CMO Predictions — 2026 Priorities Redefining Marketing Leadership

Julia Nimchinski:
Welcome to the show! What a panel! Jonathan Metric, 4X CMO, CGO, and partner at Cigar. Welcome to the show, how are you doing, Jonathan?
Jonathan Metrick:
Doing well, thank you, Julia. It’s an exciting chat. It’s gonna be tough to follow that, but us marketers are gonna take a run at it, so let’s see how we do.
Julia Nimchinski:
Let’s do it. Before we start, one question to you, and then the stage is yours. What’s your top prediction for GTM and AI for 2026?
Jonathan Metrick:
Top prediction. I think, you know, I’ve been hearing a lot… I spend a lot of time talking to CMOs and founders and marketers, leading up to this time of year, and there’s a big push toward generalists. I’m hearing a lot of the word of kind of, like, going wide, generalist marketers that are going to be able to do a lot of jobs to be done, just given how quickly the space is changing. So, I think less… less specialists, more generalists. Not my take.
Julia Nimchinski:
Amazing. Take it away.
Jonathan Metrick:
anything. Awesome, great. Well, welcome, everyone, to the CMO Predictions Panel, and we’ve got some incredible, panelists that I’ll be introducing to you in a second to kind of dive into what are some of the predictions that marketing leaders are, you know, predicting for go-to-market folks leading into 2026. My name is Jonathan, as Julia mentioned. I’m a four-time CMO, I’m the Chief Growth Officer at Cigard. We are a $32 billion fund focused on fintech financial services, and I spend, you know, the vast majority of my time speaking to founders, marketers, CMOs about, you know, marketing and scaling and growth. And one thing that everyone, I think, can agree on is how quickly the marketing function is changing. It is under rapid state of evolution. That’s both exciting and overwhelming for many folks, I think, who are in the function. AI automation is shifting the way marketing is tackling our traditional jobs to be done, how we think about our team compositions, the tools we use, and even the channels that we acquire customers on. So, what is ahead of this evolving landscape into 2026? We have an incredible panel of leaders who are helping shape the marketing industry that are going to chime in on their predictions of what they see coming ahead as they’re leading their teams. But before we do, just a bit of, kind of, logistics. We’ll spend about 45 minutes going through our various predictions across the panel. But feel free to use the Q&A function if you have questions that pop up. We’ll be going through 5 different types of predictions, but feel free to ask questions along the way. I’ll try and get to some of them, or most of them, but then we will have a small section at the end for Q&A. So, without further ado, I’d just love to have each of the panelists introduce themselves, if they could just say their name, their role, their company. And then just one sentence teaser on your prediction, before we dive in. And so maybe to kind of kick things off, Matt, if you want to kind of do a quick introduction for yourself, and we’ll start round-robining introing everyone on the panel.
Matt Hammel:
Great. Hey everyone, Matt Hamill, I’m one of the co-founders of AirOps. We are a platform that the best marketing teams in the world use to take action and win this, brave new world of AI search. One teaser for predictions is I’ll touch a little bit on what we see OpenAI doing in the near term, which will have some impact on marketing budgets, and a little bit on how teams are reorganizing as well. So, excited to dig in.
Jonathan Metrick:
Amazing. Alright, can’t wait. Christine, you want to go next?
Christine Royston:
Sure. Hi, everyone. I’m Christina Royston. I’m the officer at Wrike. We’re an intelligent work management platform where anybody can build, connect, and automate their workflows. And a teaser for my predictions, I’ve got two. One is cultural, and one is technical, so I’ll talk a little bit about the interplay between those.
Jonathan Metrick:
Awesome. Okay, next up, maybe, Shawna.
Shawnna Sumaoang:
Hello, everyone. My name is Shauna Sumawang. I am the CMO for a company called Dito. We’re an AI-native customer platform, and so my 2026 prediction, obviously, is very much centered around the customer and how GTM teams really need to start to think about a continuous customer orchestration loop, rather than kind of a linear funnel.
Jonathan Metrick:
Awesome. Okay, and, to Pindark.
Deepinder Singh Dhingra:
Hi everyone, I’m Dipinder, I’m the founder of RevShore. We are a full funnel AI solution for go-to-market teams, helping them optimize the end-to-end buyer journey, right from anonymous visitor to close one and beyond. So, my teaser for 2026, is that the boundaries between sales, marketing, SDR, BDR motion, etc. are going to start blurring, because information flows are going to get faster and faster due to agentic AI. And, you know, what one needs to do is full funnel will truly need to come To life in 2026.
Jonathan Metrick:
Awesome. Alright, and rounding us out, Joan.
Joan Jenkins:
Yeah, hello, I’m Joan Jenkins, so I’m the CMO at Mind Tickle. If you don’t know MindTickle, so we’re an AI revenue enablement platform, so helping go-to-market teams build skills, get knowledge, and take that action to close more deals. So, my teaser is it’s a brand new world going into 2026 for CMOs. New things are needed. new skills are needed. I’ll have two predictions, one on marketing and one on sales.
Jonathan Metrick:
Awesome. Okay, well, for folks who are just joining us, again, this is the CMO predictions panel. What are we predicting from the go-to-market function from the marketing side for 2026? And we’re going to be going through 5 rounds of predictions. We’re going to do lightning rounds of about 5 minutes each for folks to kind of give their prediction, but if you have questions, feel free to use the chat feature. I’ll try to get to some of those as we’re going through each of them, but we’ll have a little bit of a section at the end for any Q&A. So to maybe kick things off, Joan, you just introduced yourself, but maybe would love to have you dive in, you know, over the course of 5 minutes. What do you see as kind of what’s ahead for the marketing function looking into 2026?
Joan Jenkins:
Yeah, alright, so let’s start with marketing. So, first of all, you know, there’s a lot going on. So, we really wanted to center this on… you know, we’ve all heard the term GTM engineer. So, if you don’t know the term, I think I hear it at least 3 times a week, but it’s the person who… usually it’s a role who helps support those, you know, automated workflows and AI and, say, go to market, but my prediction is this is not going to stay just a single job title. what I see as the new norm is this really becoming that cross-functional go-to-market engineering initiative across the org, where I’ve seen it at least be successful, and that needs to include the entire go-to-market org. So, marketing, rev ops, engineering, sales, customer success, and marketing has to have a key part of this. And what does that mean? That means marketing needs to have more of the technical skills to succeed. So, across each of the roles, technical skills need to, you know, increase… doesn’t… not just marketing ops, but just across the board. So, really, that builder mindset becomes the norm. So, creativity plus technical skills is really the new superpower. You know, you can partner with the AI specialist, of course. There’s always going to be a specialist that can deep dive, but you need to understand the workflows, automations, and how those get built to really generate better ideas and optimize faster. I’m doing this currently with my team, my BDR team, my marketing team, and the engineering team, and it’s been very successful. It’s helped us move, I would say, operate 5X faster. So that’s marketing. So, quickly…
Jonathan Metrick:
So maybe just summarize that, Joan, so I guess it’s more of, like, a technical up-leveling across the marketing.
Joan Jenkins:
Yeah.
Jonathan Metrick:
function, both technical and maybe systems, AI-related, less of the, we’re going to have the AI specialists do all this work, we’re actually going to take on some of these capabilities ourselves and up-level the function.
Joan Jenkins:
Yeah, it’s really up-leveling the function, and it’s also working across the teams, because it can’t just be in a silo either, so it’s marketing up-leveling, expanding their skills, but also working across the team on those GTM engineering initiatives.
Jonathan Metrick:
caught up.
Joan Jenkins:
Yeah. Alright, prediction for sales, so… I’m seeing this more and more recently, and I think it’s just going to explode going into 2026, is AI-first buying. So, that’s going to become the norm. So, you know, and this impacts sellers, so… Sellers will only get pulled in once the buyer’s AI has already done the research. Now, in 2025, this year, we saw sellers coming in late, you know, kind of, I would say, mid-conversation on the buying cycle. But in 2026, sellers are going to be brought in even later to the conversation, because AI is going to be doing a lot of that upfront work. So, this puts pressure on the seller, even more pressure on the seller, if they didn’t have enough pressure in 2025. So those first impressions are gonna make or break a deal. So they’re gonna have to differentiate right away, and… and they’re gonna really have to… understand and answer specific technical marketing, you know, technical and challenging questions, so… they’re going to need to rely on some AI tools that help them, you know, get prepared faster. So, I see AI role play as something going into next year that’s only going to increase. It definitely exploded in 2025. It’s going to become the norm in 2026. So those… those are my two predictions, and I’ll leave with the final thought. So what does this mean for CMOs? Definitely… co-own the GTM Engineering Initiative. Make sure you and your team across the board really combine that creative and technical skills in your hiring. And then, secondly. you know, with the buyers, with the AI, buyers relying on AI for that research, that’s kind of good news for the CMO and the marketing team, because you can really own that buyer’s research experience end-to-end. So, really making sure that, you know, you’re playing a big role, you know. Owning this end-to-end and helping to shape that outcome with the buyer. With that, that’s it for me.
Jonathan Metrick:
Very cool. Any kind of, any thoughts across the panel? Any, you know, I feel like other folks might resonate with that with some of the predictions or teasers we had for the predictions, but any other comments to Jones? perspective.
Christine Royston:
I can jump in here. I love that term, go-to-market engineering. We’re trying to take, sort of, a first step with that, in scaling across the team, we just created a new role in my team, who’s focused on AI and marketing automation, and so he’s reporting directly into me, not to IT, but his role is to really bridge the gap that you’re talking about, where he’s kind of figuring out what could our initial roadmap be, but then I’ve also been So tasked him with, how do we enable the team? So that they are actually building these workflows themselves. they feel like they’re upskilled on how to leverage AI, so that starts to disseminate across the organization.

Jonathan Metrick:
Very, very cool. Awesome. All right, well, maybe switching gears a little bit, Matt would love to hear a little bit around what you’re thinking in terms of, you teed up, kind of, what’s the future of chat GPT, so you… you piqued my interest at minimum, but maybe, you know, would love to hear a little bit about your prediction looking into 2026.
Matt Hammel:
Yeah, absolutely. I, I would say the first part of this won’t be too controversial of an opinion, but it’s basically that I think within Q1, latest Q2, ChatGBT will launch ads. But I think the interesting way that I would predict they do it is to actually retain a plan that will be ads-free. And so, what that means, I think it’ll effectively create… two new channels, or basically create one new channel and supercharge an existing channel that marketers and their, you know, budget partners in finance are grappling with how to invest in and how to test into. The first is the sort of ad placement network within ChatGPT. I think as any new kind of, like, ad channel immersions, that’s gonna… that’s gonna require a lot of experimentation, and there’ll be kind of, like, winners and losers, and probably some low-hanging fruit. But, figuring out how that works within the rest of the kind of ad stack will be important. That’ll also create another channel, for lack of a better term, we’ll call it kind of VIP SEO within ChatGPT. Effectively, what you’ll have is kind of the biggest power users on ChatGPT, people who spend more of their time there, who are willing to pay, you know, $200 a month Potentially even more for a subscription, who likely have higher income. Who are then more likely to be influenced by, non-ad content. And so the need, especially if you start to see some of the sort of, like, budget and the eyeballs continue to shift from. traditional, Google search, that’s gonna create an imperative for, you know, creating what we call information gain content that, ChatGPT and other models can consume, and then answer those kind of really deep questions that, consumers and users of those platforms have when they’re deep in a conversation with a model, potentially evaluating a product or a service. So, exciting times, and we’re excited to be kind of at the forefront of that.
Jonathan Metrick:
How should some of the marketing teams be thinking about that, be it founders, be it CMOs, be it folks who are heading up the content verticals?
Matt Hammel:
Yeah, I would… on the second… on the second front there, I think connecting right back to the broader discussion on go-to-market engineering, I think what we’ve seen is a… a really, kind of well-equipped, somewhat technical, but very trusted with respect to taste and brand, individual being able to Really kind of spearhead a faster velocity experimentation, motion that looks kind of closer to A-B testing than it does traditional kind of waterfall content marketing in a way. And that’s not just creating new content, it’s kind of refreshing and analyzing what’s already on the site, and actually helping drive off-site PR initiatives as well. So we see it as one system, and we call it content engineering, but it’s very much a go-to-market engineering function that’s needed to both build and maintain the workflows, but then have I have the trust of the marketing team to… to make sure that content that’s going on the site, that’s being then, like, served through these models, is, is, you know, meets all the requirements that an enterprise has, which is sometimes a gauntlet, so…
Jonathan Metrick:
Content engineering, hearing it first, folks. I’ve not heard that before, but now all the sub-functions of marketing are becoming engineering something, so… could be a macro trend, I think, to the function. Very cool.
Matt Hammel:
30% branding and 70% true, which is what most think, but we truly believe it, and that’s where we’ve seen teams be really successful.
Jonathan Metrick:
Miracle.
Matt Hammel:
And then, number two is actually very much related to that. So, we’re seeing this, and we’ve actually, kind of spearheaded this internally within Air Ops, but, a lot of functions that typically relied heavily on outsourcing to agencies, bringing some or part or specific parts of the workflow in-house, and a couple of reasons for that. One is. I think cost savings and speed. I think the traditional agency model, and this isn’t to speak for all agencies, there’s a lot of great ones out there, but I would say the sort of, like, median agency has interestingly caught up slower to adopting to both these new channels and new ways of working in some ways than larger in-house teams have. The second reason I would say, is that you need someone who’s really close to your internal context to get AI systems and workflows to work really well, so knowing where the best, product data, PMM data, kind of like brand truth and governance, about how you talk about your products lives, and the data you have to kind of differentiate is usually done better by someone internally, and so, it reduces the overhead of working with an agency. And then lastly, just, we’ve seen kind of, like, high-leverage individuals have, in some cases, like, 5 to 10x the impact of. of the kind of, like, old agency model, where you have an in-house person, freelancers, an agency, one or more agencies working together. There’s still some… still some functions where that makes sense for in a role for agencies. I think it just, it’ll trend towards insourced.
Jonathan Metrick:
Yeah, interesting, and I think that’s kind of similar, my tee up at the beginning of the session around generalists. As we’re bringing in some of these functions, and we’ve got folks kind of playing different hats, where we’re discovering what sort of tool stack we’re… we’re going to be using as the space is evolving so quickly, that kind of profile being brought in-house versus outsourced is an interesting kind of macro trend. Other… other kind of… other folks on the panel, Matt’s predictions, either around kind of you know, chat GPT ads expanding into a new channel, or kind of in-housing some of the traditional agency functions. Are other folks, you know, does that resonate with others?
Deepinder Singh Dhingra:
Yeah, I can… I can, I can add to what, Matt and second, what Matt just mentioned. I think the channel around ChatGPT and other LLM tools definitely is going to increase. Obviously, there’s a lot of focus on AEO through better content engineering.
Jonathan Metrick:
And what is AEO for folks who may not know that?
Deepinder Singh Dhingra:
yeah, answer engine optimization, which essentially means creating content that LLMs and other models can consume, whether that’s ChatGPT or cloud perplexity, etc, that then represents your business, your value prop, in its best light. That becomes very important, so it definitely becomes a very, very important content channel And a dark funnel channel to attract prospects into your funnel.

Jonathan Metrick:
I’m pumped. Awesome. Well, maybe shifting gears a little bit, Christine would love to hear a little bit about how you’re thinking of marketing teams’ focus area leading into 2026.
Christine Royston:
Sure. So I… I think as we… 2026, we’re finally moving past this shiny object phase of AI. If I think about the last couple of years, we’ve been talking a lot about exploring and acquiring new tools, but when I think about predictions, you know, this is not about buying more tech. It’s really about these these two walls that I think we’re gonna hit as we try to scale the tech and the AI that we have, and as I mentioned before in my teaser, one is cultural, one is technical. So, starting with the cultural piece, I think, you know, the biggest barrier that we have to seeing ROI with AI is not going to be budget, it’s going to be belief, belief of our teams. So we recently conducted some research at Wrike, about the age of connected intelligence, and one of the statistics that really stood out to me was that 87% of employees have serious concerns about AI data accuracy and privacy. So if… if you’ve got 9 out of 10 people on your team. That are residuals that are meant to help them. Because they’re afraid of output, we’re definitely going to run… run into an issue, because this is causing a trust gap. And so I think if marketers can’t prove that the systems that they’re using have safe, valid outputs, then driving adoption is going to be really difficult. So I… this year, successful leaders are really going to have to focus on not rushing deployment, but really starting to audit their workflows and thinking about how they leverage AI for trust, because if we don’t have that trust, the teams are not going to use it. So that’s the first prediction. If I move into the second technical barrier. And it’s really focused on moving into this era of connected intelligence. If you think about most marketing stacks and many of the AI tools that that some of us are using. They’re really isolated, and if… If we have great point solutions, but they don’t talk to each other, then we’re really not maximizing the impact of those tools. And our research backs this up as well. What we found was 42% of employees are turning to shadow AI, or unapproved tools, to get their work done, because this… the ecosystem is too fragmented. So if you think about all of us talking about agentic AI right now, agents to do the work for us, that agent is only as good as the context that we give it. And if we don’t have the data in… outside of silos, then the agents can’t really operate effectively. So I think that shared context is… is something that’s important. And next year, we really have to stop… stop building these silos with our technology and start building this unified data foundation, so that we can really scale with AI, and make sure that our tools, as well as our agents, are talking to each other. It’s really better for business results if our… Data is connected for people, because they’re going to start to trust the tools and trust the data that they’re leveraging. So if I think about what those two predictions mean for CMOs, you know, the pivot is not buying more technology in 2026, but about auditing what do you have, not only to prove that it’s safe and reliable. Also, to think about how you can build this more unified platform. So, stopping that purchase of point solutions and thinking about how you create that shared data foundation so that your solutions are connected and your agents have all the data that they need to act on your behalf.
Jonathan Metrick:
Very interesting, and I think that kind of connects a little bit earlier with Joan’s comment around this kind of movement forward of AI adoption is not just a single functional focus. You’re working with your cross-functional peers, be it sales, the data team are probably going to be involved if we’re going to be unifying our underlying data foundation. In terms of maybe just how… if there was… I like your tee-up of, you know, not just choosing more tools, but really doing an audit of the technology, and is it doing what we really need it to do? And if it isn’t, how do we connect those systems? Who do you envision as kind of the key cross-functional partners that a CMO listening to the session might want to be, you know, proactively trying to, you know, create linkages with or partner with to get that done?
Christine Royston:
I mean, if I think about, you know, our organization and the way I’ve seen some other organizations structured, you know, if there is a centralized operations team that’s looking at operations and tools and systems across all functions, marketing, sales, CS, I think those teams often have the best view into, you know, what is that total stack we’re looking at as a team? And if you think about go-to-market as a cross-functional collaboration across all those teams, you know, where is it that you can potentially get some leverage, because there are tools that are providing similar insights that you might be able to leverage, or they’re thinking about how to connect those dis… connected system. So, you know, I certainly look at my marketing team as partnering with that organization, as well as, you know, depending on sort of where your engineering and product teams are focused. We build solutions for marketers, so I’m lucky that I can lean on my engineer… engineering partners to kind of say, hey, this is what I’m trying to do, can we possibly, like, build that in the product that we have?
Jonathan Metrick:
That kind of theme resonated with curious other folks on the panel, you know, the need for data connectivity. or, you know, partnering cross-functionally to really unlock some of these, you know, we see a lot of, you know, kind of the potential, dreaming the art of the possible when it comes to AI, but actually to do that, there’s a lot of kind of if-then statements, but curious if other folks on the panel are, you know, does that… Christine’s kind of prediction of needing for data connectivity, cross-functional collaboration, is that resonating with other folks’ marketing orgs?
Joan Jenkins:
Yeah, I would say definitely, I mean, you know, I made the point earlier about the GTM Engineering Initiative, and I think that touches on a couple of things. It’s not only the teams, it’s the tools as well that are working across the board, especially as we get into some of the workflows and AI. It becomes even more important that you are looking across the organization. not just teams, but tools, like… like Christine mentioned. I think that’s… that seems like that’s a theme that’s coming out.
Jonathan Metrick:
You know, one of the key pieces I’m definitely hearing across my portfolio with folks who are looking to deploy agents, Christine, as you kind of mentioned, is, you know, the data that you’re training the agent on is really somewhat indicative of how effective the agent will be. And, you know, setting very clear parameters on what this agent can do and can’t do, but uploading, you know, uploading examples of what does good look like? But you need the data for that, right? And you need the data to train the agent on effective conversion tactics and these sort of pieces. So, yeah, the better the underlying foundation, I think the better able you are able to automate through an agent and actually, you know, take advantage of some of the, I think, the potential for AI. But if you don’t have the underlying data foundation, you’re going to start with that.
Christine Royston:
Right, right.
Joan Jenkins:
Yeah.
Jonathan Metrick:
Great. Shauna, maybe you want to switch gears a little bit to kind of your prediction, but just as folks are coming through, if there’s any questions that are in the, you know, as you’re listening to these pieces, feel free to put those into the chat. Happy to kind of tackle those as we’re going toward the end, but shifting gears a little bit to Shauna and kind of how you’re seeing the marketing landscape looking into next year.
Shawnna Sumaoang:
Yeah, no, thank you, Jonathan. And to piggyback on a lot of the things that the panelists have already said, especially Christine, I think we carried forward with us a lot of the team and department silos that we had with our previous tech stacks, and what I love about AI is its ability to start to kind of break down those complex silos that we’ve built up, and I think one of the places where I see it being really impactful for businesses is the ability to break down the… the silos that we’ve built up around our own customer journey models. I… I have spent, many years in marketing. Most recently, I owned the post-acquisition marketing efforts for a company called HiSpot for several years, and I spent a lot of time trying to build very complex customer journey models that would, you know, map every touchpoint and every moment from the moment that they first interacted with us all the way through to their renewal with us. by the time I ever finished one, it felt like it was almost immediately out of date. We would see a new buyer behavior show up, a new channel would emerge, a new internal team might change a process, and so it felt like I was constantly chasing a moving target. And so I think it’s really powerful. I heard a… I also attend a lot of events to learn from other CMOs in this space, and Earlier in the year, a CMO at an event had said that, They feel like the ground is moving faster than we can keep our feet under us. And I think that that is true. We’re all feeling that. The game has changed, but the playbook that we were using before, it just hasn’t caught up. And so, I think that’s why my first prediction for 2026 is that companies that win will stop relying on, again, kind of their funnel-based playbooks that they’ve been running for the last decade, and start to think about kind of the continuous customer orchestration loop that they need to have in place and that AI empowers. Customers, they’ve never moved in neat stages. All of us who have been in the space know this. They’re constantly sending us signals through conversation and through feedback, and through usage, and sentiment, and now AI makes it possible to listen to all of that in real time and act on it. But I think you know, to Christine’s point, I think a lot of organizations you know, they’re still working in their respective silos, you know, in their chapters. Marketing does something, then sales does something, then customer success does something, and it’s not creating this kind of unified experience for the customer. And so, I think in 2026, that’s where this really needs to shift, and where customer voice needs to become almost imbued in the infrastructure of our businesses. Instead of just being, you know, used by marketing to create more content. So I think that that’s definitely one, prediction, and to be honest, I must have missed the homework assignment that there should be two, but I… I’ll give you my second one, just kind of off the cuff. I think in a world, just on the fly, in a world that’s filled with a lot of AI, generated content, to be candid, I think authenticity is gonna continue to surface as the most valuable currency, that we have available to us, especially in the go-to-market world. I think, you know, again, every company can generate almost perfect-sounding content, like, at the drop of a dime with ChatGPT or Claude, and, you know, similar to what Christine shared about that trust gap. Noise becomes cheap, and… and…
Jonathan Metrick:
Yeah.
Shawnna Sumaoang:
what buyers trust and what their agents are gonna trust, is gonna be the lived truth of your customers, and so I think authentic customer voice really becomes that anchor.
Jonathan Metrick:
In terms of, kind of, Shawna, I love your point around, kind of, listening more to the customer voice throughout the journeys and connecting that data. What would you say are some of the things that you would hope would be uncovered from, kind of, actually looking at that sort of information, or maybe you’re seeing that in your organization, but kind of what would that unlock look like if people actually were to connect these things and actually listen to more of a customer voice throughout the buying process?
Shawnna Sumaoang:
I have seen it used in some really powerful ways. So in the… pre-sales world, listening to your buyer, understanding what that sales process, that sales experience was like, helps you course-correct what your sales teams might be doing wrong, and you’re getting it straight from the source, instead of trying to psychoanalyze internally and have your sales managers coach to call recordings. Another example, that I see frequently is instead of having your product management organization building your product innovation and your product roadmap based to feedback that they’ve heard through a game of telephone, you know, through the CSM team to the product marketing team, back to the product management team. Instead, by capturing customer voice in a single system that they all have access to, they can start to essentially get feedback directly from the source, directly from their customers, in terms of what they need to build, into their innovation, into the roadmap that their customers are actually going to use and actually going to adopt. And those are just a few examples, of what we’ve started to see Dito customers leverage our platform to do within their organizations.
Jonathan Metrick:
Yeah, and I think it does kind of connect back to, again, the central theme we’re hearing time and time again here of these value-added solutions are going to create, you know. cross-functional connectivity and value, and one of the examples you gave was sales, another, you know, was going to get product, you know, upstream product innovation ideas. So I think there is this component of Stepping outside, as you teed up at the beginning of your session, stepping outside of kind of these functional silos that we got pretty comfortable with, given, you know, product had its tools, sales had its tools, marketing had its tools. this new wave of kind of AI tooling may actually have more of a connectivity, but the benefit of that, if we can get across the data line, which Christine teed up, is that multiple people can be looking at a much more robust data set that can then hopefully allow us to move, you know, pieces materially forward for different functions. Other folks on the panel is kind of the, you know, connectivity of customer information. And, you know, going into, be it sales or product, is that something resonant, that you folks are working toward, or have heard, kind of, an area that you think could move forward for organizations looking forward to next year?
Deepinder Singh Dhingra:
Yeah, we definitely… we definitely see that the whole information flows, like, whether that’s customer voice coming back into the top of the funnel, like, how is that going to be used by product teams, or even marketing teams and content teams, to take that authentic customer voice. And reflect the authentic value match with our customer voice back into the market. So that’s definitely a key theme that we are seeing, yeah.
Jonathan Metrick:
Awesome.
Joan Jenkins:
I’ve used that a lot on really using AI, listening to that customer voice, and having that inform new marketing messages that I’ve done and created. So, yeah, there’s a huge use case for that.
Jonathan Metrick:
Yeah, I think so many of us would love to spend.
Christine Royston:
you know, many hours a day talking to customers, but, you know, we have other things top of the list, so I think if we can kind of democratize that access to customer feedback, whatever format it comes in, whether it’s a sales call, our success meeting, an email reply, I think it helps to make all of our marketing programs and our go-to-market strategy much better.
Jonathan Metrick:
Yeah, I’m seeing, you know, the two main use cases I’m seeing across my global portfolio of fintech financial services firms when it comes to AI adoption is there’s two. There’s one is the automation of kind of repeatable, kind of annoying, you know. Human time-intensive tasks. So, from an automation lens. The second is actually, you know, analysis or activities that ideally we would like to do. But we just didn’t have the time or capacity or, you know, headcount to do so. And this, I think, you know, probably gets into both, but ideally, everybody should be having access to listening to a lot of customer calls and seeing the feedback, but we haven’t really had the time, capacity, energy tooling to do so. Can AI finally unlock that and bring more of that information to more people in the organization to make more informed decisions? So, very interesting kind of future, hopefully, for us. Devender, would love to kind of hear your take on, you know, kind of how you’re seeing, you know, the space looking into 2026 from your vantage point.
Deepinder Singh Dhingra:
Yeah, absolutely. So, what we, what we predict is that there’s going to be a whole agentic AI explosion. The number of agents that both marketing teams, sales teams, SGR, BDR teams, all go-to-market teams are going to start using are just going to explode. So, earlier, if there were, like, 30 tools across the GTM tech stack. Right? They’re going to be now 300 agents. So now imagine 300 agents that are acting on millions or hundreds and thousands of contacts, depending on the size of the company that you are. and executing billions of interactions. What’s going to happen in a world where you might even have a risk of runaway agents, right? So if these agents are not going to be talking to each other, and are not going to share the same context, that’s going to lead to not the agentic singularity, but that’ll probably lead to the agentic apocalypse, right? So how do you prevent that? And that’s kind of basis of art prediction. We believe what’s going to end up happening is that agents will start to get stitched together more and more. So whether those are agents for simple automation, or whether those inbounding agents, prospecting agents, content agents, sequencing agents, nurturing agents. customer voice analysis agents, right? All of these agents are going to start getting more and more stitched, which means that the information flows from the top of the funnel, right, from the marketing motion into the SDRBJ motion into the bottom of the funnel, and back from the bottom of the funnel to the top of the funnel, are going to start getting blurred, and that’s why what we predict is that it’s going to be a blurring of the sales and marketing boundaries. And I think maybe even the funnel might dissolve, right, as was pointed out earlier, right, there’ll be more closed loop. But our perspective definitely is that even if not the teams and the individuals in the GTM org are going to merge, or are going to start doing the same role, definitely the information flows will make it seem like the boundaries are really blurring, alright? And so that’s one of our key predictions, that if you want to kind of stop the agent apocalypse, you will really have to instrument All of the agents, which means you’ll have to instrument all of the data behind the agents, the context behind the agents, the guardrail behind the agents, so that they’re sharing the same connected context. and the shared intelligence, especially as you’ll have to design your GTM motion to interact with buyer agents and agents of all sorts at every stage of the funnel. So imagine the dark funnel becoming even darker, right? Because, let’s say, the sales motion is going to start much later into the cycle, right? You’ll have to really think about that coordinated, agentic workflows, and that’s really our prediction, that’s what I think that’s where we all will be moving towards.
Jonathan Metrick:
Very cool. And what would you say are some of the kind of parameters that you see folks needing to proactively look and institute to try and avoid… I don’t know if you’ve done some early work in this area, but for folks just to… who might be starting on that, what are areas they should proactively look to kind of create guardrails around?
Deepinder Singh Dhingra:
Yeah, definitely. The first thing is the data. How do you stitch the data and create a common semantic layer, or a data model, and I think it was mentioned, a unified data model, so that every agent is feeding of the same context. So if you imagine an SDR agent that just thinks about a click as the… as high intent, and is starting to send messages to every buyer just based on a click. that is generalized to every ICP, right? You want to have personalized context delivered to every agent at the right time, but the same context delivered to every agent, right? Your inbound agent, your outbound agent, your calling agent, the content agent, your campaign refinement agent at the top of the funnel should be feeding off the same context. So context engineering becomes very important. Obviously, the guardrails around brand and, I think what you mentioned around what is good in terms In terms of what are the right conversion tactics. All of that needs to be fed in with… based on the learning and the expertise of your GTO Motion for your product, for your category, and your ICP. So how do you figure out, you put those guardrails? Not just security guardrails, which are important from from LLM security and AI security perspective, but guardrails about your business, about your brand, and about the value proposition messages. But also, what does good look like? What does good look like in terms of the campaigns, the messaging, the responses? the calling, the nurturing, as well as the, you know, in the sales cycle, what does good tactics look like, right? I think those are the key guardrails you have to put, and then you need a system of coordinated agents, right? You know, you need an orchestration layer for agents, so you can monitor these agents. and provide input to these agents, right? And so that’s going to be… so investing in systems and orchestration of agents is going to become very important, and that’s where I think the role of a centralized op team that has purview into all of the different tech that is happening and the different workflows, right, they might need to kind of figure out how to orchestrate these agents and create a central command and control center for AgentTech AI.
Jonathan Metrick:
It’s an interesting point, and I think, you know, to some degree connected, but also, to some degree, slightly in opposition to this idea that you know, more of the workflows are going to flow into individual teams, and then the converse of that is, you know, these systems need to be all connected, so maybe there’s a central team that are… that are kind of tackling these elements. How do you, or folks on the panel opening it up, like, how do you reconcile that? Like, how should, let’s imagine, you know, I’m a CMO, I’m hearing all those great, you know, bits of technology that are showing up in AI, I want to take advantage of these ideas. I haven’t done anything yet. Should I be creating a central team to organize this? Should I be creating, you know, someone within the functions to kind of lead in? Like, how do I reconcile those two approaches that we kind of seem to have teed up? Dependra, you can start, or anybody, just kind of… how would you think about resourcing it?
Matt Hammel:
My perspective on this, from working with larger enterprises, the ones that have had success Deploying agents in production, and a lot of times what are called agents in… enterprise are actually just workflows, given the number of checkpoints that are associated with them, and the sort of, like, smallness of the tasks they’re allowed to do unsupervised. But, the enterprises that have had real success in deploying agents to production. are the ones that have a… the champion for the initiative in the line of business that is actually responsible for the number within the right connective tissue to central IT, and where that, like, orchestration layer is kind of adopted for and bought, I think, is less important than the actual workflows and understanding of the process and what the humans are doing. being driven and led by and held accountable at the line of business level, be that marketing or sort of finance or whatever function it would be. I think central AI innovation teams or committees are where Magentech AI goes to die.
Jonathan Metrick:
Yeah. Yeah, I would… more of the traction I’ve heard is kind of within the function. That said, to Hendra’s point, I do think the underlying data foundation, or maybe the general governance, some sort of individual internal to the team’s got to be kind of QA-ing that, because obviously if we have different data governance rules across different teams, I think that may be, you know, a bit of a challenge. One question from the audience, kind of which I thought is connected to this, you know, let’s imagine we’re trying to build this out for the first time. how do we want to be thinking about recruiting teams for skills around this area that are so emergent, they maybe didn’t even exist? You know, we teed up at the beginning with Joan, go-to-market engineer. That’s a term that was created, I think, you know, one to two years ago. It didn’t exist two years ago. How are we thinking about resourcing These sort of individuals that are going to be moving forward this tooling which may be so emergent that, you know, we can’t just put out a bunch of, you know, be an AI expert and, you know, have 10 years of experience in it. How do we think about that?
Joan Jenkins:
I think one of the ways to think about it, even whether you’re approaching, you know, hiring new people, or thinking of the tools that you need, is really define what… what is… What is it that you’re trying to solve? Because sometimes we jump and we make decisions based on the latest cool tool, or somebody has experience in AI, but what’s truly the challenge you’re trying to solve? And then go from there and figure out how best to solve it, whether that’s hiring or tools or whatever it is. I think that’s key.
Deepinder Singh Dhingra:
Yeah, I think it’s going to be a lot of training, right, and education internally, rather than looking for people with specific skills. Definitely, you’d like people who have this innate curiosity of, you know, working with technology, but just understanding how the technology is applied, how does prompting work, how do agents really think and act? You don’t have to understand the math. or the details behind it, but just have an appreciation, and that’ll require a lot of training, right? Obviously supported whether those are by tools or by teams, right? And so the human-agent collaboration aspect. becomes more and more important. So the human needs to understand the agent, and the agent needs to understand the human or your business, right? So I think that’s going to be the way forward.
Jonathan Metrick:
Yeah, pieces I’m seeing across our portfolio is definitely, you know, there’s… There are the kind of early adopters out there, folks who are, you know, the first one to have the iPhone, the latest, you know, new gadget. Sometimes those folks tend to want to be putting their hands up and say, oh my goodness, I’d love to try out this tool. And, you know, that isn’t everybody in the team, but there may be some folks who proactively lean into that. And I think, you know, empowering those individuals who are excited organically around testing new tools and, you know, systems. And then I think, you know, there’s another cohort, maybe, that are really good around once those are identified, maybe it’s someone separate that’s really good about expanding it across the entire team, the entire organization, once it’s been validated. So I think thinking through strategically who’s generally excited about this sort of stuff. Who loves the new technology and tactics, and giving them a bit of a budget and some time to test. I’ve seen kind of move the needle a little bit moving forward. I think, you know, one topic we’ve been discussing extensively about new tooling, you know, how we’re gonna activate AI across the organization, you know, the ground moving so quickly, we’re trying to, you know, we can’t even keep up with it. what are, you know, just round robin really quickly, what is something that is foundational you think is just never gonna change? That if, you know, there’s a bunch of folks that are on the call as a CMO, yeah, we’ve got to, you know, watch AI and automation and the new tools and channels. let’s not forget what. If there’s one thing you just kind of tee up as, this is a foundational principle that regardless of where AI goes. you know, this is still going to be essential. Round Robin, you know, curious what you folks would advise the group.
Joan Jenkins:
I would say in-person still matters. No matter what. you know, you can’t build your whole marketing plan, based on AI automation. it still counts as seller. You know, the world has changed. You can do a lot of it virtually, but nothing… you know, a big impact of being in person, whether it’s events or whatever it is, one-to-one communication in person, you just can’t… Can’t beat that.
Deepinder Singh Dhingra:
Yeah, I think… Yeah, I think the basics, in addition to what Joan mentioned in terms of the in-person, I think just the basics of good marketing, the basics of goods selling, are not going to change, whether that’s done through people or through agents, right? So really instrumenting that into any workflow or playbooks that you have, that is… that is something that you’re not going to change. And along with that, I think everyone is now talking about how brand and taste are back. Right? Versus just going, you know, full throttle into demand performance marketing, etc, right? And so I think that also doesn’t change.
Christine Royston:
Yeah, I think to… to build on that, I think the… the… the ongoing focus, that continuous improvement of your customer journey, the customer experience that you’re delivering, that is something that is going to continue. I think You can get agents to synthesize massive amounts of data and make recommendations on that customer experience. You can set up rules for agents to work… to act on. But I think, you know, we are all still, as leaders and as humans, going to need to talk and align cross-functionally on what is that ideal customer experience, because that is… how people age with you, that’s what makes your brand, you know, your brand, and I think that is still going to be really important moving forward.
Shawnna Sumaoang:
Yeah, I’ll piggyback on that one as well. I think, the thing that doesn’t change is to the point on authenticity, it’s real human truth. I think AI can create a lot of content, and it sounds great, but it can’t replace that feeling you get when you’re actually talking to Another human being about what mattered to them, or what has… changed for them, you know, in their career or in their business, and it just… it can’t replicate the nuance of someone sharing their experience in their own words, and so, for me, I think it’s a lot about kind of that human truth element. You know, I think while AI speeds up everything, the heart of marketing, it stays the same. Like, we have to understand people, we still have to listen to them, and We still have to tell the truth about what they care about and how we can help.
Jonathan Metrick:
Matt?
Matt Hammel:
Piggybacking on what a few others have said, a real focus on the end user, both in marketing, but in how product and marketing come together. it might sound ironic, but I think it’s becoming more important and will maintain its importance despite agents potentially doing work on behalf of the user. In a lot of ways, that’s where the knowledge of how this work gets done and can go faster and be done at a high quality come, and so… the more you can focus, if you’re in software, on the end user, or in B2C, I think, The better off you’ll be, and the better stories you’ll tell.
Jonathan Metrick:
Yeah, I think it’s, we host a kind of growth summit every year for all of our CMOs and CROs across our global portfolio in New York in October. And the theme, you know, unsurprisingly, was AI in action. And I think what was so fascinating to see across all these different use cases of portfolio companies was the areas that really where AI had added a lot of value was really just amplifying the superpower that the organization was already really good at. And so, you know, organizations that were really data-savvy were using AI to kind of do even more data analysis, or, you know, very creative-driven organizations were using AI to, you know, amplify static imagery to then video imagery in a way that’s more scalable and cost-effective in these sort of areas. So I do think You know, it isn’t necessarily replacing, it’s amplifying areas that I think organizations are already really good at. And I love these kind of, you know, tee-up themes of in-person still mattering, right? If there’s a lot of AI slop out there, and there’s a lot of digital noise, you know, an in-person connectivity may actually drive further beyond. I think just to kind of close out this session, you know, we started this, you know, CMO predictions topic with the fact that, you know, many of us feel excited, but I think also equally kind of overwhelmed with the rapid pace of change that’s happening within the marketing function because of AI. There’s so much more tooling, our teams are changing, how we go about doing the jobs to be done for marketing is shifting, even though I think the core principles necessarily aren’t. We started off, you know, Joan, you’d kind of teed up the idea that go-to-market engineering and these kind of cross-functional areas are changing the way that we may need to collaborate with our cross-functional peers, but a bit of a challenge to say, you know, marketers need to step up to really understand the interconnectivity and the tooling between, you know, these new AI solutions, but also working cross-functionally to make sure that we’re up-leveling technically, but working with product and sales to get these pieces done, don’t let them do it. Matt, I think you had a great tee-up on, you know, the evolution and the future of chat GPT ads, and this may be teeing up a new channel for marketers to explore, and we need to be aware of that to be kind of bidding and getting in front of them, but I think, you know. the other component I thought was really interesting was maybe that some of the work that we might have outsourced to an agency historically might actually be coming in-house, and the need for faster cycle times may create some more opportunities in-house for certain generalist marketing functions to kind of take over because the speed of change is so rapid. Christine, you know, I love your tee up on kind of unified data foundations, right? And we’ve got a lot of promise But if we don’t have connected data, and we got disparate, you know, different systems across different teams. We’re not going to have some agent that helps sales and marketing and product do a whole bunch of stuff if the data isn’t connected, and we haven’t really thought through, you know, what sort of information are we feeding these systems and the agents, you know. Depender, you kind of dovetailed onto that as well, where if you create an agent and let it go awry a bit. you know, we need to create guardrails around this if it’s connected, and they’re going to kind of go off and make, you know, agentic use cases for our marketing flows. And then finally, Shauna, you know, I love your tee-up of kind of the voice of the customer information, and you know, I think one of the… a lot of us around the room have, you know, been… love the kind of the Gong call recordings, but how much time, you know, are you really cutting out a whole day or an afternoon to listen to every single sales call? Well, we should, but ideally, you know, if this is a way for us for ADA I had to kind of automate those components. I think that’s a really exciting future in front of us. That was, you know, an exciting conversation. I think 2026 is going to be a really hot year. You know, what’s ahead? Who knows? The landscape is moving very, very quickly. But, you know, maybe just closing thoughts for any, you know, folks. If you have a closing thought for the group, you know, feel free to chime in on that, but I thought this was a really interesting conversation. We’ve got maybe, what, two, three minutes left? But if you guys have a closing thought you want to leave with anybody, with a standoff prediction, now’s your time. Otherwise, I’ll close this out.
Deepinder Singh Dhingra:
Yeah, I just… I just have one. I think the teams that win, the GTM teams across marketing and sales that win, will not be those that have more AI, but those that’ll have more coordinated AI, that are feeding off each other, yeah.
Joan Jenkins:
I’d say it’s an important time for marketing, and jump in, and, you know, dig in in 2026. Should be a good ride.
Christine Royston:
I would say we are all still figuring it out. This is an ongoing learning process, so, you know, encourage people to share their ideas and best practices in various forums, ask questions. I feel like, you know, we can all learn from each other as this continues to evolve.
Jonathan Metrick:
One quote we had kind of wrapping up our, Grow Summit that we hosted this year was, you know, don’t get ready, get started. And I think they’re, you know, the tools are rapidly evolving, and the landscape’s changing, but there’s no better time to just dive in, right? And I think there’s a little bit of a, well, if it isn’t perfect, I’ll wait for the ground to settle. And, you know, that’s not the case. I think you’re going to learn the most by diving in and playing with the tools and experimenting, and even if, you know, the toolset changes. you know, the fact that you’ve been playing with it is going to make you further along, and it reminds me of the kind of earliest days of, you know, kind of when SEO and paid search and social media started rolling out, and the folks that dived in first, I think, had a real arbitrage window to kind of take advantage of some of these new opportunities of growth that popped up in that mix. So I love that kind of don’t get ready, get started. you know, plus one to that, Christine. Matt?
Matt Hammel:
I was just gonna… I’m predicting full agentic singularity in marketing in 2026, so anything less will be a big letdown after this panel, but this is… this is.
Jonathan Metrick:
Absolutely. And we’ll be looking at Julia. Julia will absolutely, you know, we’ll be having this panel next year. Maybe all of us will be agentic, it will just be avatars, it won’t even be us, but we’ll have maybe trained them. Good point on that one. Shauna, any kind of closing thought as we wrap up this panel on your side? Just kind of… I can’t beat Matt’s closing. He just made it all come full circle, so thank you. Amazing, amazing. Well, thank you so much for the panelists. Hopefully, folks listening enjoyed this session. Lots moving in the marketing space, lots of new tooling, lots of new ideas, but I did like kind of the reminder that, you know, while many things change, the core foundations and jobs to be done in marketing are evergreen, and I think never losing sight of those pieces. So, thank you so much, folks, for taking the time to listen in. Appreciate the panelists taking time out of their busy days to let us know what’s going to be ahead for 2026 in our gentic, singularity future.
Julia Nimchinski:
Thank you so much, phenomenal panel. Jonathan, before we go, you gotta tell us, what are you looking for in startup future investing in 2026?
Jonathan Metrick:
Yeah, I mean, I think, you know, what’s fascinating about the startup landscape, you know, we’ve transitioned from the growth at all costs era, which was, like, if you’re growing, you’re good, to, well, wait a minute, we actually need you to be profitable, because the interest rates have gone up, and capital is now more expensive, and it’s not free. And, you know, we’re on the other side of, you know, we want growth, we want profitability, and now it’s actually a combination of both. And I think if you take a look at the startups that are valued, higher in terms of multiples, of course, there’s the AI cohort, which is, you know, is it a bubble? I don’t know, but they’re definitely getting valued quite highly. But all the other folks, it is a combination of profitable growth. growth. And if you were to force rank them, growth is actually more important than just profitability. But I think the businesses that are being valued the best outside of the AI cohort are really businesses that have robust growth engines. and solid unit economics. And so I think that combination with the heavier weighting to growth, I think is going to set you well, for valuation in 2026.
Julia Nimchinski:
Love it. What’s the next triple, triple, double, double? Quadruple?
Jonathan Metrick:
Well, again, are we in the AI world? Are we in the agnic AI world, or are we back into reality?
Julia Nimchinski:
similarities.
Jonathan Metrick:
Yeah, think about… 10X, I have no idea. Petaxing, pet axing. Yeah.