Black Friday and Cyber Monday have become a stress test not just for ecommerce infrastructure, but for performance marketing itself. Budgets spike, competition explodes, and the margin for slow decision-making collapses to near zero. MAI.co believes it’s cracked that problem—not with bigger teams or smarter dashboards, but with autonomous AI agents.

The company, which provides AI-driven performance marketing for direct-to-consumer brands, says customers using its platform saw an average 63% increase in revenue during the BFCM period year over year, with some brands recording more than six-times growth compared to last holiday season.

Those are bold numbers in a period where many brands struggle simply to hold ground as ad costs surge. MAI’s wager is that continuous, machine-speed optimization—not manual media management—is the only way to compete during peak moments.

Why BFCM Exposes the Limits of Traditional Performance Marketing

For years, agencies have promised hands-on optimization during major shopping events. In reality, Black Friday weekends expose the constraint no one likes to admit: humans can’t keep up.

According to MAI, its AI agents reviewed an average of 39.1 Google Ads campaigns per client per day, executing 32.4 optimizations daily. That level of iteration—budget shifts, bid adjustments, signal interpretation, and anomaly detection—would be nearly impossible for a human team to manage in real time, especially across dozens or hundreds of accounts.

The comparison matters. During peak periods, performance gaps are rarely caused by poor strategy. They’re caused by delays. By the time a human notices an issue, debates its cause, and implements a change, the opportunity window has already closed.

MAI’s system aims to remove that lag entirely.

Optimization at Algorithm Speed

At the core of MAI’s platform is a network of autonomous AI agents designed specifically for Google Ads. These agents continuously evaluate performance signals, testing changes and measuring their impact through reinforcement learning and a fast feedback loop tied directly to ecommerce data.

Rather than automating a single action, the agents manage the system end-to-end: monitoring spend efficiency, reallocating budget, and responding to shifts in demand or conversion rates as they happen.

That architecture reflects a broader trend in MarTech. As platforms like Google Ads become increasingly opaque and algorithm-driven, success depends less on manual tweaking and more on feeding the system clean, timely signals—and reacting instantly when those signals change.

MAI is positioning itself as the connective tissue between ecommerce systems and Google’s AI-led buying engine.

What That Looks Like for Brands on the Ground

For brands, the impact shows up as scale without destabilization—a rare combination during holiday spikes.

Boring Mattress CEO Daehee Park says the company onboarded MAI ahead of Black Friday with one clear objective: scale profitable spend without sacrificing efficiency.

The result: stable performance while tripling daily ad spend, a scenario that often breaks traditional account structures. Park also highlighted MAI’s daily transparency updates, which explain not just what the agents are doing, but why—an important counterbalance to the “black box” criticism often associated with AI marketing tools.

That theme appears repeatedly in customer feedback. While the agents operate autonomously, MAI emphasizes visibility into decision logic to keep operators confident in high-stakes moments.

From Manual Monitoring to Continuous Vigilance

Beyond optimization, MAI pitches its agents as always-on watchdogs—something most internal teams can’t realistically maintain.

During BFCM, the agents flagged problems like sudden conversion-rate drops caused by broken website elements faster than customers’ own monitoring systems. In a compressed buying window, detecting those issues minutes or hours earlier can be the difference between a record day and a lost one.

That kind of vigilance reframes performance marketing from campaign management into operational insurance. When revenue velocity peaks, the cost of downtime spikes alongside it.

For brands like Italic, that responsiveness stood out. COO Avi Arora described MAI as feeling like an extension of the internal team during Black Friday, helping identify when to push spend and when to pull back—decisions that are easy in hindsight and brutally hard in real time.

Scaling Where Google Is the Only Channel

For some D2C brands, the stakes were even higher. NutritionFaktory, which relies exclusively on Google as its marketing channel, credits MAI with delivering 110% year-over-year revenue growth over Black Friday.

In catalogs with thousands of SKUs, human-led budget allocation becomes guesswork under pressure. MAI’s agents continuously redistributed spend across products based on performance signals, scaling what worked and throttling what didn’t without waiting for intervention.

This use case underscores where AI agents may be most disruptive—not as assistants to media buyers, but as primary operators in environments where speed beats intuition.

The “Toothbrush Problem” of Performance Marketing

MAI co-founder and CEO Yuchen Wu summarizes the company’s mission with a metaphor that resonates with any performance marketer: the constant urge to check numbers.

Wu describes this as the “toothbrush problem”—the need to manually review performance metrics multiple times a day just to maintain confidence that nothing is breaking. It’s a cognitive tax that grows heavier during peak periods.

By handing that layer of vigilance and adjustment to autonomous agents, MAI aims to free human teams to focus on strategy, product, and messaging—the areas where human judgment still carries the most value.

It’s also part of a larger democratization story. Advanced reinforcement learning and real-time optimization were once the domain of large enterprises with in-house data science teams. MAI’s pitch is that growth-stage brands can now access the same capabilities without building them internally.

What This Signals for the Performance Marketing Industry

MAI’s BFCM results point to a broader inflection point.

Manual optimization—whether in-house or at agencies—was designed for a slower era of digital advertising. Today’s platforms reward those who respond fastest to volatility, not those with the largest teams. As ad ecosystems consolidate around AI-driven buying, the advantage tilts toward systems that can operate continuously, learn autonomously, and execute instantly.

That doesn’t eliminate the need for marketers. But it does shift their role from operators to architects—defining strategy, constraints, and success metrics while machines handle execution at scale.

If MAI’s reported results hold across more categories and longer timeframes, autonomous agents could move from experimental add-on to baseline expectation, especially during revenue-critical moments like BFCM.

For now, the message is clear: in peak commerce, speed isn’t a nice-to-have—it’s the strategy.

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ServiceNow is placing a sizable, long-term bet on Canada’s public sector—and on the idea that AI adoption at government scale requires more than just software licenses.

The company announced a CA$110 million multi-year investment aimed at helping Canadian public sector organizations adopt AI securely and at scale. The commitment includes expanding Canadian-hosted, AI-ready digital infrastructure, strengthening data residency and security controls, and significantly increasing local expertise through a new Canada Centre of Excellence and approximately 100 high-skilled, Canada-based jobs.

For ServiceNow, which positions itself as the “AI control tower for business reinvention,” the move signals a deeper shift: governments are no longer experimenting with AI on the margins. They’re demanding production-ready platforms that meet sovereignty, privacy, and operational requirements from day one.

Why This Investment Matters Now

Public sector AI adoption has reached an inflection point. While governments globally have piloted automation and analytics initiatives for years, scaling AI across citizen services brings a new level of scrutiny around data location, security, and accountability.

Canada is no exception. Federal departments, crown corporations, provincial governments, and major municipalities are under pressure to modernize services while respecting strict regulatory environments. ServiceNow has already established a footprint across these organizations, and this investment builds on that groundwork rather than starting from scratch.

The message is clear: AI for government can’t be bolted on from outside the country. It needs local infrastructure, local people, and local governance.

Canadian-Hosted Infrastructure, Built for AI

A core pillar of the investment is expanding Canadian-hosted infrastructure designed specifically for AI workloads in the public sector.

Running the ServiceNow AI Platform in a secure, domestic environment allows government organizations to automate workflows and improve service delivery without compromising on data residency or privacy requirements. Advanced operational controls are intended to ensure public sector customers can meet compliance obligations while still moving faster than traditional IT modernization cycles allow.

This approach reflects a broader trend in government tech procurement: cloud-first is no longer enough. For sensitive workloads, governments increasingly require sovereign cloud capabilities that provide the flexibility of modern platforms without exporting data or control beyond national borders.

ServiceNow’s strategy aligns with that shift, positioning the company as a long-term infrastructure partner rather than just an application vendor.

From Platforms to People: Building Local Expertise

Technology alone doesn’t drive transformation—execution does. That’s where the company’s new Canada Centre of Excellence comes in.

The Centre will expand ServiceNow’s in-country expertise with roughly 100 new high-skilled roles, focused on helping Canadian public sector customers accelerate deployments, apply AI effectively, and realize value faster. These roles are designed to support implementation, optimization, and ongoing evolution of ServiceNow environments, not just initial rollouts.

This emphasis on people is notable. Many public sector modernization efforts stall after deployment, when internal teams struggle to operationalize new capabilities. By investing directly in local delivery and advisory capacity, ServiceNow is addressing one of the most persistent friction points in government digital transformation.

Chris Ellison, Group Vice President and General Manager of ServiceNow Canada, framed the move as both an economic and technological commitment.

“This is a major investment in Canada’s digital future,” Ellison said. “We’re creating high-skilled jobs, expanding our local footprint, and helping the Canadian public sector modernize how it serves citizens.”

AI at Scale, Without Sacrificing Trust

One of the consistent challenges facing public sector AI initiatives is trust—both internal and public-facing. Citizens expect faster, more responsive services, but they’re equally concerned about how their data is handled and how automated decisions are made.

ServiceNow’s pitch is that centralized workflow automation and AI-powered decisioning can actually increase transparency and accountability when implemented correctly. By standardizing processes, improving visibility across departments, and embedding controls into workflows, agencies can reduce manual errors and make outcomes more predictable.

That balance—efficiency with oversight—is increasingly critical as governments move from pilot projects into large-scale AI adoption.

Evan Solomon, Canada’s Minister of AI and Digital Innovation, highlighted that tension in welcoming the announcement.

“Advancing secure AI adoption and digital sovereignty is essential to building a resilient Canadian economy,” Solomon said, pointing to the importance of partnerships between public institutions and industry in driving trusted innovation.

A Strategic Bet on Public Sector Modernization

While the announcement is framed around AI, the implications extend beyond machine learning alone. ServiceNow’s platform is fundamentally about workflow orchestration—connecting people, systems, and data across large organizations.

For government entities, that means rethinking how work moves between departments, how cases are managed, and how services are delivered to citizens. AI accelerates those workflows, but the underlying transformation is organizational.

By committing capital, infrastructure, and talent locally, ServiceNow is positioning itself as a strategic modernization partner rather than a point solution provider. It’s a move that mirrors how hyperscalers and enterprise software vendors increasingly approach government markets: slow to enter, expensive to sustain, but sticky once embedded.

The Competitive Landscape

ServiceNow isn’t alone in targeting public sector AI adoption. Large cloud providers, systems integrators, and enterprise software companies are all racing to define their role in government AI strategies.

What differentiates ServiceNow’s approach is its focus on control, governance, and orchestration rather than raw compute or analytics. In heavily regulated environments, those traits often matter more than cutting-edge model performance.

By anchoring its Canadian strategy around digital sovereignty and local capability, ServiceNow is responding directly to concerns that have slowed adoption for some global vendors.

A Long-Term Commitment, Not a One-Off Announcement

Importantly, ServiceNow is framing this investment as part of a long-term commitment to Canada, not a standalone initiative tied to near-term revenue goals.

As public sector needs evolve—from service automation to predictive insights and cross-agency coordination—the company says it will continue investing in people, technology, and partnerships to support that evolution.

For Canadian government organizations, the significance is less about the headline dollar amount and more about what it enables: the ability to move forward with AI initiatives confidently, without waiting for regulatory clarity or infrastructure readiness to catch up.

The Bigger Picture for Government AI

ServiceNow’s CA$110 million investment underscores a reality that’s becoming harder to ignore: AI-driven government transformation requires industrial-scale commitment.

It’s not enough to pilot chatbots or automate isolated processes. Real impact comes from re-architecting how work flows across institutions—and doing so in a way that’s secure, compliant, and trusted.

By putting infrastructure, talent, and governance on Canadian soil, ServiceNow is betting that the future of public sector AI will be built locally, even if the platforms are global.

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The company announced it has been named a Leader in the 2025 Gartner Magic Quadrant for Marketing Work Management Platforms, a recognition that carries added weight this year. With the latest report, monday.com becomes the only platform positioned as a Leader across three separate 2025 Gartner Magic Quadrants: Marketing Work Management Platforms, Collaborative Work Management, and Adaptive Project Management and Reporting.

It’s the second year running that the company has achieved this triple-Leadership status, underscoring how quickly monday.com has evolved from a flexible project tool into a broad, AI-powered work operating system.

Why This Recognition Stands Out

Marketing work management is one of the most crowded and fast-changing categories in enterprise software. As marketing teams juggle campaigns, content, creative workflows, digital production, and cross-functional coordination, expectations for visibility and speed have skyrocketed.

Gartner’s Magic Quadrant evaluates vendors based on Ability to Execute and Completeness of Vision, and monday.com’s consistent placement at the top suggests it’s delivering in both areas—not just shipping features, but aligning them with how modern marketing teams actually operate.

The story gets stronger in Gartner’s companion report. In the 2025 Gartner Critical Capabilities for Marketing Work Management Platforms, monday.com ranked among the top three vendors across all evaluated use cases, signaling depth as well as breadth.

In a category often defined by point solutions or legacy tools retrofitted for marketers, that consistency matters.

A Leader Since Day One

This isn’t a one-off win. monday.com has been recognized as a Leader in the Marketing Work Management Platforms Magic Quadrant every year since the report’s inception. That track record is notable in a space where vendors frequently rise and fall as customer expectations shift.

The momentum reflects a deliberate strategy: build one platform that scales across marketing, product, operations, PMOs, and executive teams—without forcing organizations to stitch together disconnected tools.

For marketing leaders in particular, that promise of a single, connected system has become increasingly appealing as campaigns grow more complex and timelines get tighter.

From Managing Work to Doing the Work

According to monday.com, the key differentiator behind its recent recognition is how deeply AI is embedded into the platform.

“We’re pioneering a shift from tools that simply help teams manage work to a platform that can actually do the work for them,” said Daniel Lereya, Chief Product and Technology Officer at monday.com.

That distinction reflects a broader trend in MarTech. As AI moves from experimental add-ons to core infrastructure, platforms are expected not just to track tasks, but to actively accelerate execution.

With monday AI, the platform automates routine but time-consuming activities—creating tasks, summarizing feedback, routing approvals, and surfacing insights in real time. The goal is to reduce managerial overhead and allow teams to focus on strategy and creative impact rather than administration.

Designed for How Marketing Actually Works

monday.com’s appeal to marketers lies in its flexibility. Rather than forcing teams into rigid templates, the platform adapts to different workflows, from campaign planning and content calendars to creative reviews and cross-channel execution.

Marketing teams can manage their entire lifecycle in one place:

• Campaign planning and prioritization

• Content and creative pipelines

• Digital production workflows

• Cross-team collaboration and reporting

This adaptability is increasingly important as marketing organizations operate more like internal agencies, supporting multiple stakeholders with different timelines and success metrics.

Enterprise Visibility Without the Complexity

For marketing leaders, visibility and governance are often as important as creative freedom. monday.com emphasizes enterprise-grade controls—real-time dashboards, data management, and security features—that allow leadership teams to track progress without slowing execution.

That balance between autonomy and oversight is difficult to achieve, and it’s one reason many large marketing teams struggle with fragmented tool stacks. By consolidating workflows into a single hub, monday.com aims to reduce handoffs, duplicated work, and blind spots.

The platform’s broader suite—including monday campaigns, monday CRM, monday dev, and monday service—extends that visibility beyond marketing, connecting execution to sales, product, and leadership teams.

Implications for the Work Management Market

monday.com’s continued rise highlights an important shift in enterprise software: customers increasingly favor horizontal platforms that can flex across use cases rather than narrow best-of-breed tools that require heavy integration.

Gartner’s recognition suggests the market is rewarding vendors that combine usability, scalability, and embedded intelligence—especially as AI reshapes expectations of what “work management” should deliver.

For buyers, the signal is clear. Work management tools are no longer just task trackers. They’re operational backbones that influence speed, accountability, and decision-making across the organization.

The Bigger Picture

By maintaining Leader status across three Gartner Magic Quadrants, monday.com is reinforcing its position as a long-term player rather than a fast-growth challenger. The consistency of its recognition points to a platform that’s evolving alongside its customers, not chasing trends one feature at a time.

As marketing teams continue to navigate tighter budgets, higher expectations, and growing complexity, platforms that genuinely reduce friction—not just visualize it—are likely to define the next phase of work management.

For monday.com, Gartner’s 2025 reports serve as validation of a clear bet: that the future belongs to platforms that don’t just organize work, but actively help teams get it done.

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As Latin America’s iGaming market enters its most consequential phase yet, Optimove is moving to cement its presence in the region—starting with Brazil.

The player engagement and CRM marketing company has announced a strategic partnership with Salsa Technology, one of Latin America’s most established iGaming platform providers. The deal makes Salsa the first Brazilian platform provider to integrate natively with Optimove, offering operators a significantly faster and more streamlined route to advanced, AI-driven CRM marketing.

In a market shaped by new regulation, intensifying competition, and rising acquisition costs, the partnership signals a broader shift in LATAM iGaming strategy: growth will increasingly come from retention and personalization, not just player acquisition.

Why Brazil Is the Strategic Center of Gravity

Brazil has quickly become one of the most closely watched iGaming markets globally. Regulatory clarity is reshaping the competitive landscape, drawing both regional incumbents and global operators into the market. At the same time, margins are tightening as brands compete aggressively for attention across digital channels.

That combination has made player engagement and lifetime value a board-level priority for operators. Blanket bonuses and generic promotions are losing effectiveness, replaced by a need for real-time, behavior-based personalization that aligns with responsible gaming requirements.

Optimove’s partnership with Salsa is designed to address exactly that inflection point.

What the Integration Unlocks for Operators

At a practical level, the deal removes one of the biggest friction points operators face: implementation complexity.

By integrating directly with Salsa’s platform, Optimove reduces the number of technical and operational steps required to activate its CRM and personalization engine. For operators, that means shorter onboarding cycles, faster time-to-value, and fewer data handoffs between systems.

The integration creates a more fluid connection between core platform data and Optimove’s player engagement layer, enabling operators to act on player behavior as it happens rather than after the fact.

For Salsa clients, the advantage is immediate and exclusive: they gain the fastest access to Optimove’s CRM marketing capabilities in Brazil, without needing bespoke integration projects or prolonged setup timelines.

Real-Time Personalization, Built for LATAM

Salsa’s regional footprint plays a critical role here. With strong operations in Brazil and teams across Uruguay, Peru, Mexico, Portugal, Spain, and Malta, the company has built a reputation for localized, regulation-ready technology tailored to Latin American operators.

Pairing that infrastructure with Optimove’s real-time CRM and AI-driven personalization tools gives operators a powerful combination: local compliance and global-grade marketing intelligence.

With the integration in place, operators can orchestrate automated, multichannel journeys across the player lifecycle—triggered by real-time behaviors such as gameplay patterns, deposit activity, or lapses in engagement. Instead of relying on static campaigns, marketers can respond dynamically, pushing relevant messages at the moment intent forms.

AI as a Retention Engine, Not a Buzzword

Optimove’s platform brings predictive, generative, and agentic AI into the mix—tools increasingly viewed as essential rather than experimental in mature iGaming markets.

These capabilities help operators anticipate churn, identify high-value players earlier, and tailor incentives and messaging with far greater precision. Over time, that intelligence compounds, improving campaign performance while reducing promotional waste.

The emphasis on responsible, data-driven engagement also aligns with Brazil’s evolving regulatory framework, where transparency and player protection are becoming central pillars.

For Optimove, this partnership reinforces its broader Positionless Marketing philosophy—giving marketers the autonomy to execute personalized experiences without being constrained by rigid campaign structures or channel silos.

A Strategic Response to Market Maturity

According to Heloisa Bianchi, Partnerships Manager for Optimove in Latin America, the timing reflects deeper market forces at work.

Brazil and the broader LATAM region, she notes, are entering a phase defined by regulation, competition, and a pivot away from acquisition-heavy models toward retention-first strategies. In that environment, operators need intelligence and speed, not just scale.

Salsa’s role as a regional technology anchor makes it a natural partner. As one of the most influential platform providers in the market, Salsa sits at the core of operators’ day-to-day operations. Embedding Optimove at that level brings CRM marketing closer to the heart of the gaming stack.

Salsa, for its part, views the partnership as a strategic enhancement to its platform rather than a bolt-on feature. By integrating advanced personalization and automation, the company strengthens its value proposition to operators navigating Brazil’s new regulatory chapter.

Implications for the LATAM iGaming Stack

The deal reflects a wider trend across iGaming and MarTech: best-of-breed CRM and engagement platforms are moving closer to the core platform layer.

As competition intensifies, operators can no longer afford fragmented stacks where player data, messaging, and decision-making live in separate systems. Native integrations like this reduce complexity while increasing responsiveness—two qualities that matter far more than feature checklists in fast-moving markets.

For LATAM operators, the partnership also signals that global vendors are no longer treating the region as “next.” It’s now central to product strategy and investment.

The Bigger Picture

Optimove’s integration with Salsa Technology is less about a single partnership announcement and more about what it represents: the acceleration of retention-led, AI-powered marketing in one of the world’s most dynamic iGaming regions.

As Brazil formalizes its regulatory framework and competition intensifies, operators that invest early in personalization and lifecycle marketing will likely define the market’s next phase. This partnership lowers the barriers to entry for doing exactly that.

For the LATAM iGaming ecosystem, it’s another sign that the infrastructure behind player engagement is evolving—fast—and that CRM marketing is no longer a downstream function, but a core growth lever.

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For many chiropractic practices, claim denials and documentation errors aren’t just an occasional headache—they’re a persistent revenue leak. Industry estimates suggest that as much as 30% of expected collections can be lost due to compliance and documentation issues, creating mounting financial pressure and raising the risk of payer audits.

ChiroTouch believes it has a fix. The company has announced Compliance Scan, a new AI-powered feature embedded within its existing AI assistant, Rheo, aimed at helping chiropractors reduce compliance risk before claims ever leave the building.

Rather than treating compliance as an after-the-fact cleanup exercise, Compliance Scan is designed to work as an always-on guardrail, reviewing documentation and billing in real time and flagging potential issues when providers still have the chance to correct them.

Compliance moves upstream

Traditionally, compliance checks happen too late—after a claim is denied or during an audit. Compliance Scan flips that model. The tool automatically reviews SOAP notes and billing codes before submission, cross-checking patient complaints, exam findings, diagnoses, and billed procedures for consistency and defensibility.

If something doesn’t line up, the system surfaces the discrepancy immediately. That could mean a diagnosis that isn’t adequately supported by exam findings, or a billed procedure that doesn’t clearly map back to documented complaints. Either way, the provider is alerted before the claim ever reaches a payer.

The goal is simple: make every claim audit-ready from the start.

“Compliance Scan gives chiropractors confidence that their documentation meets compliance standards every time,” said Tami Howard, Product Manager for AI Solutions at ChiroTouch. “By embedding audit intelligence directly into the workflow, Rheo transforms compliance from a manual burden into an integrated, automated safeguard.”

Built into the clinical workflow

One of the more notable aspects of Compliance Scan is how tightly it’s integrated into everyday clinical processes. The feature can be triggered automatically when a provider completes a note or makes changes to CPT or diagnosis codes, eliminating the need for separate reviews or external tools.

Once activated, the system provides real-time scoring and validation, giving clinicians a quick, understandable view of documentation risk. Rather than burying feedback in dense reports, Compliance Scan uses clear indicators to show whether a note is billing-ready or needs attention.

Providers can jump directly to flagged sections with a single click, make edits, and re-scan instantly. The emphasis is on speed and clarity, not adding yet another layer of administrative work.

Visibility beyond individual notes

Compliance issues rarely show up as one-off mistakes. They tend to follow patterns—certain providers, diagnoses, or procedures that consistently carry higher risk. Compliance Scan addresses this with a centralized dashboard that aggregates compliance data across notes.

The dashboard uses color-coded indicators to highlight risk levels and allows filtering by note status, signatures, and billing readiness. Over time, historical analytics help practices track whether compliance scores are improving and quantify reductions in audit risk.

That longer-term visibility could be especially valuable for larger practices or groups managing compliance across multiple providers, where standardization and oversight are ongoing challenges.

Why it matters now

The push for cleaner documentation and defensible claims is intensifying. Payers continue to tighten audits, and rising administrative costs are squeezing margins for independent practices. At the same time, clinicians are increasingly vocal about burnout tied to paperwork and compliance overhead.

AI-driven compliance tools like Compliance Scan signal a broader shift in healthcare software toward preventive automation. Instead of asking providers to memorize evolving documentation rules or rely on external audits, platforms are starting to bake compliance intelligence directly into clinical workflows.

ChiroTouch is positioning Rheo not just as a productivity assistant, but as a revenue-protection layer—one that aligns subjective complaints, objective findings, diagnoses, and billing in a way that’s both defensible and efficient.

Less guesswork, fewer denials

At its core, Compliance Scan is about removing uncertainty. By automatically validating documentation and surfacing risks early, chiropractors can submit claims with greater confidence, reduce denials, and spend less time responding to audits.

Just as importantly, it aims to do this without disrupting how providers already work. Notes are written as usual, billing is completed as normal—but now there’s an AI-driven safety net watching for gaps and inconsistencies.

For practices navigating shrinking margins and growing payer scrutiny, that kind of built-in compliance intelligence may quickly move from “nice to have” to essential.

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Search engine optimization is once again in flux—and this time, the shift isn’t about backlinks or keyword density. It’s about artificial intelligence quietly reshaping how search engines interpret content, predict intent, and present answers to users.

Digital Silk, the award-winning digital agency known for brand strategy, custom websites, and digital marketing execution, has released a new set of insights exploring how AI-driven search experiences are redefining SEO—and why brands may need to rethink long-standing assumptions about visibility.

The takeaway is clear: AI isn’t just improving search; it’s changing what it means to be “found” online.

From rankings to relationships with AI systems

For years, SEO strategy revolved around climbing blue-link rankings. Today, that model is starting to blur. Search engines are rolling out generative summaries, adaptive ranking systems, and real-time interpretation layers that increasingly sit between users and organic results.

Instead of directing users to ten clickable links, AI-powered experiences often provide synthesized answers at the top of the page—sometimes reducing the need to click through at all. The result is a search landscape where visibility depends as much on interpretability as it does on traditional ranking position.

Digital Silk’s analysis frames this moment as a transition period. Brands aren’t dealing with a single algorithm update; they’re adjusting to search engines that learn, adapt, and respond to context in real time.

That shift helps explain why many organizations are reassessing how AI may influence user behavior and result formats—especially as search platforms move deeper into predictive and generative territory.

Marketers are already adjusting

The industry, it seems, has gotten the message. According to HubSpot’s 2025 State of Marketing Report, 64% of marketers say AI or automation tools are now an important part of their strategy. That’s not a future-facing statistic—it reflects how teams are operating right now.

Even at the content creation level, habits are changing. HubSpot also reports that one in two writers now uses AI tools to help boost content performance. While opinions vary on how much automation is too much, the signal is hard to ignore: workflows are being rebuilt to align with AI-driven discovery.

Digital Silk’s insights position this shift as both an opportunity and a risk. Brands that adapt thoughtfully can gain an edge. Those that cling to legacy tactics may find themselves losing ground—even if their rankings technically remain intact.

What AI is changing inside search engines

At a functional level, AI is influencing search in several interconnected ways:

• Generative summaries are pulling information from multiple sources and presenting a synthesized answer, often before users scroll.

• Intent prediction allows search engines to infer what users want next, not just what they typed.

• Real-time content interpretation evaluates freshness, context, and factual consistency more dynamically than older models.

Together, these features alter how content is evaluated and surfaced. Ranking signals still matter, but so does a page’s ability to feed AI systems with structured, reliable, and clearly contextualized information.

Digital Silk notes that brands are responding by paying closer attention to how their content is technically framed—not just how it reads.

Structured data and technical precision take center stage

If AI systems are the new gatekeepers, they need clean inputs. That’s where structured data, schema markup, and technical accuracy come into play.

Digital Silk’s insights highlight structured data as a growing focal point for brands navigating AI-powered search. Properly tagged content makes it easier for search engines to identify entities, relationships, and core facts—elements that generative summaries rely on heavily.

This doesn’t mean brands should abandon storytelling or long-form content. But it does mean clarity matters more than ever. Ambiguous claims, inconsistent facts, or poorly organized pages are less likely to be trusted by machine-driven evaluation layers.

In an AI-mediated environment, credibility is encoded as much in structure as it is in style.

Accuracy becomes a ranking signal—indirectly

One subtle but important theme in Digital Silk’s update is the rising importance of factual accuracy and source credibility. Generative systems don’t just rank pages; they learn from them.

That raises the bar for brands publishing content meant to perform in search. Inaccurate or thin pages aren’t just less useful to users—they may also be sidelined by AI systems prioritizing trustworthy sources.

This trend aligns with broader movements already underway, from Google’s emphasis on experience and expertise to growing scrutiny of content provenance. AI magnifies those priorities by making synthesis and comparison instantaneous.

In practical terms, this means:

• Claims should be supported by evidence.

• Sources should be transparent.

• Content should be updated regularly to reflect current realities.

SEO, in this context, is drifting closer to digital publishing discipline than tactical optimization.

Monitoring rankings isn’t enough anymore

Another consequence of AI-driven search is that rankings alone no longer tell the full story. A page might technically rank well and still lose traffic if generative summaries satisfy user intent before a click occurs.

Digital Silk underscores the importance of monitoring behavior, not just positions. How are users interacting with AI-powered results? Where are impressions rising but click-through rates falling? Which content formats are being quoted, summarized, or ignored?

Answering those questions often requires more specialized analysis than traditional SEO reporting tools provide. That’s part of why the agency sees growing interest in deeper, more interpretive SEO support—work that looks beyond surface metrics to understand how algorithms are using content.

Why brands are rethinking SEO partnerships

As AI introduces more variables into search performance, many organizations are realizing that DIY optimization may not be enough. The rules are still forming, and best practices are evolving in real time.

Digital Silk’s insights suggest that brands are increasingly looking for partners that can interpret algorithmic signals, not just implement checklists. This includes understanding how AI-generated summaries might surface certain pages, how intent modeling reshapes keyword targeting, and how technical decisions influence discoverability across emerging formats.

In short, SEO is becoming less predictable—but also more strategic.

Leadership perspective: planning for uncertainty

“AI is accelerating shifts in search behavior and creating new variables for brands to consider,” said Gabriel Shaoolian, CEO of Digital Silk. “Our latest insights outline how these developments may shape SEO planning and why many organizations are assessing the need for more specialized analysis.”

That emphasis on planning stands out. Rather than prescribing a rigid playbook, Digital Silk frames AI-driven SEO as an evolving discipline—one that requires flexibility, observation, and a willingness to rethink assumptions.

The message isn’t that old SEO is dead. It’s that old SEO, on its own, may no longer be sufficient.

The bigger picture for digital marketing

Zooming out, Digital Silk’s update fits into a broader industry pattern. AI is collapsing boundaries between search, content, and user experience. Visibility is no longer just about being indexed—it’s about being interpretable by machines designed to answer questions directly.

For brands, that raises strategic questions:

• How do you optimize for engagement when users may never click through?

• How do you balance AI-assisted content creation with originality and trust?

• How do you measure success when impressions, summaries, and citations matter as much as visits?

There are no definitive answers yet. But agencies and marketers paying attention now will be better positioned as the rules solidify.

SEO enters its next chapter

Digital Silk’s insights don’t declare the end of SEO—they mark its evolution. As AI reshapes how search engines understand and present information, brands will need to think less about gaming algorithms and more about aligning with them.

That means clarity over cleverness, structure over shortcuts, and credibility over volume.

The companies that adapt won’t just rank well—they’ll become reliable sources in an AI-curated web.

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Grocery retail runs on razor-thin margins, fast-moving inventory, and decisions that often can’t wait until tomorrow morning. For Schnuck Markets, Inc.—one of the largest privately held supermarket chains in the U.S.—that reality exposed a growing problem: its data was everywhere, but insight was nowhere.

Now, the Midwest-based grocer is betting on Domo to change that.

Domo (Nasdaq: DOMO) says Schnucks has deployed its AI and Data Products platform as an enterprise-wide reporting layer, giving operators, managers, and executives a shared, real-time view of the business. The move marks a sharp break from the spreadsheet-heavy reporting models that still dominate much of the grocery industry.

The goal isn’t better dashboards for their own sake. It’s faster decisions on staffing, promotions, inventory, and customer experience—on the store floor, not weeks later in a boardroom.

A familiar grocery problem: too much data, too little clarity

With 113 stores across multiple Midwest states and more than 80 years in operation, Schnucks had accumulated a classic enterprise challenge. Data lived in dozens of systems spanning HR, finance, merchandising, supply chain, marketing, and store operations. Reporting often required manually stitching together spreadsheets, printed reports, and emailed files.

That fragmentation came at a cost.

Teams across departments were looking at different numbers, often generated at different times. Store managers reacted to yesterday’s data. Executives debated whose version of the truth was correct. And real-time operational insight—the kind modern retail demands—was mostly out of reach.

This isn’t unique to Schnucks. Many grocery chains still rely on legacy reporting approaches built for slower, less complex retail environments. But today’s grocery landscape—shaped by inflation, labor shortages, omnichannel fulfillment, and heightened customer expectations—leaves little margin for lag.

Rebuilding reporting around a single source of truth

Schnucks turned to Domo with a clear mandate: centralize data and make it usable by everyone, not just analysts.

Domo now serves as the grocer’s enterprise reporting platform, aggregating data from across the organization into a single, cloud-based system. HR metrics, financial performance, supply chain signals, merchandising data, marketing results, and store-level operations all flow into one environment.

The difference isn’t just consolidation—it’s accessibility.

Executives see high-level performance indicators across regions and states. Store managers get role-specific KPIs relevant to staffing, production, and service levels. Department leads monitor operational metrics in near real time rather than waiting for end-of-day summaries.

Instead of reacting to what already happened, teams can respond to what’s happening now.

From hindsight to foresight on the store floor

That shift is precisely what Schnucks was after.

“We no longer ask ‘What were my sales yesterday?’ but focus on ‘What do I need to do moving forward to improve the customer experience?’,” said Colin Lloyd, Director of Business Analytics at Schnucks.

It’s a subtle but important change in mindset. Grocery retail has long been retrospective, built around weekly reports and historical trends. Domo pushes Schnucks toward continuous decision-making, where data informs staffing levels, production planning, and customer service adjustments throughout the day.

Store teams reportedly use the platform daily, not as an abstract analytics tool but as an operational guide. When staffing needs shift or production demands change, managers see it immediately. That immediacy matters in an industry where under-staffing hurts customer satisfaction—and over-staffing erodes margins.

AI and low-code apps bring analytics closer to the business

A key enabler of this shift is Domo’s low-code App Studio. Schnucks used it to build interactive dashboards that surface company-wide KPIs in visually intuitive ways.

Rather than forcing users to adapt to rigid analytics reports, Schnucks tailored experiences by role. A corporate executive doesn’t see the same view as a department manager, and they shouldn’t. Each dashboard highlights what matters most to the decision-maker using it.

This is where platforms like Domo are increasingly competing—not just on analytics horsepower, but on usability. Traditional BI tools often struggle to gain adoption outside analytics teams. Low-code environments, by contrast, aim to bring data into daily workflows without requiring SQL fluency.

For grocery retailers, that accessibility can be a competitive advantage.

Marketing and merchandising move at near-real-time speed

One of the most tangible changes has shown up in marketing execution.

Schnucks’ marketing and merchandising teams can now monitor promotion performance in Domo as little as 15 minutes after launch. That’s a radical improvement over traditional retail reporting cycles, which often measure outcomes hours or even days later.

If a promotion underperforms, teams can adjust pricing, placement, or messaging while the campaign is still running. If it overperforms, they can scale it faster or ensure inventory availability keeps up with demand.

In a category where promotions directly influence foot traffic and basket size, that agility translates to real revenue impact.

It also reflects a broader industry trend: retailers are increasingly borrowing playbooks from digital commerce, where real-time performance optimization is table stakes.

Breaking down silos without breaking workflows

One reason Domo resonated with Schnucks is its ability to unify data without forcing massive changes to existing systems.

Rather than ripping out legacy tools, Domo sits above them, ingesting and normalizing data. That architecture allows Schnucks to modernize analytics incrementally while preserving operational continuity.

For many retailers, that balance is critical. Large IT overhauls carry risk, especially in businesses that operate seven days a week. Platforms that can layer intelligence on top of existing systems are often more attractive than ground-up replacements.

Mark Boothe, Chief Marketing Officer at Domo, framed Schnucks as an example of what happens when organizations democratize data.

“Schnucks demonstrates how critical it is for retailers to break down data silos and enable all teammates with insights to drive smarter, faster decisions,” he said.

The grocery industry’s data reckoning

Zooming out, Schnucks’ deployment highlights a broader shift underway in grocery retail.

As margins tighten and competition intensifies—from discount chains, big-box retailers, and online players alike—data-driven execution has become less optional. Retailers that can’t see across their operations in real time risk falling behind those that can.

Historically, grocery has lagged other industries in advanced analytics adoption, in part due to operational complexity and thin margins. But pressures around labor optimization, dynamic pricing, and demand forecasting are accelerating change.

Platforms like Domo position themselves as enablers of this transformation, offering analytics, app development, and now AI-driven insights in a single stack. The challenge will be proving sustained ROI in an industry that measures success in basis points.

AI is the next layer Schnucks is preparing for

Schnucks isn’t stopping with dashboards.

The grocer is preparing to integrate Domo.AI, aiming to surface AI-powered insights directly to frontline teams in real time. While detailed use cases haven’t been disclosed, the direction aligns with industry momentum toward predictive and prescriptive analytics.

Instead of asking users to interpret charts, AI systems can flag anomalies, predict demand shifts, or recommend actions—essentially shortening the gap between insight and execution.

In grocery, where managers balance hundreds of variables each day, AI-assisted decision support could become a major differentiator, especially if it’s delivered directly into operational workflows.

Operational data as a customer experience lever

What stands out in Schnucks’ approach is how closely analytics are tied to customer experience.

Better staffing decisions reduce checkout wait times. Smarter production planning minimizes out-of-stocks and food waste. Faster promotion optimization ensures customers see relevant offers when they matter most.

Data, in this model, isn’t a back-office function. It’s an on-the-floor capability.

That framing mirrors a larger Martech and RetailTech convergence, where operational data increasingly shapes customer-facing outcomes. Analytics platforms that can bridge those worlds—linking internal efficiency with external experience—are well positioned as retailers modernize.

A blueprint for data-driven grocery retail

Schnucks’ Domo deployment doesn’t reinvent grocery retail, but it modernizes how insight flows through the organization.

By consolidating data, tailoring analytics by role, and pushing intelligence closer to decision-making, the grocer has shifted from reactive reporting to proactive operations.

Other grocery chains facing similar challenges—fragmented data, slow reporting cycles, misaligned teams—will likely see a familiar story here. The tools may vary, but the imperative is the same: in today’s retail environment, insight delayed is opportunity lost.

As Schnucks layers in AI-driven capabilities, the next test will be whether predictive intelligence can scale across hundreds of stores without overwhelming users. If it does, the company may offer a compelling case study for how grocery retailers can turn data into a daily operational asset—not just a quarterly report.

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SeatGeek wants to make sure that when fans ask an AI assistant what to do this weekend, the answer includes its events—and, ideally, a ticket already in hand.

The high-growth ticketing platform announced it is a pilot partner in Google’s new agentic AI search experience, positioning SeatGeek among the first ticketing companies whose event inventory can be fully interpreted and acted upon by Google’s AI systems. The move signals a deeper shift in how discovery and conversion are converging as search evolves from typing queries into browsers to delegating tasks to intelligent agents.

For SeatGeek and its sports, concert, and venue partners, this isn’t just another search integration. It’s a bet on where fan journeys are headed next.

From search results to search actions

Google’s agentic AI experience represents a notable departure from traditional search. Instead of returning a list of links, the system is designed to understand intent, plan multi-step tasks, and act on users’ behalf—searching, comparing, and executing when prompted.

That’s a meaningful change for ticketing, an industry historically dependent on search rankings, paid listings, and comparison shopping. SeatGeek’s integration allows Google’s AI to ingest its structured event data and actively use it when answering broader queries such as “What should I do in Chicago this weekend?” or “Find me good courtside seats for a Knicks game.”

Today, the agentic ticketing experience is available to U.S. users opted into Google Labs, with broader access for Google AI Pro and Ultra subscribers, and through Google’s AI Mode. As these experiences expand, they create a new discovery layer that sits above conventional SEO.

For SeatGeek, early participation helps ensure its events don’t get lost as interfaces shift.

Why structured data suddenly matters even more

SeatGeek says it has worked closely with Google to ensure its event listings and structured content can be easily read, interpreted, and acted on by agentic AI systems. This underscores a larger trend reshaping search strategy: success increasingly depends on how well machines—not humans—understand your data.

While structured data has long played a role in SEO, agentic systems raise the stakes. AI doesn’t just extract snippets; it evaluates inventory, pricing context, availability, and seat quality to decide what actions to recommend or execute.

For rightsholders—teams, venues, and artists—this represents a shift in leverage. Platforms that invest early in clean, rich data pipelines stand to gain disproportionate visibility as AI intermediates more of the buyer journey.

Early signs of an AI discovery edge

SeatGeek claims its efforts are already paying off.

Using Profound, a tool that tracks how brands surface across large language model outputs, the company has observed that SeatGeek appears in AI-generated search responses at a higher rate than other major ticketing platforms when prompted with similar event-related queries.

While the AI search landscape is still fluid—and competition from incumbents remains fierce—the signal is notable. As more discovery shifts into AI assistants and agentic search tools, visibility may hinge less on brand recall and more on how AI models assess data completeness and relevance.

For partners, this could mean that choosing a ticketing platform increasingly affects AI exposure, not just traditional traffic.

A more fragmented fan journey—and SeatGeek’s response

SeatGeek’s leadership is clear-eyed about the challenge.

“Fans no longer start their journey on just one channel,” said Russ D’Souza, co-founder of SeatGeek. “They’re asking questions across AI assistants, new search experiences, and tools that can plan or take actions for them.”

That fragmentation has been building for years, driven by mobile apps, voice assistants, and now generative AI. Agentic search accelerates the trend by collapsing discovery and transaction into a single interaction.

SeatGeek’s strategy is to make its inventory portable across these surfaces—whether fans are browsing Google Search, interacting with AI Mode, or engaging through future planning tools that haven’t yet fully emerged.

What agentic AI unlocks for ticketing

Agentic AI goes beyond answering questions. It can sequence actions: scanning inventory across platforms, comparing options, and acting when instructed.

SeatGeek’s participation in Google’s pilot offers several potential advantages to rightsholders:

• Broader AI-driven discoverability: Events surface naturally when fans ask open-ended or planning-oriented questions.

• Richer representation of inventory: AI models can better understand seating quality, availability, and pricing context from SeatGeek’s structured data.

• New conversion paths: As fans adopt task-based search and AI assistants, bookings may happen without the traditional click-through funnel.

This represents a fundamental shift from optimizing for clicks to optimizing for actions—an adjustment ticketing companies will need to make quickly as AI-native interfaces gain adoption.

Industry context: Ticketing meets agentic commerce

SeatGeek isn’t alone in preparing for this future, but it is early.

Across ecommerce and travel, companies are racing to ensure AI agents can transact against their inventories. Ticketing, with its time-sensitive products, fluctuating prices, and complex seating maps, is a particularly challenging—and potentially lucrative—use case.

By leaning into agentic search now, SeatGeek positions itself as a more AI-ready partner compared with rivals still optimized primarily for browser-based discovery. The competitive implications could be significant if agentic commerce gains mainstream traction.

Building toward a broader AI discovery stack

The Google integration complements ongoing investments SeatGeek has been making across AI-driven discovery. These include user-generated content programs, richer inventory metadata, and experiments in multimodal search that blend text, visuals, and contextual signals.

Taken together, the strategy suggests SeatGeek is preparing for a future where discovery happens everywhere—across traditional search, social feeds, AI assistants, and planning tools that act autonomously.

“This is only the beginning,” said Suzy Evans, Senior Manager of Search at SeatGeek, pointing to a longer-term roadmap focused on AI search and distribution leadership.

Looking ahead to 2026 and beyond

Google’s agentic search features are rolling out gradually, and mass adoption is far from guaranteed. But if AI-driven planning and action become a routine part of how consumers discover experiences, early integration could offer compounding advantages.

SeatGeek’s enhanced AI discoverability is expected to expand through 2026 as Google introduces new agentic capabilities. For now, the pilot gives SeatGeek a front-row seat—and a say—in how ticketing evolves when search stops being just about answers and starts being about doing.

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