Interview
Beyond Static Floors: What Publishers Need to Know About Dynamic Flooring in Programmatic Advertising
marketing 23 Jan 2026
1) When publishers talk about their “programmatic floor strategy,” what does that typically look like in practice today? How are most teams approaching this?
In practice, most publishers’ “floor strategy” is still largely manual and backward-looking.
Typically, teams set static CPM floors in GAM (via UPRs) or in their wrapper/Prebid configuration, segmented by broad buckets like geo, device, or placement. These floors are usually based on historical averages, past performance, or rough heuristics rather than real-time demand signals.
They revisit and tweak them periodically — weekly, monthly, or around seasonal moments — but the core approach rarely changes: fixed prices applied across highly variable auctions.
So most “floor strategies” today are really just static rule sets designed for a slower market. They are not built for the speed, variability, or complexity of modern programmatic auctions.
2) Modern programmatic auctions happen in milliseconds with DSPs constantly repricing based on real-time signals. How should that reality influence the way publishers think about setting price floors?
It should completely reshape how publishers think about pricing.
If buyers are making decisions in real time, then floors cannot be static. They need to reflect what is actually happening in that specific auction — not what happened last week or last month.
Instead of asking, “What should my average floor be?” publishers should be asking, “What is this impression worth right now, given who is in the auction and how they are behaving?”
That means treating floors as dynamic, responsive signals — not fixed thresholds — that react to live demand, bidder behavior, and competition in real time.
3) Many publishers measure price floor success by looking at CPM changes, often comparing this week to last week. Is this the most effective way to measure this?
No — it is one of the weakest ways to evaluate floors.
CPM alone is an incomplete metric. It only tells you the price of impressions that are sold. It says nothing about suppressed demand, lost bidders, or auctions that never cleared.
On top of that, before-and-after comparisons (this week vs last week) are fundamentally flawed because the market is constantly changing. You end up measuring market volatility more than pricing impact.
This is why floor changes often look “good” or “bad” depending on timing — not because of the floor itself, but because of shifting demand conditions.
4) What other metrics should publishers actually be tracking to understand whether their floor strategy is working?
The single most important metric is holistic RPM — revenue per thousand ad opportunities (requests) across all programmatic channels (Prebid, Amazon, AdX, Open Bidding).
This metric captures:
● Price impact (CPM)
● Volume impact (fill)
● Buyer participation and routing effects
Crucially, this must be measured per ad unit first, then aggregated to site level. Site-wide averages hide too much.
Beyond holistic RPM, publishers should also track:
● Bid density (bids per auction) — a proxy for competition
● Win rates by bidder — to see which partners are reacting to floors
● Timeouts / drop-offs — signs of demand suppression
● Clearing price distributions — where auctions are actually settling
Together, these give a much clearer picture of whether floors are helping or hurting.
5) What happens at the auction level when a publisher sets a floor that doesn’t align with what DSPs are willing to bid in that moment?
Two things typically happen, depending on the direction of misalignment:
If the floor is too high, DSPs don’t negotiate — they exit. Bidders reduce participation, route budgets elsewhere, or stop bidding altogether. Fill drops, and auctions become less competitive.
If the floor is too low, auctions clear too easily. Bidders don’t need to bid aggressively, competition thins out, and you end up leaving meaningful value on the table — selling inventory below what buyers were actually willing to pay and losing yield in the process.
In both cases, revenue is lost — it just appears differently: either as lower fill or lower effective prices.
6) When publishers analyze floors using site-wide or monthly aggregated data, what critical dynamics are hidden from view?
A lot.
Aggregated data hides:
● How different ad units respond to floors
● How specific bidders behave in specific geos or devices
● Time-of-day demand patterns
● Differences between mobile vs desktop, app vs web, or browser types
You might see “healthy” site-wide CPMs, but underneath that some placements could be massively underpriced while others are choking demand.
Most auction-level behaviors — like bid density shifts or bidder pullback — get completely washed out in monthly rollups.
7) How do price floors influence which demand partners and campaigns even enter an auction? Is it just about setting a minimum price?
It’s more than just having a minimum price.
In modern auctions, price is a signal — but a static price is a weak one. Without a reliable, real-time signal, DSPs have to guess the likely clearing price, which makes pacing and confident bidding harder, even when the audience match is strong.
Dynamic floors strengthen that signal. By adjusting in real time, they give buyers a clearer view of where the market is clearing right now, which makes them more willing to participate and bid at the true value of the impression.
Dynamic floors can also help unlock demand. When a higher, real-time clearing price is visible, it can influence which campaigns DSPs prioritize for that impression.
Finally, dynamic floors improve how inventory moves through SSP and DSP throttling systems. Requests that are priced in line with real demand are more likely to clear — and therefore more likely to pass throttling and access available budgets.
In short, dynamic floors act as a real-time market signal that shapes participation, routing, and budget access — not just a guardrail on price.
8) If a Head of Programmatic wanted to properly test whether a floor change improved performance, what would that test need to look like?
They would need a true real-time A/B test, not a before-and-after comparison.
That means:
● Splitting traffic into a floored cohort and a control cohort running simultaneously
● Measuring holistic RPM per ad unit across both groups
● Ensuring both cohorts see the same demand conditions
● Tracking bidder behavior (bid density, win rates, drop-offs) alongside revenue
Only with this setup can you isolate the true impact of pricing from normal market volatility.
9) What patterns have you seen when publishers shift from static to more dynamic floor strategies? What typically changes in their auction outcomes?
The most consistent pattern is that auctions become healthier.
Typically we see:
● Higher holistic RPM (often 10–16%)
● Slightly higher CPMs (around 7-11%)
● Stable or improved fill
In other words, pricing aligns better with real demand. Publishers capture more value without suppressing liquidity.
Perhaps most importantly, they stop seeing the wild performance swings that plague static floor setups.
10) Where do you see price floor strategies evolving as the programmatic ecosystem becomes increasingly algorithmic on both buy and sell sides?
On the web, pricing is becoming more holistic, unified, and visible inside the auction.
Two recent shifts are accelerating this:
● Amazon now participates in Prebid as a bidder, meaning Prebid floor decisions can directly influence Amazon demand instead of pricing it in a separate silo.
● Google now allows separate pricing for AdX demand in GAM, giving publishers far more control over how AdX is treated relative to other demand.
Prebid floors themselves aren’t new — they’ve existed since early header bidding. What’s changing is that more major demand sources now sit inside a common pricing perimeter.
As a result, I expect floors to become more holistic and consistent across channels, delivering clearer RPM impact and giving publishers greater control over how their inventory is positioned and monetized.
Stop spinning, start gaining traction in 2026
marketing 22 Jan 2026
Marketers are at a strategic turning point. AI is on track to generate nearly 90% of online content. Gartner predicts that 30% of a brand's perception will soon come from generative AI, not human-created work. In this new reality, marketers are asking: How can we build real connection with our customers?
The answer, increasingly, is storytelling. The Wall Street Journal recently reported that companies are “desperately seeking storytellers” as AI floods every channel with look-alike content. Why? Because stories are central to the human experience. Stories help us learn. They make us care.
The real tension in 2026 won’t be whether marketers can produce things, but whether what they produce will matter. As sameness becomes the enemy, the teams that gain traction will be the ones who tell a damn good story.
Disciplined storytelling will cut through the noise.
Research shows that 75% of marketers say AI is now more important to their strategy than it was last year, but more content doesn’t always equal a deeper connection. Producing more video, more copy, more social, more bylines—none of that will matter unless it’s anchored in a coherent story.
The real differentiator in 2026 will be discernment. Producing more will be easy. Producing what actually moves an ideal customer to action will not.
The best brands will resist “random acts of marketing” (a term coined by MKT1 founder Emily Kramer) and create less, better: one core story, reinforced everywhere with precision and intent. When everyone is shouting, consistency and clarity quietly resonate.
Marketers who think in systems, not just trends, will win.
For years, marketing rewarded speed above all else. Now, speed without structure is collapsing under its own weight. The real edge in 2026 will come from how well teams connect the dots between moments, platforms, and outcomes.
The strongest marketers will stop chasing every trend and start architecting connected journeys that mean something to customers, where product, paid marketing, organic social, communications, search, and customer care reinforce each other under a singular, differentiated narrative. This is much harder to get right.
Some narrative bets will fail, but the right ones—the ones grounded in real customer insight and a deep understanding of what customers are up against—will shine. As AI makes execution easier, the connective tissue behind it becomes the true differentiator.
Consumers will reward brands with a unique voice.
The strongest brands in 2026 will stay grounded in who they are, while staying deeply attuned to who their customers are becoming. Authenticity and empathy won’t compete with one another, but rather compound each other.
What’s changing is how voice gets managed. As brand health, message testing, and continuous feedback loops become standard, voice is shifting from a creative flourish to a measurable asset. The SurveyMonkey Trends 2026 report shows a 167% increase in brand attribute measurement templates and a 75% rise in brand tracking, clear signs that brands are now engineering—not guessing about—their impact.
Trust will become the most measurable metric.
AI casts a long shadow over trust, truth, and responsibility. According to HubSpot and SurveyMonkey, 70% of consumers notice AI in marketing communications, but only 47% trust brands to use it responsibly. In 2026, communicators won’t treat trust as an abstract brand value. They’ll track it in real time and link it directly to performance.
With continuous feedback and brand health data becoming standard, marketers will measure how trust builds, erodes, and recovers—then connect those shifts to business strategy. Data from marketing’s brand health tracker should make its way into every executive business review, influencing everything from product decisions to taglines, your refund policy to what your support chatbot says.
Turn motion into momentum in 2026.
Volume and timeliness are out—AI has leveled that field. The real difference-makers will be the humans who bring judgment, restraint, creativity, and connection back into the work.
Going forward, marketing success will belong to the brands that rise above AI-generated sameness with clearer thinking, sharper stories, and a deeper understanding of the people they’re trying to reach. Brand strength will be defined by how deliberately leaders guide creation, and how often they ask the question: why? That responsibility can’t be delegated to technology or tools. It belongs to humans.
What Makes a Great CMO in an AI-Accelerated, Martech-First Market
marketing 20 Jan 2026
Great CMOs are being judged less on marketing output and more on revenue outcomes. That change is not philosophical. It is operational. When pipeline targets rise and buying cycles get messier, the CMO becomes the person who turns strategy into a working system across people, process, data, and technology.
This is also where many teams struggle. Misalignment between Sales and Marketing is expensive. It shows up as wasted time, abandoned deals, duplicate tooling, and budget that can’t be defended. In a martech-first world, alignment breaks for a very specific reason: teams have more tools, more data, and more dashboards, but no shared operating model that turns those inputs into repeatable decisions. AI doesn’t introduce a new problem so much as it speeds up existing ones, making weak alignment and unclear ownership harder to hide.
For many mid-market companies, the gap isn’t ambition. It’s leadership bandwidth. That’s why fractional marketing leadership is showing up more often: not as a “part-time marketer,” but as senior CMO-level ownership focused on building the revenue system and the team habits that make performance repeatable.
The CMO’s Core Job: Revenue Alignment, Not Activity
A strong CMO makes marketing accountable to pipeline, revenue, retention, and expansion. They do it by creating shared definitions and shared accountability with Sales, RevOps, and Customer Success. That means agreeing on what a qualified opportunity is, what pipeline coverage is required, and what “good” looks like at each stage.
The practical implication is that marketing does not “hand off” and disappear. It stays connected to the buyer journey through enablement, nurture, sales readiness, and post-sale growth signals.
They earn their keep by aligning the revenue team around the scorecard, the definitions, and the decisions, not by shipping more assets.
Strategy That Holds Up Under Execution Pressure
A strategy is only useful if it survives contact with the calendar. Great CMOs build a full-funnel plan that connects ICP and segmentation, positioning and messaging, demand creation, nurture and conversion, and expansion and retention.
They also treat brand and demand as one system. Brand makes demand generation cheaper by improving response rates and win rates. Demand gen keeps brand honest by forcing clarity, proof, and focus.
Most importantly, strong CMOs are comfortable saying no. Fewer initiatives, executed well, create more throughput than an overloaded roadmap that nobody can measure. It brings outside-in prioritization and decision discipline when internal teams are stuck in reactive execution.
Turning Martech into a Growth System
A great CMO treats the martech stack like infrastructure. It must be purpose-built, integrated, and adopted. Buying tools is easy. Building a growth system is harder, and it shows up in three choices.
First, they prioritize data flow over feature checklists. The core is clean movement of data across CRM, marketing automation, web analytics, and BI. If lifecycle stages and attribution fields do not travel reliably, reporting becomes politics.
Second, they design for capacity. A complex stack with low adoption is worse than a simpler stack that the team actually uses. This is where governance matters: documented lifecycle stages, required fields, routing rules, and naming conventions for campaigns so performance can be compared over time.
Third, they run an alignment cadence that makes the stack useful. Regular sales and marketing syncs and closed-loop reporting become standard operating practice for teams that want real feedback on lead quality and pipeline movement.
Measurement Executives Trust
Great CMOs build measurement that the CEO, CFO, and CRO can use to make decisions. They define a KPI model that connects daily work to revenue outcomes through leading indicators, pipeline metrics, and efficiency metrics.
They also acknowledge the limits. Attribution is directional, not absolute. It works best when combined with pipeline review, cohort performance, and channel experimentation. The goal is consistency, not perfection.
Measurement is one of the fastest ways to make alignment real because it forces shared definitions and exposes tradeoffs, especially in organizations where marketing leadership is being shared across a fractional CMO, internal operators, and agency partners.
When Fractional Marketing Leadership Is the Right Move
Fractional marketing can be the right fit when you need senior revenue accountability but can’t justify a full-time CMO, have channel executors but no one owning the full-funnel plan, or are drowning in tools and reporting and need governance plus a clear measurement model.
What matters most is not the title or the engagement model. It is whether the person in the seat can see the whole revenue system, recognize where it is breaking down, and make clear decisions about what moves the business forward now versus what can wait. In an AI-accelerated, martech-heavy environment, that judgment is the difference between motion and momentum.
How AI Is Rewriting Trust and Buying Behavior in 2026"
marketing 12 Jan 2026
AI Digital recently released their annual Media Trends report. Can you briefly summarize what you’ve learned and why it matters for marketers right now?
The report shows that the media has crossed a structural threshold. AI now determines visibility, credibility determines recommendation, and premium environments determine performance. The research makes clear that many marketers are still optimizing for reach and efficiency in systems that no longer govern outcomes — for example, brands are still buying on CPM when AI systems don't prioritize based on volume anymore. Right now, the gap between brands adapting to this shift and those clinging to legacy playbooks is widening fast, and it shows up directly in performance.
One of the report’s big takeaways is that AI is changing how discovery works. What does that mean for marketers who still rely heavily on clicks and last-click metrics?
Discovery is no longer a journey, it is a decision moment. AI-driven answers are collapsing multi-touch funnels into single conversations, reducing organic click-through rates by 34–64%. The report is blunt: if a brand is not present inside the AI response, it is not part of the consideration set. This forces marketers to move beyond clicks and last-touch metrics toward measuring visibility, credibility, and influence earlier in the decision cycle. The priority should be ensuring brand information is structured and authoritative enough to appear in AI-generated answers.
Why does trust matter more now, and what should brands focus on to build it?
Trust now operates at two levels, human and machine. Consumers remain skeptical of AI-generated content, and AI systems themselves prioritize reliable, consistent signals when deciding what to surface. Our research has shown that transparency, premium inventory, and verifiable data inputs are no longer brand hygiene, they are performance levers. Brands that treat trust as infrastructure consistently outperform those that treat it as messaging.
How does first-party data factor into building trust and driving performance today?
First-party data is the strongest signal brands actually control. The report shows that campaigns anchored in first-party identity deliver lower CPAs and higher conversion rates, while third-party dependency continues to erode. AI Digital frames unified CRM, CDP, and clean-room infrastructure not as a tech upgrade, but as the foundation for privacy-safe personalization and sustainable performance.
Many marketers are investing in AI tools, but still struggling with performance. Where do you see teams getting stuck or overlooking foundational issues like data and transparency?
AI amplifies everything — good strategy and bad. Teams see breakthrough results when they ensure clean data, transparent supply chains, and clear success metrics before adding AI to the stack. Without these fundamentals, automation simply scales inefficiency faster. We consistently see performance improve only after teams address basics like fraud exposure, measurement integrity, and signal quality. The AI isn't the problem; it's a mirror that reflects the strength or weakness of what's already there.
Why are channels like CTV, retail media, and audio becoming more important, and how are brands underusing them today?
These channels combine attention, trust, and closed-loop measurement, but remain underutilized. CTV now reaches nearly all U.S. households and supports real attribution. Retail media is approaching $69B with full-funnel capability built on first-party data. Audio and podcasts deliver high engagement with relatively low saturation. The report shows brands still undervalue these channels because they don't fit traditional reach-and-frequency or last-click attribution models, even though they increasingly shape how decisions are made. Marketers need to rebuild their measurement frameworks around these environments, not force them into outdated metrics.
Redefining Marketing Seniority in the Age of AI: Why Adaptation Matters More Than Experience
marketing 7 Jan 2026
What traps do traditional marketers fall into when they rely on past knowledge instead of exploring new AI capabilities?
Incorrect assumptions and stagnation. Past knowledge is great, but without layering it with AI, marketers put themselves in a position to be chasing current trends and user behaviors constantly.
As people adopt AI across all aspects of their lives, they begin to expect more, whether it’s quicker answers, higher quality, or more relevant information. Relying on past knowledge without the addition of AI can lead to marketing in ways that don’t resonate with current consumers and cause marketers to react too slowly to underperforming tactics.
Today, a marketer with just a few years of experience can run fully automated, personalized campaigns using AI. How does this change the definition of “seniority” in marketing?
Seniority is traditionally defined by length of time in a career, and for good reason: knowledge is gained with experience. The longer someone worked in the field, the more experience and working knowledge they had, making them more valuable. In most cases, this was the only way to gain that experience.
AI is quickly leveling that playing field in many areas. For instance, let’s say a team is running a paid ad campaign. The veteran marketer would traditionally be leading the team, using his experience to guide the design of the ad. They’d choose the copy, the creative, the target audience, etc. If the campaign was underperforming, they’d have to make assumptions for the reasons why and make changes accordingly. Today, AI tools can assist with all of these tasks by relying on large sets of data, helping to optimize campaigns before they go live, and then adjusting, if needed, once they do.
Marketers who master the AI tools necessary to run optimized marketing programs become the ones trusted to lead efforts moving forward. Experience has its place, but it no longer serves as a defining predictor of success.
You’ve said that marketers who don’t adapt will be left behind. What signs are you seeing today that confirm this shift is already happening?
Businesses are becoming either AI-first or integrating AI into their products and day-to-day processes. Those who don’t adapt to AI won’t understand the business and how to help it grow. To see this in the world, look at the description of any current job opening. Nearly everyone, whether it’s marketing, engineering, or sales, specifically mentions proficiency with AI tools as a requirement.
You’ve emphasized that AI won’t replace people, but people who use AI will replace those who don’t. How do you explain this distinction to marketing leaders who are hesitant?
Let’s look at it from a brand perspective. Let’s say a company wants to run a video campaign that helps with brand positioning in the market. There are nuances to a company’s brand, competitors, and current events that only humans know. AI won’t understand each of these and create a perfect campaign from A to Z. However, AI can help provide ideas, creative suggestions, targeting segment recommendations, and the tools to create the final product.
In this scenario, AI doesn’t replace humans; it helps them execute faster and smarter.
In your opinion, what are the areas of marketing where AI can boost performance without needing a full transformation?
This can happen at nearly every level of an organization. I already mentioned paid ads and the ability to produce content more quickly, and there are plenty more. Let’s look at email marketing.
Brands rely on automated emails because they generate 37% of all email sales from just 2% of messages sent. Traditionally, building automated email flows took time and best practice knowledge. That’s no longer the case. Email platforms offer pre-built workflows and email templates to streamline creation, and infuse AI throughout the process to create more efficient messaging based on individual brands.
What once took marketers days, if not weeks, to create (if they had the knowledge), can now be done in minutes by junior team members. This is the type of transformation that’s possible with AI.
You recommend that marketers make time each day to learn and experiment with AI. What does that look like in practice, 15 minutes, a daily workflow test, or something else?
There are many different AI tools and platforms, learning them all is next to impossible. Gradually increasing knowledge is the key to long-term success. I recommend people spend at least 15 minutes each day learning something new with AI. Of course, if you can spend more, you should.
Spending 15 minutes each day can easily be done by anyone. For example, let’s say you already use ChatGPT for something simple, like brainstorming ideas. Spend 15 minutes experimenting with the prompt and learning which ones produce more or less favorable results. Then, push the boundaries to see if you can get a “pie in the sky" result, even though it may not seem or be possible. Failing can help users understand the limits of a tool, which further informs them of alternate means of achieving a goal, whether it’s through promoting or combining the output with other tools.
If you have more time to invest each day, make a list of things you wish could be streamlined in your day-to-day or “genie in the bottle” projects you wish could be done, and start testing different tools to help you accomplish these goals. With AI, even failure is winning.
Looking ahead, what will differentiate marketers who thrive in an AI-driven future from those who fall behind?
The ability to stack information, combined with critical thinking. As I mentioned, learning how different platforms can achieve different goals is essential. This will be the first line of separation. However, if we assume many modern-day marketers will learn these new tools, there will be a need for a second set of differentiation. Here is where critical thinking, and maybe even experience, come into the equation.
If we have 20 marketers who understand the fundamentals and maybe even advanced use cases of different platforms, no one is ahead or behind. Using critical thinking to determine how different programs can benefit their own department while connecting the results to different areas, like sales, marketing, and customer success teams, is how marketers can prove value to an organization.
I mention experience here because while seniority may be redefined, experience is still experience. Those with experience tend to understand how different teams operate and see the bigger picture. Combining experience with critical thinking, marketers can determine when AI-generated outputs don’t perfectly align with organizational goals — something only humans can determine.
When marketers become reliant on AI and assume every output is “correct,” the result is blandness, unoriginality, and stagnation. This is what separates the pack.
Speaking the CFO’s Language: How Unified Measurement Is Redefining Marketing’s Role in Growth
marketing 7 Jan 2026
This gap exists because marketers aren’t suffering from a skills problem, they’re suffering from a structural one. Fragmentation has created an ecosystem where insights live in different tools, teams, and formats, making it incredibly difficult to create a single version of the truth that finance can trust.
CFOs aren’t skeptical of marketing, they’re skeptical of proxies. When outcomes are defined by CTRs or impressions instead of revenue, margin, or brand growth, finance understandably remains skeptical.
Growth is the shared goal, but the metrics used to define and measure growth often diverge. CMOs don’t always get the insights they need to consolidate campaign activity into specific KPIs that align with CFO goals. Instead, CMOs often speak in terms of brand lift, impressions, and engagement, while CFOs focus on revenue, margin, and cost efficiency.
It’s not that either set of metrics is wrong, it’s that they’re sitting at different levels of the business. And that’s where the disconnect lives. Marketers need systems that move beyond tactical KPIs and surface the indicators CFOs ultimately make decisions on: incremental revenue, customer value, margin contribution, and efficiency.
That’s where martech becomes the translator. Unified measurement systems that can take the full funnel-media, creative, performance-and connect it to the financial outcomes that matter to the business. According to Perion’s research, marketers with unified measurement systems are much more likely to report alignment with their CFOs. When marketing brings business-level KPIs to the table, the conversation shifts from justification to shared strategy.
The gap isn’t ambition– it’s operations. While 73% of marketers in Perion’s study say a unified measurement system is a “must-have” for the future, only 23% have one in place today. Years of layered tools, vendors, and workflows have created systems that are difficult to stitch together without cross-functional buy-in from IT, finance, and external partners.
Budget and capacity constraints also play a role. A unified system drives long-term efficiency, but getting there requires a short-term investment in integration and change management. In a “do more with less” environment, that can feel like a hurdle instead of a path forward.
And finally, habits are hard to break. Many teams are still optimizing around legacy metrics like clicks or completion rates. Reorienting around business outcomes like customer LTV or margin contribution requires a mindset shift, and the infrastructure to support it.
One misconception is that marketing metrics are too abstract to reflect true business value. When CFOs see impressions or engagement scores without a clear link to revenue or retention, it’s understandable that they question impact.
MarTech can help close this gap by connecting performance signals to business KPIs, translating activity into outcomes. Unified systems don’t just report what happened, they explain why it happened and what it’s worth financially.
Another misconception is that marketing is a cost center. That perception persists when performance data is fragmented across platforms or hidden inside black-box tools. When MarTech consolidates data, simplifies attribution, and ties spend to outcomes, it reframes marketing as a growth engine. It makes the math-and the value-visible.
From where I sit, fragmentation is something marketing teams feel every single day and it’s not going to become less complicated. Every year there are new screens and tactics for marketers to reach their preferred audiences. It’s the time lost toggling between platforms. It’s reconciling data that doesn’t match up. It’s teams optimizing toward different metrics with systems that don’t speak to each other.
I’ve seen it firsthand: one team is optimizing toward ROAS, another toward attention or lift, and the systems they use don’t talk to each other.
Our research found that 70% of marketers are still struggling with performance gaps due to disconnected platforms, and 71% say it limits their ability to prove value. That’s a staggering indicator of the problem’s scale.
This is why I’m such a strong advocate for unified systems. When you have a connected view of your campaigns across media, creative, performance, and outcomes, all in one place, you move faster, and you speak the same language as your CFO, which changes everything. It turns marketing from a cost center into a growth engine the entire business can rally around.
Agentic AI is absolutely where the industry is headed, but brands need to approach it with both optimism and practicality. The idea of intelligent systems that autonomously optimize across channels in real time with minimal human input is incredibly compelling, but most organizations aren’t structurally ready for that level of automation just yet.
WIll it transform everything overnight? No. But for brands investing in unified systems and outcome-based measurement, agentic AI is absolutely realistic. The technology is ready, the unlock now is integration and operational alignment.
Start with mindset. Technology and process are critical to success, but without the right mindset, neither will stick.
From there, process is essential. Shared dashboards, monthly checkpoints and consistent definitions of success make collaboration real. Technology brings it to life by creating visibility and accountability.
One thing we’ve learned at Perion is that when marketing shows up as a growth partner, not just a budget line, the tone of the conversation shifts immediately. And that shift starts with mindset.
Zefr Awarded New U.S. Patent for AI-Powered Content Classification Process
marketing 24 Dec 2025
Silent Partner’s Contactter.ai Platform Counters the Auto Industry’s Lead Follow-Up Crisis
marketing 24 Dec 2025
Why has lead follow-up become the automotive industry’s most overlooked threat to revenue growth?
How does fragmented communication across SMS, email, and phone impact dealership sales performance?
How can unifying communication channels into a single platform enhance consistency and solve the inefficiencies that multiple disconnected tools create?
How does Silent Partner’s Contactter.ai platform provide instant, dealership-specific communication while improving response time?
How does Contactter.ai perform as an “AI Closer” to reshape the customer journey from first contact to final sale?
How can AI-driven engagement rebuild trust and consistency with customers in a way humans alone cannot?
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