Julie Lyle is a Board Director at Dstillery, the leading predictive AI audience targeting company. She is also President and Board member of TCC Global, where she leads efforts to help retailers worldwide drive customer loyalty, develop data strategies, and implement marketing programs at scale. Julie began her career as an entrepreneur before moving into executive roles at Walmart, Prudential plc, and Barnes & Noble, building deep expertise at the intersection of commerce and consumer behavior. In addition to her operating roles, she is a seasoned investor and advisor, serving on the board of meetsynthia.ai. Julie is widely recognized for her perspective on how brands can build lasting customer relationships through data and personalization.

What’s the most unexpected opportunity you’ve gotten in your career?

Early in my career, Mitch Hart, who is a venture capitalist and Founder of The Hart Group, recognized my potential and gave me the role of functionally reporting to 5 portfolio company CEOs, while administratively reporting to Mitch as Chairman. Through that job, I learned to successfully navigate internal politics, build trust among competing executives, and master conflict among big personalities. It was challenging, and the skills I gained were invaluable.

What’s the worst career advice you’ve gotten?

“If people allow you to take advantage of them, they deserve what they get.” I categorically disagree with that. I believe we all have a moral imperative to do well by doing good, as companies and as people. At the end of the day, your integrity is all that matters.

What was a heartbeat moment for you in your career?

It was when my mother was diagnosed with cancer. While she fought valiantly, she eventually had to be transitioned to hospice care. I was CMO of Walmart at the time, and I immediately resigned so that I could go and take care of her. It was a heartbeat moment for me because I loved my job. It was difficult to go from managing multi-billion-dollar profit and loss statements to changing bedpans and IVs, but I wouldn’t change that decision for the world. Those last months I had with my mom will always be precious to me.

Who is one person you’d love to give flowers to from your career that influenced your journey? What advice or lesson did you learn from them?

I would give flowers to Dan Burnham, who, when he was CEO at Raytheon, gave me a “tough love” challenge to be more intentional about how I managed my career. I took his advice to heart and developed a 7-part marketing plan with time and action schedules that I implemented and followed for the next 25 years. It had an incredible impact on my career growth and income. I eventually turned it into a curriculum and convinced Walmart to offer the program to 2,500 female managers in an effort to empower them with a clear path for growth and development. Even though it was 18 years ago, I still receive notes from women who used those growth insights to achieve success. I’m grateful to Dan for challenging me, bringing out my best, and allowing me to pay it forward.

Where have you caused some good trouble in your career?

During the 2008 global financial crisis, I was leading a team charged with accelerating growth in Prudential’s Asian insurance business. Market confidence was at an all-time low, and we had very little brand awareness in Asia. After careful research, we went against industry standards and took a big risk. We shifted our entire marketing strategy from typical financial services messaging to focus on families with children under the age of 12. We developed music videos, apps, games, and characters around a financial learning program for kids. We shifted our advertising dollars from the major news and financial media outlets to Nickelodeon. It was a huge success, and our sales, recruitment, and brand recognition soared.

Want to nominate a Female Quotient “Troublemaker” you admire? You can do so here.

The post From Walmart to the boardroom: Julie Lyle’s lessons on leadership and growth appeared first on Dstillery.

For decades, loyalty has been won at the moment of choice, from the shelf to the feed to checkout. Today, that paradigm is breaking down.

A new model called agentic commerce is emerging, where AI systems increasingly act on behalf of consumers, researching, evaluating, and completing purchases. Gartner reports that a growing share of digital commerce interactions will soon be influenced or executed by AI agents. At the same time, research from McKinsey & Company finds that over 70% of consumers already expect brands to anticipate their needs, accelerating the shift toward automated decision-making.

This fundamentally changes the role of marketing.

Dstillery POV:
The most important decisions are no longer happening in the moment; they are being modeled in advance. Brands are no longer just competing to be chosen by consumers; rather, they are competing to be pre-selected by the systems that decide. 

The Collapse of the Decision Moment

Historically, marketing strategy has focused on influencing behavior within the consumer journey. This has largely been done through promotions, channel retargeting, and checkout conversion optimization. 

It is essential to note that this model assumes a human actively navigating options. Agentic AI removes that assumption, is apt to learn from behavioral signals, evaluate options, and execute purchases autonomously. Research from Deloitte projects that customers will increasingly hand over not just recommendations, but also purchasing authority to autonomous agents, prompting brands to consider how they earn influence and embed into automated workflows. 

Dstillery POV:
The “moment of decision” is no longer a moment; it’s a model. What used to be a discrete interaction is now the output of continuous prediction. The implication is that brands that rely on actively intercepting consumer decisions will lose ground to those shaping the model upstream. 

From Influence to Prediction

We are moving beyond influence into something more powerful: predictive preference modeling. Traditional commerce has historically focused on influencing decisions, optimizing conversions, and reacting to consumer behavior. In the age of agentic commerce, AI systems can effectively predict consumer decisions, optimize the likelihood of selection, and model behavior before it occurs. 

Dstillery POV:
The next stage in the evolution of marketing is prediction, not persuasion.

At Dstillery, this is the foundation of how modern audience targeting should work. AI is not just identifying who a consumer is: it is identifying and predicting who they are likely to become in a given context. This distinction matters.

In an agentic marketplace, the brand that wins is not the one that always reacts the quickest. Instead, it’s the one already aligned with the anticipated outcome. 

The Commoditization Trap

As AI intermediates transactions, traditional levers of differentiation erode. Price, convenience, and availability become instantly comparable, continuously optimized, and algorithmically interchangeable. 

According to NielsenIQ, in categories like grocery, purchasing is largely driven by habitual, repetitive behaviors, with returning buyers accounting for a significant share of brand revenue. When those habits are handed to AI, the system defaults to efficiency unless directed otherwise.

Dstillery POV:
If your brand is only winning on efficiency, you are training the system to replace you. Without strong preference signals, brands become indiscernible and substitutable. 

What AI Can’t Optimize (Yet)

AI excels at optimizing functional outcomes, such as finding the lowest price, the fastest delivery, and the highest-rated option. However, loyalty is not built purely on functionality.

Research from Accenture indicates 63% of consumers show increased loyalty to brands that align with their values.

Dstillery POV:
The most valuable signals are the hardest to quantify and the hardest to displace. These signals include emotional connection, brand trust, cultural relevance, and perceived identity fit. Preferential signals don’t disappear to agentic systems; instead, they become inputs. The question is whether your brand is generating them strongly enough to be recognized.

The New Loyalty Equation

In an agentic world, loyalty is no longer built at the point of purchase. It is built before the system begins evaluating options. 

Dstillery POV: The Pre-Decision Advantage

The brands that win will: 

• Model intent early, moving upstream from conversion to intent formation
• Create durable signals that AI systems can learn from, such as behavioral patterns and contextual relevance
• Reinforce preference continuously, ensuring signals are not episodic, but instead, persistent
• Align with machine logic, structuring brand signals in ways legible to AI systems
  

The Role of AI-Powered Advertising

In an agentic landscape, fragmented signals create blind spots, causing identity-based approaches to struggle as signal loss increases. What’s needed is a system that can operate without dependency on identifiers, while still modeling high-intent behavior. This is where our approach at Dstillery becomes critical.

Dstillery POV:
Identity is not required to understand intent: prediction is. 

Dstillery’s AI-driven approach synthesizes behavioral and contextual signals across environments to identify patterns of emerging intent and build custom AI audiences based on likelihood to act. This enables brands to engage consumers earlier in the decision cycle, influence both human- and AI-mediated outcomes, and shape preferences before they are formalized into action. 

From Targeting to Training

As AI reshapes advertising, the role of targeting is evolving. Finding demand is no longer enough – brands must also train the systems that ultimately determine who wins it.

Dstillery POV:
Impressions now serve a dual purpose: driving immediate conversion while simultaneously informing future decision systems. This shift reframes campaigns as both performance engines and signal generators. In turn, media evolves from a distribution channel into a persistent data ecosystem—one where every outcome contributes to cumulative impact.

Brands that understand this will think beyond short-term ROI, invest in long-term signal strength, and build compounding advantage gradually. 

An Opportunity to Lead

What feels experimental today will quickly become standard. The shift into the agentic commerce era creates a clear divide between brands that optimize for today’s decisions and brands that shape the decisions of tomorrow. According to Deloitte, over half of leading retailers believe AI agents will handle most customer interactions within five years. McKinsey reports that agentic AI commerce will likely generate up to one trillion dollars in the United States alone by 2030, with global projections as high as five trillion. Ultimately, agentic AI isn’t the add-on in commerce: it’s the infrastructure. 

Dstillery POV:
The brands that win in an agentic world won’t just participate in the ecosystem; they will influence how it thinks.

Preference Is the New Currency

Loyalty has always been rooted in economics. In an era where convenience is assumed, comparison is automated, and decisions are delegated, preference becomes the most valuable currency of all.

Dstillery POV:
If you are not shaping preference, you are competing on leftovers. The question isn’t if your brand wins at the moment of choice; it’s whether it has already been chosen before that moment arrives. 

The post Rebuilding loyalty in the age of agentic AI appeared first on Dstillery.

The conversation has shifted

The most noticeable change from years past: nobody needed to be convinced that AI matters. The interesting debates were happening further upstream, around execution, differentiation, and what “useful AI” actually looks like in practice.

One phrase that kept surfacing was the shift from “human in the loop” to “human in the lead.” It reflects something we think about a lot at Dstillery: automation that accelerates doesn’t mean automation that replaces judgment. The organizations moving fastest aren’t chasing autonomy for its own sake. They’re embedding AI into the places where manual work creates the most drag.

There was also a lot of honest conversation about “pilot purgatory,” the gap between experimenting with AI tools and actually integrating them into scalable workflows. That gap is where many companies are still stuck, and it came up repeatedly as a more urgent challenge than any technological limitation.

Mark Jung on agents actually used in the wild

A highlight for us was Dstillery’s VP, Head of Product & AI, Mark Jung’s session, “Agents Built For Audience Buying Workflows… Actually Used In The Wild.” The framing was intentional: not a roadmap, not a demo of what’s coming, but a look at how agentic AI and Dstillery’s DS-1 platform are already running inside real audience-buying workflows today.

Mark walked through how Dstillery’s agentic AI advertising platform, DS-1, helps teams move faster on audience discovery, reduce activation timelines, and cut down on the manual operational work that tends to slow campaign execution. From audience creation to activation, the session showed how agentic AI can streamline workflows that traditionally take days into processes completed in minutes. The core argument: you don’t need fully autonomous systems or perfect industry standards to start generating real operational value. The work is happening now.

That landed well in the room, and it matched the tone of the broader conference. Less interest in flashy demos, more interest in what’s actually solving problems.

What we’re thinking about next

Consumer intent signals are getting more fragmented as AI assistants become a bigger part of how people discover and decide. That makes downstream behavioral modeling more important, specifically the kind that can work across fragmented signals. It’s a dynamic we’re watching closely.

There was also consistent emphasis on data quality and governance as AI takes on a larger operational role. The reality is that AI is only as strong as the data and systems behind it, and as automation scales, those foundations become even more important. For us at Dstillery, that’s where the real opportunity lies: building AI that’s not just fast, but reliable, scalable, and grounded in high-quality data.

The post AdExchanger’s Programmatic AI 2026: Dstillery’s key takeaways appeared first on Dstillery.

The multimodal difference

While everyone’s been obsessed with Sora 2 putting their faces into videos, the advertising world has quietly been building something more practical: AI that can understand any type of data and apply those insights across any advertising channel.

“What you need is prediction based on behavioral data, and that’s why we wanted to use multimodal AI for prediction in digital ad targeting,” explains Melinda Han Williams, Chief Data Scientist at Dstillery. “By multimodal, we mean you can use different modalities of data — not just different sources. The difference between looking at words and what they mean and looking at behaviors and how you’ve seen them strung together as sequences in the past.”

Think of it like how humans naturally combine visual cues, sounds, text and context to understand a situation. When you see a waiter slip on a wet floor near a caution sign, your brain instantly processes multiple inputs to grasp the full story. Multimodal AI aims for that same fusion of understanding.

From brief to campaign in one model

Here’s where it gets interesting for advertisers. Instead of building separate models for search, display, CTV and everything else, multimodal AI creates one unified understanding that works everywhere.

“What sets multimodal AI apart is in its flexibility,” said Taejin In, Chief Product Officer at Dstillery. “Whether you have rich first-party data or just a basic campaign brief, multimodal AI can transform virtually any starting point into precise, actionable audiences.”

The system can take first-party data, CRM lists, search keywords, website URLs, or even just a paragraph describing your target audience. It then builds a model that can activate across user segments, contextual targeting, private marketplace deals and custom bidding algorithms — all from that single input.

This new way to approach targeting doesn’t just simplify workflows for overworked advertising professionals, but it also delivers superior outcomes. For example, an automotive brand using a curated CTV deal created by multimodal AI saw a much higher 98% video completion rate compared to traditional ID-based targeting, while an auto insurance company with direct response goals surprisingly discovered that contextual targeting outperformed 12 other tactics in their campaign, including traditional id-based lookalikes.

Why this matters now and in the future

Multimodal capabilities in digital ad targeting clearly provide a lot of value today – the application of more data to build more precise targeting, the ability to understand and reach audiences without relying solely on identifiers, and the ability to reduce vendor sprawl and simplify workflows.

However, something that might not be obvious is how multimodal AI is foundational for tomorrow’s AI agents and agentic advertising systems. “For agentic AI and advanced AI agents to truly deliver superior value, they need to understand all modes of data; you can’t do that without multimodality,” In explains, “Imagine if an AI agent can only reason from contextual signals. It would be missing behavioral signals which we all know are a much better predictor of intent”

With a multimodal foundation, these agentic AI systems and protocols will be able to understand multiple modes of data (web behavioral, CTV, in-app, search, user segment, evaluate and make decisions in a shared space, and interact with external systems all with different modalities.

The reality check

Multimodal AI isn’t magic. Success depends on having the right seed data and being willing to test and optimize. The most successful implementations happen when brands view it as a partnership, not a set-it-and-forget-it solution.

But for advertisers tired of managing dozens of disconnected models and tactics, multimodal AI offers a compelling alternative: one model that understands your audience across every signal and can activate that understanding anywhere in the programmatic ecosystem. In a world where consumer journeys zigzag across channels, that unified view isn’t just convenient — it’s essential.

The post WTF is multimodal AI in advertising? appeared first on Dstillery.

For the first time since AI entered the mainstream with tools like ChatGPT in 2022, it wasn’t a sidebar in advertising — it was central to how brands approached the Super Bowl itself. Super Bowl LX reflected a broader shift in how agencies and brands are using AI to inform strategy, shape investment decisions, and decide when they’re ready to compete on marketing’s biggest stage.

1) AI Is No Longer About Tools—It’s a Defining Position

AI-driven decisioning wasn’t just influencing how ads were created, but which brands chose to show up at all.

That shift was especially evident in the rise of first-time Super Bowl advertisers. Performance-minded, growth-focused brands were willing to make high-stakes investments in live TV, using AI-backed insights to validate reach, relevance, and scale before committing to the biggest media buy of the year.

We want to give a shout-out to our TV advertising platform partner, Tatari, on securing and managing the ultimate in premium media placements for four first-time Super Bowl brand advertisers—Life360, Manscaped, Ro, and Tecovas—each making a deliberate bet on relevance, reach, and measurable outcomes. These brands didn’t simply “jump in” to the Super Bowl; they’ve been expanding beyond their original target markets and hero products. While AI may have played a role in their media strategies and ad targeting decisions, what’s clear is that the legacy brand-building playbook still has a valuable role, with agency partnerships and relationship currency more important than ever.  

This theme of blending top performance and playing to win extended beyond the game itself. At Adweek House, these brands joined a panel led by Tatari’s SVP of Marketing, Amit Sharan. See the full story here: First-Time Playbooks for the Big Game

2) AI-Powered Targeting Is Now the Confidence Engine Behind Media Decisions

Super Bowl advertising has always been a media decision as much as a creative one. What changed this year was the confidence behind those decisions.

Rather than relying on broad demographics or legacy assumptions, advertisers leaned on AI-powered audience prediction and targeting to reduce risk and increase certainty. AI is no longer optimizing campaigns after launch—it’s shaping upfront investment decisions, giving brands the confidence to take bigger bets with expectations.

3) AI Is Embedded in Prediction and Engagement (Not Just Creation)

Every Super Bowl ad is ultimately a prediction: a bet on what audiences will remember, share, or act on. AI is accelerating that predictive capability across both media and messaging.

This year, that showed up through AI-forward brand narratives and competitive positioning that used AI as both a capability and a differentiator. The most effective advertisers weren’t chasing novelty—they were using AI to align insight, timing, and engagement to drive impact.

The Bigger Signal

Super Bowl LX made one thing clear: AI in advertising has moved beyond experimentation. It’s shaping confidence, precision, and prediction—helping marketers ensure the right messages reach the right audiences at the right moment.

The post What Super Bowl LX teaches us about AI in advertising: how AI shifted from experiment to strategy at the Big Game appeared first on Dstillery.

Multimodal learning is the next frontier in machine learning. It allows machines to combine different types of data to understand more than is possible from any one source.

Think of the example of understanding a scene in a play. As humans, our brains seamlessly integrate visual cues, auditory input, text, and prior knowledge.  In a scene, we may see a waiter fall and drop a tray of drinks, we hear the crash of objects falling, we hear language spoken as someone reacts to the accident, and we read a caution sign saying the floor is wet. From this input, we easily conclude that the waiter slipped on the wet floor and dropped the drinks. This is due to our brains combining visual cues, auditory input, text, and prior knowledge.  Without the combination of all of these inputs, we risk drawing the wrong conclusion about the scene. Auditory input alone might tell us an accident occurred, while visual cues may show a waiter slipping and drinks falling – but without reading the sign that says the floor is wet, we can’t fully understand why it happened.

Multimodal machine learning allows machines to perform a similar fusion of different data modalities, giving machines the ability to understand context, respond naturally, and make smart decisions in complex environments. 

At Dstillery, we are experts at using AI to perform ad targeting. A powerful data modality in this space is user browsing behavior. The success of ad targeting campaigns is often measured by a conversion event performed online, such as purchasing a product or visiting a homepage. The journeys a user takes across the internet can be very predictive of whether they are likely to perform a particular online conversion or not.  

For example, online retail sites visited by a consumer tells us the user’s style and the amount they may be willing to pay, and gives clues to demographic attributes such as gender and age.  It is intuitive to see how this leads to useful features in an ad targeting campaign for a clothing brand. With the recent development in LLMs and generative AI, highly accurate text features can be generated for almost anything. This gives a second highly performing data modality we can use in our ad targeting campaigns.

We have built a modular, scalable multimodal architecture to integrate these data modalities and produce high-performing CTV ad targeting. Our patent-pending approach combines web behavioral features and text features to generate richer predictions, better personalization, and smarter automation of CTV ad targeting. 

Our Approach: A Modular Multimodal Architecture for CTV Ad Targeting

Our CTV ad targeting system is built on a modular multimodal architecture that combines a foundation model with a lightweight mapping model. The foundation model is trained on large-scale web browsing behavior, while the mapping model enables us to extend that model’s capabilities to other data types — such as text — by projecting them into the same embedding space.

Foundation Model: Learning Behavioral Embeddings from Web Journeys

At the core of our system is a foundation model trained on web visitation sequences, producing what we call MOTI embeddings — short for Map Of The Internet. These embeddings are learned using self-supervised learning on billions of sequential website visits. The model is trained to predict the next website in a user’s browsing journey, allowing it to learn the behavioral patterns and intent behind web visits.

This results in a rich embedding space that captures user behavior across the open web — not just what sites users visit, but why and in what context. MOTI embeddings provide a strong signal for predicting future behavior, especially web-based conversions.

Mapping Model: Extending MOTI Embeddings to New Modalities

To enable multimodal learning, we train a mapping model that projects from text space into the MOTI embedding space. This allows us to represent any domain described by text — such as CTV content metadata — using the behavioral signal embedded in our foundation model.

We train this mapping model by aligning two modalities:

• MOTI embeddings for a large set of websites.
  
• LLM-generated keyword embeddings extracted from the same websites using generative AI.
  

By training a model to predict MOTI embeddings from LLM embeddings, we learn a cross-modal projection that allows us to map new text inputs (e.g., CTV parameters) into our MOTI space — effectively teaching the text modality to “speak the language” of web behavior.

Brand-Specific Models: Optimizing for Conversions

Clients often wish to drive outcomes measurable by web conversions — such as site visits, keyword searches, or product views. Since MOTI embeddings capture real behavioral intent, they serve as high-performing features for building client-specific models trained on conversion outcomes.

These models learn what types of behavior (in MOTI space) are most predictive of desired outcomes for each brand — allowing us to personalize targeting at scale.

CTV Targeting: Scoring Content Using Behavioral Signals

Once we’ve trained a brand-specific model in MOTI space, we can use it to score any other domain that can be described in text. For CTV ad targeting, we use generative AI to extract semantic features from content metadata — such as series, title, genre, language, rating, and channel.

These features are embedded using an LLM, then mapped into MOTI space via our mapping model. This allows us to use the brand’s web conversion model to score and rank CTV inventory based on how closely it aligns with high-performing web behaviors — creating a seamless link between behavioral intent and CTV content.

This architecture enables us to fuse two distinct data modalities — behavioral browsing data and structured text — through a shared representation space. It’s a powerful, scalable approach to multimodal learning: one that leverages foundation models, bridges across modalities, and delivers measurable performance in production systems. You can think of it as teaching the text modality to speak the behavioral language of MOTI embeddings, allowing both modalities to contribute meaningfully to targeting decisions.

FIGURE: TSNE visualization of CTV attributes (series, title, language, rating, channel, genre) in the same space as websites in MOTI. As indicated by the left dotted circle, CTV attributes related to soccer, as well as soccer related domains are close by in the MOTI space.  Another example indicated by the right dotted circle shows CTV attributes and nearby domains are related to Italian content. 

A powerful, flexible approach to multimodal learning

Our CTV ad targeting solution is an effective and product-ready form of multimodal learning. 

Scalable: Leveraging an existing foundation model trained on large amounts of streaming data allows the training of CTV models at scale. Training a model for each of our clients is fast and efficient.  

Flexible: As text features from LLMs improve, or new CTV shows are aired, we can represent this new data in our multimodal model without retraining our large and computationally expensive foundation model.

Composable: As text features have emerged over the past couple of years, if there is a new data modality that is useful for ad targeting we can simply train a new mapping model and produce a multimodal solution with a new modality

Interpretable: Our solution is highly interpretable because we can query the joint representation space easily, giving us a clear understanding of the relationship between modalities

Results in the real world

Our modular multimodal targeting system has delivered strong, measurable performance across verticals, proving its value in real-world, ID-free environments.

Automotive Brand: Outperforming ID-Based Targeting

An automotive client sought to reach high-intent car shoppers using an ID-free CTV strategy. Using our modular multimodal architecture, we built a custom brand model for the campaign.

– The ID-free CTV model outperformed the client’s ID-based audience from day one, ultimately achieving a 98% higher video completion rate (VCR) by the end of the campaign.
– The model also delivered better cost efficiency, producing more CTV conversions at a lower cost per conversion (CPC), obtaining a CPC of $9.13.
– The client’s agency concluded that adding ID-free targeting alongside traditional ID-based models significantly expanded reach without sacrificing performance.

Kitchen Appliance Brand: Sustained Success Across CTV and Display

A kitchen appliance brand partnered with Dstillery to raise awareness for their new products using CTV and display media. They measured success using VCR and clickthrough rate (CTR) as KPIs.

– The campaign achieved a 94% average VCR on CTV beating the client benchmark of 70%.
– Display ads delivered a 0.14% CTR beating the 0.10% benchmark.
– Top-performing CTV channels included AT&T TV, Discovery Channel, and Food Network Kitchen — all aligned with high behavioral intent signals captured in our MOTI embeddings.

Multimodal AI for CTV: A Real-World Blueprint for Smarter Ad Targeting

Our solution is a framework for practical multimodal AI.  When there are two complementary data sources and a unimodal foundation model exists for at least one of the modalities, a fast and efficient solution to multimodal machine learning is to train a model to learn a mapping from one modality to another.  This produces an efficient multimodal solution that is interpretable and adaptable. You don’t need to train all modalities together from day one. Multimodal learning can be layered, modular, and immediately impactful. Multimodal learning isn’t just about giant models – It’s about combining signals to see more clearly and building systems that grow with your product.

The post Data Science Deep Dive: modular multimodal system for CTV ad targeting appeared first on Dstillery.

At Dstillery, supporting women is embedded in our culture. Half of our executive team is comprised of women leaders with a range of expertise who are setting strategy, driving innovation, and inspiring the team every day. Leadership isn’t just about titles; it’s about impact. And we’re proud to create an environment where women are empowered to lead with confidence and vision.

Creating a Culture Where Everyone Thrives

Change doesn’t happen in a single moment – it takes ongoing commitment. At Dstillery, we ensure that women’s voices are not only heard but amplified. We’re focused on creating a workplace where everyone, regardless of role or experience, feels encouraged to speak up, share ideas, and take the lead. By breaking down barriers and fostering a healthy culture of collaboration, we create space for real innovation.

Women at the Forefront of Innovation

One reason we celebrate International Women’s Day is to recognize how far we’ve come and to keep building the pipeline for future women leaders. At Dstillery, we’re all about mentorship, growth, and opportunity — giving women at every career stage the support they need to succeed.

A great example of this can be seen during a recent interview for CNBC’s CMO NOW series, with our Chief Marketing Officer, Patti Boyle, filmed during her time at Davos 2025. She highlights the importance of women advocating for themselves, the power of mentorship in shaping future leaders, and how Dstillery’s culture is designed to help women thrive. When women are empowered to lead, the entire industry moves forward.

Honoring the Women of Dstillery

This International Women’s Day, we’re celebrating the incredible women who make Dstillery what it is. Their drive, expertise, and leadership push our company forward, and we’re proud to recognize their contributions – not just today but every day.

We’re sharing a team photo of our amazing women on LinkedIn to mark the occasion. But this isn’t just a one-time shoutout – it’s a reflection of our ongoing commitment to supporting, elevating, and empowering women all year long.

The Future is NOW Women are leading NOW

The future isn’t about small steps – it’s about creating workplaces where women are valued, heard, and empowered at every level. At Dstillery, we’re committed to being part of that change.

So this International Women’s Day, let’s go beyond the celebration. Let’s commit to making real, lasting change – not just for today, but for the future.

Happy International Women’s Day!

Click here to view this featured post via The Advertising Club of New York.

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BrXnd AI Founder Noah Brier has emerged as a leading authority on how brands can and should use, and are using, generative AI tools at all stages of the marketing process. Brier’s team consults with companies of all shapes and sizes about using AI, publishes a newsletter and Marketing AI landscape, and hosts in-person events.   

Reflecting on a day of learning and inspiration, I am thoroughly convinced that AI is not overhyped.  It is having real impact.

The first half of the day was mostly about the state of AI and trends in how brands are using it.  The second half was essentially demos/pitches.  Both were impressive and thought-provoking.  A few themes emerged: 

– The consensus view is that we are still very much in the discovery & experimentation stage with AI

– The LLM model itself is a tool, and our collective imagination in using it to solve business challenges is the value-add 

– ROI is hard to measure at this stage and it is premature to try and assign dollar values 

Having sponsored and attended BrXnd NYC’s 2024 event, we saw some amazing progress. There were fewer demos of working AI technology in action. Tools were not ready, so the conference was heavy on trends and themes and light on specifics. We have come a long way.  

The audience for the products is marketers and agencies, and there were some VERY cool demos of tools that simplify and speed up agency and digital media workflows, opening up new opportunities.  Here are a few of our favorites: 

– Pencil creative canvas 

– McKinney’s Brand Attention Index 

– JellyFish Share of the Model (how do brands show up across models?)

– Springboards.ai creative brainstorming 

– Social Department video social asset extraction 

– And my personal favorite, the Waldo.fyi research assistant

These are very cool customer-facing tools that help brands and agencies do their work better, faster, and more cheaply. Eight months ago, they did not exist. Now, they are compelling workflow tools gaining adoption among our clients. I am blown away by the pace of the progress.  

Above all, I am convinced after the BrXnd AI showcase that we are only just getting started. 

The post BrXnd AI LA: the future of AI for brands is now appeared first on Dstillery.

The Power of Sponsorship & Next-Gen Leadership

On stage with Anna Marks, Camilla Maggiori, Bijou Abiola, and our exceptional moderator Amber Coleman-Mortley, we explored what it means to build pathways for emerging talent — not just in theory but through deliberate, meaningful action. These conversations are particularly relevant in the rapidly evolving landscape of AI-driven marketing, where guiding the next wave of women’s leadership is just as critical as technological innovation itself.

Dstillery & AI Innovation: Aligning with UN SDG #9

Throughout the week, a common theme emerged: the role of AI in driving sustainable innovation. Dstillery’s commitment to AI-driven insights and ethical marketing practices aligns with United Nations Sustainable Development Goal #9 (Industry, Innovation, and Infrastructure) — reinforcing the importance of responsible AI development that fuels both business growth and societal progress.

From collaborative discussions on AI regulation to exploring investments in growth-oriented technology, Davos provided a platform to engage with industry leaders on how marketers can harness AI responsibly while maintaining a human-first approach to advertising.

Leadership in a Shifting Business & Political Landscape

Beyond the panels and networking lounges, I had the opportunity to participate in exclusive roundtable discussions hosted by Steve Clemons, Chairman & CEO of Widehall, Washington, DC. These conversations delved into the evolving terrain of leadership, business, and politics, highlighting the challenges and opportunities executives face in a landscape shaped by geopolitical shifts, economic pressures, and rapid technological transformation.

A Seat at the Table: CNBC International’s “CMO Now” Series

One of the most exciting outcomes of the week was getting invited to record a video interview for CNBC International’s “CMO Now” series, set to air later this month in the lead-up to International Women’s Day. This conversation allowed me to discuss the intersection of AI, marketing, and leadership, further reinforcing the pivotal role of AI-driven insights in shaping modern business strategies.

Looking Ahead: Davos 2026 and Beyond

Among the highlights of Davos 2025 was the invaluable time spent with our newest Dstillery Board Member, Jim Weiss, Founder & Chairman of Real Chemistry, and the outstanding EVP/Chief of Staff, Danielle Clark-Brown. As we look to Davos 2026, one thing is certain: the convergence of AI, leadership, and innovation will continue to shape how brands engage, connect, and grow.

For Dstillery, the takeaways from Davos reinforce our vision: to remain at the forefront of privacy-friendly AI innovation, actionable data insights, and digital advertising solutions that drive measurable business impact. I look forward to continuing these conversations, taking actions to continually grow and lead, and making plans to return and reunite in Davos for WEF 2026.

Stay tuned for our CNBC International interview airing later this month, and let’s keep the conversations going!

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Performance Marketing: The Sprint to ROI

Performance marketing can sometimes be misconstrued as solely about clicks, conversions, and measurable returns. More accurately, well-planned performance marketing is vital to any strategy, ensuring campaigns are tied directly to outcomes. Yet, in our hyper-quantified world, the danger can lie in focusing solely on short-term wins at the expense of long-term growth. Both pursuits are valuable and not mutually exclusive.

The industry leaders headlining MediaPost’s agenda highlight the sophistication of modern performance strategies, from precision targeting to multi-touch attribution. The key is not just to measure performance but to optimize campaigns while maintaining a broader brand vision.

Brand Marketing: The Marathon to Mindshare

Brand marketing, on the other hand, builds a company’s reputation, affinity, and trust over time. This “slow burn” is no less essential in driving sales; in fact, research shows that brand equity can multiply performance results.
In my MarketingProfs article, “Brand Awareness Measurement: A New Perspective,” I explored how companies often struggle to measure the true impact of brand campaigns. Our solution at Dstillery applies award-winning innovation, including advanced AI modeling and predictive behavioral targeting, to ensure your campaign is reaching your best customers. The future lies in connecting the dots between brand strength and performance metrics.

Why Choose? The Case for Integration

The most effective marketing strategies no longer draw hard lines between performance and brand. Instead, they fuse these disciplines into a seamless, cohesive approach. At Dstillery, we’ve worked with global agencies and brands to align short-term and long-term goals using data-driven decision-making. For example:

– Re-framing Target Market Groups: Use Dstillery Insights to refine your target market prioritization or adjust for seasonality. If campaign results reveal that GenZ audiences respond more positively to short-form video ads, then shift your strategy to allocate more budget toward platforms like Instagram to maximize engagement.

– Simultaneous KPIs: Measure short-term ROI alongside brand health metrics, like awareness and favorability, to ensure no aspect of your marketing suffers.

– Iterative Feedback Loops: Performance data can fuel brand decisions and vice versa, creating a virtuous cycle of continuous improvement.

Takeaways for the MediaPost Performance Marketing Summit

The MediaPost Summit promises a deep dive into the future of performance marketing, but attendees should look beyond tactical excellence. The challenge — and opportunity — is to balance these skills with brand-building strategies that can scale and sustain success.

Here are a few questions to ponder as you prepare for the summit:

– How can we use performance data to refine social strategies?
– Are our KPIs reflecting both short-term wins and long-term growth?
– What technologies or methodologies (like those in Dstillery’s portfolio) can help bridge the gap between branding and performance?

We’re also a proud sponsor of the event and will share insights during our presentation titled, “How To Target & Reach The Right Audiences in 2025.”

A 2025 Marketing Mindset

In 2025, the most successful marketers won’t think of performance and brand as separate entities. They’ll see them as two sides of the same coin, working together to achieve exponential growth. Whether you’re attending the MediaPost Performance Marketing Summit or reflecting on your plans for the year ahead, the time to integrate these strategies is now.

Curious to learn more? Click here to explore how Dstillery’s top-performing audiences help brands achieve measurable results while building long-term equity.

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