Why Customer‑Led Strategy Is The New Marketing Imperative 

The strongest marketing strategies reflect the customer, not the business model. 

Across industries and company sizes, the most common misstep we see — whether from a first-time marketer or a seasoned operator — is building a strategy around the business model instead of the customer. It’s an easy trap: revenue targets, product priorities, and internal narratives feel concrete. Customers, on the other hand, move fast. They shift behaviors, expectations, and attention long before most organizations adjust. 

But in 2026, the brands that win are the ones that reverse the order. They start with the customer, then align their positioning around what those customers value. 

This is the strategic reset many marketing teams need. 

Start with the Customer. Every Time. 

Most marketing leaders will tell you they already start with the customer, and they’re half right. They have the personas. They’ve done the research. But there’s a meaningful difference between having customer data in a slide deck and genuinely building your strategy around customer priorities. The former is a starting ritual. The latter is a discipline that has to run through every decision that follows. 

To start with the customer means asking, before any channel is selected or budget is allocated: where is this customer right now, and what do they need from us at this stage of their journey? The brands getting this right aren’t just mapping touchpoints; they’re aligning business priorities with customer priorities from first awareness all the way through to advocacy. That alignment is where sustainable growth lives. 

Channel Strategy Is a Customer Question, Not a Business One. 

When leadership asks, “where should we be showing up?”, the answer can’t come from internal preference or competitive benchmarking alone. It must come from a clear picture of where your customers are, what they’re doing, and what they’re looking for at each stage of their journey. This becomes especially important as the channel landscape shifts — particularly around AI search and traditional SEO, two channels that are often conflated but serve meaningfully different purposes. SEO points users toward options; it works well when customers are exploring. AI search gives a direct answer, generated uniquely for every query, and the practice of optimizing for it commercially is still being developed across the industry.  

The practical takeaway: traditional SEO is still the right starting point, but it is no longer the whole strategy. You need both running in parallel, and you need visibility into how your brand is showing up in AI-generated answers — not just where you rank. We’ve recently launched a tool to help clients do exactly that. 

If You Only Track One Thing, Track This. 

Once the strategy is in motion, the instinct is to jump straight to revenue metrics: pipeline, conversion rates, ROAS. Those matter, but they’re lagging indicators. They confirm what already happened. If you want to know whether your customer-first strategy is actually working, start with engagement. Not impressions or follower counts, but the signals that show people are paying attention and finding genuine value: time spent with your content, repeat visits, shares, saves, replies, and the quality of inbound conversations. These tell you whether the message is landing with the right customer, in the right place, and that’s the foundation everything else is built on. 

Engagement is the leading indicator that pipeline, conversion, and retention will follow. If it’s not there, no downstream optimization will fix it. If it is, you’ve validated the approach and earned the right to scale.

The Takeaway for CEOs and CMOs 

The brands winning right now are not necessarily the ones with the biggest budgets or the most sophisticated tech stacks. They’re the ones with the clearest picture of their customer, and the discipline to let that picture drive every strategic decision. 

Reverse the order. Start with the customer. Understand where they are, what they need, and how they engage. Build your channel strategy around that understanding. Measure your progress with the signals that tell you whether the foundation is working before you pour resources into scaling it. 

That’s not a new idea. But in a market moving this fast, it’s the one that matters most. 

Ready to build a strategy that starts with your customer? Let’s talk about where your brand stands today — and what it takes to grow from here. 

At Adobe Summit 2026, one thing became clear: AI has moved beyond theory and into practical application, but humans are still in the driver’s seat. 

The Mod Op team returned from Adobe Summit in Las Vegas with a fresh perspective on Adobe’s rapidly evolving ecosystem. Across keynotes, live demos, hands-on sessions, and networking events, we saw how smarter systems, cleaner data, and more connected automation are helping marketers keep pace with rising customer expectations. 

I chatted with Mod Op Director, Marketing and Automation Strategy Tracy Smith, Senior Manger Marketing Automation Grace Han, and Manager, Campaign and Journey, Lindsay Chu to get their biggest takeaways from this year’s Adobe Summit. Here’s what they had to share.  

From AI Hype to AI in Practice 

A major theme throughout Summit was that Adobe is no longer positioning AI as a distant possibility. This year’s focus was on practical, embedded AI tools that support real marketing work now. 

One of the biggest announcements was Adobe CX Enterprise, an end-to-end agentic AI system designed to help manage the customer lifecycle. Closely tied to that was the introduction of CX Coworker, an AI-powered assistant built to support audience segmentation, creative asset development, performance analysis, editing, implementation, and reporting. 

What stood out most was not just the scale of Adobe’s AI vision, but its usability. The focus centered on helping teams move faster across three critical areas: 

• Content supply chain 
• Customer engagement 
• Brand visibility 

The Summit team also noted a stronger emphasis on Adobe Journey Optimizer, Business-to-Business edition (AJO B2B), signaling Adobe’s continued push toward more connected, journey-based orchestration across the B2B marketing ecosystem. It allows designing hyper-personalized journeys with the help of AI virtually in seconds.  

The platform was positioned as a more modern, drag-and-drop environment for omnichannel orchestration, with Marketo continuing to power critical back-end marketing operations. What made AJO B2B especially noteworthy was the amount of attention Adobe gave it, suggesting it will play a larger role in how B2B marketers design and manage connected customer journeys going forward. 

Marketo’s AI Evolution is Built for Efficiency 

For anyone working in marketing operations, some of the most exciting updates came from Marketo. Several stood out as meaningful time-savers for enterprise teams: 

• Interactive webinars with generative AI 
• Image to HTML Converter 
• GenAI for copy, image, and subject line generation 
• Adobe Express integrations for quicker image edits 

These updates matter because they reduce friction in day-to-day execution. Instead of switching between disconnected tools or rebuilding assets from scratch, marketers can increasingly work within a more unified environment. That is a big deal for operational teams. It means less time spent on manual production and more time focused on campaign quality, segmentation, testing, and performance. 

One standout demo showed Marketo’s generative AI transforming a hand-drawn napkin sketch into a functional email template in minutes. The workflow was simple: upload the sketch, and Marketo builds the email structure directly from the drawing. It still requires human refinement, but the technology can take teams from idea to a highly developed prototype from a single image, dramatically accelerating the path from concept to execution. 

The Rise of AI Agents in Marketing Operations 

One of the most talked-about innovations was Marketo’s growing use of AI agents, including: 

• Prebuilt Agents 
• Callable Agents 
• Model Context Protocol (MCP) Server 
• Product Knowledge 

Prebuilt agents offer a range of practical features. They can run quality assurance (QA) on a program, generate a downloadable QA report, and support lead imports by cleaning data, removing duplicates, and normalizing state and country values before records are brought in through the Import Leads agent. The current prebuilt agents are: 

• QA Agent – Ensures program meets quality assurance and generates a QA report 
• Import Leads Agent – Simplifies lead intake by cleaning data, removing duplicates, and normalizing values like country and state 

Other features, such as the Lead Investigation Agent and Create Program Agent, are still in development. Once available, the Lead Investigation Agent will help marketers save time by surfacing activity history and answering questions such as why a lead did not become an MQL. 

Callable Agents can be triggered directly within Smart Campaign workflows to automate tasks such as bot detection and data standardization, normalization, and enrichment. They can also automatically fix inconsistencies (for example, company names, job titles, and country codes) before a record is stored or routed to the CRM. This eliminates the need for manual cleanup or downstream fixes, reducing errors and preventing broken workflows. 

What makes them especially valuable is that they bring AI execution into existing marketing operations processes, helping teams reduce manual work, improve data quality, and move faster without leaving Marketo. 

The  MCP Server was another notable announcement. It is designed to connect Marketo with external AI tools such as ChatGPT, Claude, and others, opening the door for more connected AI workflows. 

The Product Knowledge  feature also impressed the team as an in-product coach that can provide step-by-step guidance backed by Adobe documentation. 

Better Interfaces Support Better Adoption 

Adobe’s evolving user experience, particularly within Marketo, introduced a modernized interface and redesigned email builder that make it easier for teams to create, edit, personalize, and QA assets with greater speed and efficiency. Adobe is listening to user feedback: improvements to HTML access, drag-and-drop editing, reusable fragments, and brand alignment checks suggest a more mature and practical product direction. 

For brands managing complex campaigns across audiences and business units, usability matters. Better interfaces support faster adoption, fewer errors, and more scalable execution. 

Beyond Marketo: Other Standout Summit Demos 

While Marketo updates drew a lot of attention, there are several broader Adobe innovations worth watching. 

• Adobe Firefly + Sharpie: One hands-on session showed how  Adobe Firefly can turn simple drawings into AI-generated content. The demo underscored how quickly rough ideas can become usable visual assets, social content, and design outputs.

• Brand Audit with Adobe: Adobe’s Digital Opportunities Portal can generate a brand performance report based on company name. For teams focused on visibility and digital maturity, this kind of audit functionality offers a useful shortcut to identifying opportunities.
• Adobe Pro and Express: Another standout area was the growing set of AI tools built into Adobe Pro and Adobe Express. Features like PDF spaces, AI Assistant, brand review, audience-specific summaries, and even podcast generation show how Adobe is embedding automation into tools marketers already use every day.
• Adobe Experience League: A practical resource for ongoing learning, especially its AI training content and Acrobat updates.

What This Means for Brands Right Now 

Another important thread from Summit was Adobe’s view that the traditional marketing funnel is no longer as linear as it once was. Customers are increasingly using LLMs and AI tools to research vendors, compare solutions, and narrow consideration sets before ever arriving at a website. That means brands need to think beyond conventional funnel planning and begin preparing content, structure, and discoverability for an AI-shaped buyer journey. 

Adobe’s introduction of  LLM Optimizer  reinforced this shift. As AI-mediated discovery becomes more common, brands will need to think more strategically about how they appear in AI-generated answers and recommendation flows. 

Top 10 Tips for Future Adobe Summit Attendees 

Adobe Summit is always packed with learning opportunities, but it is also a lot to navigate. Here are a few of the most useful takeaways from the Mod Op team for anyone attending in the future: 

• Arrive the day before.  You’ll have time to get your badge, get oriented, and start the week with less stress. 
• Plan your schedule early.  Sessions fill up fast, so use the Summit app as soon as it opens. 
• Block time for meals.  If you don’t schedule lunch, the day will absolutely run away from you. 
• Wear comfy shoes.  This may be the most important tip of all. The attire is business casual and sneakers are completely acceptable. Your feet will thank you. 
• Hydrate constantly.  Conference air, hotel air, desert air — it all adds up. Bring a water bottle and refill whenever you can. There are plenty of refill stations throughout the conference. 
• Take breaks.  You do not need to make every single session. 
• Find the meditation room or recharge spaces. When you’ve spent hours surrounded by thousands of people, a few quiet minutes can make a huge difference. 
• Sit a few seats in. It’s a simple networking trick: people are more likely to sit next to you, which makes it easier to strike up conversations. 
• If you’re getting certified, do it before the conference starts. That way you can spend the event focused on learning, networking, and having fun. 
• Accept that you may get lost in The Venetian. Everyone does. 

And yes, if the therapy dogs make a return, they are absolutely worth a visit. 

Final takeaway 

Adobe Summit 2026 made one thing clear: the future of marketing will be shaped by connected systems, stronger automation, practical AI, and a growing ability to identify data anomalies faster and more accurately. But success will still depend on the people guiding the strategy behind it all. 

That is where Mod Op comes in. As an Adobe partner with Adobe Certified Masters – Marketo Engage Architects on our team, Mod Op helps clients turn marketing automation strategy into scalable, high-performing execution across all major marketing automation platforms and CRMs, including Adobe.

This year Mod Op attended the POSSIBLE Miami Marketing Conference alongside several of our clients – including DoubleVerify, Media.net, and Yahoo. As one of the marketing and ad tech industry’s premier annual conferences, the event brought together vendors, platforms, and decision-makers from across the ecosystem. 

While not every attendance scenario was equally valuable, there’s plenty to unpack about what the conference revealed and what it means for the industry moving forward. Here are just a few key takeaways:  

Takeaway #1: Know Your Audience, And Whether They’re Actually There 

One of the most eye-opening realizations from POSSIBLE was that the conference’s value proposition depends heavily on who you are. 

If you’re an ad tech vendor selling to other ad tech vendors, POSSIBLE delivered. The opportunity to connect with fellow vendors, explore partnerships, and showcase solutions in one place was genuinely valuable. But if you’re an ad tech player hoping to land conversations with brands and agencies, the picture became murkier. Brands and agencies were certainly present at the conference, but the dynamics made organic conversations challenging. The high volume of vendors on the floor meant brands and agencies were naturally more guarded about their availability.  

This raises an important question for the conference itself: what will POSSIBLE 2027 – and beyond – look like? 2026 may be a pivotal year for POSSIBLE. For the conference to remain a must-attend, it needs to create genuine cross-functional value beyond vendor-to-vendor connections. The key takeaway is that if you’re specifically targeting brand and agency relationships, POSSIBLE may require a different approach than traditional vendor networking events. Understanding these dynamics upfront will help you determine if the conference aligns with your specific goals. 

Takeaway #2: Agentic AI is the Buzzword (But Adoption is Still Early) 

If you spent more than 30 minutes on the conference floor, you heard one word repeated constantly: agentic. 

Nearly every company with something new to show was launching agentic solutions, discussing the potential of agentic AI, or positioning themselves as leaders in autonomous ad tech capabilities. The industry’s enthusiasm was palpable – and for good reason. Agentic systems promise to reduce manual overhead, improve efficiency, and create new possibilities for campaign optimization and automation. 

That said, the reality check is important: we’re still in the very early innings of adoption. Most of these solutions are in their infancy, and real-world AI agent deployment at scale is still limited. However, what’s encouraging is the speed at which companies are moving. The fact that so many players are investing heavily in agentic capabilities suggests the industry believes in the potential, and that momentum typically signals innovation that’s worth paying attention to. 

The companies that get agentic right in the next 12-18 months will likely establish significant competitive advantages. The rest may struggle to catch up. 

Takeaway #3: The Real Value is in the Room 

Here’s something that might seem obvious in hindsight, but it bears saying: the programming and agenda at POSSIBLE are secondary to the experience itself. 

The real value is in networking. The conference’s physical setup – everyone housed in one location for the entire event – creates an environment where you’re constantly bumping into people, having conversations, and making connections. You’re not rushing between multiple venues. You’re not juggling competing tracks. You’re just… hanging out, talking to people, and building relationships. 

That intimacy and accessibility is powerful. Whether you’re trying to close a deal, stay updated on industry movements, or simply maintain relationships with peers, that concentrated environment creates unique opportunities. In an industry increasingly driven by data and automation, there’s something valuable about the old-fashioned art of face-to-face connection. 

Looking Forward 

POSSIBLE 2026 clearly defined where the industry is heading and what truly matters to be successful in today’s marketing landscape. 

As the industry continues to evolve, events like POSSIBLE will remain important – but only if organizers can create equal value for brands, adtech players and agencies. Until then, ROI will depend on what attendees aim to get out of the experience. If you’re there to network and stay plugged into industry trends, it’s worth the investment. If you’re hoping for something more structured or predictable, it might be worth reconsidering. 

Either way, the conversations that happen in that Fountain Blue lobby will likely shape marketers’ decision-making through the remainder of 2026 and beyond. 

The brands winning in 2026 aren’t the ones with the biggest budgets or the boldest bets – they are the ones who’ve mastered the art of purposeful experimentation. Technology is expanding the boundaries of what’s possible in marketing faster than most teams can operationalize it, and the brands thriving are those who’ve stopped waiting for certainty before they act.  

Building a culture of experimentation isn’t about throwing ideas at the wall. It’s about creating conditions where smart, low-risk innovation becomes a repeatable discipline, built into how teams work every day. And, in a marketing environment that never stops evolving, that matters more than ever. 

Inside the Experimentation Mindset 

Real innovation requires teams to challenge ‘seemingly known’ assumptions, share what isn’t working, and keep going anyway. We tapped leaders from across Mod Op to find out how they’re fostering that mindset and the results they’re already seeing. 

What does a culture of experimentation actually look like day to day?  

According to Mod Op’s Chief Technology Officer, Tessa Burg, for many marketing teams, experimentation is already happening, though it isn’t always recognized as such. Testing headlines in paid search, rotating creative variations, trying new CTAs in emails or on landing pages: that’s all experimentation in real time. The key is making it intentional. “The most effective approach is to meet teams where they are,” says Tessa. “Point to areas where the call for innovation is already being answered and say, ‘Look, you’re already doing it. Now let’s apply that same thinking more intentionally.’ From there, it’s about layering in structure gradually: encouraging early bets, building toward measurable hypotheses, and scaling what works.” 

For Co-Chief Creative Officer, Steve O’Connell, the most active experimentation ground right now is AI workflows. “With new technologies and platforms being released every day, teams are constantly trying new tools out, both as a collective creative department and also individually, on their own time. This results in a lot of conversations of people sharing how they found a way to make their lives easier.” 

Where do you see the most opportunity for marketing teams to test and innovate in 2026?  

When it comes to where the most room for innovation exists, Steve sees pitches as an underrated experimentation runway. “The goal of a pitch is usually to show off how you think and the ideal way a partnership would unfold, so it naturally offers space for teams to think about what could be, as opposed to how things have to be based on the pressures of day to day.” 

Patty Parobek, SVP of Artificial Intelligence Transformation, however, naturally points to AI: specifically, AI-driven personalization, generative engine optimization (GEO), and agentic AI across content creation, creative production, and ad management, with built-in human oversight. Emerging paid channel tactics, like advertising within AI platforms, are also worth watching. But she flags something less obvious: the client relationship itself. “A major unlock is identifying and prioritizing customer segments or clients who are themselves bold and ambitious. Partners who are genuinely ready to experiment alongside you will accelerate what’s possible far more than any internal initiative alone.” 

Marketing teams need to rethink promotion as part of the marketing mix. Sasha Dookhoo, VP of PR, believes they need to stop treating promotion and PR as a megaphone and start using it as an active testing ground for brand relevance and trust. “The biggest opportunity for marketers is in AI discoverability, because if your brand is not showing up in LLM answers, you are disappearing before the click even happens.” Marketing teams also need to better utilize their executive bench. “By turning executive voices into a performance channel, brands can drive credibility and build the pipeline.” Right now, leadership content has become very one dimensional, being pushed out in long-form content and social promotion; as such, a multi-pronged approach is critical. “Thought leaders need to be leading relevant conversations and driving collective industry knowledge. The winners in 2026 will be the brands that test faster and build authority across every single touchpoint of their target audiences.” 

What’s the biggest obstacle holding teams back from embracing experimentation? 

“Ask any practitioner what’s standing between their team and more experimentation, and the answer is almost always the same. It’s time,” says Patty. “Teams rarely lack curiosity or willingness; they lack the bandwidth to prioritize learning alongside everything else on their plates.” Her prescription: leaders must model it visibly by engaging with new ideas, carving out resources, and setting concrete starting goals that don’t feel overwhelming, like committing to one new experiment per month and sharing findings back with the team. “Significant cultural change doesn’t happen without that visible, sustained leadership commitment behind it.” 

Steve agrees. “Most days and weeks, teams are trying to hit deadlines and get deliverables out the door. But AI is beginning to shift that dynamic. As AI tools shave time off routine tasks, teams are starting to reclaim the bandwidth to explore and staying ahead depends on it.” 

Start Small, Stay Consistent 

The possibility mindset isn’t wishful thinking – it’s a practice. In a landscape where technology is rewriting the rules of what marketing can do, the agencies that will define the next era are the ones building the muscle to try, learn and adapt faster than the competition. A culture of experimentation isn’t a departure from rigor; it’s rigor applied to the unknown. Start where you are, test something small, and let the learning compound. That’s how the possibility becomes strategy. 

There was a time when consequential marketing decisions felt unmistakably human. 

A pricing shift would trigger debate. A targeting strategy would raise hesitation. Someone in the room would ask whether the short-term lift justified the longer-term signal that the strategy sent. The discussion might have been imperfect, but the decision was visible – and owned. 

Today, that same adjustment is more likely to be executed automatically, and might follow a pattern like this… 

A pricing engine recalculates in real time. A personalization model reshuffles exposure. A predictive scoring tool suppresses certain audiences. The dashboard updates. Performance improves. No meeting required. 

 This new pattern is progress, and a redistribution of authority. But, left unmanaged it is the precondition for zombie governance: oversight appears intact, but real decision-making is absent. 

Modern marketing organizations now oversee systems that make thousands of consequential decisions per hour: who sees which message, who receives which offer, how media budgets reallocate, and how journeys evolve mid-stream. Automation delivers speed, consistency, and measurable gains that no CMO can afford to ignore. But as execution accelerates, leaders will have to address question: is judgment scaling with it? 

Optimization Is Not the Same as Judgment 

Optimization systems are extraordinarily effective at achieving defined objectives. If the goal is conversion efficiency, they will pursue it relentlessly. If the objective is margin discipline, they will adjust inputs accordingly. They operate exactly as designed. 

Judgment operates differently. It emerges when objectives collide — when efficiency conflicts with fairness, when margin pressures strain loyalty, and when personalization crosses into discomfort. Judgment requires someone willing to weigh trade-offs in context and assume responsibility for the outcome. 

As marketing stacks mature — AI-driven targeting, dynamic pricing, predictive segmentation, real-time content orchestration — most organizations strengthen oversight. through dashboards, validation cycles, compliance reviews, and reporting cadences. While, these mechanisms are necessary, oversight is not governance. Oversight confirms that a system performed within tolerance. Governance asks whether what it produced aligns with brand authority and long-term trust. 

A pricing engine can increase yield while alienating loyal customers. A targeting model can improve efficiency while narrowing exposure in ways that feel exclusionary. A personalization system can drive engagement while eroding the sense that anyone is truly listening. In each case, the system functions, but the brand may not. 

How Zombie Governance Takes Hold 

The real risk is not spectacular failure. It is an incremental displacement. 

Recommendations become defaults. 

Defaults become norms. 

Norms become outputs that no one revisits unless something breaks. 

Over time, human involvement shifts from deliberation to validation. This is zombie governance in its most recognizable form, instead of asking whether an outcome is appropriate, teams confirm that the logic was applied correctly. Intervention begins to feel inefficient. Override slows throughput. Throughput affects metrics. Metrics influence incentives. 

No one sets out to diminish judgment. It recedes quietly, through rational delegation. 

Internally, performance remains strong. Externally, customers begin to experience decisions as precise but impersonal. Appeals route back into the same system logic. Trust this before metrics show stress. 

Because nothing appears broken, nothing feels urgent. 

What Governance at Machine Speed Actually Requires 

If automation is now core marketing infrastructure, governance must mature with it. That requires moving beyond performance management toward an authority architecture. 

Marketing leaders should be asking: 

• Where does real decision authority reside?
  Is there meaningful capacity to intervene in context, or only at quarterly review cycles? 
• Who owns automated outcomes?
  When decisions generate backlash, is responsibility explicit? 
• Is override culturally safe?
  Does your organization reward responsible intervention — or quietly penalize it because it disrupts efficiency? 
• Are you measuring trust, not just efficiency?
  Do you track legitimacy signals, escalation patterns, and brand friction alongside ROAS and conversion rates? 

These are not philosophical questions. They are competitive differentiators in AI-shaped markets. 

Why This Matters Now 

You are not optimizing alone.  When competitors deploy similar AI-driven frameworks, variation declines, experiences converge, and markets become more synchronized and more brittle. Individually rational optimization can collectively erode differentiation and trust. In that environment, performance will normalize while judgment will differentiate. 

Brands that endure will not simply be those that optimize fastest. They will be those that demonstrate visible stewardship — organizations where leadership remains substantively present in the decision-making systems. 

Execution will continue to accelerate. That trajectory is set. 

The open question for modern CMOs is whether governance will keep pace — whether you merely supervise your systems or can still meaningfully govern them. 

Ready to Assess Your Governance Maturity? 

If your organization is scaling AI-driven marketing, personalization, or pricing systems, now is the time to evaluate whether governance has kept pace with performance. 

At Mod Op, we help enterprise marketing leaders design data, AI, and activation ecosystems where efficiency and accountability scale together — ensuring that brand authority remains intact as automation expands. 

If you’d like to assess whether judgment remains visible inside your marketing architecture, let’s start the conversation. 

This Women’s History Month, we sat down with Tessa Burg, Mod Op CTO, to talk about the leadership lessons she’s collected across her career, from building her first website at 16 to the ups and downs of co-owning a strategic marketing agency during a global pandemic, and now guiding AI and Innovation strategy at Mod Op. 

You host Mod Op’s Leader Generation podcast, which is focused on leadership and growth. What have you learned from guests that shaped how you think about women in leadership? 

We launched it right as the world shut down in 2020. Within days, every business saw clients pause or cut budgets, and everyone was suddenly navigating the same uncertainty. The Leader Generation podcast became a space for shared learning, a way to talk openly about leadership, personal journeys, and what it really means to support other people. 

Our first guest was Linda Owens from Nestlé Professional, who talked about managing and elevating your career. She’s now Head of Global Digital Marketing at Sherwin-Williams. What struck me about her, and so many guests since, is how many women have had to build their own path without a clear roadmap. Nobody handed them a playbook. They figured it out and then turned around and shared what they learned with others.  

A lot of meaningful relationships grew out of those early episodes, and because it inspired everyone involved, we’ve kept it going. 

That theme of navigating without a roadmap comes up a lot in your own story too. How do you lead with confidence when the answers aren’t always clear? 

My grandmother used to say: “If you speak to people like adults, they’ll act like adults.” I think about that a lot. Being transparent about what you don’t know isn’t a weakness. It’s what builds trust. None of us knows exactly what the next two or three years will look like, but we have to imagine it together. That requires involving people with different perspectives, including ones we don’t always agree with. 

There are also hard truths worth naming: Across our industry, many entry-level and mid-level roles, as they exist today, will be eliminated by advancements in technology. That’s not the same as saying people will be eliminated, but it does mean we have to define the work of the future and invest in getting there. Asking questions and listening is far more important than having the answer. 

You mention the importance of involving people with different perspectives and investing in their futures. Was there someone in your career who did that for you at a critical moment? 

So many times. It makes me want to tear up, honestly. I’ve been given so many opportunities and pushed, and I’m just so grateful. 

The first one was when I was 16. I wasn’t exactly well-behaved and didn’t go to school a lot. I loved desktop computer games: King’s Quest, AOL chat rooms. My uncle’s company needed a better web presence, and he told me he thought I could help his team build a website and get them more business. That was the first time anyone ever thought I could really do anything. 

I took community college classes, and was able to successfully contribute to launching the site. Later I started my own company where we implemented one of the first e-commerce engines, before Google existed. From there, because I had those skills, I got an internship at a Fortune 500 company. I was the only female in internal IT, which at the time just meant they knew their team needed to be more representative of the population. I got to work with teams in India, and my first boss was based in Bangalore. He became my reference for my next five jobs because of how much of an impact he had on me. 

After that, I actually met someone in a mall who got me into a branding and advertising agency as an account coordinator. I’d never taken a marketing class in my life. And in everything that followed, someone would open a door, put up a really big challenge, and tell me they believed in me. I try so hard to do that for people on my team now. If you’re looking for your next big leap, find out what the big challenge is and ask for that opportunity. 

You’ve taken that same spirit of opening doors and giving people big challenges into your leadership at Mod Op. How do you balance that drive for innovation with enablement and operational stability?  

My answer has changed a lot over the last four years. Early on, we pushed hard to integrate AI into our work — and it didn’t land the way we expected. The harder we pushed, the slower things moved. What actually worked was building confidence internally, one person at a time. We announced a pledge to invest $10 million in AI, but it was really a $10 million investment in our people. 

Today, we think about innovation in two tracks: 

Productivity, which makes us faster and smarter 

Possibility, which is about extending those capabilities to create better, more personalized experiences for the brands we work with.  

With about half the company now through our internal AI transformation program, our teams are showing up to conversations with CMOs and brand leaders from a place of genuine experience, not just theory. That authenticity is what builds real partnerships, and it’s proving to be the fastest path to doing meaningful work together. 

As Mod Op’s CTO you oversee AI and ML adoption, governance and innovation. With the insight from your role. Do you think we’ve hit peak AI, and what’s your biggest prediction for the year ahead?  

Oh no. We’ve barely scratched the surface of what is possible with AI. Currently, AI ranks around issue number 35 in terms of public concern in this country. That’s way too low. In change management, the pain of not changing has to be higher than the pain of changing. Most people haven’t hit that point yet, and that’s concerning because there are no real controls on AI right now. 

The AI landscape is still largely shaped by a handful of major players. Schools are behind, and some don’t even allow AI tools. Everyone should be channeling their anxiety into upskilling and pushing for schools to build curricula for the jobs of the future. When you look at LinkedIn, there are a lot of job postings and not enough people who meet the requirements. Addressing that gap is the real issue. 

What specific AI skills should people be developing right now? 

I think everybody should have basic data science skills. I know that sounds controversial, but understanding the fundamentals of statistics, machine learning, and feature selection helps you unlock what AI tools are actually doing so much faster. Back in 2014, I dragged our now-VP of Transformation to a week-long data science bootcamp. She had to learn Python first. She was not happy at first but saw the benefit afterward. Those foundations matter. 

The other thing I’d add, and this might sound even more surprising, is philosophy. You have to be able to dream. If you can’t theorize about what problems are going to emerge and why, no amount of technical skill will help you build solutions for the future.  

–  

During Women’s History Month, Tessa reminds us that innovation isn’t defined by tools or trends but by the people willing to dream boldly and support one another along the way. As AI reshapes industries, her leadership shows how curiosity, and inclusion can guide us through uncertainty and toward a future where more people get to participate in building what’s possible. 

In the race to adopt artificial intelligence, many large enterprises are identifying exciting use cases, from AI-powered chatbots to underwriter co-pilots. But what separates genuine, transformative AI adoption from expensive pilots that fail to scale? The answer lies not just in selecting use cases with high business impact, but in rigorous prioritization based on foundational readiness. 

We recently worked with a large insurance organization, analyzing four AI use cases across customer service and underwriting. The finding? Successful AI implementation depends on addressing underlying data and system capability gaps first. 

The Trap of Fragmented Systems 

Understanding why AI initiatives stall requires looking beneath the surface at what’s actually missing. Many organizations envision AI augmenting their workforce, equipping service agents with a co-pilot that offers real-time insights and a 360-degree view of the customer. However, this vision quickly clashes with reality. For this insurer, foundational challenges plagued their environment, including a fragmented data landscape, system silos, and the absence of a unified customer view across key systems such as policy, billing, and claims. 

When the core challenges are rooted in data integration, system architecture, and process automation, focusing resources solely on advanced AI tools is a costly mistake. AI thrives on rich, contextualized, and timely data. If customer data is scattered, incomplete, or lacks consistent cross-system synchronization, AI algorithms will struggle to provide personalized support or accurate predictions. 

For example, in the case of our insurance client, initiatives such as AI-driven claims chatbots or policy file review tools were fundamentally constrained by the fact that data is updated in scheduled batches rather than continuously in real time, as well as by long-standing gaps in capturing the end-to-end customer journey across digital channels, call centers, and abandoned Interactive Voice Response (IVR) interactions.  

The key takeaway is clear: foundational elements must be addressed before implementing AI-based solutions. 

AI and Prioritization: More Than Just Strategic Alignment 

Every potential AI project evaluated in this engagement with our insurance client aligned with the organization’s strategic objectives, such as enhancing culture, improving customer value, and boosting financial health. But strategic fit isn’t enough. For genuine, scaled AI deployment, enterprises must prioritize solving infrastructural problems that unlock multiple future use cases – not just one. 

Here’s the critical factor that often gets overlooked: without a Unified Data Services Platform, each AI initiative solves a singular problem while creating another – a collection of disconnected tools that are unable to scale. Instead, organizations should prioritize investments that establish core technical prerequisites, such as consistent data governance and a centralized customer data platform (CDP). 

Building a robust data infrastructure supports the progression of AI maturity across the organization, from AI-assisted recommendations to guided workflows and eventually task automation. This means that the projects that fix data latency, label historical data for accurate model training, or standardize integration architecture should receive urgent attention and commitment from senior leadership. 

Building for Scale, Not Just Pilots 

AI is not a shortcut around fragmented systems. Scalable, responsible AI adoption depends on an explicit platform strategy and on prioritizing the unglamorous but essential work of data integration, governance, and unification.  

Organizations that invest in these foundational capabilities first position themselves to deploy high-impact AI solutions that are not only innovative but also scalable, sustainable, and trusted. In enterprise AI, the path to transformation starts below the surface 

So, before you launch that next AI pilot, ask yourself: are we building solid ground, or just piling innovation on top of dysfunction?

Account-based marketing, or ABM, has evolved from a niche strategy into a core approach for modern B2B organizations. ABM, in a nutshell, is a highly targeted B2B marketing strategy where marketing and sales teams work together to target and engage specific high-value companies with personalized campaigns designed exclusively for them.   

Instead of marketing to a broad audience and generating many leads, ABM focuses on:  

• Identifying target companies (accounts) 
• Understanding key decision-makers within those companies  
• Delivering tailored messaging and content  
• Aligning marketing and sales efforts to win that account  

As buying behaviors change, traditional lead-based models are becoming less effective, especially in complex B2B environments with long sales cycles and multiple decision-makers.   

To get a ground‑level view of ABM’s evolution, we spoke with Rachel Zavala, a Senior Manager, Campaign & Journey Automation at Mod Op, about how ABM has developed, why it matters today, and how organizations can successfully operationalize it using integrated platforms. Here’s what she had to say, drawing on more than 15 years of experience in marketing automation and operations.  

What companies should know about ABM?  

Account-based marketing isn’t a tactic, and it isn’t a campaign type. It is a strategic operating model for B2B growth, and that distinction matters because companies often adopt the language of ABM without restructuring their go-to-market motion to support it. Modern B2B buying involves larger buying committees, longer evaluation cycles, and significant amounts of anonymous research, which is why most analysts now recommend an account-led approach for high-consideration B2B engagements.   

What most companies should understand before launching ABM is that:  

• ABM is built around accounts and buying committees, not individual leads.  
• Sales and marketing must operate as a single team, with shared targets and a shared definition of success.  
• Data quality is foundational. Without trustworthy CRM and marketing automation data, ABM efforts will plateau quickly.  
• It is a long-term commitment, not a quick-win program.  

Companies that succeed with ABM typically begin with a clear ideal customer profile (ICP), a focused target account list, and a small set of repeatable plays before they scale. The teams that struggle most are usually the ones trying to do too much, too soon, with too little internal alignment.  

What are the core elements of an effective ABM strategy?  

A strong ABM strategy is grounded in fundamentals before it’s grounded in technology. The core elements typically include:  

• A defined ICP and target account list (TAL) that integrates firmographic, technographic, behavioral, and intent signals.  
• A clear understanding of the buying committee within each account, including champions, economic buyers, technical evaluators, and influencers.  
• Tightly aligned marketing and sales motions, ideally with shared definitions of stages, marketing qualified accounts (MQAs), and pipeline contribution.  
• A coordinated, multi-channel engagement plan across paid media, email, web personalization, content, sales outreach, and events.  
• Marketing automation and ABM platform integration, so account-level signals translate into action across systems like HubSpot, Marketo, Eloqua, or Pardot.  
• Account-level measurement focused on engagement, account progression, and pipeline influence rather than only lead-based metrics.  

The piece marketers most often underestimate is measurement. ABM should be evaluated at the account level, not the lead level. Without that mindset shift, even the most expensive platform investment will look like underperformance.  

What is the dark funnel?  

The dark funnel is the portion of the B2B buyer’s journey that happens anonymously and outside your owned channels. It includes review sites like G2 and TrustRadius, peer communities, podcasts, Slack groups, vendor comparison content, syndicated articles, and search activity that never results in a form fill or website conversion.   

This is meaningful because most modern B2B buyers don’t engage with vendors until they’re already deep into evaluation. By the time someone fills out a form, they may have already shortlisted you or excluded you. That’s why intent data, third-party signals, and anonymous research patterns are now central to how mature ABM programs prioritize accounts. The dark funnel requires you to listen better.  

What tools are available on the market? 
 

A handful of ABM platforms come up consistently in industry research and analyst reviews like the Gartner Magic Quadrant for ABM Platforms and Forrester evaluations. The most commonly used today include:  

• Demandbase  
• 6sense  
• ZoomInfo Marketing  
• AdRoll ABM (formerly RollWorks)  
• Terminus / DemandScience  
• Common Room  

In addition to a core ABM platform, many companies use adjacent tools to enrich their data, expand their intent signals, or strengthen account-level personalization and outreach. Common examples include:  

• Intent and signal data: Bombora, TechTarget, G2  
• Sales engagement and outreach: Salesloft, Outreach  
• Contact and firmographic data: ZoomInfo Sales, Cognism  

What are the pros and cons of these tools?   

Platform decisions usually come down to fit, integration depth, and how well the tool supports your team’s workflow rather than standalone features.  

Valuable insights and functionality across leading platforms:  

• Integrated intent data  
• Account scoring and predictive insights  
• Audience activation in advertising and on the website  
• Lead-to-account matching  
• Sales alerts and prioritization  
• Account-level reporting  

Considerations to keep in mind:  

• Investment level — enterprise ABM platforms typically require a meaningful annual investment.  
• Setup expertise — these platforms are powerful, and getting the most out of them works best with internal expertise or a partner who can help configure, optimize, and maintain them.  
• Data readiness — ABM platforms perform best when CRM and marketing automation data is clean, complete, and well-structured, which often makes data foundation work part of the rollout.  
• Implementation runway — most platforms take a few months to fully operationalize, so building in time for setup, integration, and team enablement leads to stronger long-term outcomes.  

Can a company start ABM without a subscription to an ABM platform?  

Yes. The most common misconception is that companies need a platform to “do ABM.” They don’t. What they need is clarity around target accounts, a defined buying committee model, content built for the journey, real alignment between sales and marketing, and disciplined measurement. A platform can’t replace strategy, but it can accelerate scale.  

Many organizations are better off starting without a platform, especially mid-market companies, early-stage B2B teams, and organizations still building out their marketing operations. A team can lean into ABM strategies leveraging tools already in their stack.

How soon can a business see measurable results from an ABM campaign?  

ABM is a long-cycle strategy, but it tends to drive stronger pipeline impact, larger deal sizes, faster sales cycles, and improved win rates. It can generate meaningful leading indicators well before pipeline impact materializes.  

Early on, companies typically see better data quality, sharper account targeting, and more visibility into intent and engagement. The companies that see results fastest are usually the ones that start focused, with a well-defined target list and tight cross-functional coordination.  

What are marketers often missing about ABM?  

In my experience, marketers most commonly miss the following:  

• They under-invest in the ICP. Without a strong ICP, ABM becomes “targeted lead gen” rather than account-based marketing.  
• They treat ABM as a channel. ABM is a strategy that runs across channels, not a tactic that lives in one platform.  
• They under-utilize sales alignment. ABM fails when sales doesn’t use the insights or doesn’t agree on the target accounts.  
• They expect quick wins. ABM accelerates revenue, but it does not bypass the realities of long, multi-stakeholder B2B buying cycles.  
• They measure with lead metrics. MQLs and form fills do not capture buying-committee progression or anonymous research.  
• They overlook the dark funnel. If marketers ignore the anonymous research happening, they miss a huge portion of the journey.  

ABM works best when companies treat it as a long-term go-to-market commitment supported by data, automation, and strong sales and marketing alignment, rather than as a quarterly campaign. Organizations that embrace this mindset turn ABM into a meaningful, scalable growth engine, and for teams willing to commit, it offers a data-driven path to sustainable growth in an increasingly complex B2B environment. 

If you are ready to put ABM into practice, our team can help you build the data foundation,  connect the right systems, and run programs that prove impact in pipeline and revenue.

Organizations across every industry are investing heavily in data to drive decisions, improve efficiency, and unlock new value. In the world of AI, data has become more important than ever to feed models, train models, and unlock data as a competitive advantage. Yet many leaders overestimate the strength of their data foundation. This is rarely due to a lack of skill or effort. More often, teams work so closely with their systems that gaps, inconsistencies, and workarounds become normalized over time. 

When was the last time your organization stress-tested its data foundation? Common patterns include: 

• Systems seem integrated, but data flows inconsistently behind the scenes. 
• Reporting looks comprehensive, but teams rely on manual work–arounds to fix quality gaps. 
• Business units use the same terminology, but with subtly different definitions. 
• Structural issues are overlooked because teams have learned to work around them. 

Real Case Example: Two Revenue Centers, One Company, No Unified Sales View 

Data foundation challenges like, inconsistent data flows or manual workarounds, rarely surface without structured, objective questioning. As a result, organizations underestimate the complexity and overestimate the readiness of their data.  

In fact, one of our recent engagements identified this scenario impacting a client and limiting their ability to make strategic decisions about their business. Our client operated two revenue centers (product and service) under a single entity. Sales efforts were combined, but there were no unique identifiers or opportunity IDs linking the full journey of a sale across both centers. Neither group was able to use data about their customers and offerings definitively to drive decision-making.  

Further challenging this organization was the fact that leadership thought they knew what their customers and potential customers wanted from a value and pricing perspective, yet no market or customer research had been performed in the past six years to determine if their customer hypotheses were valid. 

On the surface, performance reporting looked solid. But once assessed, it became clear that: 

• Opportunities could not be tracked across teams. 
• Leaders could not see where pipeline leakage occurred. 
• Performance metrics were incomplete. 
• Strategic gaps and growth opportunities could not be clearly identified.  

Despite active pipelines and strong sales teams, the business could not answer basic questions about the full revenue lifecycle. Leaders had believed they had “good data”, but the assessment revealed structural barriers that had gone unseen for years.  

The Mod Op strategy team worked with the organization to design and implement a unified opportunity structure and consistent data capture approach. As a result, the organization gained visibility that directly supported better forecasting, strategy, and resource allocation. 

The Value of a Structured Data Assessment and Roadmap 

An effective assessment does more than highlight issues. It defines the path to value.  

A strong assessment evaluates data quality and completeness, system integration and architecture, governance maturity, and process consistency and controls. The resulting roadmap provides: 

• A clear view of immediate risks 
• Actionable recommendations 
• Quick wins that build momentum 
• A realistic sequencing of investments 
• Alignment across technical and business leaders 

This structure helps organizations focus on their resources where they will generate the greatest impact. The most effective assessments come from people who have actually run data organizations such as former operators who have built and owned these environments themselves. There’s a big difference between checking boxes to complete a standardized assessment and knowing which questions reveal underlying issues. Experienced operators recognize when surface level answers mask deeper, systemic problems. Having lived through these challenges firsthand creates an instinct for where the real issues lie, following problems down to their root causes and tracing opportunities to their full potential. 

Objective Insight Clarifies the Path to Value 

At the end of the day, every organization aspires to be data-driven, but self-assessment often clouds the real picture. An objective third-party view – using internal and external data – provides the clarity, structure, and guidance needed to turn data into a true strategic advantage. Leaders who challenge their assumptions and embrace external insight position themselves to make better informed strategic decisions and smarter investments that uncover hidden value, and build a scalable, data foundation that can drive a competitive advantage. 

For leaders ready to move from aspiration to execution, inviting an independent view is often the most impactful place to start. Let’s talk!

Last year, Mod Op Co-Chief Creative Officer Steve O’Connell predicted that “Brands need to learn the hard lesson that people don’t like AI-created ads.” 

So far … he seems to be on track. People don’t love AI ads — at least when they can tell they’re AI-made ads — but brands keep creating them.  

A Reality Check on AI‑Generated Advertising

The disconnect between consumer sentiment and brands’ strategy isn’t accidental – it’s wishful thinking. 

“Companies will keep hoping we reach the point where consumers accept generative AI-produced spots,” Steve shared back in December. “But while the novelty of AI-generated content still exists, the amount of slop is going to grow and the pushback against it will harden.” 

To better understand this gap, we reached out to Steve to get his thoughts on AI-created ads and what separates success from failure.  

What specific qualities make AI-generated ads less appealing to consumers compared to human-created content? 

It’s the AI-generated people. I think audiences, in general, don’t love knowing something is AI-generated, but it’s seeing artificial actors and the uncanny valley that really triggers the ‘ick’ response. There’s a lack of genuine emotional depth that audiences immediately detect – the eyes are just a bit too lifeless. 

Have you seen any brands successfully leverage AI in a way that resonates with audiences, or are all attempts falling short? 

I’d venture to say brands are having success and we don’t even realize it. AI crushes it when it comes to generating inanimate objects – it’s really hard to spot. Same with food photography, which is so retouched anyway. So, I’m sure some brands are using generative AI behind the scenes, staying quiet about it, and having a lot of success. 

The biggest win I’ve seen with a client doing pure generative AI and shouting about it was Kalshi’s “World Gone Mad” spot from last year. Since the concept was about things getting out of control, it made sense to leverage AI. And that seemed to give them more of a pass from the critics. 

What do you think brands could do to make AI-generated ads more authentic or engaging? 

Anytime you’re creating something that would already be done with computers, like visual FX, generative AI is fair game. Audiences tend to accept, or ignore, AI when it’s applied to technical processes rather than creative ones. 

For creative applications, the other path brands have tried is sharing the story behind the spot, focusing on the human effort that went into the production. Although that attempt fell flat for McDonald’s with their holiday spot— it may have softened the blow  

I think brands just need to be selective and not overuse generative AI. More importantly, don’t replace human actors with technology.  

How do you see the consumer pushback against AI-created ads evolving over the next few years? 

This is so hard to know. Logic would say that AI generation will get better and better. If AI-generated video ever becomes indistinguishable from the real thing, consumers simply won’t realize it. And most probably won’t put in the effort to do the research. So, it’s very possible we’ll see a slow progression toward total acceptance, since the economics of generative AI – speed and cost savings – make it increasingly attractive to brands.  

That said, if we see a giant movement against AI usage overall, which is likely given how fast we seem to be moving without guardrails, it’s possible companies will be forced to make a pledge about their AI usage and their willingness to refrain. This isn’t likely to happen but predicting the future beyond a year or so has almost become comical at this point.  

What advice would you give companies eager to use generative AI for their advertising while avoiding consumer backlash? 

Silly company.  

Kidding. I would say start small. We’ve used AI in ways that supplement productions we’ve filmed traditionally. When we wanted to add a little something extra we thought about in the editing room. Or when we wanted to fix something we couldn’t get exactly right on set. Those use cases seem totally fine, since AI is being used to enhance something humans made. 

When it comes to generating a whole spot entirely from AI, my first suggestion would be not to make technology the story when it doesn’t have to be. Normally brands get credit for using new technology and being on the leading edge, but the reception to AI advertising has been mixed at best. Consumers care more about the message and creative, than the tools used to make it — so be transparent about your use of AI, but let the work speak for itself. 

That said, be careful if you’re going to generate actors with AI. That’s the hardest to get right and the first thing audiences will notice. 

Do you think there’s a future where consumers won’t be able to distinguish between AI and human-created ads — or will the difference always matter? 

It’s hard to imagine we won’t end up with AI-generated spots as good as the real thing. I’m not saying an AI actor will ever be as good as Daniel Day-Lewis or Viola Davis. But in most cases, generative AI does mediocrity pretty well. And it will deliver the same results for advertising.  

A lot of brands out there accept mediocrity because it’s good enough. But I’m a creative, so I live by the old saying that good is the enemy of great. I think there will always be a difference between AI-generated and human-made ads. Most brands won’t think that difference matters. Some will. And it’s almost always the brands embracing creativity, authenticity and genuine storytelling that tend to come out on top. 

Moving Forward with AI in Advertising 

The tension between AI capabilities and consumer sentiment isn’t going away anytime soon. Brands have a choice: chase efficiency at the risk of alienating audiences, or find the balance between technological innovation and human creativity. Steve’s advice points to a clear path: use AI where it enhances production, be transparent when it matters, and never compromise on the human elements that make advertising memorable. The brands that figure this out early will have a significant advantage as the landscape continues to evolve.