruise ship on the ocean are the ones that, um, a can't get all the way through the planning process and therefore can't actually get anything implemented.

Um, And so really starting small is

beneficial. So

I'm still working, You know, one-to-one advising executives on like how they individually should

use AI

so that they can then be prescriptive to their teams on their expectations around

using ai.

And then their teams [00:27:00] can help them. You know, they do things like hackathons to come up with ideas for team centered workflows, and then that gives them the baseline understanding of how we might then do this orgwide across, You know, more mission critical systems.

And so I think that the mistake is when we try to jump ahead in the learning too fast, um, and don't have that good base foundational knowledge of how it impacts, um, our stakeholders internally.

Natalie Nathanson: I

like that and I, You know, I hadn't really thought about like the hack of. On almost as like a litmus test kind of a function. Um, we did something like that in our, in my marketing firm, uh, last quarter and, uh, created kind of a made up brand and kind of bringing it to market within a, I dunno, four hour window or so.

And it was, You know, both like the, the risk free environment to work

in low stakes because it's not, uh, kind of a, a real world situation, but I think also as a litmus test, um, it's, uh, it's very beneficial and seeing where those friction [00:28:00] points are what seems like it should be kinda easy to do or AI can layer in, but it's actually not working the way that it should.

So I think there's just so

many benefits to something like that.

Kristina McMillan: One other thing I would offer, um, You know, for CEOs and, and this, this applies to Go-to-market, but applies to the organization at large as well, is, is it's really well, a, you have to recognize the value of what AI

can bring.

Once you do, you have to make sure that your team understands that bringing AI in is not going to replace the humans. Now, it may mean we don't need to grow as fast from a human headcount standpoint to get to where we need to get to in our revenue goals. That's a good thing. Um, but I am seeing organizations where, um, you have pockets of people who are very, very AI reluctant.

And it's often because when we say great, like you're, you're a content writer, AI can help with all of that. What it's causing for people is this like, well, if that's what I did before. I have no idea what I'm gonna

do next.

So it doesn't mean that the [00:29:00] organization is gonna fire that person, but their jobs are gonna change.

Their roles are gonna change. The, um, what defines good in their role is gonna change. And if we don't do some thinking about that first to help the humans transition and be ready to let go of, of those old things to ai, um, we're sort of creating that, um, uncertainty. Um, and that creates chaos and that creates, You know, un un productiveness in, in the teams that we have.

So thinking through, like, here's an example. Um, if we did have somebody who was developing content and that was all they did, 'cause that was like so much to do, um. AI is gonna help relieve some of that. So does that open up, um, strategic responsibilities for that person that their job is actually gonna be a little more, um, campaign planning or, You know, um, brainstorming,

You know,

on a brand level where we wanna take things and do they have the capabilities?

So how can we equip them to step into those new

responsibilities

so that we can release some of those old responsibilities to ai and we still keep the talent that we need.[00:30:00]

Natalie Nathanson: I'm glad you mentioned that because, You know, on the podcast I talk about Alon of different types of, of transformation in organizations and psychological safety for the team is just such an important one. Um, and I think that really, You know, underscores it and I think it doesn't help when, uh, kind of individuals are seeing, You know, plastered all over LinkedIn, kind of the.

Uh, like, Hey, we slashed this many roles and it's, it was easy. All because of ai, right? And so for every leader to make sure that they're communicating that authentically and, and clearly to their

team.

Kristina McMillan: And it's worth noting maybe just like a, a little bit of a. Gossip from the industry, Alon of those teams that slashed are now rehiring because they slashed prematurely because they didn't have a good sense for how AI was going to execute. And the quality, the taste, and the judgment of what was executed was subpar.

And so we're bringing back people to manage the quality of the taste and the judgment.

Natalie Nathanson: Yes. That's a great, that's a great point. I wanna talk a little bit about, [00:31:00] uh, kind of ROI and kind of measuring impact, uh, in kinda AI Go-to-market activities. And, You know, you gave the example of, uh, the sales team and kind of setting the target of 10 hours. I wanna just hear from you how, how you think about kinda setting, uh, and capturing ROI and uh, and metrics in general in this world.

Kristina McMillan: Yeah.

well it's, it's first worth noting that.

Mm-hmm.

A couple months ago on LinkedIn, there was like a whole slew of posts that went crazy around, um, bashing productivity as the ROI in ai.

And I just wanna say like, that is a mistake. Productivity is absolutely a win. Um, now we know there's research out there that shows that when we create more time and space for people to do a job, um, it doesn't mean that they will do the job faster. Like whatever amount of time you have to do the job. If we allotted two weeks, it typically takes two weeks.

So what do we do to try and capture that product? Those productivity gains is we need to move the goalposts. So a thing that used to take two weeks. [00:32:00] Once we learn that we can do it in less, we need to move the goalpost forward so that we can then put more, more beneficial activities behind it. It's also things like, um, I saw some really great research, um, that showed that they looked at, You know, the amount of time saved and then what was actually done and.

uh, Execs were dismayed, they were like happy but also dismayed at the fact that most of that time saved went to work-life balance. And so people who were absolutely burnt out and working

too hard

before were getting better balance. And they wanted that, but they were also like, that's a really hard

thing to

justify spending money on.

Um, And so I think the first thing is, is productivity is act absolutely a win. And we need to figure out how to measure. So like the example I gave where they measured it in hours saved per rep per week. A great one. To be able

to do

that, you need to understand what it looked like before, what was the baseline before, like what were, for example, the number of activities that we could get done in a given period or how long did certain activities take us?

So that [00:33:00] after with AI we can then compare, right? It's like we went from X activities to Y activities or it took, You know, this long and now it's, You know, 50% less time. Like we just have to be able to measure that. But that said. AI is one of those things that there's Alon of little things that

add up. So it's really

hard to say AI contributed to better conversion rates. When I mentioned before how, You know, all of the individualized

and even the

team level AI use

cases are

focused on productivity. After that is the, the use cases that focus on improving quality. That's where we're seeing changes to things like conversion rates.

So for example, if we know conversion rates tank, we can use AI to analyze, um. Behaviors or activities or campaigns or whatever it was to then be able to figure out how we make better decisions that then yield better conversion rates. Um, but I think Alon of people are expecting, like, if I do a thing here, I wanna see it immediately all the way down the road.

And it's just like, You know, we used to say with SDRs, you can't expect an SDR R [00:34:00] to control the quality that is beyond their role, right? So they can generate a great qualified meeting, but it's not their job to close it. So we can't hold, we can't necessarily always hold them accountable to whether things close because it's not within their control.

The same goes

for ai.

It can only have so much of a sphere of control. And we have

to put that

into perspective. That said, we are starting to, um, measure things like,

um,

Go-to-market, uh, spend,

uh,

per person. right? So it's like we wanna look at things like, are we improving the

dollars? Per

person that we're generating in revenue and are we improving the cost metrics of what it costs, um, for those people?

And so that's a little more of a zoomed out version. We, we would look at that across all Go-to-market spend. We'd also look at that across marketing spend or sales spend. It's, are we getting more productive with the, um, humans and the dollars that we have? And how is AI contributing to that? Um, I think those metrics are still fairly, um, early, but we can see in our own [00:35:00] portfolio that those that are very AI forward have better outcomes.

They are

just more

productive. Like, You know, the old SaaS metrics of like, we look at revenues per revenue per employee as a thing. And like somewhere between 300, 500,000 used to be good. Now it's like with AI that could

be closer

to a million per employee or, or farther. And so. It's looking at things like that overall organization effectiveness and outcomes, um, where we're rolling up all of those little AI, um,

initiative.

Natalie Nathanson: Yeah,

and I think from, even from my own organization's experience, just seeing kind of how that has incrementally evolved as we've kind of, maybe it's moved up, up the maturity stage or, uh, just, uh, gotten more ingrained with our use. Because I remember initially, uh, we were having the team kind of track like estimated time savings on individual tasks and then kinda aggregating that and, and looking at the impact.

But as You know, from the, staying with this individual example, as each person gets more kind of AI native and it's just [00:36:00] ingrained in how they work, it gets harder to even put those time estimates next to it because it's everywhere and it's in, in everything you do. Um, but just to show that it really does need to progress, uh, how you look at that as an organization and kinda revisit that.

Kristina McMillan: Yeah. I think it's also also worth saying that for organizations that have adopted ai, it's really important to think at this point, what would happen if I took it out like there. That's a really important signal in an organization because as we've given it to employees, and as long as we've, we've got the right, You know, securities and the right, like, You know, protections to make sure that we're protecting our data,

it's,

it's important to note that if people are feeling like it's making their job easier, better.

You know, faster, et cetera. There is value

in that.

And so we, the example of the sales reps, the

thing being

sent to their inbox, they're not directly using Chachi PD to do that, but they love the outcome and that [00:37:00] matters. That has a value in the organization in terms of retaining that talent in terms of, um, uh, motivating those people to keep doing their jobs.

We're seeing the same in marketing. Like there are Alon of folks who are like, ah, I wouldn't wanna do my job

with a,

without ai. Like, it just, it would be such a drag to go back to doing it the way I was doing it before. And I think that that's an important qualitative, um, factor to keep in

mind.

Natalie Nathanson: I like that it's like simple and like, yet so compelling, uh, to, as a, as a thought exercise to draw that out.

Kristina McMillan: Yeah.

Natalie Nathanson: I wanna take a quick side step, uh, staying on our AI theme, but, uh, a little bit. Sidestep from Go-to-market, and I know you have a passion and a compelling point of view on kinda AI as an equalizer.

Can you talk a little bit about what's behind that?

Kristina McMillan: sure. So

I

just, uh, I

am deeply passionate and write Alon about, um, supporting ambitious women to achieve their goals.

And I think one thing that I get really excited about and that I've advised, [00:38:00] um, uh, female executives, female entrepreneurs, female founders on is, um, never before have we had the opportunity to be able to step forward materially and create a competitive advantage to try and normalize and equalize

World

in that way.

And I think AI provides that. So I've been advising Alon of folks to, to look at it this way. Um, most of organizations measure.

um,

Success in a person's ability to develop good ideas and disseminate them. And often when we have lots of good ideas, what we don't have time to do is put together that deck, right?

Or put together, You know, um, flesh it out so that I've, I've done the brainstorming

to think

about the impact of those ideas and then to, again, create that, those assets to, to talk to my team about them, and then figure out the operational plan to then go enact them. That all takes time. Then that

cycle, we're,

we're gated by how fast we can move ideas through that cycle.

Often the people that [00:39:00] move ideas through that cycle the

fastest aren't

necessarily the smartest, but the ones that move it through

the fastest

tend to have the best outcomes. And so what AI can do, and what I'm encouraging Alon of women to really lean into is all of those ideas, all of those great things that you wanna go do, leverage AI to get the idea fully through that cycle, figure out how you're going to use it to frame the decision,

um,

to flesh out the idea, to create that operational plan, to then, um, uh, create that story narrative for the organization and then get it into play.

I think there can be a massive, um, equalizer. And then I, I am encouraging Alon of folks too, to think about it in terms of how do I make myself the best. Whatever it might be, CEO, founder, CMO, whatever it is, AI can help you as like a, a support team that you don't necessarily have, right? So things like creating an AI copy of yourself.

Um, not, [00:40:00] not in like

a cyborg kind

of way, but in like a, how do I mimic my own brain to create a second brain that I can then converse with. So giving it context around what my role is, what my challenges are, what things I'm thinking

about, and

have it ideate with me or help it do my thinking in advance of a meeting to be able to be super crisp and clear

in that

meeting.

So decision framing or pre-thinking. It can also do things like, um, help me write up and frame my ideas in

a, in a

strategy much, much quicker. And that could be things like emails, it could be things like, um, literally just getting my thoughts down because often, You

know, I'm,

I'm a big believer of the school of thinking is writing. And so the, the

more I

can get that stuff down,

the better.

And then it can do things that we just

are,

breaks our brains. So things like it can analyze patterns that we are not necessarily able to see as, as quickly as AI might. So it might be looking at, um, I don't know, the transcripts from all the status updates on a certain [00:41:00] project and it can start recognizing when we're like falling behind.

It can start recognizing like, this has been a problem now for a while. So instead of us having to tax our brains to constantly do that, we can. put Some of that cognitive burden of that pattern matching

onto ai.

So there's just so many use cases for how it can just make us a more, um, robust and prepared and, um, thoughtful strategic executive that I think it's a huge opportunity that I would encourage anyone to take advantage of.

Natalie Nathanson: Yeah, I love that. And I think, You know, AI as a, as a thought partner is by far my personal favorite use case. And I feel like I just keep peeling back the onion on different ways, uh, for myself. Um, and the, the, what am I not thinking of is one of those best ones, making sure that

I am, uh, continually kind of feeding the right.

Context and transcripts and, and all of that. And I'm finding there's like a, a layer of like organiza the same way you have to think of like organizational data management is now like personal data management. Like what [00:42:00] information puts you have to put in, knowing obviously how to work with it to get out of it what you want, but then like making sure you close the loop so that you're kind of feeding that context back in on what was the decision, what was the outcome.

Um, And so it's a different way of thinking, but it's one that is like exciting and powerful.

Kristina McMillan: Yeah. And I think it also, um, it not only accelerates

that cycle,

but it genuinely makes the job more fun. It is, yes, they're not real people. AI is not real in that way, but the ability for us to get excited, like they used to say, ideas, multiply when they come in

contact with

other ideas, and AI is a way to facilitate that.

And I think that that

can be

really powerful without increasing the cognitive burden on the human. And that's, that's the game changer.

Natalie Nathanson: Yes. And it doesn't hurt that they're often very complimentary. Like, oh, what a great idea.

Kristina McMillan: I've encouraged mine. I'm like, I need you to be a little more serious.

Like, don't just tell me what I want to hear. Like

Natalie Nathanson: Same. Same,

Kristina McMillan: Make a comment or a compliment. Hard

earned, like

[00:43:00] Yeah.

Natalie Nathanson: yes. Tough love. Tough love from our uh, gen AI assistants.

Kristina McMillan: That's

right.

Natalie Nathanson: Um, so I'd love to shift gears a bit and, um, beyond kind of some of this discussion of like Go-to-market and ai, uh, kind of internal to an organization. There's obviously a ton changing in on the buyer side in uh, kind of what's happening in the world.

So, You know, from your perspective would be interested, what are kind of some of those shifts happening and buyer behavior that can impact how organizations need to adapt?

Kristina McMillan: I mean, I think, um, I'm a bit of a broken record out there with everyone

else, but

like the buyers are absolutely overwhelmed with the amount of content that is coming their way.

Um, at first it was great 'cause AI made that possible, but now we've absolutely flooded the zone

and

that has made our prospects extremely leery and weary of. Taking in all of

that content.

And the problem is, is we raised the bar, like the content is now good enough in many cases. And so

to stand out as

a human [00:44:00] requires an extraordinary level of effort.

Um, and buyers are craving that human connection. So I'm getting really excited about some of the stuff that I'm seeing people do with, um, getting more creative about personalization, stacking signals. So it's like there's

no way maybe

AI could have put this together. Things like, yes, there's

the classic

signals of, You know, job change or initiatives I saw, but then it's like, hey, saw you just move to this new city.

Um, like really trying to make it more personal and then doing things that AI can't do, like gifting, um, finding channels that literally cannot be done by AI today. Handwritten notes, all of that kind of stuff. I, I think that buyers. Because of the fatigue, they are craving more of those human touches. Um, and they are craving, um, somebody to cut through the noise.

And so I'm seeing that lead to where Go-to-market teams need to be doing even more of the work for their customer than they ever have before in [00:45:00] any,

uh,

sales cycle. So it'd be things like, You know, if I wanna ask for, You know, we're having good conversations and I wanna ask for other people in the organization to talk to.

The old way was we had to go find those people. Like, can you introduce me to this person? It's like, let's do the heavy lifting of finding the

person.

Now we. Also do the heavy lifting of finding the person, but do everything to create the, the use case so that that person doesn't even have to create the message of the value to that person.

So as much of that burden that we can take

off of

the cus of the customer, the prospect, the buyer as possible, um,

that

is, is getting attention, they are engaging with that, but it, it does put Alon more burden on an,

on the Go-to-market organization

to create those moments.

Natalie Nathanson: Well, and to your point from earlier, right? If you're freeing up that time because of the productivity gains, that's a perfect example of where you wanna be spending it. It's kind of what's human, it's what requires that judgment. What you can't, uh, do in that kind of mass personalization, uh, [00:46:00] kind of environment.

Kristina McMillan: Absolutely.

Natalie Nathanson: I'm curious, You know, we're, we're talking Alon, uh, internally and with clients around, You know, because of the buyers being overwhelmed and it's hard to know which signals are trustworthy and all of that. Just kind of a growing importance on, on brand as we talked about a little bit earlier in the conversation, but then also on like partnerships and the ecosystems that you're in.

And so I'm curious if you're seeing shifts in, uh, kind of Go-to-market models, uh, kind of a difference in reliance and kind of going to market direct versus partnerships or anything along those lines.

Kristina McMillan: Um, I mean, I wouldn't say that I've noticed

anything.

materially different. It, it obviously depends on the business, like those, that, that where partnerships are complimentary and open

up access

to prospect bases that we wouldn't have before. Definitely folks are still doing that. I think that where I am seeing interesting things now is, again, back to the comment on brand.

Before it might have been this brand is partnering with this brand, now it's this person is [00:47:00] partnering with this person and they happen to both be at these different companies. So leveraging that more personal.

um,

element

to brand

and then the, the, as we are seeing, You know, different models or partnerships or things like that, um, it is about those personal connections now becoming

evident.

Um, I think that's really interesting. I also think, um, um, You know, PLG is still very much a thing.

I

think we're seeing some creativity in pricing models, like as terms of how people are buying technology and therefore people are just getting really creative about pricing in general right now, especially if you're building on top of ai.

Um, so things like outcome-based pricing, I'm even seeing that trickle into changing the way services are pricing. Um, but I don't know that there's necessarily any one set way that is the best practice now. But all that to say is like, now is the time to experiment. Take all of the assumptions off the table and say, what can we do to absolutely delight our [00:48:00] buyers?

Everything's fair game in terms of how we, we change things. Um, and I, I, I want folks to keep that in mind because that is where the, the coolest creative ideas are coming today is take out all those old assumptions of how we did things and let's just figure out what might work in this situation and then we can scale it from there.

Natalie Nathanson: I think that's great advice and I wanna ask. Thinking about, uh, You know, founders that are really looking at how to engage with their board or their investors in this world where Go-to-market is changing, are there certain things you feel like founders have to get right on the Go-to-market side to build that confidence?

Uh, with, with those audiences these days?

Kristina McMillan: Yeah, I think, um, and this is, this is maybe an oldie but goodie, um, but it always comes back around. So I often see there's a rush to scale, um, before we've even figured out what works. And this applies to AI as well, focusing on that unit of.

one, like

How [00:49:00] can we just do one thing really amazing and that could mean like we absolutely knocked it outta the park for that one account.

Once we figure out that, then

we can

figure out how to scale it. But I think rushing past that. Trying to scale and then running into problems like we don't know why they bought, or we don't know why they turned, or like we sort of know, but like

it

feels like maybe that's not the real reason, like it just leaves us blind in so many ways.

And I see Alon of teams having to revisit

that unit of

one as a result. And so I would encourage anyone, like Es, especially if.

We're

rolling this up to the board, the board wants to know repeatability, right? Scalability and repeatability of the success that we're having. And so if we can't really articulate that because we have those specific unit of one examples, then we gotta go back and figure that

out because

that's what gives us the confident to make

the decisions to

then do the scale in the first place.

So I, I just feel like that's, it's so critical to not rush

[00:50:00] past that.

And I'm not the first one to say this, You know, like lots of people have said do unscalable things, right? Um, but I see that pattern happen again and again. Um, and teams have to revisit. Um, the other thing is, is uh, to not be afraid to test and try different things, but to not get so, um, caught up in the testing that we miss when one good thing happened.

And so it's, it's like a paying attention, like slow down to speed up type

of thing,

where we really have that. And AI can be very beneficial to this, which is. Let's get really, really good hygiene around how we assess the outcomes. Not just like, great, there's revenue. Yay, we want that. But can we go back?

And, You know, back in the day, 20 plus years ago, I used to be a part of a market research firm that did win-loss studies. And um, it was a big thing. And you paid Alon of money because we had to call 50 of your customers and like some of the ones that didn't. And we would try and get those insights for you.

You don't have to do that anymore. Like [00:51:00] you don't have to spend hundred thousands of, You know, dollars on doing that. You can do that yourself every quarter. And when I think about the organizations that have scaled the fastest and had the most unicorn like growth, it's those that had the operational hygiene to understand.

When those patterns were emerging and before they would do it without ai, now we can do it with ai it's even easier. But taking the time and developing the organizational cadences and hygiene to look is really important. And those are the things that give us greater confidence in the board meeting

Natalie Nathanson: That is so

powerful Yeah, I love that. So powerful. And I think just a great reminder of the importance of having the discipline and the foundations and then doing things in, in new ways in that experimentation. So I think that's a great place to wrap up the conversation. And as we do that, if listeners wanna get in touch, can you let them know the best way to do that?

Kristina McMillan: Sure. Find me on LinkedIn. I would love that. And then I would just also [00:52:00] say like, if you're an ambitious woman and you want support and figure out how to leverage ai, let me know. Um, I wanna make every single one of them dangerous. That's not to say I wouldn't take, You know, the call from anyone else, but, um, it's just definitely a personal passion of mine.

So reach out to me on LinkedIn. I'd love to continue the conversation. Um, and I wish everyone the best.

Natalie Nathanson: Thank you, uh, that is very generous, uh, uh, very generous offer and thank you so much, uh, for this conversation. I really loved hearing about so much of what you shared and what we discussed around kinda how you're actually seeing AI drive, kind of real transformation in those examples, uh, in Go-to-market and what needs to happen to make that possible.

I love the focus on, You know, that true personalization and the impact of kind of people driven brand and branding efforts. Um, so really appreciate so much of what you shared.

Kristina McMillan: Thanks so much for having me, Natalie. It was great.

Natalie Nathanson: All my pleasure and thank you two to everyone that's listening. If today's conversation [00:53:00] sparked something for you, and I'm pretty sure that it did, please pass this along to another leader in your network. We know these are the conversations that really help us scale smarter, build stronger, and create more resilient Go-to-market engines.

So thanks again, Christina, and this has been another amazing conversation on Shift and Thrive. I'll see you all next time.

That's a wrap for this week's episode. For show notes and more visit Shift and thrive podcast.com. A special thank you to our sponsor, magnitude Consulting, bringing you the thinking power of a growth consultancy and the getting it done Power of a full service marketing agency to help B2B companies fuel their growth.

For more information on magnitude and to get your complimentary transformation readiness assessment, visit magnitude consulting.com/. Get ready. Thank you so much for listening. We'll see you next [00:54:00] week.