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What’s the best part of being a modern-day SaaS CMO?

The best part of being a CMO in SaaS is the compounding nature of work. Unlike more static business models, SaaS rewards consistency and iteration as everything you build stacks. Each campaign, piece of content, customer insight, and data signal feeds into a larger system that gets more efficient with time. Instead of just executing in the moment, you’re building a flywheel that accelerates with every turn.

There is also an abundant density of signals to gauge. In SaaS environments, within a short period of time, you are able to know whether a campaign, message, or pricing change is working. This quick feedback is made possible through a consistent stream of behavioral data from their first touch through customer engagement. That immediacy sharpens decision-making and allows for rapid optimization.

Finally, the connection between marketing and business performance is wonderfully direct. In SaaS, marketing isn’t a support function, it’s a measurable contributor to Annual Recurring Revenue (ARR), Net Revenue Retention (NRR), and customer lifetime value. That level of accountability brings gratification and discipline to the role. I think this is a really exciting time to be a marketer.  All disciplines in Marketing are more important than ever to corporate performance.

As a CMO; how have you helped marketing teams build a 360-degree view of their customer across disparate platforms (CRM, product, support) to better enable personalization?

Regardless of the maturity level of the SaaS company, the information that marketing teams need to be effective is spread across many different systems. With customer and prospect data held in the CRM and support information in another, customer engagement information comes across several channels. In addition, data such as provider intent signals, inquiries from partners, and field team data must all be pulled together to develop a useful 360-degree view.

Having a customer data platform that contains everything needed to score, prioritize, and personalize outreach through your martech and GTM infrastructure to ensure relevant engagement is critical to success.  In my experience, outreach prioritization governance is key to avoid overwhelming or confusing customers.  Done right, it drives action and measurable lifts across GTM.

How are you using AI and automation to scale marketing productivity and enhance personalization without losing the human touch?

I think about AI in marketing across three distinct layers, and the human touch is important in each.

The first layer is team productivity. At this point, AI should be embedded into the day-to-day work of every marketer. Whether building account research briefs, analyzing campaign performance, or refining messaging, AI can dramatically increase speed and efficiency. It’s less about replacing human thinking and more about removing friction so teams can focus on higher-value strategy.

The second layer is orchestration. This is where AI and automation start to run the marketing engine itself. Modern marketing teams are increasingly powered by agents that manage workflows like data hygiene, segmentation, routing, and identifying funnel risks. I often think of a world-class marketing team as a Michelin-starred kitchen where everything operates with precision that is invisible to the customer.

The third layer is personalization and engagement, and this is where I’m a bit more measured. AI is incredibly useful for preparing content and pulling from approved data sources. However, I still see significant value in human review for customer-facing interactions to ensure quality. That said, the AI output from our marketing tools at Five9 is becoming increasingly sophisticated and reliable.

Ultimately, the goal isn’t to replace the human touch, but to create robust experiences. It’s about producing experiences that help customers understand the value and move toward deeper engagement – whether that’s a discovery call, an executive briefing, or an insightful Customer Advisory Board (CAB) conversation.

Tell us about a time you used an existing marketing technology in your stack to solve a new business challenge. What was the tool, and what was the outcome?

At a previous employer, we used 6sense in a very standard way—driving top-of-funnel intent data and prioritizing accounts for new business. At the same time, we started noticing some concerning signals within our existing customer base. In response, I wondered: could we use that same intent data from 6sense to understand when our own customers might be exploring alternatives?

Instead of treating the platform purely as a net-new acquisition tool, we flipped the use case and began monitoring our existing accounts for spikes in research activity around competitor categories or adjacent point solutions. When those signals appeared, we flagged those accounts to the customer success team and paired that outreach with targeted content designed to reinforce our differentiation, often surfacing value propositions or capabilities the customer may not have fully adopted yet.

It was a case of asking a different question of an existing tool. I’ve found that we as marketers are often not using the full extent of the marketing tools that we have purchased.

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Five thoughts on the future of AI and marketing and the overall shift it will lead to in martech?

• The Martech stack will consolidate: AI will allow key systems to quickly expand their functionality and replace former point solutions. Marketing software that doesn’t incorporate robust AI feature sets could quickly be replaced by other adjacent competitors, or frankly, internal AI teams who make these tools themselves.
• Marketing operations will become AI operations: This shift is already occurring. Talent across marketing operation teams will shift to those who can design agent workflows, engineer excellent prompts, evaluate model output, and create/manage AI-driven campaigns end-to-end.
• AI-driven discovery will be the predominant customer action: As buyers are shifting to research via their favorite LLM, they are developing little patience for studying content, forms, and perhaps even watching videos. Marketing teams must redesign their tools to ensure their company ends up in a customer’s AI research.
• Brand and data will become critical to the company moat: In this overly saturated AI era, differentiation comes from a unique POV, proprietary data, insights, and brand equity. Brand is becoming more important than ever as AI cuts creative cycle time, putting greater weight on the reputation customers have of a company when making high-stakes decisions.
• Marketing measurement shifts from deterministic to probabilistic and causal: As mentioned, today we have rich intent signals and tons of customer data to analyze. However, as the web gets darker, and customer discovery moves to AI, our understanding and gauge of customer influence will need to evolve. Moving forward, marketing must move to even greater test and control models in order to measure wider outcomes and come up with more probabilistic models that measure influence and ROI.

Five martech innovators and innovations you’d like to highlight more about in this martech conversation before we wrap up?

The pace of innovation in marketing right now is extraordinary. Between rapid advancements in AI automation and ongoing consolidation across the Martech landscape, the environment is evolving at a speed that is challenging to stay on top of. That’s why it’s difficult to single out any one innovator, as the list will likely be different just 6 months from now.

What’s more important than any individual company is the broader shift happening across the current ecosystem. Tasks that require long cycle times, like data analysis, lead scoring, and list augmentation, are now being automated and orchestrated at a rapid pace. Marketing teams are now able to quickly design relevant customer journeys to their target accounts and contacts.

Regardless of the specific innovation or innovator, deploying these journeys with impactful execution across marketing channels, partner channels, and sales organizations will be the measure of success in the current martech ecosystem.

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About Five9

Five9 empowers organizations to create hyper-personalized and effortless AI-driven customer experiences that deliver better business outcomes. Powered by Five9 Genius AI, the Five9 Intelligent CX Platform is trusted by 3,000+ customers and 1,400+ partners globally.

About Jay H. Lee

Jay H. Lee is the Chief Marketing and Growth Officer at Five9. A growth-focused leader in enterprise software and fintech, Jay brings more than 20 years of experience driving go-to-market transformation, scaling global marketing organizations, and delivering measurable business outcomes.

Prior to joining Five9, Jay served as Chief Marketing Officer at Icertis, where he led the company’s global marketing organization and helped position the business for its next phase of growth.

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Hi Jana, take us through your time in martech and your role at Cognitiv?

I’ve spent more than two decades at the forefront of advertising innovation. First helping lead the adoption of programmatic across EMEA during its early emergence, launching programmatic at Spotify, to now helping marketers access AI-driven solutions to scale growth. I joined Cognitiv 8 years ago, at the time we were an early player in the deep learning space, with fewer than 10 employees. Joining a start-up is always a gamble, but I felt confident in the technology and the founding team and saw it as a rare opportunity to learn about AI.

Today as SVP of Partnerships, I focus on redefining how brands and media companies leverage deep learning AI to drive performance. In a media landscape that’s more complex than ever, that means building strategic partnerships that help publishers unlock new revenue streams while enabling brands to engage consumers in more meaningful, data-driven ways. I have developed strategic partnerships with major SSPs and DSPs to bring the industry’s most advanced AI-driven curation to media buyers.

We’d love to learn more about your new enhancement, AudienceGPT. Why should marketers pay attention to it?

AudienceGPT is a fundamental shift from reactive audience targeting to predictive, intelligence-driven marketing.

Traditionally, audience segmentation was manual, time consuming, static, and relied on outdated signals like clicks or page visits that didn’t tell you much about the actual stage of the journey a consumer was in.

AudienceGPT solves this by using Cognitiv’s deep learning advertising platform to develop synthetic consumer journey profiles that can then be found programmatically. The result is a more adaptive, predictive approach to audience strategy that aligns media delivery with true consumer intent. Audiences can be activated across web, CTV, social, and audio, meeting advertisers where they are.

Modern marketers manage different types of data and workflows today. What top best practices come to mind for those looking to optimize how they clean and use data to power better outcomes and customer journeys?

During my time at Cognitiv, I’ve evaluated probably 100 data providers across contextual, attention, measurement, and audience segments, so I’ve seen a wide range in data quality and approaches.

A few best practices really stand out. First is understanding the origin of the data, whether it’s deterministic or modeled. Deterministic data, especially in its raw form, tends to be more reliable and transparent, whereas modeled data can introduce assumptions that aren’t always clear or consistent.

Second is freshness and relevance. Marketers often overlook how frequently data is refreshed. An audience labeled as a “travel intender,” for example, is only as valuable as the recency and signal behind that classification. You have to ask: what behaviors actually qualified this user, and how recent were they?

Finally, validation is critical. At Cognitiv, we’re fortunate to test data directly by running it through our models offline to see whether it actually improves predictive accuracy. That kind of rigorous testing helps separate data that sounds good in theory from data that truly drives performance.

Ultimately, the best outcomes come from combining transparency, recency, and real-world validation, rather than relying on labels or assumptions alone.

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What’s the most exciting thing about how AI is leading to a shift in marketing processes and standards as well as a shift within marketing teams in terms of how teams are structured today?

AI is reshaping marketing in a way that feels very similar to the early days of programmatic, but at a much faster pace.

From a team perspective, the traditional silos between media, data, and analytics are starting to break down. We’re seeing hybrid roles emerge, people who understand both the strategic and technical sides of marketing, and are usually proficient in deploying and working with AI.

From a process standpoint, there’s a tendency to think about AI primarily as workflow automation. And while it can help with that, the bigger opportunity lies in real-time prediction and decisioning. That’s where the biggest performance gains will come from.

Five thoughts on the future of AI and martech?

1. Audience targeting shifts towards moments of intent: The combination of contextual signals, real-time behavior, and understanding of content will outperform audience segments. This goes beyond assigning someone to a segment, to predicting their likelihood to act in that moment based on live inputs.

2. Data quality becomes the true differentiator: The future will be built on better data—deterministic where possible, transparent in methodology, and validated against outcomes.

3. AI shifts from automation to intelligence: Today, AI in marketing is primarily focused on automating execution, not redefining strategy. The next phase will move beyond efficiency gains to deliver real intelligence—powering better decisions rather than just optimizing the manual levers we’ve relied on.

4. Personalization will scale without manual effort: AI will enable truly individualized experiences without the operational complexity that used to limit scale.

5. CTV Moves from awareness to performance: CTV is a great channel for reach and scale but we’ll increasingly see it used as a medium to drive performance. The ones who win in CTV will go beyond content targeting.

Some top martech innovations and martech innovators that you’d like to shout out to in this conversation?

Two martech innovators I want to shout out are Magnite and Index Exchange – specifically Paul Zovighian, VP, Marketplaces at Index Exchange, and Zach Pucci Global, Enterprise Sales at Magnite. Both are helping push real-time curation forward in a way that’s shifting intelligence to the sell side and accelerating innovation across the ecosystem.

Real-time curation turns live data signals into actionable inputs for AI, allowing for accurate, real-time predictions. This drives improved performance for buyers in the moment, not after the fact.

Cognitiv is a leading advanced performance partner powered by deep learning. Leveraging cutting-edge AI technology and data science since 2015 to more accurately predict consumer behavior and understand nuance, Cognitiv connects brands with their customers in more precise, relevant, impactful moments at scale. Cognitiv’s Deep Learning Advertising Platform provides marketers with unprecedented flexibility, activating as a Dynamic Deal run through the DSP of your choice, as a managed service DSP, or through its industry-first ContextGPT product. Cognitiv is on a mission to bring intelligence to advertising.

About Jana Jakovljevic

Jana, SVP of Partnerships at Cognitiv, brings two decades of experience driving innovation across the advertising industry. Before joining Cognitiv, Jana was the Global Head of Programmatic Solutions at Spotify, where she successfully launched the company’s programmatic arm and pioneered the first Private Marketplace (PMP) for audio ads. At Magnite (formerly Rubicon Project), Jana held various management positions, building out international buy-side partnerships and playing a foundational role in the company’s journey from start-up to IPO. Known for landing at companies that are at the forefront of the media landscape, Jana is now focused on leveraging AI to propel the ad industry forward. Her dedication to disruption and passion for constant improvement make her a key agent of change, unafraid to break the status quo in the name of innovation.

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Hi Max, what’s a day at work like as a CEO in martech?

No two days look alike, which is both the challenge and the appeal. A lot of my time goes into conversations at the intersection of business and technology: understanding what marketing and data teams are actually struggling with, and translating that into product thinking. In martech specifically, the pace of change is relentless. New privacy regulations, shifting platform dynamics, the AI wave: you’re constantly having to update your mental model of the landscape.

I try to carve out time in the mornings for deep thinking before the meeting load kicks in. Running and skiing in the Alps help me reset. But the honest answer is that being a CEO in this space means you’re perpetually juggling the urgency of today with the strategy of tomorrow.

What’s wrong with how marketers today choose, deploy and integrate their martech stacks?

The most fundamental problem is that the customer is rarely the starting point. Martech decisions tend to be driven by internal logic (what the vendor promises, what the team already knows, what the budget cycle allows, etc.) rather than by asking: what does the person on the other end of this actually experience, and does our data infrastructure make that experience better or worse?

That sequencing problem has consequences that compound. The average enterprise today runs dozens of martech tools, each holding a fragment of the customer picture. But because those tools were chosen independently rather than as parts of a coherent whole, they rarely agree on who a customer is, what they’ve done, or what they need next. The result is a degraded customer experience. People receive irrelevant messages at the wrong moment through the wrong channel, because the system of record is too fragmented to know any better.

The deeper issue is that most stacks were built around third-party data assumptions that no longer hold. The architecture was designed for a world where you could fill gaps in your customer understanding by buying data about people from somewhere else. That world is contracting fast. What replaces it has to be built on genuine first-party relationships. Too many organizations are still patching over that gap rather than rethinking the foundation.

There’s also a governance blind spot that ultimately hurts the customer too. When tool decisions are made in marketing without legal, IT, and compliance in the room, you get a stack that looks commercially attractive but creates real risks around how customer data is handled. And these are risks that erode the trust that makes the customer relationship possible in the first place.

What martech stack optimization tips do you think more marketers need to pay closer attention to?

A few things I’d highlight:

Always start with your customer, not your tool wishlist. Before you add anything new to the stack, ask: do we have a clear, consistent picture of our customer data, or at least how we can obtain the data we need? If the answer is no, adding more tools will compound the mess.

Audit your existing stack ruthlessly. Most teams discover, when they actually sit down and map it out, that they’re paying for tools that overlap significantly or that nobody is using at full capacity. Consolidation (where it doesn’t compromise capability) almost always pays off.

Treat interoperability as a first-class requirement. When evaluating any new tool, the question shouldn’t just be “does it do what we need?” but “how cleanly does it plug into everything else?” Poor integrations are where data quality goes to die.

Invest in data quality before you invest in analytics. It sounds basic, but the signal-to-noise ratio in most marketing data environments is terrible. Better models and better campaigns both depend on better underlying data.

Finally, think carefully about where sensitive data flows, as this can present a serious business continuity problem in addition to the more obvious legal implications. Knowing where your customer data goes and who has access to it has become a core competency for modern marketing teams.

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How can modern marketing teams create better data cleaning and data unification processes?

The first shift is cultural: data quality has to stop being treated as someone else’s problem. In too many organizations it lives in a technical backwater, handled by a small engineering team that nobody pays much attention to until something breaks. Elevating data quality as a marketing concern as well as an IT concern changes what gets prioritized and resourced.

On the process side, you need standards before you need software. That means agreeing on what a “customer” is, how identity gets resolved across channels and devices, what counts as a valid email address, and so on. These decisions sound mundane but they’re foundational. You can’t clean data if you don’t have a shared definition of what clean means.

For unification specifically, the challenge is almost always organization at its core rather than technical. Data lives in different systems because different parts of the business own different relationships with the customer. The CRM has one slice, the e-commerce platform has another, the ad platform has a third. Unifying that requires not just technical connectors but trust between teams: agreement on who can see what, under what conditions, and for what purposes. Getting that governance layer right is actually the hard part.

Identity resolution has matured significantly, and the best approaches increasingly combine deterministic and probabilistic methods depending on the context. Many teams still apply a single method rigidly where a more flexible strategy would serve them better. The key is understanding which approach fits which use case, rather than treating it as one-size-fits-all.

A few thoughts on how AI-powered martech is leading to a complete rejig in marketing?

AI is accelerating a shift that was already underway: from campaigns built around broad segments to experiences shaped around individuals. That personalization around scale changes the fundamental unit of marketing strategy.

Here’s the thing that doesn’t get said enough: AI doesn’t create competitive advantage on its own. It multiplies what already exists. If your data is siloed or poorly governed, AI will only amplify the issue. The organizations seeing the best results from AI-powered martech aren’t necessarily those with the most sophisticated models. They’re the ones with the most solid, best connected first-party data foundations.

That’s driving a fundamental rethink of data strategy. For a long time, the dominant instinct was to stockpile and ring-fence proprietary datasets. Today’s marketers are realising that intelligence compounds when data is connected via secure networks. No single brand has a complete view of the customer journey. But through privacy-respecting collaboration across brands, retailers, and publishers, marketers can feed AI richer and more diverse signals without ever exposing raw data. That network effect is where the real AI advantage lives.

Five martech thoughts to leave us with before we wrap up?

1. First-party data is not optional. Every strategy that still depends significantly on third-party data has a shelf life, and that shelf is getting shorter. The teams who’ve invested in owning their customer relationships directly will have a structural advantage that compounds over time.
2. Less stack, more depth. The arms race of adding tools has to end somewhere. The best-performing marketing teams I see are the ones who’ve made fewer, better choices when it comes to their tools and actually mastered what they have.
3. Collaboration between data owners is the next competitive frontier. Some of the most interesting marketing use cases require combining data across organizations — retailer and advertiser, publisher and brand, etc. — without either party giving up control. This kind of privacy-respecting data collaboration is still early, but the teams that figure it out will unlock insights their competitors simply can’t access.
4. Treat compliance as a design constraint, not an afterthought. Privacy regulations aren’t slowing down, and neither is enforcement. The organizations building data practices around compliance from the start will spend far less time and money fixing things later.
5. The AI opportunity in martech is real, but it has to be earned. You don’t get the benefits of AI by adopting AI tools. You get them by doing the unglamorous work of building clean, unified, well-governed data foundations and then letting AI do what it’s actually good at on top of that.

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The company specializes in secure data collaboration and offers a platform for data clean rooms, as well as the Collaborative Audience Platform: a unified layer that adds CDP- and DMP-style capabilities to the clean room for real-time segmentation, identity, activation, and shared audience products.  Decentriq has secured significant funding, acquired international customers, and established partnerships with major technology companies such as Microsoft.

About Maximilian Groth

Maximilian Groth is co-founder and CEO of Decentriq, a technology company founded in Switzerland.

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Hi John, take us through some of your best marketing moments over the years?

When I look back on my career, the moments that stand out most are when marketing created real value, both for customers and for the business.

Great marketing starts with authentic connection. It’s about understanding the business challenges your audience is facing and creating experiences that feel frictionless and genuinely helpful. When customers can clearly see how your solution helps them move their business forward, that’s when marketing becomes even more impactful.

I’ve also seen firsthand how powerful it is when marketing investment is directly tied to revenue growth. Some of the most defining moments in my career have been when organizations shifted their mindset, from viewing marketing as an expense to recognizing it as a true growth lever. When that connection becomes clear, it changes how companies operate and how teams align.

And beyond outcomes, the moments I value most are about the people. As a leader, there’s real joy in setting a bold vision and raising the bar in a way that stretches a team’s capabilities. Creating that constructive discomfort, and then watching people exceed what they thought was possible, is one of the most rewarding parts of my job.

What are some of the top trends that will define advertising and lead the ad tech ecosystem in 2026 in your view?

Advertiser expectations are reshaping the ad tech space this year, particularly around fee transparency and the growing impact of AI across the buyer journey.

Transparency has long been a friction point in digital advertising, and it’s re-emerging as a central issue. Advertisers want clearer visibility into fees, supply paths, and how value is distributed across the ecosystem. That scrutiny is only increasing as budgets face greater accountability.

At the same time, AI is fundamentally reshaping how campaigns are planned, activated, and optimized. We’re moving beyond AI as a reporting layer to more agentic systems that can actively manage decisions in real time, improving efficiency, performance, and yield across the lifecycle of a campaign. As those capabilities mature, they’re compressing timelines and reducing operational friction for buyers.

The intersection of these trends makes this a transformational moment for ad tech. Advertisers are demanding smarter execution powered by AI, and clearer economics across the supply chain. The platforms that can deliver both will define the next phase of growth for the industry.

As a marketing leader, how are you using modern martech and ad tech to align with business growth goals?

At PubMatic, we’re embedding AI across the full spectrum of our marketing capabilities to ensure our work is tightly aligned to growth.

That starts with strategy: using AI to help refine positioning for key audiences and sharpen how we communicate value. It extends into the development of sales enablement materials, targeted campaigns, and campaign assets. From there, we leverage AI in activation and optimization within our marketing automation systems, and ultimately in measuring performance and impact.

We’re also building internal AI agents that support planning and prioritization across go‑to‑market teams, while integrating best‑in‑class external AI tools where they accelerate execution.

By leveraging AI from strategy through measurement, we’re creating stronger alignment between marketing activity and measurable business outcomes.

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How do you see the scope of marketing and advertising changing today, given that new AI-powered martech and ad tech impact how marketers and advertisers are trained and hired to form modern teams?

As AI-powered martech and ad tech make more aspects of execution measurable and optimizable, the expectation for ROI has increased, even in traditionally brand-led environments. At the same time, we’re seeing strong growth in high-impact formats and moments like CTV, mobile, and live sports. These environments allow marketers to connect with audiences in meaningful moments and measure performance with far greater precision.

That shift is changing how teams are structured and hired. Modern marketers need to be comfortable operating at the intersection of brand and performance. They need data fluency, an understanding of AI-enabled tools, and the ability to translate insights into business impact.

Four martech and marketing best practices you’d leave our readers with before we wrap up?

Be a growth partner.

Marketing should be directly tied to revenue and business outcomes. When you align your work to growth, you elevate marketing to a true value creator.

Create frictionless, value-driven experiences.

The goal isn’t just to reach your audience. It’s to help them solve real business challenges. The more relevant and seamless the experience you can provide, the more trust and impact you build.

Invest in continuous capability building.

The skills that made marketers successful in the past won’t be enough on their own. AI, data, and evolving formats require ongoing innovation in how teams operate and the skills they develop.

Organize for speed and adaptability.

The digital advertising ecosystem is evolving swiftly. Teams that embrace experimentation, agility, and cross-functional collaboration will stay ahead of the curve.

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PubMatic is a leading AI-powered ad tech company delivering digital advertising performance. Through an intelligent, unified platform that connects buyers, publishers, data partners, and commerce media networks, PubMatic delivers superior performance with greater transparency, control, and efficiency.

About John Petralia

John Petralia, is CMO at PubMatic

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Hi Jen, what are you most looking forward to as Siteimprove’s new CMO?

I love a good scale story. High-growth companies give marketers the opportunity to tell meaningful stories about transformation and market shifts, and Siteimprove is at a fascinating moment in that journey.

We’re entering a new phase of digital discovery shaped by agentic AI, where content strategy, analytics, search visibility, and user experience are being redefined in real time. That creates a huge opportunity for marketers. The brands that succeed will be the ones that understand how content is discovered and evaluated not just by people, but by machines.

What drew me to Siteimprove specifically is that we’re building an Agentic Content Intelligence platform while staying grounded in a mission I care deeply about: accessibility. Too often accessibility is treated like a compliance checkbox. In reality, it’s the foundation of digital performance. If someone can’t access your content, the experience stops there. And increasingly, AI systems recognize that too. Accessible, structured digital experiences are simply more discoverable in the AI era.

Helping organizations connect those dots is a story I’m incredibly excited to tell.

What top marketing trends will dominate the B2B SaaS ecosystem in 2026?

The biggest shift I see is that we’re moving from AI experimentation to AI cost rationalization.

Over the past few years, marketing teams have been piloting tools, testing workflows, and experimenting across the AI landscape. That exploration phase was necessary, but now leadership teams want to see results. They’re asking which tools actually drive productivity, pipeline, and measurable growth. So 2026 will be the year when marketing leaders take a hard look at their AI stacks and make real decisions about what stays, what goes, and what truly delivers value.

At the same time, AI is changing how content is discovered. Marketers have always built strategies for different audiences. Now we have to design content for another persona entirely: machines.

AI systems increasingly interpret and surface content before a human ever clicks a link. That means clarity, structure, and accessibility are becoming strategic advantages. Accessible digital experiences tend to be easier for both people and machines to understand, which directly impacts discoverability in the AI era.

For marketing leaders, the takeaway is simple: success isn’t just about driving traffic anymore. It’s about making sure your content can be understood, trusted, and surfaced by AI systems in the first place.

Can you take us through some of the martech you’ve often relied on to drive outcomes?

At the center of everything for me is the CRM. Marketing’s ultimate goal is to drive revenue, and the CRM is where you understand customers, relationships, and outcomes.

Beyond that, I’ve always valued technologies that help teams understand intent and behavior. When you know what customers are looking for, how they interact with your digital experiences, and where friction appears, you can make much smarter decisions about where marketing should focus.

What has changed dramatically over the past few years is how embedded AI has become in that process. It’s no longer one tool or one workflow. AI is now part of the day-to-day operating system for modern marketing teams. I rely on it constantly for research, analysis, content development, and decision support. Used well, it becomes a true force multiplier.

The challenge for marketing leaders today isn’t access to tools. It’s building a stack – and a team – that knows how to turn all that data and intelligence into action.

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How do you use AI powered martech these days to boost marketing goals, some top of mind tips to share with fellow marketers?

One of my immediate priorities at Siteimprove is making sure we hold ourselves to the same standard we expect from our customers. We already use the platform to monitor accessibility across our digital properties, and now we’re doubling down on using it to guide our broader content and performance strategy.

The goal is simple: build a marketing engine that continuously improves itself. We analyze how our digital content performs, identify where experiences fall short, improve them, and measure the impact. That’s the kind of feedback loop AI should enable.

My advice to other marketers is to start with amplification, not replacement. The most effective AI use cases are often the ones that remove friction from work your team is already doing.

Account-based marketing is a great example. Historically, ABM required enormous manual effort to personalize messaging, creative, and targeting for individual accounts. AI can now automate much of that groundwork so teams can focus on strategy and creativity instead of repetitive execution.

Those kinds of workflow improvements are where AI becomes transformative.

A few martech and marketing takeaways you’d like to leave us with before we wrap up?

The pace of change in marketing has always been fast, but AI has accelerated it dramatically.

We used to adapt to predictable shifts – maybe a new platform release, or a search algorithm update every couple of years. Now the environment evolves almost continuously. The moment a team thinks they’ve mastered something like AI-driven search optimization, the rules move again.

That means flexibility is becoming one of the most important capabilities a marketing organization can build. Flexible strategies, flexible teams, and flexible technology stacks.

But even with all of that change, the core principle hasn’t moved.

It’s still about the customer.

The tools will evolve. The ecosystem will shift. AI will reshape discovery and decision-making. But the brands that win will be the ones that remove friction, deliver accessible digital experiences, and create real value in the moments that matter.

Technology changes. Great customer experiences don’t.

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Founded in 2003, Siteimprove transforms access to the digital world by providing an agentic content intelligence platform that unifies accessibility, analytics, SEO/AEO, and content strategy. Today, global 2000 clients across manufacturing, government, higher education, financial services, and healthcare rely on Siteimprove.ai, an agentic content intelligence platform to deliver both content that performs and that is compliant. Based in Copenhagen, Bellevue, Minneapolis and London, Siteimprove is a single, actionable source of truth for digital content and development teams across many of the largest global enterprises, government entities and learning institutions. Siteimprove is majority-owned by Nordic Capital.

About Jen Jones

Jen Jones is a transformative enterprise technology marketing leader known for scaling global organizations and elevating category‑defining platforms. As the CMO at Siteimprove, she leads global brand, communications, demand generation, content, insights, performance and analytics, and product and partner marketing. Her work focuses on strengthening category leadership, deepening enterprise value, and aligning go‑to‑market teams to accelerate growth.

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Hi Mike, tell us about yourself and more about Prescient AI in brief?

I’m Mike True, Co-Founder and CEO of Prescient AI. Before starting the company, I spent my career in enterprise AI and analytics sales at companies like IBM, Oracle, and App Annie, where I helped clients generate millions in revenue through AI-powered solutions [1]. I hold a B.S. in Marketing from Salve Regina University.

I founded Prescient AI in 2019, and honestly, it didn’t start where it is today. We originally set out to build a predictive model for the music industry — helping artists figure out optimal tour schedules and venue recommendations. When COVID-19 hit, and live events disappeared overnight, we had to pivot fast. That’s when we turned our focus to solving marketing attribution and measurement challenges in e-commerce [4].

Today, Prescient is an advanced Marketing Mix Modeling (MMM) platform serving 100+ omnichannel brands — including Saatva, Hexclad, Jones Road, MaryRuth’s, and Coterie — and we raised $20M from investors including Headline and Blumberg Capital [9]. We work without pixels or cookies, deliver actionable insights within 36 hours, and can forecast future campaign performance three months out with around 90% accuracy [3]. In July 2025, we launched what we believe is the first fundamentally new MMM framework built entirely from scratch since the technology was first introduced in the 1960s [17].

How are brands today using predictive AI to power measurement and attribution tactics in modern marketing workflows?

What I’m seeing is a real shift — brands are moving away from piecing together siloed ad platform reports and toward unified, AI-driven measurement. At Prescient, our approach is built on dynamic MMM, which is a statistical, probabilistic model that ingests data from ad platforms, Shopify, Amazon, Google Analytics, and even offline sources to help brands understand which channels are truly driving revenue [14]. Unlike the old Nielsen-style annual MMM studies, our model refreshes every single day, so marketers can reallocate budgets and optimize campaigns in near real time [12].

The brands I work with are using predictive AI in a few powerful ways:

• Measuring halo effects: We help brands quantify how upper-funnel channels like YouTube or CTV indirectly drive Amazon or retail sales. BrüMate, for example, discovered that nearly 20% of its CTV-driven revenue came through Amazon — something its traditional tools had completely missed [11].
• Running budget simulations: Our Optimizer tool lets marketers model different budget reallocation scenarios and get predictive media plans back in seconds [12].
• Triangulating measurement: I always tell our clients, don’t rely on just one source. Combine MMM with incrementality testing, post-purchase surveys, and MTA to validate performance from multiple angles [15].

Marketing Technology News: MarTech Interview With Fredrik Skantze, CEO and Co-founder of Funnel

What’s wrong with most marketing attribution norms today, and why should marketers move away from click-based attribution?

The core problem with click-based attribution — whether that’s last-click or multi-touch attribution (MTA) — is that it can only measure what can be tracked through a click. That means entire categories of high-performing channels get systematically undervalued or ignored entirely. I’ve said this many times: “MTA is great for click-based channels, but it’s not very good for something like linear TV, connected TV, podcasts, YouTube, or TikTok — where they’re more view-based channels.” [12]

There are a few other issues I see constantly:

• Platform-reported ROAS is inflated and overlapping: Every channel claims credit for the same sale. I’ve seen a brand running nine channels with search ROAS ranging from 298% to 1,750% — that’s clearly impossible when channels overlap that heavily [16].
• iOS 14+ and privacy regulations have broken pixel-based tracking: The signal loss is real, and it’s only going to get worse. Relying on MTA or last-click in a post-iOS 14 world means you’re making decisions on incomplete data [19].
• No single tool should be your “source of truth”: I’m very direct with all of our clients about this — “The MMM is not your source of truth. The MTA is not your source of truth. Your incrementality test is not your source of truth. The source of truth is the marketer.” The best practice is triangulation [15].

My best practices around attribution:

1. Use MMM as your backbone for planning and forecasting.
2. Run incrementality tests to validate specific channel performance.
3. Use MTA only for bottom-funnel, click-heavy channels where it’s appropriate.
4. Layer in post-purchase surveys to capture what data simply can’t.
5. Trust the ensemble of models plus your own judgment — never a single platform’s numbers.

What are most marketers getting wrong in the AI/martech implementation process today?

From working with hundreds of brands, I see the same mistakes come up repeatedly:

• Building on outdated foundations: Many martech tools out there are just modernizing old math — legacy 1960s-era regression models dressed up with a new interface. Our CTO Cody Greco and I made a deliberate decision early on that we weren’t going to do that. As Cody put it, “Building on old technology would limit our ability to solve the complex measurement challenges facing today’s marketers.” [17] That’s why in July 2025, we launched a completely new MMM framework built from the ground up.
• Over-indexing on bottom-of-funnel Google and Meta: I see this all the time — the majority of budgets flowing into easily trackable, bottom-funnel channels while upper-funnel brand investments go unmeasured and underinvested [7].
• Trusting a single measurement source: Any marketer who relies on just platform-reported ROAS or a single attribution tool is seeing a dangerously incomplete picture.
• Accepting slow time-to-value: Legacy solutions have been clunky, expensive, and take months to onboard. That’s not good enough anymore. Brands should expect insights within 36 hours and point-and-click integrations [13].
• Ignoring Amazon and retail revenue in media models: For omnichannel brands, a massive portion of revenue has historically been completely disconnected from paid media measurement. That’s a blind spot we’ve been on a mission to fix [18].

As martech evolves and old marketing models are replaced, what trends do you think will reshape B2B SaaS marketing and the martech ecosystem going forward?

This is something I think about a lot. Here’s where I see things heading [9]:

• Predictive models will replace cookies as the primary measurement lens: The demise of third-party cookies and the tightening of GDPR and CCPA regulations mean that privacy-compliant, statistical models like MMM will become the default infrastructure. The privacy-first internet isn’t a future scenario — it’s already here [19].
• Advertising on autopilot: The era of semi-manual media buying is fading fast. AI-driven models will increasingly recommend and execute optimizations automatically, with back-tested confidence. I see a future where brands are running fully automated, dynamically optimized campaigns across every channel [8].
• The rise of “Compound AI”: The next phase of MMM involves multiple specialized AI agents — for forecasting, creativity, audience analysis, and saturation analysis — collaborating continuously to deliver adaptive recommendations in real time [14].
• In-house brand teams replacing agencies: As automation handles media buying, I believe brands will build lean, data-empowered in-house teams and reduce their dependence on traditional agencies. The tools are now accessible enough to make that happen [8].
• AI agent advertising: This one fascinates me. As more search and commerce shifts to LLMs and AI agents rather than Google, entirely new attribution models and ad formats will have to emerge for the “agent world” [7].
• Consolidation in martech: We’re already seeing the big players like Publicis actively looking to acquire AI measurement companies — Prescient was cited as a potential target — which tells you how seriously the industry is taking this shift [1].

Five martech and marketing best practices you’d leave us with before we wrap up?

These are the things I come back to again and again with every brand I work with:

1. Triangulate your measurement — Never put all your trust in a single attribution model. Use MMM, incrementality testing, MTA, and post-purchase surveys together. Each one tells part of the story [15].

2. Give upper-funnel channels a fair shot — Stop penalizing CTV, podcasts, or YouTube for not generating clicks. Use view-based and halo-effect measurement to reveal what they’re really contributing to your bottom line [12].

3. Future-proof your martech stack against data loss — Build infrastructure that doesn’t depend on cookies or pixels. That transition is already underway, and brands that wait will be caught flat-footed [19].

4. Demand speed from your measurement tools — If your MMM only updates quarterly, it’s too slow to be useful. Modern platforms should recalibrate daily and deliver insights within 36 hours. Agility is the whole point [12].

5. Remember: you are the source of truth — AI models are incredibly powerful advisors, but they don’t replace the marketer. You’re the one synthesizing data, culture, creativity, and business context to make the final call. Lean on the models, but trust yourself [15].

Marketing Technology News: The Death of Third-Party Cookies Was Just the Start. Are You Ready for Consent Orchestration?

Prescient AI’s platform delivers fast, daily marketing insights across the full funnel: quantifying halo effects, isolating seasonal impact, and identifying the real levers of performance across DTC ecommerce, marketplaces, and retail.

About Mike True

Mike True, is Co-Founder & CEO of Prescient AI

Source References

[1] Prescient AI – About Us & Meet the Team – November 7, 2025

[3] Prescient AI LinkedIn

[4] EP007: Michael True | Equation of Excellence / Fermat Commerce

[7] 5YF Episode #28 Transcript – Focal VC

[8] 5YF Episode #28 – Focal VC Founder Resources

[9] Future of Advertising with Prescient AI CEO Mike True | 5YF #28 – April 14, 2025

[11] What is Media Mix Modeling (MMM)? – Prescient AI Blog – September 23, 2025

[12] S12 E6: Redefining Media and Marketing Measurement – Limited Supply Podcast – May 14, 2025

[13] Investing in Prescient AI: Next-Gen MMM – Headline VC

[14] MMM Explained – Darkroom Agency Observatory

[16] Marketing Mix Modeling: How Multi-Channel Brands Stop Wasting Ad Spend – Ecommerce Coffee Break – August 13, 2025

[17] Prescient AI Unveils First New MMM Since 1960s – PPC.land – July 16, 2025

[18] Prescient AI LinkedIn – Amazon Measurement Launch

[19] Future of Advertising with Prescient AI CEO Mike True | 5YF #28 – April 14, 2025

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Hi Sherry, tell us about yourself and your role at Criteo.

My career has grown alongside retail media itself. I was part of the early days of the industry, helping build some of the first retail media programs with Walmart and later leading Triad Retail Media. Back then, we were proving that retailers could turn their first-party data and shopper relationships into a powerful growth engine for brands.

Over the past two decades, I’ve seen retail media evolve from a nascent idea into a core pillar of modern commerce. As President of Retail Media at Criteo, I focus on helping retailers and brands scale that opportunity globally and build for the future of commerce, where retail media plays a central role in driving growth, loyalty, and measurable results across every touchpoint.

How is Retail Media shaping up today, and what top trends will define the market through 2026?

Retail media is entering its next phase of maturity. Over the past decade, growth has been fueled by sponsored search, onsite display, offsite media activation, and marketplace advertising. But as commerce becomes more connected and responsive to shopper behavior, discovery is evolving beyond simple keyword search toward more intuitive, personalized experiences.

Looking toward 2026, I see three major shifts shaping the market.

First, retail media will become more seamlessly embedded across digital touchpoints. This will support richer product discovery experiences while preserving retailer control over inventory, pricing, and shopper relationships.

Second, we’ll see the emergence of new, more native ad formats that feel less like traditional ads and more like helpful recommendations, creating incremental opportunities for brands rather than simply reallocating existing spend.

Third, advanced automation and optimization will become essential. As digital shelf space becomes more competitive, retailers will rely on sophisticated decisioning systems to balance sponsored and organic results, maximize performance, and protect the customer experience.

For brands resetting their agentic commerce workflows and experience: what top tips would you share with them?

As agentic commerce evolves, brands should start by recognizing that discovery is becoming more conversational and context-driven, but it is still anchored in retailer environments. AI-driven experiences rely heavily on structured product data, clear attributes, and strong content signals. Brands that invest in making their product information accurate, differentiated, and easy to interpret will be better positioned as recommendations become more dynamic.

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Can you talk about a few brands from around the world who you’ve seen build unique retail experiences with agentic AI?

While retailers ultimately own and operate the commerce experience, we’re seeing innovative brands lean into these new environments in thoughtful ways.

In markets like the U.S., as retailers introduce more guided or conversational shopping features, leading brands are investing in richer product content, enhanced attributes, and contextual storytelling that help their products surface naturally within those experiences.

Globally, the brands that stand out aren’t necessarily building standalone AI experiences themselves. Instead, they’re partnering closely with both retailers and emerging players like LLMs to support incremental discovery and ensure their brand is presented in these new shopping environments.

Five thoughts on the future of retail from your perspective?

First, retailers remain central to commerce because they control the fundamentals: trust, pricing, loyalty, fulfillment, and customer relationships. Technology will continue to evolve, but those assets are enduring competitive advantages.

Second, discovery will continue to diversify. Consumers will move fluidly across retailer sites, marketplaces, social platforms, and emerging interfaces depending on need and context. Winning retailers will meet shoppers wherever they are while maintaining a consistent, trusted experience.

Third, trust will become an even more powerful economic driver. As commerce grows more personalized and automated, transparency and reliability will directly influence conversion, loyalty, and long-term brand value.

Fourth, digital shelf space will become more strategic. As assortments expand and attention becomes scarcer, retailers and brands will need smarter merchandising, better data, and more sophisticated optimization to ensure relevance and performance.

Finally, retail media will solidify its role as a foundational revenue engine. When integrated thoughtfully into the commerce experience, it strengthens partnerships with brands and supports sustainable, incremental growth.

Top of mind best practices for brands looking to optimize their retail media outlook and output in 2026.

Brands need to think beyond campaigns and focus on impact. In 2026, the winners will be those who align retail media investment with merchandising strategy, category growth, and customer lifetime value — not just short-term ROAS.

They should also move early on emerging formats and experiences, but with discipline. Testing is critical, yet every activation should be measured against incrementality and long-term brand equity.

Most importantly, retail media performance will hinge on partnership. The brands that treat retailers as strategic growth collaborators, rather than media channels, will unlock the greatest value.

Marketing Technology News: The Death of Third-Party Cookies Was Just the Start. Are You Ready for Consent Orchestration?

Criteo is the global commerce media company that enables marketers and media owners to drive better commerce outcomes.

About Sherry Smith

Sherry Smith is President of Retail Media at Criteo

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Hi Fredrik – thanks for taking the time to be part of the MarTechSeries chat. Tell us about Funnel’s recent funding round – the highs and lows around it.

We secured an $80 million debt facility from HSBC Innovation Banking and Hercules Capital, and for us, this is a huge endorsement of our technology and the future of our marketing intelligence platform. HSBC Innovation Banking have spent years backing the world’s best technology companies, supporting high-growth, venture-backed businesses and their investors, and this additional capital supports Funnel’s strategic initiatives. This includes further global expansion, continued AI-first product development, and operational efficiency improvements as Funnel scales its platform.

We’re approaching profitability, we have a proven business model, we’re growing quickly, and the facility gives us the headroom to keep building. Specifically, we’re looking to accelerate the conversational analytics and agentic measurement capabilities that marketers urgently need as they navigate the post-cookie landscape and demand better visibility into campaign performance across every channel.

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How is Funnel redefining the scope of modern marketing intelligence? 

When we started Funnel in 2014, there were around 1,000 marketing products in the world. Today, there are 13,000, with 3,000 launched in the last year alone. The data complexity marketers face has exploded, and most tools weren’t built for it. Cloud data warehouses and BI tools were designed for IT teams, not for marketers who need to act quickly and can’t wait for a technical team to run queries. At Funnel, we built something different: a platform that automatically collects, models, and surfaces marketing data across 700-plus connectors, so marketers can understand what’s working without needing a data scientist in the room.

We then acquired Adtriba to bring in best-in-class measurement, triangulating marketing mix modelling, multi-touch attribution, and incrementality testing into one unified platform. It is one thing to offer clients data and information about their marketing spend; it is quite another to give them marketing intelligence, and that’s what we’re building toward.

How can modern marketers make the most of marketing intelligence to course correct in 2026 and beyond? What are they not doing enough of here? 

Our joint research with Ravn Research last year told a sobering story that 86% of in-house marketers and 79% of agency marketers can’t determine the impact of each marketing channel on their overall performance. More than two-fifths of in-house marketers say that when they report results, they don’t analyse the “why” or identify the actions they need to take next. Rather than lacking data, marketers are lacking the right foundation for collecting, measuring and actioning it, which means unifying all of your data sources, automating reporting, and committing to consistent measurement.

Many marketers are skipping this step and jumping straight to AI experimentation, which only delivers intelligent insights when the data underneath it is clean and structured. The other blind spot is the shift from SEO to GEO — 64% of marketers expect generative engine optimisation to eclipse traditional SEO within two to three years, yet fewer than half are actually training their teams for it. Marketers must therefore move with the times, adopting marketing intelligence.

Can you highlight some brands from around the world that are fuelling better marketing plans and strategies with improved marketing intelligence?

We work with around 2,600 customers directly and reach another 60,000 global brands through roughly 1,000 media agencies. Brands like Uber, Adidas, ASOS, and Samsung are using Funnel to get a clear, unified view of their marketing spend across every channel. On the agency side, our five-year global partnership with Havas, announced last year, spanning all 40-plus of their offices worldwide, is a good example of how marketing intelligence scales. They use our platform to deliver sharper, more consistent insights across their entire client portfolio.

One particular case showing off marketing intelligence in action comes from Sephora’s European marketing operation. The team had a data problem that will be familiar to many large organisations: every week, the central data team waited for reports to arrive from local markets across Europe, spent an entire working day consolidating them, and only then could it present findings to senior leadership (a slow and exhausting process).

Working with Funnel and data agency Hanalytics, Sephora implemented a stack where marketing data is ingested, cleaned and prepared as one table in Funnel, sent to BigQuery, transformed using dbt, and visualised in Looker Studio. The impact was immediate as the central team got a full working day back each week, and what started as a senior leadership report expanded to include operational reports for local markets too. Everyone from regional teams to the C-suite now works from the same data, and a company that once spent its time gathering information now spends it acting on it.

A few thoughts on the future of B2B SaaS marketing and martech?

Marketing has always been part art, part science; the difference now is that the science is becoming non-negotiable as the platforms marketers have relied on for decades – Google, Meta, TikTok – are increasingly black boxes. AI handles the bidding, optimises the targeting, and generates the creative, leaving marketers with less visibility into what’s actually driving results at the very moment when understanding that has never mattered more. B2B lead generation automated by agents, AI-generated copy and creative at scale are all happening now, and marketers who assume their current measurement approach can keep pace may well be caught out.

What I find genuinely fascinating is how measurement itself is evolving technically. We’re using neural networks that understand the sequence of marketing touchpoints, not just the touchpoints themselves, because whether a branded search came before or after a direct visit completely changes what drove a conversion. The future belongs to organisations that treat measurement as a priority rather than just a way to report to their superiors. If AI is doing more of the marketing, knowing what’s working is one of the only competitive edges that remains entirely yours.

Top martech innovators — people or companies — you’d like to shout out before we wrap up?

It would be the AI-first companies working on a completely new approach to solving marketing’s different problems. There are a lot of them, and many have a really strong and exciting vision of where they want to go. Many of them are not quite there yet, but give it another year or so and another couple of iterations both for them and the foundational models, and I think we will see some very exciting new AI martech companies emerge and reach scale.

One such product that we are trying out ourselves is Day AI, which is an AI-first take on the CRM space. We are currently evaluating how it stacks up against our existing CRM. The vision of what it can be is quite transformative compared to an existing CRM system.

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Funnel helps thousands of marketers at brands like Havas Media, Home Depot and Publicis to choreograph their data and unlock insights that move their businesses forward. Connect, explore, visualize, measure and more — all in one place.

About Fredrik Skantze

Fredrik Skantze, is CEO and co-founder of Funnel.

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Take us through your time in the martech ecosystem and more about your role at PubMatic.

I’ve spent over 15 years working at the intersection of strategy, analytics, and large-scale digital platforms — across consulting, enterprise technology, and now advertising infrastructure.

What has consistently drawn me to this space is that ad tech isn’t just about media delivery; it’s about market architecture. It shapes how brands connect with consumers, how publishers monetize content, and how transparency and trust scale across the open internet.

Over the years, I’ve worked across commerce enablement, brand safety, political advertising transparency, and AI-driven automation. A consistent lesson has emerged: when you improve the intelligence layer of infrastructure, you fundamentally improve decision quality.

At PubMatic, I lead developing and marketing products across AI-driven analytics, performance solutions, curation, and vertical specific initiatives such as political and pharma ads. Increasingly, my focus is on agentic AI, building systems that move beyond static workflows to fast, contextual reasoning, and translating that into measurable outcomes for publishers and buyers globally.

We’d love the top highlights on your recent AI Insights launch — how does it enable end users?

Much of AI in ad tech has historically been incremental, summarizing dashboards or automating isolated tasks. The more meaningful shift is architectural with AI.

With AI Insights, we focused on structuring data from the ground up so the system can reason across signals rather than simply display them. That enables real-time benchmarking against relevant peer sets, detection of demand shifts, and surfacing of next-best actions directly within the workflow.

The shift is assisted, automated, and agentic decision-making.

For example, during election cycles, publishers with political inventory often see volatile demand patterns across geographies and content categories. AI-driven benchmarking can highlight where pricing strategies or packaging approaches are underperforming relative to comparable publishers, where is the demand coming from across PACs, geographies and enabling faster, data-backed adjustments while maintaining policy and transparency standards.

The broader vision is agentic workflows, where AI doesn’t just summarize information but helps operators navigate complexity with context. As more structured signals are integrated, the value compounds directly to growth and profitability.

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What’s trending in the CTV space today? What will dominate the market in 2026?

CTV is increasingly being treated as performance-grade media with premium storytelling layered on top.

Today, we’re seeing stronger demand for transparent supply paths, growth in curated marketplaces, tighter alignment between CTV and commerce signals, and more scrutiny around measurement consistency.

The deeper shift is sophistication. Buyers are no longer optimizing for scale alone; they’re optimizing for validated, structured supply.

By 2026, three forces will likely define the market. First, AI-driven optimization across CTV supply, dynamically adjusting pricing and packaging in real time. Second, outcome-based evaluation, where campaigns are judged by business impact rather than impressions alone. And third, intelligent curation, where inventory is transacted based on contextual quality and performance intelligence rather than volume.

CTV will increasingly operate like high-quality programmatic performance media, while retaining the creative power that makes it distinct.

What are some top tips publishers should keep in mind as martech and ad tech evolve?

Three priorities stand out.

First, build structured data foundations. AI and automation only deliver value when signals are clean, interoperable, and contextualized. This sometimes needs to be built from the ground up.

Second, operationalize transparency. Across political advertising, brand safety, and CTV, transparency increasingly drives premium demand and long-term trust.

Third, adopt AI with intent. The goal isn’t full automation; it’s embedding AI-assisted reasoning into workflows so human operators can make faster, higher-quality decisions.

Publishers that combine premium environments with intelligent, structured supply will lead the next phase of programmatic growth.

How is AI fueling a complete shift in the industry?

AI is reshaping ad tech across three dimensions.

It reduces operational friction in reporting and campaign management. It amplifies intelligence by identifying patterns across supply and demand. And most importantly, it begins to influence market design, enabling adaptive packaging, dynamic pricing strategies, and predictive optimization at scale.

The real shift isn’t speed; it’s decision quality. Markets become more data-native and less intuition-driven.

But this only works when AI is built on structured data foundations and clear governance. The companies investing architecturally, rather than layering AI superficially, will define the next decade of digital advertising.

Five ad tech and martech thoughts you’d leave our readers with before we wrap up.

• The future of media is curated and intelligent, not infinite and opaque.
• AI will augment expert operators before it replaces them.
• CTV will demand performance-grade accountability.
• Transparency will increasingly correlate with revenue performance.
• Infrastructure innovation will quietly determine which ecosystems lead.

PubMatic is a leading AI-powered ad tech company delivering digital advertising performance. Through an intelligent, unified platform that connects buyers, publishers, data partners, and commerce media networks, PubMatic delivers superior performance with greater transparency, control, and efficiency.

About Punit Shah

Punit Shah is a global product marketing and go-to-market leader specializing in AI-driven analytics, programmatic curation, and regulated advertising innovation. At PubMatic, he leads product strategy and market expansion for the company’s AI and next-generation analytics portfolio, driving adoption and revenue growth across international markets.

__________

Tell us little about yourself, and more about your role at Infobip

For the majority of my career, I’ve been focused on helping ambitious companies (OpenMarket, Amdocs Inc.) grow their business and build high-performing teams in various sales and business development roles.

When I came to Infobip as Vice President of Revenue for North America and Global Strategic Accounts, I saw this as an opportunity to do the same for a “behind the scenes” company – a successful company that powers virtually every touchpoint of the customer experience for some of the world’s most renowned brands. Now as Group President of North America, I leverage my past experience working for major messaging and digital communications companies to point to why Infobip is business-critical for brands to create an unrivaled omnichannel experience for their end consumers, resulting in long-term brand loyalty, engagement, and meaningful customer connections that set the foundation for these companies to become a brand people love.

Can you talk to us about the latest AI in marketing trends that are impacting the ecosystem today?

Last year, specifically during the holiday season, cemented AI as a business must-have. With AI becoming a tool that more consumers are embracing, we’ll really start to see AI maturity take off with brands exploring what kind of value it brings outside of customer support automation. This year, AI agents are expected to handle up to 95% of customer engagements, going beyond round-the-clock personalized support. We’ll see more AI chatbots taking over more routine tasks (banking inquiries, order tracking, cost comparison, product discovery, etc.), while advanced voice bots are emerging as a tool to unlock more opportunities for automation and enhanced customer conversations.

We’ll also see data management becoming more of a priority. Customers want to feel seen and heard, and personalization is key to achieving this. As AI evolves, business interest in smaller, domain-specific language models is gaining traction because of their ability to support hyper personalization by providing accurate, context-aware responses with data privacy and compliance in mind, which is a top consumer preference.

Additionally, conversational AI will continue to improve its ability to mimic human conversations, from speech patterns to awareness. Having a holistic digital presence is still important for businesses to reach various consumer audiences and embedding conversational AI across communications channels make it easier to meet customer expectations at scale; from personalized promos and boosted customer retention to actionable insights that can help marketers optimize messaging, segment audiences, and improve campaign performance over time.

What AI-powered martech features are customers leaning more towards today?

Before I answer this question, I want to share some additional context to deepen the understanding of how agentic AI and conversational AI differ: conversational AI mimics human-like interactions while agentic AI drives outcomes and efficiency.

Consumers have long been skeptical about AI, showing hesitancy to embrace the tech and use it in their day-to-day. That changed last holiday season – consumers felt pressure to tighten their purse strings due to impending tariffs and rising costs. Because of AI agents’ ability to instantly cost compare and discover new products, consumers leaned on the tool to streamline their shopping processes as they fulfilled their gift lists – a trend we’ll continue to see with AI creating a more accessible end-to-end experience.

Additionally, consumers are increasingly adopting conversational AI. People still want to build human connections, even in their interactions with brands. Speaking to human agents might not always be feasible, especially during periods of time when demand for customer support is spiked (holiday shopping season, Valentine’s Day, other major retail events). Conversational AI addresses both needs: maintaining natural human conversation patterns while enabling human agents to focus on more complex issues. In marketing, this deepens customer loyalty further by providing product recommendations, amplifying seasonal promotions, and making it easier overall to bring customers to purchase through cart abandonment notifications, back in stock alerts, and personalized offers.

How can modern marketing teams capitalize better on multichannel texting to nurture prospects and customers?

It’s important for marketers to not view AI as a standalone tool. As I said earlier, having a holistic digital presence is important in order to reach various customer segments. AI can amplify these digital channels’ effectiveness by creating more targeted conversations that resonate with various audiences.

Taking a step back, texting and mobile messaging offer more than promotional avenues. Because they boast more interactive features (appointment booking, in-app purchases, hi-res image carousels, etc.) and allow for two-way conversations to take place, brands can leverage these channels to create more meaningful interactions, provide always-on support, and bring customers to point-of-purchase. Consumers are increasingly using platforms like RCS messaging (Rich Communications Services) and WhatsApp because of their interactive features and security capabilities, making these channels ideal for boosting customer confidence and engagement.

When you add AI into the equation, the value proposition of these channels is enhanced, resulting in stronger customer loyalty, personalization, and intuitive support. When marketers blend conversational AI’s intuitiveness and ability to mimic human conversation with messaging platforms’ interactive features, an unmatched digital experience is introduced, encouraging customers to engage at every touchpoint, from carousel displays of new products to fun games and quizzes that keep them entertained, even when they’re not in the process of purchasing something.

By folding in AI into existing omnichannel platforms, marketers unlock more strategic advantages, including deeper customer loyalty, in-app shopping experiences that are more accessible for customers, and 24/7 hyperpersonalized service. On the other side of the coin, marketers receive more customer details that are provided during AI-powered conversations that help them nurture prospects and better understand what matters most to shoppers, giving them the tools needed to deliver an experience that will resonate with prospective and existing customers.

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A few ways in which you’ve been using AI-powered martech and salestech features and processes to drive impact at Infobip?

As a company that’s been at the forefront of digital innovation since its inception, we’ve evolved into a platform that’s AI native by design and a company that’s actively exploring the use of AI for various business functions beyond automation.

Our AI chatbots, including the ones on our Answers platform, handles routine internal and B2B customer queries so human agents can dedicate more time for complex issues that require intervention. We also use generative AI to craft tailored marketing messages and email campaigns at scale to meet consumer expectations for hyper personalized experiences, resulting in higher engagement rates with potential enterprise clients.

Another top priority has been using AI to analyze prospective behavior so we can pinpoint high value leads for sales teams to ensure immediate action on the most promising opportunities. The customer insights collected allow us to predict churn or identify upsell opportunities so customer success teams can intervene proactively.

These are just a few examples of how we’re making the most of AI’s ongoing evolution — AI’s value as an FAQ automation tool is cemented, and it’s critical for us to leverage our tech innovation expertise to apply the technology in a way where we deliver what our customers need and help them keep pace with the expectations of a growing digitally native audience of end consumers.

Five martech takeaways you’d like to leave us with before we wrap up?

AI is no longer a nice-to-have – it’s critical in order to keep pace with the market and consumer expectations. As the pool of digitally native consumers continues to grow, some takeaways I’d like to share are:

Marketers do not need to reinvent the wheel to deploy AI or create an unmatched experience. AI and omnichannel need each other to improve brands’ digital presence, ensure they’re on the channels that consumers find the most valuable, and make customers feel valued through hyper personalization.

If brands are still in the early stages of AI adoption, they’re falling behind. Folding AI into existing digital tools can help brands bridge that gap, amplifying the effectiveness of communications platforms they’re already using while they explore new opportunities for AI to boost automation and enhanced customer conversations.

Mobile messaging still reigns king. AI may be the shiny new object consumers are embracing and businesses are investing in, but the easiest way to reach customers at scale is still through the communications channels on their phone. It’s important for marketers to understand which channels are used most, and they must also take the time to understand which channel is most valuable for each touchpoint of the customer journey (RCS messaging for real-time delivery updates, social media for attracting new customers, in-app chatbots for support, etc.).

Brands must understand the various AI tools they have in their belt. As investments in AI continue to ramp up, it’s important that they know the distinction between AI types (ex: conversational AI vs. agentic AI) in order to reap the most value. This will help them understand where in their marketing strategies AI can be most beneficial (product launches, customer segmentation), as well as how AI can help them address consumer priorities (immediate support, personalized offers).

Creating meaningful customer experiences requires more than adopting the latest technologies. It means really knowing your customers, from how and where they prefer to communicate to macroenvironmental factors that impact their behaviors. Omnichannel strategies and AI adoption can only do so much – marketers must understand what type of personalized communication will bring them to the point of purchase and foster long-term loyalty, which AI can support.

Infobip is a global cloud communications platform that enables businesses to build connected experiences across all stages of the customer journey, with AI as the driving force of innovation. Through a single, natively built platform, Infobip delivers omnichannel engagement, identity, user authentication and contact centre solutions that help businesses and partners overcome the complexity of consumer communications while driving growth and increasing customer loyalty. Infobip is focused on enabling and accelerating AI adoption as it continues its transformation into an AI-first company. Infobip’s technology has the capacity to reach over seven billion mobile devices in 6 continents connected to over 10k+ connections of which 800+ are direct operator connections. The company was established in 2006 and is led by its co-founders, CEO Silvio Kutić and Izabel Jelenić.

About Ethan Gustav

Ethan is a senior executive with over 20 years of experience, harnessing a track record of building and leading high-performance teams that drive revenue in high-growth technology companies. He is an experienced leader principled in building high-trust working cultures and proven champion of the Customer and Employee experience. Serving in the role of Group President North America at Infobip (and member of the Executive Leadership Team), he is responsible for Infobip’s global customers headquartered in North America and is responsible for all go-to-market functions for the largest TAM in CPaaS and SaaS-based Conversational AI. He is passionate about delivering the promise of Infobip to the world: enabling and simplifying B2C interactions at scale, on any device, channel, place, and time.