The advertising industry has spent years building tools to catch bots and block fraudulent clicks. Nick Morley thinks the industry has been solving the wrong problem.

Morley scaled IAS’s EMEA business from $18 million to $90 million, selling ad verification, watching the rise of walled gardens and black-box automated campaigns up close, and recently moved from Chairman to CEO of Lunio within eight months — a shift he describes not as ambition, but as urgency. His argument is straightforward and uncomfortable: the conversation about click fraud has been too narrow, the waste is bigger than anyone admits, and AI is now automating that waste at a speed nobody can audit fast enough to catch.

In this conversation, Morley breaks down what he calls waste intelligence, why platforms that profit from invalid traffic have little incentive to fix it, and why he believes agencies, not platforms, will be the ones to finally force the industry to clean up its data.

Full interview;

Lunio says the internet is full of fake clicks, traffic, and leads. So, how much of every dollar spent on digital advertising is being stolen?

Our data shows that, on average, over 8.5% of ad clicks are invalid, translating to an estimated $71 billion in wasted ad spend globally. But looking at it purely as “stolen” dollars is only half the story. The true cost to a business is significantly higher when you factor in the missed revenue opportunities from that misallocated budget. For example, if a company spends $1 million with a 3:1 return on ad spend (ROAS), a 5% invalid traffic rate doesn’t just mean $50,000 wasted — it also means $150,000 in missed revenue. That is the real impact we are fixing.

You scaled IAS EMEA from $18M to $90M, selling ad verification. Did the industry get cleaner, or just better at pretending it had?

The industry didn’t just pretend; we genuinely made massive strides in solving legacy issues like viewability and brand safety for display and programmatic. But the battlefield shifted. Today, performance marketing relies heavily on walled gardens, social platforms, and black box automated campaigns like Google PMax. In these environments, marketers have lost a lot of their control and transparency. The bad actors simply evolved from basic botnets to highly sophisticated invalid traffic that mimics human behavior.

To combat this, you can’t rely on legacy methods. That’s why Lunio’s approach is vastly different. We have a robust data science team, well above industry standards, building superior algorithms to detect and prevent this evolved waste in real time.

Also Read: Payment Experience Is the Foundation of B2B Loyalty

You’ve said the obsession with click fraud is missing the bigger picture — so what is the real problem that the entire adtech industry is collectively choosing not to talk about?

The term click fraud implies a purely malicious, security-based problem. The bigger issue is waste intelligence. We’re talking about algorithmic inefficiencies, benign scrapers, and poor targeting, all of which drain performance budgets. The industry has historically championed the practitioner, but as ad networks push opaque, automated solutions, practitioners are losing their voice and visibility. We need to focus on cleaning up the data and giving marketers back their control across all their verticals, rather than just playing whack-a-mole with hackers.

Marketers are deploying AI to optimize campaigns at scale. If the underlying traffic data is compromised, aren’t they just automating bad decisions faster?

Absolutely. If you feed garbage into an AI optimization engine, you get garbage out, but at unprecedented speed. AI algorithms are designed to find patterns. If an AI sees a cluster of fake clicks or lead forms and attributes them to a conversion event, it will automatically optimize your budget to go find more of that exact same fake traffic. The algorithms themselves aren’t flawed, but their baseline data is corrupted. That’s why we are aggressively building out broader capabilities across Meta and LinkedIn, and adding brand safety for PMax. You have to clean the underlying data pool before you let AI take the wheel.

You went from Chairman to CEO at Lunio within eight months. What did you see from the board seat that made you decide the job needed to be done differently and by you?

Our founder, Neil Andrew, built an incredible foundation, and as he stepped back to focus on product vision, it gave me the opportunity to step in and accelerate our next phase. With over 25 years of martech and Go-To-Market experience, including scaling Efficient Frontier, Adobe, and IAS, I recognized that Lunio needed to aggressively expand its product and markets. Specifically, we have a massive opportunity to grow our already significant US client base, given the enormous market size, and I’m personally dedicating much of my time and travel to it.

We are looking to aggressively grow our market awareness and market share, and stepping into the CEO role has allowed me to execute that GTM strategy directly.

Also Read: Is AI About to Make Media Buying an Endless Experiment?

If waste intelligence is the real crisis in advertising, why are the holding companies, the platforms, and the brands not screaming about it? Who benefits from the silence?

It comes down to misaligned incentives. The ad platforms fundamentally benefit from volume; every click generates revenue for them, whether it’s from a genuine prospect or a sophisticated bot. 

For agencies, there’s historically been a hesitation to highlight inefficiencies to clients. However, that dynamic has shifted over time, and agencies are now incredibly vigilant. We see significant opportunities to grow with agencies and have proactively built our team with specialized sales and service leaders with deep agency and partnership experience from companies like IAS, DoubleVerify, and Publicis. We operate a flexible, customer-friendly commercial model that works well for agencies and brands. Ultimately, the smartest agencies realize that partnering with us to eliminate waste is a massive competitive advantage, which builds the trust required for long-term agency and brand relationships.

The post The Hidden $71 Billion Crisis in Digital Advertising appeared first on MartechView.

Customer experience has become one of the most overused phrases in business. Every company claims to put the customer first. Very few actually know what that means.

Rodrigo Aviles, Senior Manager, rpc – The Retail Performance Company, has spent his career inside that gap — across automotive, retail, and consulting — watching organizations confuse CX with a department, a survey score, or a line in a strategy deck. At Hyundai Motor Europe, he saw firsthand what happens when an industry obsessed with the thrill of the product forgets the moment after the sale. At RPC, he is now helping companies rethink the foundations.

A self-described Human-First AI advocate, Aviles is not anti-technology. He is anti-thoughtlessness. In this conversation, he makes the case that the real crisis in customer experience today is not a lack of AI — it is a lack of honesty about what problem you are actually trying to solve.

Excerpts from the interview; 

You’ve worked across automotive, retail, and consulting — three industries with very different relationships to the customer. Where did you see the biggest gap between what a company thought its customer experience was and what it actually was?

Three gaps keep coming up across industries.

The first is understanding. The definition of customer experience varies widely from one organization to the next. In some companies, CX was considered a function within customer service. In others, it sat inside marketing. In others still, people used customer experience and customer journey interchangeably, as if they were the same thing. They are not.

What I have come to believe and what I actively push in my work now is that customer experience is not a department or a team. It is a mindset. From the person at the reception desk to the C-suite, everyone has an impact on the customer, whether they realize it or not. Every touchpoint, every decision, every internal process eventually reaches the customer in some form.

The second gap is ownership. Because CX spans functions, nobody wants to claim it fully. I have been in organizations where the CX team was treated like the black sheep — where other departments would close the door on us because they assumed we were trying to take over their responsibilities. We were not. We were trying to orchestrate. There is a significant difference.

The third gap is measurement. NPS and CSAT have been the default metrics for years, and I will be honest, I am not a fan of NPS. Both metrics measure a moment. They capture how a customer feels at the end of an interaction, whether they are satisfied or not. Everything in between all the friction, the waiting, the confusion, the small recoveries goes unrecorded. And that is precisely where the real opportunities are hiding.

Also Read: Your ERP Is Holding You Back. Here’s How to Fix It.

You led CX at Hyundai Motor Europe during a period when the entire car industry was being turned upside down by EVs, software, and changing ownership models. What broke first — the product experience or the service experience?

The service experience. Without question.

I have seen a pattern across the automotive industry, and I wrote a white paper about it because I think it deserves more attention than it gets. I call it the unsexy part of the journey. Most of the resources, the energy, the excitement — all of it goes into pre-sales. Bring the customer in, get them excited, close the deal. The assumption that follows is: they bought the car, they are happy, they will stay.

But in automotive, some of the most significant revenue sits in after-sales — spare parts, insurance, and additional services. And that is exactly where the experience tends to collapse.

Think about what happens the first time a customer brings their car in for a service. Nobody knows why they are there. There is no communication while the car is being worked on. Days go by with no update. These are not dramatic failures — they are quiet ones. But they accumulate, and they erode trust.

What makes this worse is the benchmark customers are carrying in their heads. When someone brings their car in for a service, they are not comparing the experience to another dealership. They are comparing it to Amazon. They know the exact moment they press send on an order where their package is, when it will arrive, and what they will pay. No hidden fees, no surprises, constant visibility until it arrives at their door.

We cannot change that reference point. We can only decide what to do about it. And in the automotive industry, far too many OEMs have not yet asked that question seriously enough.

You call yourself a “Human-First AI” advocate. Tell me about a moment when you saw AI deployed in a customer experience that made you genuinely angry.

I was with a health insurance provider in Germany for almost six years. Then they redesigned their app — completely overhauled the UX — and everything I knew about how to navigate it disappeared overnight. I am a quick learner and reasonably tech-savvy. It still took me a significant amount of time to find basic functions.

Once I did, I submitted an invoice for a medical expense and heard almost nothing back. The app had a chatbot — marketed as AI-powered, though I remain sceptical there was much intelligence behind it. What followed was an exhausting back-and-forth across multiple channels, where I was asked the same questions repeatedly and gave the same answers repeatedly. Eventually, I left the provider.

A friend told me recently that the same company now claims to have an improved AI agent. His experience has been identical to mine.

This is the pattern I see most often. Companies encounter a problem, and instead of diagnosing it clearly, they reach for AI as the solution. But if you have not truly understood what you are trying to fix, and for whom, the technology will not save you. It will just create a faster, more scalable version of the same failure.

I use this analogy: it is like having a Ferrari in the garage when you do not know how to drive. Technology is not the problem. The lack of clarity about the problem is.

I had a manager early in my career who gave me a piece of paper the size of a business card and told me to write my idea on it. If it did not fit, I had not understood it well enough. The same discipline applies here. If someone needs half an hour to explain the problem their AI deployment is meant to solve, they are not ready to deploy it.

Also Read: Your Marketing Dashboard Is Lying to Your CFO

Customer journey mapping sounds clinical on paper. Walk me through the most surprising thing you’ve ever discovered when you actually mapped one out for a real company.

The most consistent surprise is how little companies actually know about the full journey their customers go through — including the teams supposed to manage it.

Part of this is structural. When organizations work in silos, each function only sees its own piece. Marketing sees the awareness stage. Service sees the complaint. Nobody is watching the customer move from one to the next, experiencing the whole. What journey mapping forces you to confront is that the customer does not care about your org chart. They are moving through your brand as a single continuous experience.

What struck me most in several of these exercises was how much we were unconsciously mirroring our own internal complexity onto the customer. The difficulty, the confusion, the unclear ownership — we were essentially asking the customer to navigate our internal dysfunction alongside us.

But the most valuable realization came from pairing data with direct observation. I am a strong believer in data, but I also think we lean on it too heavily as a substitute for actually watching people. In one exercise, I challenged a team to spend three hours sitting in a dealership, not collecting data, not running surveys — just observing. Who comes in, what happens, where do things slow down, and what do people look confused by?

The gaps between what the data said and what the observation revealed were significant. There were friction points we never would have identified through surveys alone, and opportunities that no amount of voice-of-customer data had surfaced. That is what I mean when I say journey mapping should never just live on a wall or in a workshop deck. There are real people moving through that journey every day. You have to go and watch them.

You’re now based in Munich, having worked across Latin America and Europe. Does culture change what good customer experience looks like — or do people fundamentally want the same things everywhere?

Both things are true, and I think the tension between them is actually useful.

Culture absolutely shapes expectations. Coming from Mexico, warmth is embedded in how service works. You walk into a restaurant, and within seconds, someone greets you, makes you feel welcome, and treats you like you matter. That is not exceptional service in Mexico — it is the baseline. In Germany, that does not exist in the same way. My wife is German, and when we visited Mexico, she found it overwhelming. Too many people checking on her, too much attention. For her, that was not hospitality — it was intrusion.

Neither one is wrong. They are simply different cultural contracts around what a positive experience feels like.

But then there is the other layer, which is universal. Regardless of where someone is from, regardless of generation or background, when something goes wrong, people want it resolved quickly. The frustration of being ignored, passed between channels, asked to repeat yourself, or left waiting for a response that never comes — that is not cultural. That is human. And the tolerance for it is shrinking everywhere because the reference points keep improving. WhatsApp read receipts. Two-minute grocery delivery. Real-time tracking. Every one of these has raised the floor for what people consider acceptable.

The challenge for companies is navigating both. They need cultural sensitivity in how they design the emotional texture of an experience, and they need universal speed and clarity in how they resolve problems. Getting one right while neglecting the other is no longer enough.

Also Read: Why the CMO Now Owns the Privacy Problem

You speak publicly about CX. What’s the question nobody in the audience ever asks — but should?

Are we using AI to help our customers or to help ourselves avoid them?

I think about this a lot. When I look at many of the AI deployments happening in customer experience right now — the agents replacing call center teams, the chatbots standing between customers and resolution — the honest question is: who is this actually serving?

In some cases, the answer is genuinely the customer. Faster, available around the clock, consistent. In others, the technology is being used to reduce the cost and friction of human contact for the company, while the customer gets a worse experience dressed up in modern language.

Klarna is an interesting example. They made significant headlines for replacing a large number of customer service staff with AI. A year or so later, they were quietly hiring humans back because the experience had gaps that the technology could not cover.

I am not against AI in customer experience. I use it constantly in my own work, and it has changed how I operate. But there is a distinction that is not examined enough: are we deploying this because it genuinely helps the person on the other end, or because it helps us avoid dealing with them?

That question — asked honestly, before any deployment decision is made — would change a lot of what gets built.

The post Are We Using AI to Help Customers or Avoid Them? appeared first on MartechView.

There is no shortage of executives willing to talk about responsible AI. There is a considerably shorter list of those willing to be honest about what it actually costs — and what it demands of the people whose names are on the org chart when things go wrong.

Peter Yeung, Chief Information Officer at Optimizely, is in the second group. A former practicing attorney with 18 years at the bar before moving into technology leadership, he brings an unusual combination of legal precision and operational candor to questions that the industry too often answers with carefully worded reassurance.

In a wide-ranging conversation, Yeung addresses the governance paradox at the heart of enterprise AI — how to move quickly without moving recklessly — and argues that accountability, far from being a legal fiction, is a structure that leaders must be willing to sign their name to. He also takes on explainability under GDPR, the data minimization debate, and the accelerating fragmentation of the global data landscape.

His answers are not always comfortable. That is precisely what makes them worth reading.

Excerpts from the interview; 

Companies are rolling out AI faster than governance can keep up. Is ‘responsible AI’ just a story businesses tell to move quickly, or do you truly think governance can match the pace of deployment?

The companies actually getting value from AI aren’t treating governance as a brake; they’re building it into how they scale. Most of us started broadly: put the tools in people’s hands, see what sticks. That phase served its purpose, but what’s working now is the opposite — picking a handful of high-impact use cases and making sure the data, controls, and workflows behind them are genuinely solid, secure, and trustworthy. Done right, governance accelerates things by cutting rework, risk, inaccuracies, and fragmentation.

That said, I’d be lying if I said governance doesn’t have a cost. The fastest innovation I’ve seen on AI happens in the messy middle — small teams shipping fast, breaking things, learning in days rather than quarters. The moment you wrap that in review boards, data classifications, and approval workflows, you do slow it down. That’s just the reality. The trick isn’t pretending the trade-off doesn’t exist; it’s finding the right balance for where you are. Too little governance and you end up with a graveyard of pilots and a compliance problem. Too much and you kill the energy that made AI exciting in the first place.

Responsible AI isn’t a layer you bolt on top of performance; it’s what allows AI to graduate from experimentation into something the business can actually rely on. But you have to be honest that getting the balance right is the work.

When AI systems use flawed or unclear data and cause harm, responsibility is often spread among teams and vendors. Right now, isn’t the idea of clear accountability in AI mostly just a legal fiction? 

As CIO at Optimizely, with both the CISO and Trust organization reporting into me, I’d push back on the idea that accountability is a legal fiction — but I understand why people frame it that way. AI accountability is more complex than in traditional systems because it spans multiple teams: the people sourcing the data, the people building or selecting the models, and the people deciding how outputs are actually used in the business. Spread that across vendors, too, and yes, it can feel diffuse. If you then include my statement above, which calls for empowering individuals within the business to innovate at speed, the task becomes daunting. 

But the way I look at it, regardless of the actor — vendor, third-party model, internal team, or individual employee — we are ultimately accountable, both internally and to our customers, for the end result. That accountability can’t be outsourced. The vendor contract doesn’t absolve us. The model provider doesn’t absolve us. If something goes wrong, our customers don’t care about the seven hops in the supply chain; they care that we own it. 

What makes that real, rather than rhetorical, is structure. We treat AI like any other critical business process: explicit ownership of data inputs, clear responsibility for model deployment, and a named, accountable owner for outcomes in production. Without that, accountability genuinely does dilute across vendors and teams, and that’s where the “legal fiction” critique starts to land. With it, you create a clear line of responsibility even in a distributed system, and you give the CISO and Trust functions something concrete to govern against. 

So it’s not a fiction. It’s just harder, and it requires leaders to actually sign their names.

Rules like GDPR require that automated decisions be explainable. But big AI systems often cannot give real reasons for their choices. Are we trying to enforce laws that no longer fit the world we live in?

Having practiced as an attorney for 18 years, I’d say the question is sharper than the framing suggests — but the answer isn’t quite “the laws no longer fit.” It’s that the laws were never as clear as people assume.

GDPR’s intent is absolutely still relevant: to protect individuals and hold companies accountable for automated decisions that affect them. That hasn’t aged. But read Article 22 alongside Articles 13–15 and Recital 71, and what you find is a requirement to provide “meaningful information about the logic involved” — with genuine, ongoing debate among regulators and legal scholars about what that actually means in practice. GDPR doesn’t even explicitly grant a “right to explanation”; it’s inferred. The framework was contested before modern AI arrived. Large models didn’t break a clean framework; they stress-tested an ambiguous one.

That matters because, in the absence of clear guidelines, the standards organizations actually have to meet are believability and traceability. Can you credibly describe how the system reached its decision? Can you trace the data, the controls, and the human checkpoints? Have you documented it clearly enough to walk a regulator, a customer, or a court through it without flinching? That’s the real test today.

So no, I don’t think we’re enforcing laws that no longer fit. We’re operating in a gap that regulators and industry need to close together. Until they do, the burden is on companies to set their own bar: traceable data, auditable decisions, guardrails on outputs, and documentation you’d be comfortable defending.

AI works best with lots of data, but privacy rules call for using as little data as possible. If companies have to choose, will they prioritize performance over principle? Are we already seeing this happen?

There’s real tension here, but the framing as a binary choice between performance and principle is a bit limiting. The premise that AI works best with “lots of data” is itself worth challenging. More data isn’t automatically better — if it’s poor quality, incomplete, or stripped of the right context, you’re just feeding the model noise. And noise-in produces worse-outcomes-out: hallucinations, bias amplification, and decisions you can’t defend. I’d rather have a smaller, well-governed, well-contextualized data set than a sprawling lake of mixed-quality inputs, while certainly following the GDPR tenet of Privacy by Design.

I think it reframes the privacy question. Privacy rules pushing companies toward data minimization aren’t necessarily working against AI performance — in many cases, they’re forcing the discipline that actually improves it. The companies getting this right are being deliberate about their data strategy: prioritizing quality, relevance, and governance over volume. That’s not a compromise position; that’s just better engineering.

Are we seeing companies cut corners on privacy for short-term performance? Yes, and it tends to come back to bite them through regulatory exposure, customer trust erosion, or models that don’t generalize the way they thought. Trust is becoming a genuine differentiator, particularly in customer-facing and enterprise use cases, and you can’t retrofit it.

The right answer is to design systems where privacy and performance are engineered in from the start, rather than treated as a trade-off you settle later. When done well, they reinforce each other rather than compete.

With decisions like Schrems II and laws like the CCPA, are we heading toward a split internet where data cannot move freely across countries? If so, what will break first: innovation or trust?

What’s interesting about the question is that it frames the split as a US–Europe divergence, when the more consequential fault line is East versus West — between western frameworks debating how to balance rights and commerce, and an eastern framework where the state’s relationship to data is structurally different. That gap isn’t closing through a successor to the Privacy Shield/US-EU Data Privacy Agreement.

So yes, we’re already in a split internet. Between Schrems II, CCPA, the EU AI Act, India’s DPDP, China’s PIPL, and a patchwork of US state laws, any global business is operating across fifteen-plus regulatory environments. My background on both the technology and legal sides of things, coupled with my ability to adjust to both business and customer needs, makes this isn’t hypothetical anymore — it’s the operating environment. We architect for it: data residency, regional processing, model deployment choices that respect where data can and can’t go.

On what breaks first — innovation and trust fail together, even if one precedes the other. If regulation becomes so prescriptive that nothing can cross borders without months of legal review, innovation slows. If companies route around the rules, trust collapses, and regulators tighten further. It’s a doom loop either way.

The companies that come through this well won’t bet on innovation at all costs over trust, or really cumbersome trust over innovation. They’ll invest in both, and accept that regulatory complexity is now part of the engineering/product/support lifecycle, not separate from it.

The post The CIO Who Says Governance Can Actually Speed Up AI appeared first on MartechView.

Every industry has a word it reaches for when it does not know what else to say. In boardrooms today, that word is transformation. It gets invoked at strategy off-sites, written into annual reports, and handed to consultants as a mandate — often before anyone has agreed on what exactly needs to change, or why.

The results are predictable. Brands that once stood for something begin to feel like everything. Customer experiences that were once a competitive advantage become inconsistent and interchangeable. Revenue targets get hit in the short term. Brand equity quietly erodes over the long term.

The problem, more often than not, is not a lack of ambition. It is a lack of an anchor. Transformation without a clear sense of what must be preserved is not strategy — it is change for its own sake.

Giovanna B. Questioni has spent her career in that gap. A transformation expert with experience across luxury fashion, food, furniture, and the digital industries, she has led mergers and acquisitions, crisis interventions, and large-scale omnichannel overhauls across global markets. Her argument is not against change. It is for coherence.

The brands that survive disruption, Questioni argues, are the ones that understand the difference between what is negotiable and what is not. Design, quality, emotional resonance — these are not variables to be optimized in a transformation roadmap. They are the reason the brand exists. Everything else is in service of them.

That distinction shapes how she thinks about omnichannel strategy — an area where many brands have spent heavily and delivered inconsistently. The instinct is to treat every new channel as an opportunity. The discipline is to treat every channel as a responsibility to serve customers without breaking what they already trust.

“Every touchpoint — whether in retail, franchising, wholesale, e-commerce, or social commerce — must deliver a distinct yet cohesive experience. Physical stores thrive on human connection and sensory engagement, while digital platforms excel through simplicity, speed, and personalization.” 

A customer who buys online and returns in-store should not feel the friction of two separate systems. The seams should never show. And the sales assistant at that moment is not a workaround — they are an opportunity for the kind of personalization no digital platform has yet replicated.

What Questioni warns against is the version of transformation that becomes its own end. “Disruption is powerful, but it must always align with customer expectations and ROI,” she says. “Without this balance, transformation risks becoming an academic exercise — one that could dilute the brand’s reputation and desirability.” Bold innovation is not the enemy of brand integrity. Undisciplined innovation is.

The second argument she makes — and the one that challenges the most deeply held assumptions in corporate strategy — is about resilience. The dominant understanding is reactive: how fast can an organization absorb a shock and return to normal?

That framing, Questioni argues, is entirely the wrong one. By the time a company is managing recovery, it has already lost the most valuable thing: time. The organizations that emerge from disruption stronger are not the ones that responded fastest. They are the ones that had already built for it.

“Resilience isn’t just about bouncing back — it’s about building the future before it arrives. It’s the difference between surviving the unexpected and shaping it into opportunity.” 

A future-ready organization, in her view, has designed agility into its operating model before crisis arrives — and has given its teams the tools, authority, and mindset to move without waiting for direction from above. The question she puts to every leadership team is not how they responded last time. It is what they have already built for next time.

Which brings the conversation to the question that defeats most large-scale transformations before they ever reach the customer: execution. The strategy is rarely the problem. The problem is that a vision designed at the top must be delivered by teams moving at different speeds, across different functions and geographies.

Most organizations respond with governance frameworks. Questioni’s response cuts closer to what the problem actually is.

“Large-scale transformation operates like a symphony — an intricate performance where each element must harmonize under the guidance of a skilled conductor.” 

The conductor she has in mind is not ceremonial. It is a deeply operational C-level leader who holds the granular and the panoramic simultaneously — who understands how individual performance feeds collective outcomes, and who can align diverse functions without erasing what makes each of them effective.

The failure mode she sees most often is not a shortage of talent or resources. It is teams executing brilliantly within their own lanes while the overall composition falls apart. “True transformation isn’t about managing chaos,” she says. “It’s about precision, collaboration, and leadership that turns vision into reality.”

The conductor is not optional. Neither is the score. And the brands that understand the difference between noise and music — between change and transformation — are the ones that will still mean something when the disruption settles.

The post The Organizations That Survive Disruption Never Had to Recover From It appeared first on MartechView.

In February 2024, OpenAI gave a small, selective group of creative professionals early access to Sora, its text-to-video generation tool. The list was short. Nik Kleverov was on it.

That alone tells you something. Kleverov is the Chief Creative Officer and co-founder of Native Foreign, an Emmy-nominated Los Angeles creative agency that has spent the last several years building what it calls AI Labs — a practice dedicated not to experimenting with generative tools, but to embedding them as foundational infrastructure across every stage of production. The agency designed the Narcos title sequence for Netflix. It produced the first brand film ever made using OpenAI’s Sora, for Toys”R”Us. It has won twelve Davey Awards for creative innovation in emerging tech.

When Kleverov talks about AI in advertising, he is not theorizing. He is reporting from work.

And what he is reporting is that most of the industry is asking the wrong question.

The Infrastructure Argument

The dominant conversation about AI in creative agencies centers on efficiency. How much faster can we concept? How much cheaper can we produce? How many rounds of iteration can we compress into a single afternoon? Those are real questions with real answers — but Kleverov argues they miss the point, and that the miss is costly.

“The biggest cost is thinking of AI as a speed hack instead of a creative system,” he says. “When it’s treated like a shortcut, there’s novelty and surface-level savings, but not strategy. When it’s treated as infrastructure, it changes how ideas are developed, prototyped, and executed from day one.”

The distinction is architectural. A shortcut is something you reach for after the idea already exists. Infrastructure is what the idea is built on. The difference between the two isn’t visible in the output of a single campaign — it becomes visible over time, in the compounding gap between agencies that have rebuilt their creative process around AI and those that have not.

For CMOs navigating vendor conversations, Kleverov frames the test simply: “The honest question is: do you have an AI workflow, or are you just tinkering? There’s a big difference between occasionally using generative software and actually rethinking how ideas move from concept to production. The companies that treat it as infrastructure will move faster, think bigger, and leave their competition in the dust.”

The Carl’s Jr. Case Study

The clearest recent illustration of the Native Foreign approach is the Carl’s Jr. campaign featuring Paris Hilton — a piece of work that required Kleverov’s team to make a series of deliberate decisions about where AI entered the process and, just as deliberately, where it did not.

The campaign, created using Freepik, leaned into Hilton’s early-2000s cultural moment, framing her as a boss overseeing an AI-automated version of the famous Starwash. The conceit was precise: in the age of AI, Paris Hilton uses it to run her operation — while she oversees it. The nostalgia was not incidental. It was structural.

“We didn’t just prompt ‘2000s aesthetic’ and call it a day,” Kleverov says. “We studied the textures, lighting, and slightly over-the-top tone of that era’s advertising and rebuilt it intentionally — but for today’s audience. Nostalgia works when it feels like memory.”

That distinction — memory versus algorithm — is where the campaign’s creative judgment lived. AI played a significant role in the production workflow, handling elements that could be generated, iterated, and refined at scale. But the scenes featuring the real, present-day Paris Hilton were kept entirely outside the AI pipeline. The boundary was not arbitrary. It was a considered decision about where human presence creates irreplaceable authenticity and where generative tools could amplify without distorting.

What Narcos Taught Him About AI

Before the AI era, Kleverov’s most celebrated work was the title sequence for Narcos on Netflix — a piece built on traditional motion design mastery, painstaking in its craft, executed without a single generative tool. It remains one of the most recognized title sequences in recent television history.

The skills that made that work possible are, Kleverov argues, more valuable now than they were then — not less. Not because AI hasn’t changed the game, but because AI has changed it in a specific way that makes certain human capabilities more critical rather than redundant.

“Tools can generate infinite options, but knowing what not to use has become the real creative skill,” he says. “The fundamentals of storytelling, pacing, and design judgment still act as the compass. Especially with AI storytelling.”

The infinite options problem is one that anyone who has spent serious time with generative tools will recognize immediately. The bottleneck in AI-assisted creative work is not generation — it is selection. Producing a hundred viable options takes seconds. Knowing which one is worth developing, and why, requires everything that cannot be prompted.

What Creative Directors Must Unlearn

That shift has implications for how creative leadership itself needs to change. Kleverov is direct about what the transition demands from Creative Directors who want to work effectively with AI as a foundational layer.

“Creative Directors have to let go of the idea that the first good idea is the one you execute,” he says. “AI rewards exploration, iteration, and divergence. The job becomes less about protecting a single concept and more about guiding a field of possibilities toward the strongest story.”

This is a significant unlearning. The traditional creative director role was built in part around the conviction and the authority to champion a single idea against the instinct to dilate, hedge, or over-iterate. That conviction remains valuable. But the context has changed. When iteration is cheap and divergence is generative rather than dilutive, the skill set shifts from protection to navigation — from defending the best idea to finding it within a field that AI has made vastly larger.

The Democratization Question — With a Caveat

The argument that AI is democratizing high-end production is one Kleverov partially accepts. More people can now make things that look impressive on the surface. Access to tools that once required significant budgets and specialist teams has broadened meaningfully. That is real.

But it comes with a structural caveat. “The gap between something that looks good and something that’s culturally resonant is still huge,” he says. “If anything, taste matters more than ever.”

This is the counterintuitive consequence of democratization in creative industries: as the floor rises, the ceiling becomes the only differentiator that matters. When every agency can produce visually polished work using the same generative tools, the question is no longer whether you can make something beautiful. It is whether you can make something that means something, and that question has always been answered by the same thing it was answered by before AI existed.

Judgment. Context. A point of view that no model was trained to have.

That is what Native Foreign is selling. And if the Carl’s Jr. campaign, the Toys”R”Us film, and the Narcos sequence are any indication, it is a point of view worth listening to.

The post AI Won’t Save Your Campaign. Your Taste Will. appeared first on MartechView.

Raj De Datta has spent over a decade building Bloomreach into one of the most quietly formidable companies in commerce technology. With $260 million in ARR and customers like American Eagle and SPANX, the platform’s AI engine — Loomi — isn’t a bolt-on feature. It’s the whole architecture. But as generative AI rewrites the rules of how people shop, discover products, and interact with brands, De Datta is less interested in celebrating how far things have come and more focused on what comes next. 

In this conversation, he makes a case for why the traditional e-commerce site is becoming infrastructure rather than interface, why personalization and automation are converging faster than brands are ready for, and why the winners of the next era of commerce won’t be those who automate the most — but those who automate responsibly.

Excerpts from the interview; 

Bloomreach just crossed $260 million in ARR, powered by Loomi AI and brands like American Eagle and SPANX. What did you get right in scaling an AI-native platform — and what nearly broke along the way?

We got a few core decisions right early. First, we built Loomi AI as the foundation, not as a feature. It is the intelligence layer that powers our applications and agents across email, search, personalization, and conversational shopping. That architecture meant that as AI advanced rapidly over the past few years, we remained ahead of the curve.

Second, we focused on real-time data and first-party context. Loomi AI combines customer and product data with real-time infrastructure, AI decisioning, and orchestration across channels. That allowed us to deliver personalization that compounds — every interaction feeds the system and improves the next one.

Third, we invested early in agentic capabilities. Nearly half of our customers now use at least one next-generation AI tool, and that adoption has more than doubled in the past year. We also saw strong engagement with our conversational shopping agent, including a significant uptick during the recent holiday season. That momentum reflects a clear product direction: move from static tools to systems that can take action.

The one ongoing challenge — though really more of an opportunity — was the pace of change in AI. Every day unlocked a new possibility. We constantly had to examine what we were building to ensure it was maximizing AI’s potential. That meant a lot of rapid iteration and required us to be willing to take products we thought were great and challenge ourselves to make them even better.

The past two years have rewritten the rules of AI almost quarterly. How do you build a durable product strategy when the underlying technology shifts this fast?

The technology will keep changing, and our strategy is to stay one layer above it. We built Loomi AI as the intelligence layer beneath all of our applications and agents. Because that foundation is consistent, we can adopt new AI capabilities as they emerge without rebuilding the core each time the model landscape shifts.

Durability comes from that architecture — every interaction feeds back into the system, so the platform continuously improves. The goal isn’t to predict the next breakthrough. It’s to build a system that seamlessly incorporates it. That’s what allows us to move quickly and keep innovating while maintaining stability for enterprise customers.

You’ve spoken about agentic commerce. What changes when AI stops assisting shoppers and starts transacting on their behalf?

When AI moves from assisting shoppers to acting more autonomously, commerce shifts from reactive experiences to outcome-driven ones. Instead of simply answering questions or suggesting products, AI can understand context, predict intent, and guide the entire journey in real time — from discovery to decision to purchase.

But this changes a great deal for brands. It means they risk losing control over customer journeys and relationships. That’s why this is such an inflection point for businesses right now. The brands actively investing in agentic commerce — preparing catalogs for agentic discoverability, building their own apps — are the ones that will be prepared for this new era of commerce.

If AI becomes the primary interface for product discovery, what happens to the traditional e-commerce site? Is the homepage becoming obsolete?

It’s more a case of “the website is dead… long live the website.” In reality, the website becomes infrastructure rather than interface, as we know it today. All of the data it houses — about your products, about your customers — remains as relevant as ever. But that data and infrastructure will transcend the website, too. It’ll extend into conversational experiences in AI apps, and into channels like email and mobile, as it already does today. The homepage isn’t obsolete. It just isn’t the front door anymore.

Every AI transformation has its blind spots. What assumptions about AI in commerce turned out to be wrong?

There were assumptions about speed and scale that were somehow both too big and not big enough. At the onset of generative AI and large language models, there was real fear that e-commerce would become irrelevant almost overnight. Obviously, that hasn’t been the case.

But at the same time — it will, and already is, wholly transforming what commerce looks like. The way people discover products, compare brands, build wardrobes — all of that is now done with AI in ways we couldn’t have imagined a few years ago. And I think the scale of that transformation hasn’t even peaked yet.

At what point does personalization cross into automation? And how do brands ensure agency remains with the consumer?

Personalization becomes automation the moment systems start acting without asking. That’s the real inflection point. For years, personalization meant better recommendations or more relevant messaging. Now, with agentic AI, systems can execute decisions across channels. The question isn’t whether that’s possible — it’s how it should be governed.

Brands need to be clear about intent. Automation should reflect what a customer has already signaled, not replace it. When AI is grounded in real customer context and real-time signals, it can reduce friction and help people complete their journey. When it drifts from that, it stops being helpful and starts becoming opaque.

Agency remains with the consumer when technology is designed to respond to their intent, stay transparent in its decisioning, and keep humans in control of outcomes. The more autonomous systems become, the more important those guardrails are.

In this next phase of AI, the differentiator won’t be who automates the most. It will be who automates responsibly — and keeps the customer at the center of that automation.

In 2030, what will feel outdated about how we shop today — and what will Bloomreach have built to stay ahead of that shift?

Today, much of e-commerce still centers on browsing: navigating pages, filtering options, and manually managing campaigns across channels. But the shift we’re already seeing is toward agentic experiences — systems that understand intent and take action in real time. As AI moves from insight to execution, customers will expect more conversational, personalized, and responsive interactions, not just better search results or product recommendations.

What will matter most is whether brands can operate across touchpoints as one connected system. Experiences won’t be confined to a single website or channel. They’ll need to work across email, messaging, mobile, search, and beyond — with intelligence that adapts instantly to customer context.

The post Your Homepage Isn’t the Front Door Anymore appeared first on MartechView.

For years, AI in retail was framed as an efficiency play: better inventory forecasting, smarter demand planning, faster ticket resolution. It was about reducing costs, shaving seconds off handle times, and streamlining back-end systems.

That era is over.

Today, AI sits much closer to the revenue engine. It influences how customers discover products, how they interact with brands, how issues are resolved, and, increasingly, whether they return. In retail, travel, and hospitality, where loyalty is fragile and competition is relentless, customer experience is no longer a support function; it is a core function. It is a strategy.

Few executives have observed that shift from both the operational and technological sides as closely as Michael Klein, Director of Retail, Travel & Hospitality Product Marketing at Talkdesk. Before moving into enterprise CX technology, Klein spent more than three decades in retail merchandising and leadership roles, including time with Adobe and Williams-Sonoma.

That background, he says, fundamentally shapes how he evaluates enterprise software.

“I’ve spent more than three decades in retail,” Klein said. “My background in retail merchandising keeps me focused on how technology actually improves the customer experience and drives real business outcomes. It’s never been about leveraging technology just for the sake of it.”

In merchandising, every decision — from assortment planning to store layout — is tied to measurable results. Klein brings that same lens to AI.

“AI is a strong example,” he said. “When applied well, it helps increase average order value, drive repeat purchases and improve retention.”

In other words, the question is not whether AI is impressive. It is whether it sells more sweaters, books more rooms, or deepens loyalty.

“At Talkdesk, we’re focused on using it where it truly makes a difference for retailers and their customers,” he added.

The Persistent Myth of the Demographic Customer

Retailers have more customer data than ever before. Yet, paradoxically, many still design experiences around broad assumptions.

“One of the biggest misconceptions is that everyone in a demographic bucket behaves the same way,” Klein said. “That Gen X shops one way, Millennials another, and Baby Boomers another.”

The reality, he argues, is messier.

“If it were that clean, personalization would be easy,” he said. “But I’ve seen plenty of Baby Boomers who are perfectly comfortable booking travel online and plenty of younger customers who want to speak to a person when something goes wrong.”

The mistake, he suggests, is building customer journeys around stereotypes rather than behavior. Digital-first consumers are not defined by age alone; they are defined by context, urgency, and preference at any given moment.

Designing around assumptions creates friction. Designing around actual signals creates loyalty.

From Cost Center to Growth Engine

Perhaps the most profound shift underway is the redefinition of the contact center.

For decades, contact centers were treated as overhead — necessary to resolve complaints, but disconnected from growth. AI is changing that calculus.

“From a customer standpoint, AI makes it easier to self-service when they want to,” Klein said. “That convenience drives satisfaction — and satisfied customers tend to spend more.”

Self-service is not about removing human interaction; it is about giving customers control. When done well, automation reduces frustration and accelerates resolution.

For brands, the internal impact is just as significant.

“AI frees up contact center teams to focus on higher-value work,” Klein said. “Whether that’s helping design a room, building a wardrobe, or solving a complex issue. Those are real revenue-driving conversations.”

By removing friction in knowledge access, documentation, and agent training, AI shifts human effort toward consultative interactions — the kinds of conversations that build trust and increase basket size.

At its best, automation does not replace people. It elevates them.

Why So Much “AI-Powered CX” Falls Flat

In a market saturated with “AI-powered” claims, differentiation often dissolves into jargon. Klein has spent years translating complex enterprise technology into language retailers actually understand — and he is blunt about what works.

“Great marketing and complex jargon just don’t mix,” he said. “Instead of relying on technical words that only developers or product managers use, brands should drop the IT acronyms and language that’s too deep in the weeds.”

Retailers do not buy platforms. They buy outcomes.

Plain language, he argues, forces vendors to clarify their value proposition. If a product cannot be explained without abstraction, it likely lacks practical grounding.

Separating Real AI From Marketing Noise

Klein is equally pragmatic about the current AI moment. Not all AI is new — and not all of it is transformative.

“We first need to be clear about which AI we’re talking about,” he said.

Retailers have long used predictive models to forecast inventory, optimize distribution, and manage replenishment. Those systems quietly shape customer satisfaction by ensuring products are in stock.

“That has a direct impact on product availability and customer satisfaction,” Klein noted.

More recently, generative and agentic AI have begun to deliver tangible value in marketing and service environments — drafting content, assisting agent,s and streamlining workflows.

Where the narrative drifts into hype, he says, is in the idea of full automation.

“We’re a long way from AI taking over everything,” Klein said. “Human oversight still matters, and consumers will want the choice between automation and a real person depending on the situation.”

The future, in his view, is hybrid — not robotic.

The Context Problem

If there is one area where brands consistently misstep, it is context.

With so much data available, companies often mistake volume for insight.

“The key to designing better experiences is relying on context signals, like timing and intent, coupled with history and preference,” Klein said.

Without context, personalization becomes misdirection. A customer who once purchased a gift for a relative may be permanently misclassified, leading to irrelevant recommendations.

“Imagine you visit a store for the first time to buy a present for your grandmother,” he said. “If the retailer caters your experience based only on your first visit, your experience won’t serve your current needs.”

The difference between intelligent personalization and awkward irrelevance often comes down to whether brands understand why a purchase occurred — not just that it did.

The Hardest Part of Modernization

Enterprise modernization is rarely blocked by technology alone.

“The biggest hurdle brands encounter is not doing anything for fear of disrupting operations,” Klein said.

Legacy systems may be aging, but they are stable. Change introduces uncertainty.

“The next hurdle is weeding out bad data,” he added. “And the third is dealing with people who are stuck in their old ways or too protective of their territory.”

Transformation demands both technical cleanup and cultural shift. It requires encouraging teams to experiment, test openly, and share ownership across departments.

Without that alignment, even the best technology stalls.

The Metrics That Will Matter Next

As AI becomes embedded in CX systems, traditional metrics such as handle time and surface-level satisfaction scores may lose their primacy.

“In the next few years, customer lifetime value, recency, and frequency will become key metrics to monitor,” Klein said, particularly in retail and hospitality.

Those measures capture relationship strength rather than transaction speed. They reflect whether AI is driving durable loyalty rather than short-term efficiency.

In that sense, the next phase of AI in customer experience is not about the volume of automation or technological sophistication. It is about commercial impact.

For Klein, that is the through line connecting his merchandising past to his product marketing present. Technology is not the hero of the story. The customer is.

And in a competitive landscape where switching costs are low and expectations are high, the brands that treat customer experience as a growth lever—not a service line item—will be the ones that endure.

The post Retail’s AI Reckoning Is About Revenue — Not Robots appeared first on MartechView.

In Silicon Valley, obituaries are often written too soon. First, it was email. Then the press release. Now, the target is the public relations agency itself.

The argument is seductive in its simplicity: if artificial intelligence can draft a pitch, summarize a strategy, and produce passable thought leadership in seconds, what need remains for the humans who were once billed by the hour to do it?

To test that thesis, I sat down with Susan Thomas, CEO and founder of 10Fold, a woman who has guided more than 500 companies toward the grueling scrutiny of the public markets. Her answer was neither defensive nor nostalgic.

“Traditional PR is dying,” she told me. “That does not mean media outreach or thought leadership campaigns are going away any time soon. What I mean is the traditional boundaries and limitations of PR no longer apply and firms must adjust quickly to adapt.”

It was not a lament. It was a warning.

For decades, public relations operated under a comfortable ambiguity. Visibility was presumed valuable. Influence was inferred. Impressions were counted and filed away in quarterly reports. In a world before dashboards and CRM pipelines, that was enough.

It is no longer enough.

The rise of measurable marketing—SEO, performance advertising, revenue analytics—has forced communications into a harsher light. Chief executives now ask questions that clip books cannot answer: How much pipeline? How much attributable growth? What business outcome changed because of this campaign?

Thomas believes the industry’s real reckoning is not with AI, but with accountability.

“AI is not the existential threat to PR and communications agencies,” she said. “To survive in today’s volatile environment, they simply have to earn their place. That means having client discussions that begin with business objectives, which serve as the basis for a communications plan. It’s about reverse engineering the desired business outcomes.”

A press release, she added bluntly, “has no inherent value.” It is a cost unless it drives measurable movement — website traffic spikes, CRM engagement, investor attention. At 10Fold, she said, teams correlate coverage with direct traffic patterns, AI rankings and campaign timelines. It is not perfect, she admits, but it is far more rigorous than impressions and advertising equivalents, the old currency of the trade.

In this framing, AI is not the villain. Mediocrity is.

The AI Paradox: Speed vs. Substance

The fear animating many agencies is that large language models have commoditized content. Thomas sees it differently.

“AI adoption is not killing agencies; it is critical for both agencies and companies alike,” she said. “The problem is that most don’t use it correctly. AI relies on data and input — something very hard to find when you have an original idea, solution, or approach.”

AI, she argues, is a powerful thought partner and time saver. But it draws from what already exists. Without original insight and a defined position, it produces content that is technically fluent and strategically hollow.

“Without a strong human point of view,” she warned, “AI-generated content quickly becomes generic and indistinguishable.”

In a discovery ecosystem increasingly shaped by large language models that prioritize credible third-party validation, indistinguishability is invisible.

The IPO Illusion

Thomas has seen another recurring misstep: high-growth technology companies failing to evolve their narrative as they move toward an IPO.

“The biggest mistake executives make,” she said, “is not understanding how communications plans must evolve as the company matures and moves toward an exit.”

Early-stage companies talk about innovation and disruption. But as they approach public markets, the narrative must broaden. Investors look for ecosystem relevance, customer validation, and operational discipline.

Hiring a CFO can signal financial maturity. Strategic partnerships signal integration. Vertical expertise signals durability. And once financial institutions are secured and paperwork is filed, the quiet period reshapes what can and cannot be said. Even subtle language shifts in press materials become regulated terrain.

Companies that fail to prepare in stages, she noted, often find themselves constrained at precisely the moment clarity matters most.

The Measurement Reckoning

Public relations has struggled to quantify its impact, not because it lacks impact, Thomas argues, but because it failed to adopt modern measurement frameworks.

“For much of its 100-year history, PR operated on a simple premise: visibility and influence were assumed to be valuable,” she said. “Anecdotal success stories are not enough in today’s environment.”

Compounding the issue is timing. Reporting delivered months after a campaign’s peak offers little strategic leverage. Measurement, she insists, must be continuous and integrated with the systems marketers trust — website analytics, CRM dashboards, pipeline models.

Agencies that sell “activity as value” will not survive. Those that demonstrate proof points aligned to business goals will.

Investors, Narrative, and the New Discovery Engine

Investors, Thomas said, are not interested in marketing spin. But they care deeply about credible narratives that resonate with buyers, partners, and future backers.

With large language models reshaping how information surfaces, third-party validation has become even more important. Discovery is shifting away from keyword density and toward authority and originality. In that environment, earned media and differentiated thought leadership are not ornamental — they are strategic assets.

Strong brand programs, supported by credible external voices, increasingly serve as signals of long-term value creation.

Crisis in the Always-On Era

Corporate reputation no longer moves in neat cycles. Social media and activist stakeholders compress timelines and amplify scrutiny. Yet Thomas resists the notion of a permanent state of emergency.

Preparation, she argues, is the antidote. Establish baseline sentiment before a crisis. Benchmark normal conditions. Define response protocols in advance.

“When leaders understand where their organization stands in normal conditions,” she said, “they are better equipped to respond with clarity and confidence.”

Beyond the Hype

Having spent decades in Silicon Valley, Thomas has seen hype cycles crest and collapse. She does not place AI among them.

“AI is beyond a hype cycle,” she said. “It is fundamentally transforming business processes at every level.”

The exuberance will normalize. Integration will deepen. The tools will become infrastructure. What will remain scarce is not automation, but originality.

The obituary for traditional PR may well be accurate. But obsolescence is not inevitable. Reinvention is.

In the age of automation, the agencies that endure will not be those that defend legacy practices. They will be those that prove — with discipline, data, and differentiated thought — that narrative, when aligned to business outcomes, remains one of the most powerful assets a company can possess.

Prove your value, or be automated out of the conversation.

The post AI Isn’t Killing PR. Bad Measurement Is. appeared first on MartechView.

In healthcare marketing, ambition is always tempered by responsibility. The industry sits at the intersection of innovation and regulation, where data promises precision but privacy demands restraint. Julius Ramirez, EVP and GM of Global Data & AI Products and Partnerships at Doceree, has built his career navigating that tension—across startups, large-scale tech platforms, and now one of the fastest-growing AI-powered healthcare marketing companies.

At a moment when artificial intelligence is redefining targeting, measurement, and engagement, Ramirez argues that the real transformation is not about speed or scale. It is about architecture. It is about trust. And it is about building systems designed for compliance from the ground up, rather than retrofitting ethics after the fact.

The Maturation of Personalized Marketing

For years, personalized marketing relied on what Ramirez calls “convenient data”—signals that were easy to access, loosely governed, and often detached from real consumer intent. That era, he suggests, is ending.

Privacy regulations are tightening globally, and healthcare has always operated under stricter scrutiny than most industries. Rather than seeing this as a constraint, Ramirez views it as a forcing function.

“Personalized marketing is maturing,” he explains. “The bar is higher. You need durable identity frameworks, consent-led data strategies, and partnerships built for compliance from day one.”

To some, this sounds expensive. To Ramirez, it is a reallocation of value. Smarter identity resolution and governed data flows eliminate waste—duplicated reach, low-fidelity targeting, and signals that should never have driven decisions in the first place. What replaces them is precision and accountability.

In healthcare, where trust directly influences engagement, that precision carries commercial weight.

Beyond Algorithms: An Operating System Approach

Doceree describes itself as the only AI-powered operating system for healthcare marketing—a claim that invites scrutiny. Ramirez welcomes it.

“The difference isn’t in the algorithm,” he says. “It’s in the architecture.”

Many AI platforms in life sciences function as point solutions—optimizing media buying, segmentation, or analytics in isolation. Doceree, he argues, built its system around healthcare’s structural realities: HCP identity, clinical context, consent requirements, and regulated data environments.

Rather than layering AI onto legacy stacks, Doceree embeds intelligence into its decision layer—connecting identity, activation, and measurement in a unified environment.

Healthcare, Ramirez notes, is not simply another vertical. Signals matter differently. Understanding care settings, treatment moments, and professional intent requires contextual intelligence—not just cookies or clicks.

When Doceree calls its platform an operating system, Ramirez says, it means AI governs how data flows and how engagement happens across endemic healthcare environments. The system orchestrates the ecosystem itself.

Ethics as Architecture, Not Afterthought

Few sectors feel the commercial pull of hyper-targeting more acutely than healthcare. Yet few sectors operate under tighter ethical and compliance guardrails.

Ramirez rejects the framing of this tension as a trade-off.

“In healthcare, the guardrails are design inputs,” he says. “They’re not obstacles.”

Doceree’s systems assume sensitivity from inception—de-identified data structures, consent-led frameworks, and strict separation between patient-level signals and healthcare professional engagement strategies. Hyper-targeting, in his view, should rely on context and professional intent—not on exploiting sensitive personal data.

The misuse of data in healthcare does more than invite regulatory risk. It damages credibility with providers and partners. That reputational cost, Ramirez argues, far outweighs any short-term performance boost.

The discipline imposed by compliance, he believes, forces better AI models and stronger long-term outcomes.

Lessons From Big Tech

Ramirez’s time in large-scale technology environments, such as Meta, shaped his thinking about measurement discipline.

In Big Tech, he says, metrics are stress-tested relentlessly. Definitions evolve. Attribution models are debated. Nothing is taken at face value.

By contrast, earlier-stage HealthTech and AdTech companies often accept “directionally right” measurement. That tolerance may work in the short term, but ambiguity compounds as companies scale.

“Eventually,” Ramirez says, “scale demands precision.”

The earlier organizations adopt rigorous definitions, feedback loops, and measurable outcomes, the more durable their growth becomes.

Intelligence Versus Automation

AI vendors routinely promise “measurably better outcomes.” Ramirez draws a clear distinction between automation and true intelligence.

Automation executes predefined logic more efficiently. AI, by contrast, should refine its own logic.

“If a system just adjusts bids or frequency caps faster, that’s efficiency,” he says. “Intelligence means it learns.”

True AI performance manifests in adaptive decision-making—systems that ingest new signals, continuously update models, and stabilize performance even as environments shift. The proof lies not in surface metrics such as click-through rates but in downstream behavioral changes and predictive accuracy over time.

Intelligence, in Ramirez’s definition, is adaptive and accountable.

The Partnership Question

In the crowded data and AI landscape, partnerships are ubiquitous. Many, Ramirez acknowledges candidly, amount to little more than logo slides.

“A real partnership changes capability,” he says. “It alters how data flows, how identity is resolved, how activation or measurement works.”

Operational partnerships require shared standards, technical integration, governance agreements, and often joint roadmaps. They are slower and more difficult to execute. But they create compound advantage.

Logo swaps, he suggests, depreciate as quickly as the press release cycle that announces them.

Five Years Ahead

Looking forward, Ramirez does not see regulation slowing healthcare AI. Instead, he anticipates transformation—but not the theatrical kind.

The shift will be toward what he calls “decisioning intelligence”: systems that understand clinical context deeply enough to determine when and why engagement drives action. Healthcare marketing, he predicts, will move away from volume-driven outreach toward precision infrastructure.

Regulation will shape the path but not halt progress. The winners, he argues, will be companies that embed compliance, privacy, and explainability into their architecture from the start.

AI in healthcare marketing will not resemble disruption theater. It will look like disciplined engineering.

And in an industry built on trust, that may be the most transformative shift of all.

The post Healthcare Marketing’s End of “Convenient Data” appeared first on MartechView.

As automotive AI grows more expressive, the challenge is no longer whether in-cabin assistants can feel human—but how far they should go. In this conversation, Olga Khryapchenkova, Lead Product Manager for AI at NIO, explores the delicate balance between emotional connection and safety in a high-stakes environment. From designing culturally adaptive voice interactions to ensuring new AI features are discoverable, Khryapchenkova offers a clear-eyed view of what responsible, user-centered AI looks like inside the vehicle. Her perspective is grounded, pragmatic, and notably free of hype—focused less on futuristic promises and more on building AI that drivers can trust.

Excerpts from the interview; 

As NIO gives its AI a “face,” how do you balance emotional connection with the risk of false intimacy in a safety-critical environment?

A visual presence creates a natural connection to both the product and the brand, which helps with perception and adoption. More importantly, it clarifies system states—when the assistant is listening, speaking, or processing—which directly supports efficiency and driver safety.

The balance lies in designing a warm, purposeful persona that enhances guidance and clarity, without drifting into emotional cues that could create unintended expectations or dependencies.

With AI now capable of tone, humor, and personality, how do you ensure cultural authenticity across markets rather than a one-size-fits-all persona?

It always starts with user research. You have to meet users where they are—listen to their pain points, collect feedback across channels, and combine those insights with strong market understanding before entering a new region. Cultural authenticity isn’t something you retrofit later; it’s something you validate early and continuously.

How do you build an emotionally aware voice assistant while meeting strict automotive demands around latency and safety?

True emotion detection isn’t on the table yet. What is feasible is strong context handling—smooth, multi-turn conversations within defined domains. That keeps interactions natural and even enjoyable while remaining efficient and compliant.

Latency is still a market-wide challenge, though progress is steady. And transparency is essential: users need clear controls and a clear understanding of what’s being captured and why.

GenAI doesn’t localize well out of the box. How do you approach multilingual, culturally adaptive voice interactions at scale?

Again, user research is central. You validate cultural nuances with real users and native speakers, remain agile, and are ready to adjust post-launch when issues surface. Over-the-air updates are invaluable here.

The principle is simple: open-minded, user-centered iteration at scale.

How do you ensure users discover and understand new AI features inside the vehicle?

Features that aren’t surfaced effectively quickly become dead features. Clear release notes are essential, and sometimes Q&A sessions or short explanatory videos help.

Equally important is collaboration. Product managers need to work closely not just with engineering, but with product marketing, go-to-market, and communications teams. Tracking adoption and engagement tells you whether the message is landing—and where it needs refinement.

How do you use user feedback to improve in-cabin AI features?

Feedback comes from multiple channels: the voice assistant itself, the companion app—within a closed-loop system—and surveys. Continuous feedback highlights friction points, while analytics reveal which features need better visibility.

That combination allows product and marketing teams to iterate quickly and communicate improvements in a way users actually notice.

Looking ahead to 2026, what excites you most about AI products?

Smarter base models, more refined AI user experiences, and richer multimodal interactions. That said, we’re still far from true productivity breakthroughs, and AGI remains a distant horizon.

There’s a lot of work ahead—which means we certainly won’t be bored in 2026.

The post Designing AI That Feels Human—Without Crossing the Line appeared first on MartechView.