More than 15 years ago, Michael Learmonth published a report in Advertising Age that showed how few of the dollars spent in digital media actually went to publishers. Back in 2010, before programmatic really took hold of our industry, his research estimated that less than half of all dollars spent to advertise on premium sites actually went to those sites, thanks to the many intermediaries and third-party tech redirects that were implicit in any campaign.  

More recent research by industry trade groups, such as the UK’s ISBA (Incorporated Society of British Advertisers), puts that number at just over 51%, despite the growth of programmatic and the arrival of real-time header bidding. Interestingly, the research indicates that a significant portion of media buys—up to 15%—disappears completely during campaign flights. Here in the US, the ANA (Association of National Advertisers) has claimed for years that ad fraud runs as high as $80 billion a year in digital video alone. This 15% disappearance has been met with a collective shrug by media buyers in digital for more than a decade now, as companies like The Trade Desk, Amazon, Meta, and Google reap billions in fees while requiring buyers to use only their internal verification tools—or else.

Why have marketers settled for this for so long? Why has it taken so long for the industry to force platforms to submit to an audit that’s not run by the platform itself? Is this about enabling transparency? Or is it more about who gets the lion’s share of that 15% that nobody can account for? Or is it both?

Also Read: How Brands Turned the World Cup Into a Cultural Play

The “Incrementality” Trap

Is the recent announcement by The Trade Desk that they’re changing how they’ll pay audience partners with a new “incremental” model just another brick in this wall of occlusion?

If data company A and data company B both sell custom, curated audiences via The Trade Desk, and both perform exactly as expected, The Trade Desk soon will be able to choose which one they pay more and which one they pay less to based entirely on its own algorithmic scale of “incrementality.” You might think this is about performance—that is, which dataset drove more conversions. But it has nothing to do with performance and everything to do with differentiation as decided by a secret algorithm.

Payments will be tied to the incremental value a partner’s identity data provides—meaning, if a data partner contributes unique signals not already captured elsewhere in the demand-side platform’s (DSP) system. The shift is intended to deprioritize duplicate data and reward differentiation.

To see how this plays out in the real world, imagine two different business-to-business (B2B) data providers who have both mapped out high-value “Cloud Security Decision Makers.” Under this new model, if The Trade Desk’s internal system determines that it already has a similar audience footprint, Data Provider B is financially penalized and deprioritized—regardless of whether Provider B’s specific data actually converts better for the advertiser.

The Trade Desk promises to introduce new tools, including APIs and scoring mechanisms, to help partners understand how their data is evaluated under the revised model. However, those tools are not yet available.

When will they become available? Who knows? What will these APIs be based on? Again—who outside of The Trade Desk knows? The platform is essentially telling programmatic buyers that what’s good for Google is good for them: “Nobody else can check our homework except us.”

Meanwhile, advertisers are complaining that this is simply about cutting costs and making more from each campaign—controlling the economics of the open web’s programmatic segment even a little more tightly with each opaque turn.

Also Read: The Hidden $71 Billion Crisis in Digital Advertising

The Marketer’s Survival Guide: Taking Back Control

Marketers do not have to wait for the ecosystem to fix itself. If you want to stop letting platforms grade their own financial homework, you need to change how you buy and validate your audiences today.

• Demand Log-Level Data (LLD): Stop settling for slick, high-level dashboard summaries. Demand raw, log-level data from your DSPs. If a platform refuses to show you exactly which impressions your data ran against and at what exact cost, they are obscuring the truth.
  
• Audit Your Data Partners Directly: Don’t let a platform be the sole judge of data quality. Test your data partners via direct Private Marketplace (PMP) deals or data clean rooms, where you can verify the uniqueness and conversion power of the data yourself.
  
• Diversify Outside the Major DSPs: If a platform treats your budget with arrogance, shift a percentage of your spend to emerging, independent programmatic environments that offer open APIs and welcome external validation.

Also Read: Are We Using AI to Help Customers or Avoid Them?

The Next 24 Months: The AI Disintermediation

If a performance media buyer compensates media sellers only for inventory that performs, the seller has the right to reserve inventory for other kinds of buys, such as impression-based campaigns. The Trade Desk says, “Not so fast. We get to decide which data is adding incremental value in cross-media campaigns based on KPIs that only we will see.”

One wonders how long the buyer community will tolerate such arrogance. Buyers will continue to pour money into this segment and wonder where their dollars are actually going—but not for much longer.

This friction is setting the stage for a structural collapse of the traditional ad-tech stack. The tech giants believe they have a permanent lock on the market because their ecosystem is too complex to bypass. They are wrong.

Very soon, enterprise brands will deploy proprietary, AI-based buying tools. Instead of logging into a clunky DSP dashboard, navigating hidden fee structures, and accepting opaque “incrementality scores,” a brand’s internal AI will talk directly to a data seller’s API. The AI will look at your first-party CRM, securely match audiences, negotiate real-time pricing, and deploy the ad directly to a publisher’s server.

By automating the negotiation and execution layers natively, AI completely cuts out the middleman’s 15% disappearing act, rendering the traditional DSP moot. Many industry insiders expect this shift to occur in as little as 2 years.

The post How Long Will Marketers Settle for Opacity in Programmatic? appeared first on MartechView.

Modern sports marketing has officially graduated from the era of product placements and aggressive calls to action. With WARC Media forecasting a $10.5 billion uplift to the global ad market during this tournament quarter, the world’s biggest brands are executing a masterclass in a discipline entirely different: selling things without actually selling them.

It’s worth pausing on that $10.5 billion figure because it tells a more interesting story than it first appears. WARC’s own analysts describe it as just a 1.1% incremental lift over a normal year, once inflation is stripped out — smaller, in fact, than the $12.6 billion bump the 2018 Russia tournament generated. In other words, most of this money isn’t new spend flooding into advertising. It’s existing budgets being redirected, with unusual intensity, toward proving cultural fluency rather than buying reach. As WARC Media’s Head of Content, Alex Brownsell, put it, brands today are expected to engage fans “across touchpoints before, during and after matches,” not just during the live broadcast.

That shift in spending logic is matched by an equally deliberate shift in creative strategy. Instead of interrupting the fan experience to pitch a product, brands are positioning themselves as essential facilitators of the fan ritual — leaning into the high-stakes drama, humor, and collective anxiety that defines international football. By building interactive digital communities, orchestrating physical creator hubs, and poking self-aware fun at tournament regulations, marketers are embedding their logos directly into the emotional memory of the games.

The scale of the moment they’re embedding themselves into is hard to overstate. This is the largest World Cup ever assembled — 48 teams, 104 matches, 16 host cities, and 39 days of competition between June 11 and July 19. FIFA expects around 6 billion people to engage with it globally, and roughly 1.5 billion to watch the final. As the tournament scales to that unprecedented 48-team format across the United States, Canada, and Mexico, a hyper-localized, social-first approach ensures brands aren’t just shouting from the sidelines. They are capturing a massive, hyper-engaged audience by proving they understand the sport’s culture as deeply as die-hard supporters in the stands do.

Here are the five campaigns dominating the 2026 World Cup by doing exactly that.

Adidas: “Backyard Legends”

Arguably, the blockbuster campaign of the tournament. Created by LOLA USA and directed by Mark Molloy through SMUGGLER, this cinematic five-minute film stars Timothée Chalamet as a hyper-serious street football recruiter tasked by musician Bad Bunny to assemble a ragtag squad capable of taking down an undefeated neighborhood crew that hasn’t lost a match in thirty years.

The Strategy: It masterfully blends generations, featuring current icons like Jude Bellingham, Lamine Yamal, and Trinity Rodman alongside brilliantly integrated, AI-de-aged legacy players like David Beckham and Zinedine Zidane. It stands out because it ditches the typical corporate “glorious destiny” tone and makes football feel playful, nostalgic, and accurate to grassroots fandom. 

The bet appears to be paying off: Adidas has already sold roughly $292 million in 2026 World Cup products, and CEO Bjørn Gulden has called the U.S. the brand’s biggest long-term opportunity.

Lay’s: “Bandwagon”

Lay’s focused its massive budget on solving a uniquely North American marketing tension: appealing to a massive audience that doesn’t traditionally watch soccer.

The Strategy: In their “Jump On, America” spot, comedian Will Ferrell drives a literal yellow-branded Bandwagon across the country, jokingly onboarding confused citizens into football fanaticism alongside David Beckham and Marshawn Lynch. By reframing “bandwagon fan” from an insult into an invitation, they strip away intimidating sports gatekeeping and pair the ad with limited-edition, globally inspired chip flavors like Argentinian Style Steak with Chimichurri. 

The timing is shrewd: a Full Circle Research study found that 75% of Americans plan to follow the 2026 tournament, many of whom are not traditional football fans — exactly the audience “Bandwagon” is built to recruit.

LEGO: “Everyone Wants a Piece”

LEGO leaned heavily into peer-to-peer social distribution rather than traditional broadcast buys, generating hundreds of millions of views by treating elite sports rivalry with blocky, lighthearted humor.

The Strategy: Created by LEGO’s in-house team alongside Wieden+Kennedy Amsterdam, the campaign features stop-motion brick versions of Kylian Mbappé, Cristiano Ronaldo, Vinícius Jr., and Lionel Messi sitting around a rotating table, taking turns assembling a massive LEGO World Cup trophy. It leans into a clever running gag that there is only room for one of them at the top of the brick pyramid, capturing the tournament’s high stakes through miniature, nostalgic animation. 

The payoff was immediate: the campaign generated 314 million views across the players’ Instagram accounts within 24 hours of release, with fans calling it a moment “generations will talk about.”

Nike: “Rip the Script”

Nike delivered a hyper-stylized, high-energy cultural tribute to the emotional roller coaster of international football, designed to capture the sheer unpredictability of the 2026 tournament.

The Strategy: Featuring a powerhouse lineup including Kylian Mbappé, Erling Haaland, Cristiano Ronaldo, and even a crossover appearance by LeBron James, the campaign avoids focus-group-tested corporate hype. Instead, it plays out like a fast-paced montage of fan culture, perfectly capturing the anxiety and chaos of tournament cycles — from group chats blowing up to the collective nerves of a nation anticipating either a historic victory or a dramatic penalty-shootout exit.

Dove / Unilever: “The Game Is Ours”

As an official personal care sponsor of the tournament, Unilever launched a massive sports partnership, but Dove’s purpose-led sub-campaign is the one driving the most meaningful cultural conversation.

The Strategy: Rooted in the insight that one in two girls quits sports due to body-type criticism, the hero film is uniquely built entirely out of the raw, joyful audio and sounds of girls playing the sport, which swells to drown out the voice of critics. Backed by “House of Fresh” physical creator hubs across major host cities, it intentionally shifts the spotlight away from elite, untouchable multi-millionaire athletes to advocate for confidence and belonging at the grassroots level.

The Bigger Picture

Taken together, these five campaigns point to something larger than clever creative. Industry analysts note that not a single crypto firm sits among FIFA’s headline global partners this cycle — a sharp reversal from Qatar 2022, when Algorand held a marquee sponsorship — suggesting brands are gravitating toward cultural credibility over speculative association. Meanwhile, Fox and Telemundo are projected to generate a combined $850 million in World Cup ad revenue, a figure that, for the first time in American soccer history, edges the tournament into Super Bowl territory.

The lesson for marketers without Adidas or Nike-sized budgets isn’t the dollar figures. It’s the discipline behind them. Each of the brands above chose one coherent theory for how to win attention and spent accordingly. In a tournament this fragmented, that clarity, more than the size of the check, is what’s actually winning.

The post How Brands Turned the World Cup Into a Cultural Play appeared first on MartechView.

Agencies have filled the execution gap for CPG brands for decades, playing an integral role in content refreshes, retail media plans, and pricing updates across entire catalogs. But now, when AI agents can optimize a product page listing in seconds rather than the half-hour or more it takes a human to do it manually, the retailer-agency relationship is starting to evolve. It has to, in order to keep up with algorithms that reprioritize listings in real time, as category competition intensifies and rising CPCs create a more dynamic marketplace.

To keep up, brands have to replace the campaign cycle with agentic execution, using AI agents to continuously optimize content, media, and pricing across all SKUs and marketplaces. But that doesn’t mean agencies will no longer play an essential role.

Also Read: Dashboards Created Visibility, but They Didn’t Solve Execution

The Agency Model Wasn’t Built for How Retail Moves Today

According to a CommerceIQ survey, 76% of commerce teams still rely on agencies, with 49% allocating 15% to 30% of their budget to agency fees alone. Those retainers keep climbing, while 55% of commerce teams say agency costs are too high relative to results. The agency model was designed around campaigns with a plan, launch, and report at the end.

Now, a competitor can update their listing and take your search ranking overnight, outbid you for the top ad placement while your agency is working on the next report, and win the sale by the time your team sees that report. Commerce teams are already seeing this play out, with 40% saying their agency’s response times can’t keep up with how fast algorithms are already moving. 

Brands need to make thousands of daily optimizations to content, media, and pricing across a handful of marketplaces. This is far beyond what any team running on a campaign calendar can handle. The demand that marketplace algorithms generate is outpacing what agencies can deliver, so brands that want to stay competitive need to find a solution that will keep up.

How Agentic Execution Does the Work That Agencies Can’t

Agencies pull reports weekly to understand what happened, while AI agents analyze performance down to the minute and flag issues, such as out-of-stock listings, as they arise. Agencies build a plan and wait for approval, while AI agents recommend the next-best action and execute it within the brand’s guardrails. Agencies manually update listings one at a time, while AI agents adjust bids and optimize content across the full catalog around the clock.

The role of agencies needs to evolve. As these agents take on operational execution, agencies can stop billing for hours spent pulling reports and updating listings, and instead focus on work that requires a human touch. 

AI agents paired with human experts outperform traditional agency and SaaS workflows by 10x to 100x in speed and operational scale. While agencies are limited by human capacity and business hours, agents execute continuously across every marketplace where a brand is present. This looks like multiple agents that operate within defined guardrails and are trained on brand-specific context:

• A content agent identifies and resolves PDP gaps at scale. 
• A media agent improves iROAS via thousands of optimizations a day.
• A sales agent flags performance risks before they show up in a quarterly review. 

This scale of execution changes what’s possible at the catalog level. Agency-led teams can typically manage only the top 20% of a brand’s catalog, so the hero SKUs get the most attention, while the rest sit untouched, with unoptimized content, stale pricing, and missed media opportunities. Agentic execution covers 100% of SKUs without incurring the agency-hour scaling costs.

Agencies continue to add value through creative strategy, brand positioning, and integrated campaigns, while agents handle the operational work that never stops and that no agency can manually keep up with at scale.

Also Read: E-commerce Doesn’t Have a Data Problem. It Has a Speed One.

How the Agency-Led Model Is Evolving

The role agencies play in e-commerce is changing. Brands that still rely on them to manage their catalogs are spending more each year to cover only a small percentage of SKUs.

Meanwhile, the brands recognizing this early are rebuilding their ecommerce operations around continuous agentic execution and are already optimizing their full catalog across every marketplace at a speed and scale the old model isn’t set up to handle.

The post The Agency-Led E-commerce Model Is Changing appeared first on MartechView.

Modernization Is Now a Business Priority

Enterprise modernization has reached a point where delays carry business risk, and careless speed poses equal danger. Many organizations still depend on ERP landscapes shaped by years of local decisions, customizations, acquisitions, compliance needs, and short-term fixes. They run the business, yet often make it harder to see clearly, respond quickly, and scale.

Selective Transformation Offers a More Practical Path

The answer is not a reckless replacement of everything that exists. It is also not a technical conversion that carries yesterday’s complexity into a newer environment. A more responsible path sits between those extremes. Selective transformation gives leaders a way to protect what still has value while removing what has become a burden. Historical data, proven controls, and essential operating knowledge can be retained. Outdated code, fragmented processes, and avoidable variation can be reduced.

A Clean Core Creates Room for Progress

A clean core is not a technology slogan. It is a management discipline. It asks the enterprise to standardize common processes, limit unnecessary customization, govern integrations, and keep the business’s core ready for improvement. When the core is crowded with exceptions, every upgrade becomes harder. Every report becomes debatable. Every innovation effort starts with a cleanup.

For senior leaders, the clean core should be viewed in business terms. Finance needs trusted numbers. Operations need reliable signals. Supply chain teams need visibility across demand, inventory, suppliers, and plants. Compliance teams need evidence that controls are working. Employees need systems that do not force them into manual workarounds. Customers need commitments that can be met. None of this is possible when the foundation is unstable.

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

Public Cloud Readiness Requires Honest Assessment

Public cloud readiness belongs in the same conversation. Public cloud ERP models are attractive because they encourage alignment with standard thinking, shorter deployment cycles, predictable upgrades, and a lower infrastructure burden. They can help growing enterprises move away from fragmented systems and toward a more consistent operating platform. Yet, public cloud should be adopted honestly. It works best when the business is ready to accept standard ways of working.

That distinction matters. Some processes are truly differentiating. Others are merely familiar. A mature modernization program separates the two. The enterprise should not customize a new core simply because the old system carried a certain practice for years. At the same time, critical regulatory, manufacturing, quality, or industry-specific needs should not be dismissed casually. Good leadership wisely uses standardization to create speed, control, and scale.

Industry Needs Should Shape the Modernization Journey

Industry realities make this balance more important. Manufacturers may need better costing across products and plants. Utilities may need modernization without disruption to regulated asset operations. Life sciences companies may need validation discipline, audit readiness, and data integrity. Automotive suppliers may need faster carveout execution, partner integration, and production visibility. Food and dairy businesses may need traceability and recall readiness. Chemical companies may need batch insight, yield control, and margin protection. These challenges are different, but they point to the same principle. The enterprise core must be standardized enough to scale and flexible enough to respect real operating needs.

AI Readiness Begins with the Foundation

Artificial intelligence has made this discussion more urgent. Intelligent tools depend on reliable data and disciplined processes. When data definitions differ across functions, recommendations become questionable. When workflows are unclear, automation may introduce errors more quickly. When integrations are fragile, digital operations become difficult to trust. AI readiness begins long before a model is introduced. It begins with architecture, data, process ownership, and governance.

Also Read: Brands Are Making ‘No AI’ Their Biggest Selling Point

Digital Workers Need Governance and Accountability

The same thinking applies to software agents and digital workers. These capabilities may assist with monitoring, analysis, exception handling, and workflow execution. Their value will depend on control. Each digital worker should have a defined purpose, a clear owner, approved boundaries, escalation paths, and performance measures. Human oversight should remain visible where decisions affect customers, employees, financial results, safety, or compliance. Without that discipline, automation can create risk.

Transformation Must Also Support People

Modernization also has a human side. Employees experience transformation through daily tasks, not through board presentations. They will judge success by whether the new process is clearer, whether training is practical, whether leaders explain the reason for change, and whether the system helps them do better work. When people are left to interpret change on their own, uncertainty grows. When they are supported, adoption becomes more natural.

This requires leadership beyond the technology function. Finance, operations, supply chain, human resources, risk, and business unit leaders must be involved early. Process owners must be willing to make decisions. Governance must be active. Change management must be practical. A modern ERP program should not be handed to IT and reviewed only at milestones. It should be a business transformation enabled by technology.

Business Outcomes Should Define Success

The measure of success should also become more grounded. A successful program should improve cost visibility, working capital control, reporting confidence, inventory accuracy, customer response, compliance quality, productivity, and resilience. These outcomes matter more than the number of features launched. They also matter more than speed if speed comes at the expense of readiness.

The current moment should be treated as a chance to simplify with courage. Aging ERP environments have forced many organizations to make decisions that had been delayed too long. That pressure can be uncomfortable, but it can also be useful. It gives leadership a reason to remove complexity and build a cleaner foundation.

Also Read: Are Brands Losing Credibility in the AI Era?

The Future Belongs to Enterprises That Modernize with Discipline

The organizations that move wisely will not confuse modernization with migration. They will protect continuity while reducing debt. They will adopt public cloud where standardization creates value. They will assess specialized operations carefully. They will prepare data before scaling AI. They will govern digital workers before granting autonomy. They will bring employees through the change with clarity and respect.

Modern ERP modernization is ultimately about confidence. Confidence that the business can change without losing control. Confidence that data can be trusted. Confidence that people can adopt new ways of working. Confidence that intelligent operations can scale responsibly. When clean core discipline, public cloud readiness, industry awareness, AI governance, and business continuity are aligned, the enterprise gains more than a new system. It gains a stronger foundation for the future.

The post Your ERP Is Holding You Back. Here’s How to Fix It. appeared first on MartechView.

For sophisticated performance marketers, running campaigns across multiple DSPs has long been a practical way to understand what is working, compare results, and preserve the option to shift budgets toward stronger performance. But even the most advanced advertisers tend to hit an operational ceiling. In most cases, they might run across two or three DSPs, not five, six, or more. The limitation is less about strategic ambition and more about time.

Every DSP has a unique set of inventory, data, workflows, and best practices. Expanding into another buying platform creates additional work, even when the advertiser sees clear value in broader comparison and testing.

That is why the next phase of agentic media buying could be so significant.

Agentic systems have not yet transformed media buying. The industry is still early in this process. But over the next 6 to 12 months, as agentic experiences mature and become more mainstream, their impact could extend well beyond automation. They could meaningfully lower the operational cost of running campaigns across more platforms, opening the door to a new model of always-on experimentation. 

DSP Agents Will Reduce Platform-Level Friction

In the near future, every DSP will roll out a similar set of agentic capabilities. These will likely include agents for campaign setup, optimization, troubleshooting, and insights.

That matters because much of the work that makes cross-platform experimentation difficult today is highly operational. A media buyer must know how to configure a campaign correctly in each DSP, understand each platform’s recommended practices, monitor delivery, interpret performance, and know when to intervene. Agentic tools can begin to absorb more of that complexity.

Setting up agents will help translate campaign goals into the right configuration. Optimization agents will recommend changes based on performance trends. Troubleshooting agents will identify why a campaign is underdelivering and recommend fixes. Insights agents will summarize what is happening and outline the actions that should follow.

None of this eliminates the need for human oversight, but it does reduce the cost of working in each environment. As that cost falls, the logic of limiting experimentation to only a few platforms begins to weaken. 

Also Read: Your Marketing Dashboard Is Lying to Your CFO

The Bigger Shift May Come From Agency-Built Agentic Operating Systems

A second transformative change in this equation is likely to happen above the DSP layer. Agencies and some large brands have wanted to build versions of a “meta-DSP” for decades. The concept has always been appealing: a single operating layer that enables a team to plan, activate, manage, and evaluate campaigns across multiple DSPs. The challenge has been execution. Traditional APIs made this difficult, expensive, and rigid. The technical complexity often outweighed the practical benefit.

Agentic systems could change that equation. In a more mature agentic environment, an agency could upload a media brief into its own agentic operating system. That system could then interact with DSP-level agents to set up campaigns across multiple platforms. It could coordinate setup, monitor performance, surface insights, and recommend budget shifts based on what is actually working.

 That would not simply make media buying faster. It would make broader experimentation operationally feasible.

 The agency’s agent could become the connective tissue across DSPs. DSP agents would handle tasks within each platform, while the agency’s agent would coordinate across them.

Always-On Experimentation Becomes a Strategic Advantage

The first conversation around agentic systems is usually about efficiency. That is understandable. Automating setup and optimization saves time. It reduces manual work. It helps teams move faster.

But the more important opportunity is learning. When the cost of running across more DSPs declines, advertisers can stop treating experimentation as a periodic exercise. They can make it part of the core operating model. Instead of asking whether there is enough time to test another platform, they can continuously evaluate where performance is strongest and where the budget should move.

Today, many brands and agencies select their core DSPs based on past performance, perceived value, or simple familiarity with how a given platform works. Those factors can become stale. A DSP that performed well for one campaign, audience, or objective might not be the strongest option for the next. Yet because each platform requires its own expertise and workflows, advertisers often keep returning to the same two or three environments rather than continually testing the broader field. Always-on experimentation changes that dynamic. As agentic systems reduce the effort required to activate and evaluate campaigns across platforms, marketers will be able to “taste and see” which DSPs are best suited to each objective, rather than relying on inherited assumptions about where performance is likely to come from.

This creates a very different competitive dynamic. Advertisers that invest in agentic operating systems will be able to compare more options and adjust with greater confidence. They will not be locked into a small set of platforms simply because those are the ones their teams have the capacity to manage. Over time, this could make experimentation less of a campaign tactic and more of an organizational capability. 

Also Read: Are Brands Losing Credibility in the AI Era?

The Role of Marketers Will Become More Strategic 

This future does not imply that media buyers disappear from the process. In fact, their strategic role becomes more important. Agentic systems can reduce executional burden, but they still need direction. Marketers will need to define objectives, determine what to test, evaluate whether recommendations make business sense, and ensure experimentation aligns with brand and performance goals. They will also need to decide how much control they want to centralize within their own agentic operating systems and how much they are willing to delegate to individual platforms. 

As DSPs introduce their own agents and agencies begin building agentic operating systems that can coordinate across platforms, always-on experimentation will become practical at scale. That means the next advantage in media buying will be less about doing the same things faster and more about discovering better ways of doing them in the first place.

The post Is AI About to Make Media Buying an Endless Experiment? appeared first on MartechView.

Here is a pattern I encounter in enterprise marketing organizations more often than I should: every metric on every dashboard is green, and the CFO still isn’t buying it.

Click-through rates are trending up. Cost-per-acquisition improving. AI engines produce thousands of ad variations with apparent precision. And yet, top-line growth is flat. Margins are compressing. Customer lifetime value is quietly stagnating.

This is not a communication problem between marketing and finance. It is a measurement architecture problem. And recent research confirms it is far more widespread than most leaders want to acknowledge. In a recent survey of marketing leaders, 92% said they believe their measurement is precise. But when those same leaders acknowledged that a portion of their marketing investment is not delivering full value due to measurement limitations, the contradiction became impossible to ignore. They cannot distinguish whether a campaign drove incremental growth or merely claimed credit for a sale that would have happened anyway.

More green dashboards. Less demonstrable truth.

The Behavioral Economics of Comfortable Numbers

What makes this pattern so persistent isn’t carelessness; it’s cognitive. Behavioral economists call it present bias: the tendency to overweight immediate, observable rewards relative to lagged, harder-to-measure outcomes. Clicks are immediate. Contribution margin is lagged. When every optimization signal a platform returns is a click metric, organizations rationally and systematically build expertise in generating clicks.

There is also a status quo bias operating at the organizational level. When dashboards are green, the institutional pressure to challenge the underlying measurement model is effectively zero. Nobody convenes a working group to question whether the metrics are right when the metrics look good. Perceived success is anesthesia. It suppresses the diagnostic instinct precisely when that instinct matters most.

This is the paradox the survey revealed. High confidence in current measurement, low ability to prove incremental impact. The confidence isn’t dishonest; it’s the product of a decade spent optimizing for the metrics the platforms made easiest to see.

Also Read: Brands Are Making ‘No AI’ Their Biggest Selling Point

Renters and Architects

For the past decade, most enterprise marketing organizations have functioned as Renters. We rented keywords. We rented cookies. We rented placements on social feeds. And because we didn’t own the land, we accepted the landlord’s definition of success.

The platforms gave us proxy metrics: clicks, impressions, and engagement rates, because these were what the platform could measure and report. We carried them into boardrooms as evidence of marketing performance. In the era of blue-link search results, this was a defensible trade-off. Volume-based visibility predictably converted to traffic, traffic predictably converted to revenue, and that chain was legible enough to manage against.

But the environment has changed, and the metrics haven’t. Search is no longer a ranked list. It is an AI-mediated conversation where agents synthesize options and surface a recommendation on the consumer’s behalf. In this environment, handing a sophisticated machine learning model a click-optimization signal isn’t a measurement strategy. It is an optimization constraint that actively limits the machine’s ability to serve the business. You are telling a system capable of profit intelligence to focus on the cheapest possible action instead.

The brands navigating this era successfully are becoming Architects rather than Renters. An Architect understands that the output is now largely a commodity; generative AI has leveled the playing field for creative production, ad variation, and placement optimization. The remaining competitive advantage lies in the quality of the inputs you feed the machine and the integrity of the measurement architecture that defines what success actually means.

Quick to Mind Is No Longer Enough

Brand strategy has long been organized around the principle of being Quick to Mind, the first association a consumer forms when a need arises. That was valuable infrastructure. It still is. But in an AI-mediated discovery environment, Quick to Mind is necessary but no longer sufficient.

Before a brand can be Quick to Mind, it must be Quick to Model.

When an AI agent synthesizes an answer to a consumer’s query, it doesn’t engage with brand purpose or creative story. It reads structured data. It examines the relationships among product attributes, proof points, and consumer intent. It looks for what I call Signals of Truth: demonstrated, structured evidence rather than brand assertion.

If your product data is siloed from inventory data, the AI bypasses you. If your CRM is disconnected from media buying, the signal chain breaks. If measurement is anchored to last-click attribution, the machine has no basis for understanding whether your offer was actually relevant to the buyer, and, critically, neither do you.

This is where the measurement problem and the AI readiness problem converge. They are the same problem. The underlying condition is Amnesiac Data: a marketing system that has no memory of what the sales system knows, no connection to what customers actually experienced post-purchase, no signal of what they valued enough to come back for. You cannot build Quick-to-Model credibility on an amnesic foundation.

Also Read: Are Brands Losing Credibility in the AI Era?

The Architecture of Truth

The fix is not another attribution platform layered atop existing fragmentation. It is a structural rewiring: building what we call a Unified Data Spine, tearing down the wall between front-office media execution and back-office profit, CRM, and customer lifecycle data.

In practice, this means shifting from ROAS (Return on Ad Spend) to POAS (Profit on Ad Spend), feeding actual contribution margins, live inventory levels, and competitive pricing signals directly into bidding algorithms. When the machine knows what a conversion is actually worth to the business, not just the revenue line it generated, the entire optimization dynamic changes. Seamless Search is one expression of this architecture: a signal-injection layer that forces algorithms to optimize paid and organic simultaneously, around margin rather than volume.

This is ultimately why we built Seamless Suite as an AI operating system rather than another analytics platform. The industry has no shortage of dashboards. What it lacks is a single intelligence layer that involves every participant in the revenue operation. The CMO setting strategic direction, the media buyer optimizing a campaign in real time, and the agentic systems executing bids autonomously around the clock, all reading from the same sheet of music.

Deployed directly into a client’s own cloud environment, Seamless Suite functions as the connective tissue between human judgment and machine execution: a unified command layer where strategic intent flows down and ground-truth performance signals flow back up. The executive sees the full composition. The practitioner plays their part in real time. The agentic systems never stop playing, operating 24/7 within the guardrails the organization has set. Everyone is in tune because everyone is drawing from the same source, a single Golden Record of business truth rather than a tower of Babel built from siloed platform reports.

This distinction from operation to orchestration is the most important one modern RevOps leaders can make. Operation means siloed teams executing separate plans against separate metrics, occasionally reconciling in a Monday morning meeting. Orchestration means humans and agents moving in coordination, responsive to a shared signal, optimizing toward the same North Star. The measurement problem and the AI readiness problem are, at root, both orchestration failures. They are what happens when the instruments can’t hear each other.

It also means embracing Marketing Mix Modeling as a discipline of causality rather than attribution credit. Attribution debates, which platform gets credit for the conversion, are a symptom of the Renter mindset. The Architect asks a more important question: which investments actually drove incremental growth? That inquiry sometimes requires the willingness to challenge perceived past successes and to discover that some were statistical artifacts of flawed measurement rather than genuine business performance. That is an uncomfortable exercise. It is also the only path to measurement that earns CFO trust, and that gives agentic systems an honest basis for optimization.

Also Read: Hyper-Automation Is Over. Agentic AI Is What Comes Next.

The Right North Star

Underlying all of this is a North Star problem. Most marketing organizations are optimizing for input metrics: clicks, impressions, conversion volume, when the true North Star should be an output metric anchored to customer lifetime value: the financial return across the full customer lifecycle, not just the moment of acquisition.

Traditional measurement architecture ends at conversion. But the right side of the customer lifecycle- loyalty, expansion, advocacy- is where lifetime value is actually built. Optimizing for acquisition cost alone while the retention side quietly leaks is how organizations achieve green dashboards and declining margins simultaneously. The left side of the funnel is winning. The right side is bleeding. And most measurement systems are not wired to see both at once.

When the North Star is correctly set, the measurement architecture is honest enough to track it, and the intelligence layer is shared across every human and agent in the organization, the green dashboard stops being a comfort signal and starts being an accurate one. Marketing becomes a genuine profit center not because the numbers look better, but because they mean something to everyone who reads them.

The machine is ready to play in concert. It can reason about profit, lifetime value, and incremental growth, but it can participate in orchestration only if the organization has built a score that everyone, human and agent alike, reads from.

Stop measuring for the dashboard. Start architecting for the orchestration.

The post Your Marketing Dashboard Is Lying to Your CFO appeared first on MartechView.

Something remarkable has been happening in boardrooms and brand strategy sessions across North America and Europe. Companies that spent nearly three years racing to announce AI integrations, AI-powered experiences, and AI-driven personalization are now doing what would have seemed professionally suicidal in 2023: publicly walking some of it back.

The hot new trend in marketing, it turns out, is hating on AI — or at least being seen to.

This is not a fringe movement. It is a strategic recalibration happening at some of the most commercially sophisticated consumer brands in the world, driven by a simple and increasingly hard-to-ignore insight: in a market saturated with artificial intelligence, the most powerful differentiator available to a brand may be genuine humanity.

The Starbucks Signal

The most striking recent example is Starbucks. In May 2026, the company retired its AI-powered inventory-counting system built by NomadGo across all its North American stores, just nine months after deploying it as a centerpiece of CEO Brian Niccol’s “Back to Starbucks” turnaround strategy.

The problems were operational and embarrassing. Employees and managers across multiple locations described the system frequently miscounting and mislabeling items — confusing similar milk types, missing items entirely during scan sessions, and, in at least one case, failing to recognize a peppermint syrup bottle in a promotional video Starbucks itself had uploaded to showcase the tool. That promotional video, along with the original blog post announcing the rollout, was quietly deleted from the company’s website before the retirement was announced.

At launch, Starbucks had promoted the technology as a way to free workers to focus on what matters: crafting beverages and connecting with customers. The floor reality inverted that promise entirely — the AI system created more work, not less, and the friction showed up precisely at the human moments the brand could least afford to compromise.

For a chain that leans heavily on drink customization and frequent limited-time items, any friction in inventory accuracy can quickly affect sales, waste, and customer satisfaction. Starbucks is not retreating from technology entirely — Niccol is rolling out a generative AI chatbot for staff built on Microsoft’s Azure platform. But the NomadGo failure is a clear signal that AI deployed without operational rigor in a brand built on human warmth and reliability can do more harm than it solves.

Dove’s Decade-Long Head Start

Starbucks may be the most recent and most dramatic example, but Dove understood this dynamic earlier than almost anyone. The brand’s “Real Beauty” campaign, launched in 2004, was built on a single contrarian insight: in a category flooded with aspirational, heavily retouched imagery, showing real women — unaltered, diverse, ordinary in the best sense — would be more commercially effective than following category convention.

In 2024, marking the campaign’s 20th anniversary, Dove formalized what had been an implicit creative principle into an explicit public commitment. “At Dove, we seek a future where women decide and declare what real beauty looks like — not algorithms. Pledging to never use AI in our communications is just one step. We will not stop until beauty is a source of happiness, not anxiety, for every woman and girl,” said Alessandro Manfredi, Chief Marketing Officer at Dove.

The timing was not incidental. As generative AI began flooding advertising with synthetic models, algorithmically optimized faces, and artificial perfection, Dove’s commitment to real images became more valuable, not less. The contrast did the work — and audiences responded.

Also Read: Why the CMO Now Owns the Privacy Problem

The Broader Backlash

Dove and Starbucks are not isolated cases. Porsche released a hand-drawn holiday advertisement with an explicit statement that no AI was used, and it received significant praise in the comments from audiences who recognized and valued the human craft behind it. Polaroid launched a billboard campaign with the line “AI Can’t Generate Sand Between Your Toes,” connecting with consumers on a personal level amid screen fatigue and phone exhaustion.

Aerie made “No AI” a trust promise, extending its long-running no-retouching stance into a clear, modern pledge. Heineken’s “real friends” wearable campaign flipped the AI companionship conversation into an offline invitation.

The year 2025 marked a clear shift, as brands began highlighting human effort and labeling their products “100% human” and “no AI”,  labels that are becoming the digital equivalents of “organic” or “non-GMO” in food marketing. Bob Hutchins, CEO of Human Voice Media, described it plainly: “We are at a tipping point where the superabundance of algorithmically-generated content, ‘AI slop, ‘ is making human-generated work a luxury good.”

Merriam-Webster agreed with the diagnosis, designating “slop” its word of the year for 2025.

The Backlash Economy

What these brands are responding to is consumer skepticism that is not a niche concern — it is mainstream, measurable, and commercially consequential.

A Nielsen study found that 55 percent of consumers feel uncomfortable with websites that primarily use AI-generated content, and 4 out of 5 said brands and media organizations should be transparent about their AI use in content creation. A 2025 study from the Nuremberg Institute for Market Decisions found that simply labeling an ad as AI-generated makes people see it as less natural and less useful, lowering ad attitudes and willingness to research or purchase.

The market, in other words, is creating a premium for authenticity precisely because authenticity has become scarce. When something abundant becomes rare, its value rises. Human-made, genuinely considered communication was once the default. AI has made it exceptional almost overnight — and the brands that recognize this before their competitors do will capture a meaningful and durable advantage.

The Risk of Overcorrection

It would be a mistake, however, to read this as a simple rejection of AI. The brands navigating this moment most effectively are not the ones abandoning technology entirely. They are the ones being selective and transparent about where AI adds genuine value and where it subtracts human value.

Starbucks, notably, is not walking away from AI — it is walking away from AI that failed operationally and degraded the human experience on which its brand depends. Dove is not anti-technology; it has created Real Beauty Prompt Guidelines to help people use generative AI more responsibly and inclusively. The distinction is not AI versus no AI. It is a judgment about where each belongs.

Also Read: Hyper-Automation Is Over. Agentic AI Is What Comes Next.

What Comes Next

The irony at the center of this moment is rich. The technology that promised to make marketing more efficient, more personalized, and more effective has, at scale, made it less trusted, less differentiated, and less human. And the brands that spent the most aggressively to automate their way to relevance are discovering that the most relevant thing they can do right now is slow down, show up as people, and say something worth saying.

Starbucks is retooling its in-store operations to put human connection back at the center. Dove is running photographs of real faces. Porsche is hiring animators. Polaroid is putting up billboards about sand between your toes.

The slop, as Merriam-Webster put it, “oozes into everything.” And the brands pulling back from it are finding that the space they reclaim is worth considerably more than what they gave up.

The AI arms race is not over. But the counter-movement has started — and, in marketing, as in most things, it’s where the most interesting money gets made.

The post Brands Are Making ‘No AI’ Their Biggest Selling Point appeared first on MartechView.

The moment a brand asks a customer for their data is one of the most important interactions in the entire customer relationship. Get it right, and you earn their trust. Get it wrong, and you lose a customer who likely won’t come back.

For years, that moment was treated as a legal problem or something to hand off to compliance and forget about. That era is over. In the age of AI, data consent has become a brand issue, a growth issue, and, increasingly, a CMO issue. 

The “Trust Gap” Is an Opportunity in Disguise

Consumer trust in data practices is eroding at exactly the moment brands need it most. A recent EY study reveals that 55% of consumers worry that organizations will fail to comply with their own AI policies.

The brands that move first to close that gap with transparent, well-designed data experiences are building a competitive moat that’s very hard to replicate.

A recent MIT Insights report notes 44% of consumers say transparency about data use is the single top driver of brand trust, ranking above security guarantees and even the ability to control data sharing. In other words: transparency isn’t a compliance cost – it’s the foundation your personalization and AI strategy is built on.

Also Read: Hyper-Automation Is Over. Agentic AI Is What Comes Next.

Why CMOs Are Positioned to Solve This Problem

The most common disconnect I see is in organizations where both legal and marketing want to build trust but define it in entirely different ways. Legal measures success in compliance rates. Marketing measures it in engagement and retention. Without a shared framework and unified KPIs, those teams end up optimizing against each other. Privacy-led UX touches legal, product, IT, and data operations, and the CMO is the one function with visibility across all of it. That makes closing this gap a CMO job, whether it’s on the org chart or not. The CMO can be the one to bridge that gap.

Good privacy UX at its core is good brand UX. Consider how brands like Zalando approach it. They use phrases like “tailor your privacy settings,” which align with their fashion identity and audience. Similarly, Porsche frames data controls around “full control,” putting customers in the driver’s seat figuratively as well as literally. Both of these are examples of intentional brand decisions that signal privacy is a core part of how the company treats its customers.

AI Everywhere Makes Privacy an Urgent Matter

If the business case for privacy-led UX isn’t compelling enough on its own, AI has changed the stakes entirely. In a recent Forrester survey of privacy professionals on the ROI of their privacy programs, the second most common answer after regulatory compliance was enabling AI adoption. Put simply: you can’t scale responsible AI without the privacy infrastructure already in place.

The need for comprehensive privacy becomes even more critical as agentic AI moves from concept to deployment. Unlike generative AI, where users make conscious choices about what to share, agentic systems act on a user’s behalf. Agents can make bookings, purchases, and data-sharing decisions without explicit input at every step. The traditional consent moment, as we used to know it, may never occur.

Also Read: AI Is Supercharging Returns Fraud. Retailers Are Behind.

The New Mandate

The window to get ahead is still open. CMOs have always been in the business of building brand equity, and how a brand handles data is now inseparable from that equity. The organizations that invest in consent infrastructure today will be well-positioned when the regulatory and competitive environment tightens further. The ones that don’t will have a hard time catching up. The difference comes down to a simple mindset shift: treat privacy as a relationship to be managed, not a disclosure to be made.

The post Why the CMO Now Owns the Privacy Problem appeared first on MartechView.

For years, digital marketing was all about scale. Brands that published frequently, optimized aggressively, and maintained a constant presence across platforms often gained the greatest visibility. 

But the rise of generative AI has fundamentally changed that reality. Today, nearly anyone can use ChatGPT, Claude, or any other AI tool to produce endless streams of blogs, social media posts, videos, and thought leadership content at minimal cost and extraordinary speed.

The content ecosystem today is saturated with AI slop, and readers aren’t blind to it. You’ve likely scrolled past bland, repetitive ads, seen those generic LinkedIn posts, and probably rolled your eyes at the blatantly AI-generated articles. It’s no wonder that as AI slop increasingly masquerades as real content, readers are becoming more selective about who they trust.

This is creating a new challenge for PR and marketing: How do you create credibility in an environment where authenticity itself is being questioned?

Also Read: Hyper-Automation Is Over. Agentic AI Is What Comes Next.

More Isn’t Always Better

Many organizations still approach PR as a numbers game, measuring success through impressions, reach, or volume. But AI has changed the metrics of success.

Let’s take marketing as an example. On Instagram, users are inundated with AI-generated ads that they tend to scroll past rather than engage with. An AI-generated ad that flashes across a social feed may technically register as a view, but that does not mean it resulted in trust or engagement. In many cases, audiences are actively tuning out.

For PR, a very similar dynamic is emerging across earned media, brand storytelling, and thought leadership. Since AI tools make it easy to produce “good enough” content at scale, readers are subconsciously placing greater value on content that feels intentional, specific, and, most importantly, human. What stands out is not perfectly polished messaging, but clarity of voice and authenticity of perspective.

In the age of AI, trust is increasingly tied to transparency. Audiences want to understand not only what brands are saying, but how they are communicating and why. If AI is being used to support content creation, brands should be open about it. Attempts to obscure or over-automate communication risks worsening this skepticism, especially among younger audiences already wary of algorithm-driven media environments.

That skepticism is growing. A recent Nielsen study found that 55% of 6,000 respondents felt “uncomfortable” on websites that primarily use AI-generated content, and 4 out of 5 respondents said media organizations must be transparent about their AI use, particularly in content generation.

Use AI to Make PR More Human, Not Less

That’s not to say AI has no place in modern PR work. The reality is that AI tools can be extremely valuable when used strategically – not just to produce content faster, but to understand audiences more deeply and communicate with greater precision.

AI is exceptionally good at identifying patterns, analyzing audience behavior, and surfacing trends. PR teams can use AI-powered social listening tools to track how conversations evolve across platforms in real time by monitoring keywords, brand mentions, and emerging narratives as they take shape. They can analyze comment sections and forum discussions to see how people react to messaging, not just how many people saw it. 

Crucially, this shifts PR back to one of its fundamentals: listening. In practice, that may involve using AI to analyze thousands of comments to identify recurring questions or frustrations, or to compare how different audience segments respond to the same message. You could also track the exact words and phrases audiences use, and reflect that language back in communications so your messaging feels natural rather than imposed.

Used well, this allows PR professionals to move beyond guesswork. Instead of broadcasting generic messages, they can tailor communications that reflect real concerns, cultural nuances, and audience priorities. AI can streamline research, assist with drafting, and help PR teams respond faster in the ever-changing media landscape. But more importantly, it can help them listen better by grounding decisions in real audience insight, not assumptions.

Still, no AI tool can generate authenticity. That remains a human trait. AI can tell you what resonates, but it’s up to you to decide why it matters and respond with judgment, context, and honesty.

Also Read: AI Is Supercharging Returns Fraud. Retailers Are Behind.

Authenticity Is Now Table Stakes

What does this mean for brands and PR professionals? With trust now harder to earn, brands must reconsider some of their long-standing assumptions about engagement. 

First, consistency matters more than volume in this new age. For years, many companies prioritized constant output to stay visible across every platform and news cycle. Search engine algorithms of the past favored high linkback rates, so writing for the SEO machine made sense as a way to drive organic search traffic.

That won’t work today, with audiences deluged with content and search itself deprioritizing content farms. 

In this new climate, brands must publish for humans. Companies that communicate clearly, thoughtfully, and consistently over time are more likely to build credibility than those that chase every trending topic or publish for visibility alone.

Second, specificity is becoming increasingly valuable. Generic messaging designed to appeal to everyone often resonates with no one. People are drawn to content that feels informed, focused, and grounded in real expertise or lived experience. That could mean sharing concrete insights, addressing niche concerns, or offering a distinct point of view instead of repeating industry clichés. Given how much content is out there, specificity will help brands sound more human and less interchangeable.

Third, prioritize engagement over impressions by measuring the quality of audience relationships. High view counts and viral reach may look impressive in reports, but they will not necessarily translate into trust or loyalty. Target a smaller audience that has a high likelihood of actively engaging with your brand, sharing your content, and building a lasting relationship with you. An approach that prioritizes honesty, clarity, and a personal touch will help you establish a real relationship that no amount of AI slop can draw away.

Finally, recognize that authenticity cannot be automated. AI may assist with execution, but credibility is still a byproduct of human insight, lived experience, and hard-won expertise. 

Also Read: The Death of Batch-and-Blast Email Marketing

The Future of PR Will Be More Human

Ironically, the explosion of AI-generated content may ultimately increase the value of human communication.

As audiences become more skeptical of messaging of all stripes, qualities like transparency, depth, and authenticity will help content stand out. The future of PR will be defined by content that can help build the strongest relationships and the deepest trust.

The post Are Brands Losing Credibility in the AI Era? appeared first on MartechView.

For more than a decade, the enterprise world chased hyper-automation. Boards, consultants, and venture capitalists all shared the same dream: big productivity gains delivered by clever workflow tools, low-code connectors, and robotic process automation (RPA). Top-tier valuations, a parade of IPOs, and billion-dollar rounds crowned automation as the next trillion-dollar frontier.

But today that narrative is collapsing — not because automation isn’t useful, but because the architecture underpinning it is fundamentally obsolete. What the market once celebrated as “hyper-automation” is now being written off as incremental plumbing — not strategic leverage.

Public markets and valuations aren’t bluffing — they are signaling a tectonic shift. 

Public Market Reality: When Automation Valuations Drop Hard

One of the poster children of hyper-automation was UiPath. Valued at over $35 billion in late-stage private funding, UiPath’s public market debut was among the largest software IPOs of the 2020 cohort. But today, the stock trades 70% below its peak, reflecting a stark reset in what public markets are willing to pay for legacy automation that fails to expand margins or deliver sustained enterprise outcomes. 

This sell-off isn’t isolated — it’s a shift in valuation narrative. Capital now demands results, not engineering complexity. Ask yourself: if automation fundamentally transformed productivity across every business unit, why haven’t legacy automation stocks maintained their valuations in an era obsessed with AI growth?

The answer is simple: legacy automation still operates like software from the 1980s — static, linear, brittle workflow logic under the hood — while the world around it has become exponentially more dynamic.

Also Read: E-commerce Doesn’t Have a Data Problem. It Has a Speed One.

The Funding Frenzy That Built Fragile Architecture

While UiPath faced valuation compression, private markets saw a secondary boom in no-code and security automation startups — each promising to elevate hyper-automation through low-code workflows and drag-and-drop playbooks.

Consider Tines, a Dublin-based no-code automation platform focused on security workflows, with hundreds of millions in funding across multiple rounds. 

Meanwhile, Torq — another “hyper-automation” platform — raised a massive $140 million in its Series D at a $1.2 billion valuation in 2026, bringing its total raised to $332 million. 

These companies embody exactly the market the last decade valued: drag-and-drop workflows that connect tools, handle alert triage, and automate repeatable tasks. Yet the core architecture for these systems remains deterministic-first — defined by prewritten steps assembled into stories or playbooks that attempt to anticipate every possible state of the world. That’s fine for checklists — less so for real business outcomes. 

Why This Architecture Is Headed Toward Zero

Here’s the uncomfortable truth:

Hyper-automation didn’t redefine how work actually gets done. It repackaged deterministic workflows with prettier UIs and AI buzzwords. The entire paradigm assumes:

• Enterprise processes are static enough to be defined in workflows.
• Users can anticipate every possible branch in a decision tree.
• You can write your way around complex human-machine interaction.

But the 2020s enterprise is not static. The business landscape, cloud ecosystems, security threats, and digital environments change by the minute. Legacy architecture cannot adapt because it starts with rules rather than objectives.

When markets recognize that a product’s DNA can’t survive the world it claims to automate, they repriced those assets accordingly. That’s exactly what’s happening with companies built on the old stack. 

Public Sentiment Is Shifting — Investors Want Outcomes, Not Workflows

The public markets — and savvy late-stage investors — are increasingly separating automation as a product from real enterprise leverage. The headlines today aren’t about bots mimicking clicks anymore — they’re about agents that act autonomously on business objectives.

Investors are no longer content to pour capital into incremental workflow stitching. Capital is chasing systems that can:

• Interpret intent, not just follow static rules
• Plan and execute across systems, not just trigger steps
• Adapt to uncertainty, not crash when conditions change

The evidence is clear: when the underlying architecture is rigid, valuations get compressed. Investors won’t buy another round of “better connectors” if the fundamental utility is low-margin and rigid. 

Also Read: Contextual Advertising: What It Is and Why It Matters

Agentic Automation Is What Comes After Hyper-Automation

The next wave isn’t a prettier drag-and-drop canvas. And it definitely isn’t another workflow builder packaged as the next big thing.

It’s agentic automation — systems built for objectives, not sequences; for dynamic coordination across tools, not fixed playbooks. An agentic system understands intent, negotiates tasks across APIs, adapts to context, and achieves outcomes without human-modeled paths for every permutation of work.

We’ve moved beyond single-thread dragons to intelligent meshes of agents that collaborate to solve ambiguous problems. This isn’t a minor upgrade — it’s an architectural rewrite of how work gets done. 

Legacy Automation Architecture Is Becoming the New Mainframe

Here’s the real market truth:

Legacy process automation, even with AI badges slapped on workflows, is rapidly approaching the same fate as outdated computing paradigms — valuable in a historical context, obsolete in a strategic context.

Public markets already charge legacy automation names discount multiples compared to true AI-driven growth platforms. Venture capital is tightening its focus on workflow stitchers while expanding its backing of agentic computation pioneers. And boards are starting to ask deeper questions about outcome economics instead of demo bells and whistles.

If legacy automation is mainframe-era tools rebranded for the cloud, then agentic systems are the next computational substrate for business execution.

That’s the disruption that will wipe out the old market cap — and unlock orders-of-magnitude value for enterprises that embrace the new paradigm.

The automation story isn’t over — it’s rewritten.

The post Hyper-Automation Is Over. Agentic AI Is What Comes Next. appeared first on MartechView.