AI is only as good as the data underneath it. When AI has better context, it can make better decisions.
John, you’ve spent your career moving from hands-on engineering and architecture into product and technology leadership, with much of that journey rooted in AdTech and MarTech. Looking back, what is one lesson from those earlier years that still influences the way you think about building technology for marketers today?
The hardest technology problems are rarely solved by adding something. A lot of my early work involved bringing together systems that were built separately, whether that meant consolidating authentication, standardizing data or connecting online and offline sources.
That definitely shaped how I think about martech today. Marketers do not need more capabilities as badly as they need the pieces to work together well enough that the technology can understand what a customer is doing and help them act on it.
You’ve worked on the problem of connecting disparate data and systems long before ‘real-time intelligence’ and AI became the industry’s language. How has that experience shaped the way you think about the gap between having customer data and actually making that data useful at the moment a decision needs to be made?
If the data can’t help you make a decision, it is basically useless. The value comes entirely from understanding how those signals relate to one another and making that context available when somebody actually needs to make a decision. A page view, purchase history, or channel interaction on its own tells you very little. Put those signals together, and you can start to understand intent.
AI is only as good as the data underneath it. When AI has better context, it can make better decisions.
Acoustic AI represents an interesting shift in the role of a marketing platform, from helping marketers find answers to proactively surfacing opportunities and suggesting what deserves attention next. What changed in your view of the problem that made this move from intelligence to action feel like the right next step?
For years, marketing technology has given marketers more metrics and KPIs, then left them to figure out what those numbers mean and what to do about them. There’s only so much help another dashboard can give you. We started to see that marketers wanted more help with that part of the job.
I think good AI should be able to generate something and suggest what to do next. A lot of platforms have focused on the first part, but generating another email is quickly becoming table stakes. Marketers want a teammate that can look across customer signals, spot something worth acting on, and suggest a next step.
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A lot of marketing has historically been built around segments, campaigns and what happened yesterday. As AI gets better at reading live behavioral signals, how do you think marketers should rethink the difference between understanding a customer’s behavior and recognizing their intent?
Behavior is what somebody did. Intent comes from understanding what those behaviors mean together.
A click or page read can be a useful signal, but on its own it is a pretty poor proxy for what somebody actually wants. Marketers need to look at how signals relate to one another. Is engagement increasing? Is purchase propensity changing? Is the customer showing fatigue? What happened immediately before this?
Consumers control the timeline now. The job of the marketer is increasingly to recognize where someone is on that timeline and respond accordingly, rather than forcing them into the timetable of a campaign.
One 2026 Salesforce study found that 83% of marketers believe customers increasingly expect two-way conversations with brands, while 69% still struggle to respond promptly because they lack the context they need. From your perspective, what does ‘enough context’ actually look like before AI can make a customer interaction meaningfully more relevant rather than simply more automated?
Personalization comes in all forms: timing, content, and channel, just to name a few. Most marketers focus on content while dismissing the timing aspect.
Customer signals have a shelf life, whether it’s browsing a product, abandoning a cart, or a sudden change of behavior. If marketers spend hours sitting in a batch process or waiting for systems to sync, AI may be acting on a version of a customer that no longer exists.
I’d much rather make a decision based on 80% of data than lose out on something we could’ve acted upon.
You’ve written recently about moving marketers away from batch-and-blast thinking and toward behavioral triggers and real-time relevance. Do you think the bigger change ahead is actually in the technology, or in the way marketers have been trained to think about customer engagement?
Simply put, I think both have to change. The batch-and-blast still runs on the business’s schedule. We’ve decided it’s time to send a campaign, so everyone gets it, whether it makes sense for them or not.
But marketers already understand why behavior matters. If someone just bought a product and you send them an email telling them to buy it, you look like you haven’t been paying attention. The goal is to make that awareness part of how every campaign works.
The technology has to keep up, too. If a customer signal has to pass through three or four systems before a message can go out, you may have missed the moment. Marketers need to plan around what customers are doing, and their systems need to let them act while it still matters.
As agentic AI starts moving from generating content to identifying opportunities, recommending actions and helping execute them, how do you see the role of the marketer evolving, and which parts of that decision-making process do you think will always benefit from human judgment?
We’ve been seeing a trend of the marketer moving closer to being a valued business strategist and decision-maker. I think AI is just accelerating that transition.
AI can be very good at watching an enormous number of signals, identifying patterns and surfacing recommendations that a person simply would not have time to find manually. It can be a great teammate when paired with the marketer, who understands the brand, the customer relationship, and the broader context surrounding a decision.
On top of that, this data that AI is helping synthesize for marketers also holds business value from a C-suite perspective. That human involvement and cross-team collaboration will always be essential.
Your background includes architecture, APIs, authentication, large-scale data and privacy-compliant targeting, while your current role brings product, engineering and AI together. When you look at the next generation of marketing platforms, what is the underlying technology challenge you think deserves far more attention than it currently gets?
A lot of tools still treat the campaign as the center of everything. That’s how marketers end up with a detailed picture of the last email they sent as opposed to a more useful picture of the person who received it.
To understand that relationship, the technology needs to know who the customer is, how they behave, what the brand sells, and what messages they’ve received (all four working together). In practice, those pieces often sit in different systems, and now we’re adding AI to the mix and expecting it to make sense of everything.
That’s the work that deserves more attention. Before an agent can give a marketer useful advice, it needs to be able to see how those pieces fit together.
Acoustic is increasingly talking about an intent-driven model, while the wider industry is also moving toward real-time decisioning and agentic customer engagement. As these ideas become more common across the market, what do you think will separate companies that genuinely understand customer intent from those that simply add AI on top of an existing marketing stack?
With content and lead generation being automated to the degree they are now, the competitive advantage is the data that connects decisions. I don’t hear a lot of people talking about this, but it isn’t easy to find and feed AI enough high-quality, connected data to distinguish genuine intent from an isolated behavior.
The companies that do have access to high-quality data will be able to recognize things like rising purchase propensity, changes in engagement, or customer fatigue, then act on those signals as a whole picture. That is very different from seeing somebody click something and immediately triggering another message.
No matter how advanced the development or product, everything will crumble if you build it around lousy data.
You’re now helping shape what marketing looks like when customer signals, intelligence and execution can operate much closer together. Looking three to five years ahead, what is one assumption about how brands understand or engage customers today that you suspect we’ll look back on and wonder why we ever accepted it?
I think we will wonder why marketing revolved so heavily around the brand’s calendar.
For decades, we have organized engagement around campaigns, launches, or “customer journeys.”
Consumers just don’t experience a brand that way, and as technology gets better at understanding intent in real time, marketers should also become more responsive to reality.
Thanks, John!
About John Riewerts,
John Riewerts is the Chief Product & Technology Officer at Acoustic, bringing extensive experience in engineering leadership across the adtech and martech industries. His background spans marketing technology, cloud-based analytics, data architecture, real-time platforms, and privacy-compliant data processing. Before becoming CPTO, he held several engineering leadership roles at Acoustic and spent more than nine years at Acxiom, where he led engineering initiatives across marketing platforms, data analytics, and enterprise architecture.
