For decades, school websites have been treated as the digital front door for parents, students, and staff. GPT AI Corporation, Inc. is now arguing that door is effectively broken—and it has data to back up the claim.

The company has launched EdGPT.ai, an AI-powered conversational platform built specifically for educational institutions, from preschools to universities. Its premise is bold: traditional school websites no longer meet modern expectations for accessibility, usability, or responsiveness, and conversational AI should replace them as the primary communication layer.

It’s a sharp challenge to long-held assumptions in education technology—and one that taps directly into mounting frustrations felt by administrators, families, and students alike.

Schools Are Overloaded With Repetitive Questions

At the heart of the problem is an administrative burden that rarely shows up on balance sheets but quietly drains time and resources. According to research cited by GPT AI Corporation, school administrative staff spend 15 to 20 hours each week answering the same questions about schedules, policies, lunch menus, admissions, and procedures—information that technically already exists online.

In practice, that information is often buried behind confusing navigation, outdated pages, or poorly designed search functions. The result: educators and staff repeatedly field phone calls and emails instead of focusing on student support.

The issue is amplified outside office hours. Roughly 68% of school-related information requests go unanswered for more than 24 hours, leaving parents and students stuck waiting for basic answers about homework rules, athletic schedules, or upcoming events. Over time, those delays erode trust and engagement.

Parents Have Stopped Using School Websites

The more troubling signal may be behavioral. Data highlighted in the announcement suggests that 73% of parents won’t return to a school website after a poor usability experience.

That statistic reflects a broader shift in expectations shaped by consumer technology. Families are accustomed to instant answers from search engines, messaging apps, and voice assistants. When school websites require multiple clicks, dense menus, or trial-and-error searching, users simply abandon them.

Instead, parents turn to Google, social media, or direct phone calls—ironically increasing the communication load schools were trying to reduce with websites in the first place. In this context, EdGPT.ai positions itself not as a website enhancement, but as a replacement for a model that no longer aligns with how people seek information.

Accessibility Is the Breaking Point

Usability frustrations are only part of the story. Accessibility failures are more systemic—and more serious.

The WebAIM Million 2025 study, which analyzed the top one million websites worldwide, found that 94.8% of home pages contain WCAG accessibility failures. Across those sites, researchers identified more than 50 million distinct errors, averaging 51 accessibility issues per page.

For educational institutions, which serve diverse populations including students with disabilities, these numbers are particularly alarming. Common failures include low-contrast text on 79.1% of pages, missing alternative text for images on 55.5%, and missing form labels on 48.2%.

“Traditional websites systematically fail users,” said Aftab Jiwani, founder of GPT AI Corporation. His conclusion is blunt: when nearly every website contains accessibility barriers, the model itself is flawed.

EdGPT.ai is designed as a direct response to that reality, promising a fully accessible, conversational interface that removes navigation and visual design barriers entirely.

How EdGPT.ai Works

Instead of clicking through pages, users interact with EdGPT.ai by asking questions in natural language. The platform delivers instant responses around the clock, covering everything from school policies and schedules to admissions requirements and campus services.

From an implementation standpoint, the barrier to entry is intentionally low. Schools provide their existing website URL, and EdGPT.ai automatically ingests publicly available information. Administrators can then upload additional documents—handbooks, calendars, policies, staff directories—to expand the platform’s knowledge base. According to the company, institutions can be operational in minutes.

The AI is configured specifically for educational contexts, understanding the nuances of school operations rather than relying on generic chatbot logic. It’s designed to work seamlessly with screen readers, voice commands, and assistive technologies, addressing many of the accessibility shortcomings baked into traditional websites.

Early Results Point to Measurable Gains

GPT AI Corporation says early adopters are already seeing tangible benefits. Schools piloting EdGPT.ai report a 65% reduction in administrative phone calls and a 75% improvement in engagement from prospective families. Some institutions have reclaimed hundreds of staff hours previously lost to repetitive inquiries.

The always-on nature of the platform is a key factor. Parents checking field trip requirements late at night or students reviewing assignment details on weekends no longer have to wait for office hours. That immediacy aligns more closely with how families actually operate—and reduces friction at critical touchpoints.

Administratively, schools report up to an 80% reduction in repetitive internal inquiries, freeing staff to focus on higher-value tasks like student support and program development.

One Platform, Many Education Levels

EdGPT.ai is pitched as adaptable across the entire educational spectrum.

Preschools and early learning centers use it to answer routine parent questions about daily schedules, pickup procedures, and meal programs. Elementary schools deploy it for homework policies, lunch menus, and after-school activities. Middle and high schools lean on it for more complex scheduling, extracurriculars, graduation requirements, and college preparation.

In higher education, the platform addresses a broader audience—prospective students, current students, parents, and faculty—covering admissions, financial aid, course catalogs, and campus services. For colleges and universities facing intense competition for enrollment, faster, clearer communication can translate directly into better recruitment outcomes.

Privacy and Compliance Considerations

In education, technology adoption often hinges on compliance as much as capability. GPT AI Corporation emphasizes that EdGPT.ai uses only publicly available information and approved school materials, maintaining alignment with FERPA requirements.

The platform is positioned as a communication layer rather than a student data system, reducing risk while still delivering meaningful improvements in access and responsiveness.

A Broader Shift in EdTech Communication

EdGPT.ai’s launch reflects a wider trend in enterprise and public-sector technology: moving away from static information repositories toward conversational, intent-driven interfaces. Similar shifts are already underway in customer support, healthcare, and government services.

What makes education different is the scale of accessibility and equity implications. When nearly all websites fail basic accessibility standards, conversational AI isn’t just a convenience—it may be a corrective measure.

Whether EdGPT.ai truly signals “the end of school websites” remains to be seen. Websites are deeply embedded in institutional workflows and compliance requirements. But as a primary interface for everyday questions, the model EdGPT.ai promotes feels aligned with how users already behave.

For schools under pressure to do more with less, the promise of reclaiming time, improving accessibility, and meeting families where they are may be difficult to ignore.

Get in touch with our MarTech Experts.

For decades, brand growth followed a familiar playbook: build awareness through advertising, reinforce memory structures, and ensure products are easy to buy. Omnicom Media’s latest research suggests that model is no longer sufficient—and may already be outdated.

In a new report, The Future of Brand Influence, Omnicom Media argues that influence today is no longer linear, predictable, or dominated by advertising. Instead, it is shaped by a fragmented ecosystem where influencers, peers, retail environments, and increasingly AI-driven recommendations play a decisive role in how consumers form opinions and make decisions.

Backed by research conducted by Omnicom Media Intelligence, the study introduces a critical evolution of classic marketing theory. Physical and mental availability still matter, but they are no longer enough. Brands must now compete on emotional availability—their ability to earn trust, relevance, and resonance across a growing web of human and machine-driven touchpoints.

Advertising Has Lost Its Monopoly on Influence

One of the clearest signals from the research is that advertising is no longer the primary driver of brand perception.

Only 32% of respondents say advertising most affects their overall opinion of a brand. By contrast, 40% point to what people are saying online, and a striking 71% say peer and influencer commentary matters more than brand advertising itself.

AI is also emerging as a powerful influence layer. Nearly half of respondents (45%) say AI-generated recommendations matter more than advertising when shaping their perceptions, putting machines on roughly equal footing with influencers (43%). For Gen Z, the shift is even more pronounced: 67% trust people on social platforms more than institutions or publications.

“Influence used to be relatively linear and predictable,” said Joanna O’Connell, Chief Intelligence Officer at Omnicom Media North America and lead author of the report. “Today, brand messaging exists alongside everything from influencer opinions to AI-generated answers—and that means brands must earn emotional relevance and trust across a much broader set of touchpoints.”

The implication is stark: brands can no longer assume that reach and frequency will do the heavy lifting. Influence is now negotiated in public, distributed spaces where brands have less control—and where credibility must be earned repeatedly.

Generative AI Is Compressing the Consumer Journey

If influence has become fragmented, it has also become faster. The rise of generative AI is dramatically compressing the path from curiosity to decision.

Seven in ten respondents say GenAI enables them to become an “expert” in almost any product or service category, helping them research pros and cons, compare brands, and validate choices in minutes rather than days. That acceleration reduces the window in which brands can shape consideration—and raises the stakes for how they show up in AI-mediated environments.

At the same time, attention is under unprecedented strain. Sixty-three percent of respondents describe their attention span as “just OK” or “not great,” while nearly four in ten say they don’t even notice ads on social platforms, despite high ad loads. Ad blockers, ad-free subscriptions, VPNs, and signal loss continue to chip away at traditional reach.

Together, these forces are creating what the report describes as a system where brand influence is frequently blocked, deprioritized, diluted, or even self-sabotaged.

Economic Pressure Is Undermining Brand Loyalty

The research also highlights a growing disconnect between how brands think they build loyalty and how consumers experience them.

More than 30% of respondents say they are now buying cheaper alternatives to their usual brands, up sharply from 19% earlier this year. While 75% say brand relatability is essential to purchase decisions, 72% believe brands care more about making money than building genuine loyalty. More than half feel brands no longer try to connect with them the way they once did.

This tension places emotional availability front and center. Consumers want brands that understand them, reflect their values, and show up with relevance—not just promotions. Yet many brands are perceived as prioritizing short-term revenue over long-term relationships, weakening trust at precisely the moment when trust has become the most valuable currency.

“Trust is migrating from institutions to individuals, and increasingly to machines as well,” O’Connell said. “That shift fundamentally changes how brands need to show up if they want to remain relevant and influential.”

Rethinking Availability in a Fragmented Ecosystem

A core contribution of the report is how it reframes the classic pillars of brand growth.

Physical availability now means more than shelf presence or distribution. Brands must ensure frictionless access across digital and physical channels, from retail media networks to e-commerce platforms and last-mile delivery.

Mental availability is no longer guaranteed by awareness alone. In an environment defined by noise, disintermediation, and AI-mediated discovery, brands must fight to remain salient when consumers are searching, scrolling, or asking machines for advice.

Emotional availability has emerged as the differentiator. It reflects a brand’s ability to connect authentically, build trust, and feel relevant in moments that matter—whether that moment occurs in a creator’s video, a retail environment, or an AI-generated response.

These shifts, Omnicom argues, point to a new marketing reality where influence is achieved by balancing machine efficiency with human connection.

From Research to Action: What Brands Should Do Next

The report doesn’t stop at diagnosis. It also outlines practical recommendations for brands navigating this evolving influence ecosystem.

On the human side, Omnicom Media advises brands to market to emotion at scale, using storytelling, live experiences, and influencer partnerships to create moments of elevated attention. Influencers, in particular, are positioned not as tactical add-ons, but as authentic brand ambassadors and scalable media channels.

Retail media also plays a central role, offering opportunities to surprise and delight shoppers closer to the point of purchase. Search, meanwhile, should be treated as a behavior rather than a channel—meeting consumers wherever and however they choose to look for answers.

On the machine side, the report urges brands to prepare for AI-driven discovery by adopting Generative Engine Optimization (GEO) strategies. As AI becomes a primary interface between consumers and information, brands must ensure their products, values, and differentiators are understood and accurately represented by machines—not just humans.

“The future of brand influence isn’t about choosing between humans and machines,” O’Connell said. “It’s about designing systems that serve both.”

Why This Matters for Marketers Now

What makes The Future of Brand Influence particularly timely is its alignment with broader industry shifts. Retail media networks are booming. Influencer marketing is maturing into a performance-driven discipline. Generative AI is reshaping search, discovery, and recommendation engines at speed.

Against that backdrop, Omnicom’s research reframes influence not as a single lever, but as a system—one where discovery, consideration, purchase, and loyalty feed into a self-reinforcing growth loop when executed well.

For marketers, the takeaway is clear: relying on advertising alone is no longer just insufficient, it’s risky. Influence today must be earned across human conversations, machine-generated answers, and moments of emotional relevance that cut through economic and attention pressures.

Brands that adapt may find themselves more resilient, more trusted, and better positioned for growth. Those that don’t risk fading into the background noise—seen by fewer people, trusted by fewer still, and increasingly invisible in a world mediated by both humans and machines.

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Zeta Global is betting that the next era of enterprise marketing won’t be driven by dashboards, but by agents that listen, reason, and act. The AI marketing cloud company announced a strategic collaboration with OpenAI to power the conversational intelligence and agentic capabilities behind Athena by Zeta™, its superintelligent marketing agent—and expanded beta access amid growing enterprise demand.

The partnership brings OpenAI models deeper into Athena’s core, shaping what Zeta calls the platform’s “next phase of development.” In practical terms, it means more natural conversations, more reliable reasoning, and more automation embedded directly into marketers’ daily workflows. It also signals how quickly agentic AI is moving from experimentation to operational reality in enterprise marketing.

From Insights to Action: Why Athena Matters Now

Athena was first unveiled at Zeta Live as an answer-driven interface for marketers frustrated by data overload. Instead of navigating reports or stitching together insights across tools, Athena allows users to ask questions in natural language and get decision-ready answers instantly.

This latest announcement pushes Athena beyond conversational analytics and into what Zeta sees as the future of marketing operations: agentic systems that don’t just surface insights, but recommend—and in some cases execute—the next best action.

“AI is moving from the edges of marketing to the center of how enterprises operate,” said David A. Steinberg, Zeta Global’s Co-Founder, Chairman, and CEO. “Athena transforms the Zeta Marketing Platform into an intelligent operating system for growth—one that can listen, reason, and act on behalf of marketers.”

That framing aligns with a broader industry shift. As marketing stacks grow more complex and data volumes explode, enterprises are looking for AI that reduces friction, not adds another interface. Athena’s promise is speed: fewer handoffs, less manual analysis, and faster movement from question to outcome.

Deepening the OpenAI Connection

Under the expanded collaboration, Zeta will align Athena’s product roadmap with advances in OpenAI’s models, allowing the platform to evolve alongside improvements in reasoning, conversation, and agentic behavior. Zeta will also have opportunities for early access to new OpenAI models and features, giving Athena a faster path to adopting cutting-edge capabilities.

“Zeta shows how advanced AI moves beyond insight and into action,” said Giancarlo “GC” Lionetti, Chief Commercial Officer at OpenAI. “By working together, we are bringing agentic intelligence directly into everyday marketing workflows, helping enterprises move faster and act with confidence.”

This is a notable step in how OpenAI is showing up in enterprise software. Rather than being positioned as a generic layer or add-on, OpenAI models here are embedded as a core engine inside a verticalized platform—one designed specifically for marketing use cases like audience insights, campaign optimization, and revenue growth.

Inside Athena’s First Agentic Apps

Alongside the OpenAI news, Zeta announced that Athena’s first two agentic applications—Insights and Advisor—have entered beta.

Insights with Athena is positioned as a conversational analytics engine. Executives can ask a single question and receive an immediate, usable answer, complete with performance drivers and ready-to-share dashboards. The goal is to eliminate the lag between curiosity and clarity that often slows decision-making in large organizations.

Instead of waiting on analysts or digging through reports, a CMO can ask Athena about emerging growth segments, audience trends, or campaign performance and get an answer in seconds. It’s analytics reframed as a conversation, not a task.

Advisor with Athena goes a step further. Designed as a goal-driven optimization agent, Advisor continuously scans campaigns and recommends—or automatically executes—next best actions based on objectives like revenue growth, efficiency, retention, or engagement. This is where Athena begins to resemble an always-on marketing operator rather than a passive assistant.

Together, the two apps reflect a shift from descriptive analytics (“what happened”) to prescriptive and autonomous marketing (“what should we do next”).

Early Results from the Field

TKO Group Holdings, the parent company of UFC and WWE, participated in Athena’s Early Access Program and has already put the platform to work.

“Athena is already transforming how our team works,” said Deborah Cook, Vice President of Data Intelligence at TKO Group Holdings. “Generating segment-based reports from a simple prompt and running ad hoc analysis in seconds has been a game-changer.”

Cook noted that tasks once requiring significant manual effort—like comparing performance across segments or identifying creative optimization opportunities—now happen almost instantly. As Athena expands into deeper geographic and performance insights, TKO sees potential for broader adoption across the organization.

That kind of testimonial underscores why agentic AI is gaining traction. Enterprises aren’t just looking for smarter tools; they’re looking for leverage—ways to compress time, reduce labor, and move faster without sacrificing control.

The Bigger Picture: Agentic AI Comes to Marketing

Zeta’s move reflects a broader trend across enterprise software: the rise of agentic AI as a new interaction model. Unlike traditional AI features that assist with specific tasks, agents are designed to operate continuously, adapt to goals, and take action across systems.

In marketing, the appeal is obvious. Teams are under pressure to deliver more personalized, data-driven experiences while managing sprawling media, CRM, and analytics stacks. An agent that can unify data, reason over it, and act autonomously could fundamentally change how marketing organizations operate.

Competitors across the MarTech landscape are racing in the same direction, from AI copilots embedded in CRM platforms to autonomous media optimization tools. What differentiates Athena is its positioning as a centralized, answer-driven operating layer—one designed to sit on top of Zeta’s broader marketing platform rather than function as a point solution.

What’s Next for Athena

Driven by what Zeta describes as “unprecedented demand” from brands and agencies, the company plans to make Athena generally available to all customers by the end of Q1 2026. Between now and then, expanded beta access will allow more enterprises to test how agentic applications fit into real-world marketing workflows.

If Athena delivers on its promise, it could mark a turning point for enterprise marketing AI—from tools that inform decisions to systems that help make them. And with OpenAI models now embedded at its core, Zeta is positioning Athena as a front-line example of how agentic intelligence moves from theory into day-to-day business impact.

For marketers navigating increasing complexity, the message is clear: the future may not be another dashboard, but an AI that already knows what you’re trying to achieve—and helps you get there faster.

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As generative AI quietly became a front-door shopping assistant in 2025, one thing became clear: throwing money at ads didn’t move the needle. Content did.

That’s the central takeaway from Bluefish’s 2025 Holiday AI Commerce Report, one of the first comprehensive analyses of how AI-driven shopping actually played out during the December holiday rush. By examining millions of AI-generated answers across major platforms, the AI marketing firm mapped which brands, publishers, and narratives shaped what consumers saw when they asked AI systems what to buy—and where to buy it.

The results should unsettle anyone still relying on paid media as a holiday growth lever.

AI assistants largely ignored ads

According to Bluefish, paid media had little direct influence on AI-generated shopping recommendations throughout the holiday season. In fact, the findings echo earlier industry research showing that up to 95% of AI citations came from non-paid sources.

Instead of rewarding ad spend, AI assistants consistently favored brands with:

• High-quality, clearly structured content

• Strong organic visibility

• Consistent messaging across owned and earned media

In practical terms, AI visibility emerged as a leading indicator of demand capture during the most competitive sales window of the year. Brands that weren’t visible to AI simply weren’t part of the conversation—no matter how much they spent elsewhere.

“Holiday 2025 proved that AI commerce is now a major channel, which requires a fundamentally different playbook,” said Alex Sherman, co-founder and CEO of Bluefish. “The brands winning here rewired their holiday strategy around high-quality owned and earned content.”

From “best deals” to “best gifts”

The report also captures a subtle but meaningful narrative shift inside AI systems as the season progressed.

During Black Friday, AI assistants leaned heavily into “best deals” language, surfacing discounts and doorbusters. But as December unfolded, that framing faded fast. Bluefish found that the influence of “best deals” content dropped by more than 30% heading into Christmas.

Taking its place: “best gifts.”

Gift guides—especially those focused on intent-driven queries like “best gifts under $100”—grew steadily more influential as AI assistants shifted from bargain hunting to thoughtful curation. This mirrors how human shoppers behave, but the speed and clarity of the transition inside AI systems caught many marketers off guard.

For brands still optimizing holiday content purely around discounts, the data suggests a missed opportunity.

A few pages shaped most AI answers

Using its proprietary Impact Score and Influence Rank analytics, Bluefish identified another striking pattern: AI recommendations were disproportionately shaped by a relatively small group of high-signal sources.

Publishers including Reddit, CNET, RTINGS.com, PCMag, and lifestyle titles like Who What Wear and Vogue emerged as outsized drivers of AI answers. While some didn’t dominate raw citation counts, their content carried far more weight in how AI systems described, compared, and ranked brands.

In other words, not all citations are created equal. Being mentioned once on the right site often mattered more than being mentioned dozens of times elsewhere—a reality that complicates traditional SEO and PR metrics.

Brands that “won” AI holiday commerce

Within this AI-shaped environment, certain brands consistently surfaced as holiday “winners” by aligning content with the narratives AI prioritized.

In beauty, Ulta stood out for its disciplined “best gift” positioning, reinforced across its own properties and third-party editorial coverage. The result: AI assistants repeatedly surfaced Ulta as a default recommendation.

In luxury, brands like Louis Vuitton, Gucci, and Ralph Lauren benefited from something harder to manufacture quickly—decades of cultural relevance. Dense coverage across curated gift guides and editorial lists positioned them as near-automatic answers to queries like “best luxury gifts,” even when newer competitors were aggressively marketing.

What this means for 2026 planning

The report suggests that leading marketing teams are already adjusting. Instead of treating AI as a black box, they’re:

• Measuring AI visibility on a weekly basis

• Treating AI commerce as a distinct performance channel

• Prioritizing fewer, higher-impact content placements over broad coverage

Looking ahead, Bluefish expects direct AI advertising to begin formalizing in 2026, adding yet another layer of complexity. But the company argues that ads alone won’t solve the core challenge: understanding how AI represents a brand and which sources actually shape that representation.

To that end, Bluefish says it is evolving its platform to help brands identify true sources of AI influence and take systematic action—before AI assistants become the default shopping interface for consumers.

For marketers, the message is blunt: if your brand isn’t legible, trusted, and well-positioned in AI’s world, it doesn’t matter how loud your campaigns are elsewhere.

Get in touch with our MarTech Experts.

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TruePath Vision, an AI computer-vision startup originally built to combat human trafficking, is expanding its mission. The company has announced the launch of a new weapon detection capability designed to help hotels, resorts, and public venues identify potential threats in real time—without installing new cameras or making heavy capital investments.

The move reflects a broader shift in physical security: away from siloed, hardware-heavy systems and toward software-led intelligence that runs on infrastructure organizations already own. For industries like hospitality, where guest experience and budget sensitivity matter as much as safety, that distinction could be significant.

From Anti-Trafficking Roots to Broader Threat Awareness

Founded in August 2024, TruePath Vision entered the market with a focused goal: protecting vulnerable populations in high-traffic environments using AI-driven computer vision. Backed by the Eagle Freedom Fund—an anti-trafficking investment arm of Eagle Venture Fund—and co-founded by Eagle Venture Studio, the company deployed its platform across hospitality and event spaces nationwide.

Its initial technology centered on real-time object detection and behavioral pattern recognition, giving operators continuous situational awareness through standard IP-based camera systems. That same foundation now underpins its weapon detection launch.

Rather than repositioning itself as a traditional security vendor, TruePath is extending an existing safety platform. The company argues that this matters because venues increasingly want integrated, multi-purpose systems—not point solutions that address only one risk scenario.

What the Weapon Detection Feature Does—and Why It Matters

TruePath’s new capability identifies visible firearms, knives, and other weapons, along with select threat-related behaviors, in real time. The software integrates directly with most IP-based camera systems already installed in hotels, resorts, and public venues, enabling rapid deployment with minimal operational disruption.

This “no new hardware” approach addresses one of the biggest friction points in physical security adoption: cost. Traditional weapon detection solutions often rely on specialized cameras, sensors, or screening equipment, which can be expensive to deploy and difficult to scale across large properties.

By contrast, TruePath’s model is software-first. Its AI runs on existing camera feeds, turning passive surveillance into an active monitoring system. For hospitality operators managing dozens—or hundreds—of properties, that difference could determine whether a solution is feasible at all.

“Our mission has always been grounded in protecting people who are most at risk,” said Jason Williamson, CEO of TruePath Vision. “By working with the camera systems venues already have, we remove one of the biggest barriers to adoption—cost.”

Designed for Real-World Security Operations

Weapon detection is only useful if alerts reach the right people at the right time. TruePath says its system delivers notifications through existing security monitoring workflows or directly to mobile devices used by security teams.

The company emphasizes accuracy and false-positive reduction—two areas where AI-based security tools often face skepticism. Its models are trained to balance sensitivity with precision, and customers can configure the system to recognize additional objects or environment-specific risks, such as unattended bags or restricted-area access.

This configurability aligns with how modern venues operate. A large resort, for example, faces different risks than a convention center or entertainment venue. A system that adapts to context is more likely to be used—and trusted—by on-the-ground teams.

How TruePath Fits Into the Broader Security Landscape

The timing of the launch is notable. Public venues are reassessing security strategies amid rising concerns about active shooter incidents, workplace violence, and crowd safety. At the same time, many organizations are reluctant to invest in visible, intrusive security measures that could negatively affect guest experience.

AI-powered computer vision has emerged as a middle ground. Instead of adding metal detectors or physical checkpoints, venues can enhance awareness behind the scenes—using software to interpret what cameras already see.

TruePath is not alone in this space. Several computer vision and video analytics vendors are racing to offer weapon detection, behavior analysis, and anomaly detection. What differentiates TruePath, at least on paper, is its positioning as a single, extensible safety platform rather than a bolt-on feature.

That approach mirrors a broader MarTech and AdTech trend: platforms that start with one use case and expand horizontally, leveraging shared data and infrastructure. In this case, safety replaces marketing as the primary outcome—but the platform logic is similar.

Implications for Hospitality and Public Venues

For hotels and resorts, security decisions are rarely just about technology. They involve brand reputation, guest trust, staff training, and long-term operational costs. A solution that promises enhanced threat awareness without visible disruption may appeal to operators trying to strike that balance.

There’s also a staffing angle. Many venues face shortages of trained security personnel. AI-driven alerts can help teams prioritize attention, potentially reducing reliance on constant manual monitoring of camera feeds.

That said, the success of such systems depends heavily on execution. Accuracy, transparency, and clear escalation protocols will determine whether AI weapon detection becomes a trusted layer of security—or another underused dashboard.

Looking Ahead

TruePath Vision positions its platform as a layered safety system that can evolve alongside emerging risks. Weapon detection is the latest addition, but the company hints at broader extensibility—adding new detection models as customer needs change.

For an industry increasingly focused on resilience and risk mitigation, that flexibility could be appealing. The real test will be adoption at scale and measurable impact on incident prevention and response times.

If TruePath can demonstrate that software alone—running on existing cameras—can materially improve safety outcomes, it may influence how venues think about security investments going forward.

Get in touch with our MarTech Experts.

HitPaw is doubling down on AI-first creativity. The company has officially released HitPaw FotorPea V5.2.0, a major update that pushes the image editor further into professional territory with smarter workflows, deeper file support, and more flexible output options.

The headline addition is AI Canvas, a new unified workspace that blends conversational AI editing with automation-heavy tools. Alongside it, HitPaw is rolling out enhanced RAW image support with intelligent denoising and upgrading its AI-generated art exports from WebP to MP4, signaling a clear focus on creators who publish across multiple platforms.

Taken together, the update positions FotorPea less as a lightweight consumer editor and more as an AI-powered alternative to traditional photo-editing pipelines—especially for creators who value speed over manual precision.

AI Canvas: Conversational Editing Comes to Image Workflows

At the center of version 5.2.0 is AI Canvas, which HitPaw describes as a single creative environment designed to reduce friction in complex editing tasks. Instead of bouncing between tools and panels, users can rely on AI-assisted commands to make meaningful changes faster.

The standout feature is chat-style image editing. Users can type natural language prompts to modify images—removing objects, replacing elements, refining textures, or even transforming entire scenes. This approach mirrors a growing trend across creative software, where prompt-driven editing lowers the learning curve for non-experts while speeding up repetitive work for professionals.

AI Canvas also introduces multi-image fusion, allowing users to combine visual elements, styles, or concepts from multiple images into one cohesive result. This is particularly useful for concept visualization, social media creatives, and early-stage design mockups, where speed and experimentation matter more than pixel-perfect control.

Other tools within AI Canvas focus on efficiency:

• One-click image upscaling enhances resolution while preserving fine details and textures, addressing a common pain point for creators repurposing older or lower-resolution assets.

• Background removal and smart cropping make it easier to isolate subjects and optimize composition, especially for product images, portraits, and promotional visuals.

Compared to traditional editors that rely heavily on manual masks and layers, HitPaw’s approach is clearly aimed at users who want results quickly—with AI handling much of the technical heavy lifting.

RAW Support and Intelligent Denoising Push Toward Pro Use Cases

Beyond AI Canvas, HitPaw FotorPea 5.2.0 makes a meaningful leap with full RAW file import support. This opens the door for photographers who prefer working with uncompressed image data and need greater control over color, exposure, and detail.

The new RAW Denoise Mode is optimized for high-ISO and low-light photography, scenarios where noise reduction often comes at the expense of texture and sharpness. HitPaw says its AI models are trained to reduce noise intelligently while preserving natural details and color accuracy.

This is an important upgrade. RAW workflows are typically dominated by established tools like Adobe Lightroom or Capture One. While FotorPea may not replace those platforms for all professionals, the addition of RAW support signals that HitPaw is serious about expanding beyond casual editing and into semi-pro and creator-driven markets.

For creators juggling photography, social content, and design in a single workflow, having RAW enhancement and AI-driven editing in one tool could reduce dependency on multiple applications.

AI Generator Gets MP4 Output for Broader Reach

HitPaw is also refining how AI-generated content is shared. In version 5.2.0, AI-generated artwork can now be exported as MP4, replacing the previously used video WebP format.

This change may sound minor, but it has practical implications. MP4 is far more widely supported across social platforms, devices, and editing tools. By switching formats, HitPaw makes it easier for users to turn AI-generated images into short animations suitable for platforms like Instagram, TikTok, and YouTube Shorts.

The update reflects a broader shift in creative tools toward motion-friendly outputs, even when the starting point is a static image. As short-form video continues to dominate content strategies, tools that simplify the jump from image to animation gain an edge.

Where FotorPea Fits in the Crowded AI Editing Market

The AI image editing space is increasingly competitive, with startups and established vendors racing to integrate generative and conversational features. What differentiates HitPaw FotorPea is its focus on workflow consolidation—bringing editing, enhancement, generation, and export into a single interface.

Rather than positioning itself as a replacement for high-end professional suites, FotorPea appears aimed at creators, marketers, and designers who want fast, AI-assisted results without deep technical complexity. In that sense, it aligns with a broader MarTech trend: tools that prioritize productivity and adaptability over granular control.

Whether AI Canvas and RAW denoising are enough to pull users away from more established platforms will depend on real-world performance—particularly accuracy, consistency, and export quality. Still, version 5.2.0 represents a clear step forward in ambition.

The Bottom Line

With FotorPea 5.2.0, HitPaw is signaling that AI-powered editing is no longer just about novelty features. By combining conversational editing, RAW image enhancement, and motion-ready exports, the company is betting that creators want fewer tools—and smarter ones.

If AI Canvas delivers on its promise of flexible, accurate editing, this update could make FotorPea a compelling option for creators balancing speed, quality, and multi-platform output.

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Avaya is making a clear statement about where it believes the future of enterprise productivity and AI-driven work is headed. The company has announced it is adopting Gemini Enterprise as its core advanced agentic AI platform, alongside Google Workspace as its primary collaboration and productivity suite, marking a deeper strategic alignment with Google Cloud.

For a company best known for enterprise communications and contact center technology, the move is less about switching productivity tools—and more about reshaping how work gets done internally to better serve customers externally.

Why Avaya Is Betting on Gemini Enterprise

At the heart of the decision is Gemini Enterprise, Google’s AI platform designed to act as a unified, intelligent interface across enterprise knowledge, tools, and workflows. Avaya plans to use Gemini as the connective tissue across its organization, helping employees access information faster, automate routine tasks, and make decisions with greater context.

Rather than deploying multiple standalone AI tools, Avaya is consolidating around a single AI layer that can span teams and functions. This reflects a growing enterprise trend: AI is no longer treated as an experimental add-on, but as foundational infrastructure.

For Avaya, that infrastructure underpins its broader AI strategy—one focused on making workflows smarter end-to-end, not just incrementally faster.

Google Workspace Becomes Avaya’s Productivity Backbone

Alongside Gemini Enterprise, Avaya is standardizing on Google Workspace, giving employees access to AI-infused versions of Gmail, Google Docs, Google Drive, and Google Meet.

The appeal here is integration. With Gemini embedded directly into Workspace tools, AI insights are delivered in context—inside documents, emails, meetings, and shared files—rather than through separate dashboards or copilots.

This matters for scale. As organizations grow more distributed, productivity gains increasingly depend on reducing friction between collaboration tools and intelligence systems. By unifying collaboration and AI under one cloud-native suite, Avaya aims to simplify its technology stack while improving day-to-day efficiency.

A Strategic Shift Toward Simplicity and Agility

Avaya’s expanded partnership with Google Cloud underscores a deliberate push toward stack simplification. Large enterprises often struggle with overlapping platforms, redundant tools, and fragmented data. Each additional system slows decision-making and increases operational overhead.

By consolidating AI, collaboration, and productivity into a tightly integrated ecosystem, Avaya is positioning itself to move faster internally—and respond more quickly to customer needs.

This is especially relevant in the communications and contact center market, where customer expectations are evolving rapidly and AI-driven experiences are becoming table stakes.

From Internal Productivity to Customer Impact

While the announcement focuses on internal enablement, the implications extend beyond Avaya’s workforce. The company frames the move as a way to accelerate innovation and deliver more value to customers.

That linkage is critical. Enterprises increasingly recognize that employee experience and customer experience are tightly connected. Smarter internal workflows can lead to faster product development, more responsive support, and more personalized communications solutions.

“Gemini Enterprise and Google Workspace will empower our employees through AI-driven insights and collaboration and next-gen workplace productivity—redefining our work environment,” said Pete Lavache, CMO at Avaya. “By reimagining workflows and unlocking greater agility across our teams, we can accelerate innovation and deliver high-value outcomes for our customers.”

How This Compares to Broader Enterprise AI Trends

Avaya’s move mirrors a wider shift across large enterprises toward agentic AI platforms—systems that don’t just generate content, but actively assist with decision-making, coordination, and execution across workflows.

Rivals and peers across the enterprise software landscape are making similar bets, whether through Microsoft Copilot ecosystems or custom AI layers built on hyperscaler clouds. Avaya’s choice of Google signals confidence in Gemini’s ability to operate as a central AI interface rather than a collection of isolated features.

The emphasis on cloud-native tools is also notable. As hybrid work becomes the norm, enterprises are prioritizing platforms that scale globally while remaining secure and manageable.

What Comes Next

The real test will be execution. Rolling out AI at enterprise scale requires more than technology—it demands change management, training, and governance. How effectively Avaya embeds Gemini into everyday workflows will determine whether the promised productivity and agility gains materialize.

Still, the direction is clear. Avaya is aligning its internal operations with the same AI-first mindset it brings to its communications solutions. In a market where speed, intelligence, and adaptability define competitive advantage, that alignment could prove decisive.

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SPAR, a leading provider of merchandising and retail execution solutions, is making a decisive leadership move as it looks to accelerate growth across the U.S. and Canada. The company has promoted Jean Richer to Head of North American Sales & Marketing, tasking the longtime consumer packaged goods (CPG) and retail services executive with driving commercial expansion across merchandising and CPG clients.

Richer will report directly to CEO William Linnane, and his appointment comes at a moment when retailers and brands are rethinking how merchandising, data, and in-store execution fit into an increasingly omnichannel world.

At the same time, SPAR disclosed that multiple members of its executive leadership team have recently increased their ownership stakes in the company—an uncommon but notable signal of internal confidence as SPAR looks ahead to 2026 and beyond.

A Strategic Promotion Focused on Revenue Acceleration

In his new role, Richer will oversee SPAR’s full commercial growth agenda across North America, spanning sales strategy, marketing execution, and go-to-market alignment. The mandate is clear: help SPAR capture more value from existing relationships while expanding its footprint with CPG brands and retailers navigating complex in-store and last-mile challenges.

Richer brings more than 25 years of executive-level experience across the CPG and retail services ecosystem. Over the course of his career, he has led sales and marketing initiatives for some of the world’s most recognizable consumer brands, including Seagram’s, Lactalis, Keurig Dr Pepper, and Anheuser-Busch. He has also held senior leadership roles within retail services firms and agencies—experience that closely aligns with SPAR’s core business model.

That blend of brand-side and services-side experience is increasingly valuable as merchandising evolves from a labor-driven function into a data-enabled, insight-led discipline.

Why Richer’s Background Matters Now

The merchandising and retail services market is undergoing a quiet but meaningful transformation. CPG brands are under pressure from margin compression, changing shopper behavior, and retailers demanding greater accountability for in-store execution. At the same time, data—from shelf analytics to performance measurement—is becoming central to how merchandising programs are evaluated and funded.

Richer’s career places him at the intersection of these trends. Having worked directly with global brands and within retail services organizations, he understands both sides of the value equation: what brands need to win at shelf, and what service providers must deliver to remain indispensable partners.

SPAR is betting that this perspective will help the company evolve its offerings toward modern, data-enabled merchandising solutions, rather than competing solely on scale or labor efficiency.

Leadership Confidence Signaled Through Share Purchases

Alongside Richer’s promotion, SPAR also announced that several members of its executive leadership team have recently increased their personal ownership in the company.

• Chief Financial Officer Steve Hennen purchased 55,000 shares

• Chief Technology Officer Josh Jewett purchased 125,000 shares

• CEO William Linnane, who acquired 173,000 shares earlier in November, now holds a total of 190,909 shares

While executive share purchases don’t guarantee future performance, they are often read by investors as a signal of leadership confidence—particularly when they occur across multiple roles, including finance and technology.

In SPAR’s case, the timing aligns with a broader narrative: leadership appears confident in the company’s strategic direction, its investment priorities, and its ability to adapt to an evolving retail landscape.

Aligning Sales, Technology, and Execution

The combination of a sales and marketing leadership change and increased executive ownership highlights another important theme: alignment.

Merchandising today is no longer just about feet on the ground. Technology platforms, analytics, and real-time reporting increasingly shape how programs are sold, executed, and renewed. With a CTO increasing his stake alongside commercial and financial leaders, SPAR appears to be reinforcing the idea that growth will come from tighter integration between sales strategy, operational execution, and technology enablement.

Richer’s remit will likely involve translating that integration into a clearer value proposition for CPG brands—one that emphasizes outcomes, not just activity.

Executive Perspective

“I am excited to have Jean leading sales and marketing across the U.S. and Canada,” said Linnane. “He brings a deep understanding of how CPG brands and retailers create value through world-class merchandising today and, more importantly, how the industry is evolving and how SPAR can serve as a catalyst for that change.”

Linnane also pointed to the leadership team’s growing ownership as a sign of long-term commitment. “I am pleased to see our Executive Leadership Team building meaningful ownership stakes in the Company, further aligning leadership with shareholders as we drive long-term growth and innovation into 2026 and beyond.”

What This Means for SPAR’s Customers

For SPAR’s retail and CPG clients, the leadership update suggests a sharper focus on growth-oriented merchandising programs—ones that balance execution at scale with better insights into performance.

As retailers demand more proof of impact and brands scrutinize every dollar spent in-store, service providers that can articulate and deliver measurable value will have an advantage. Richer’s experience across global brands and retail services positions him to shape SPAR’s sales narrative around those expectations.

Looking Ahead

SPAR’s promotion of Jean Richer and the increased ownership stakes by its leadership team point to a company preparing for its next phase of growth. The merchandising sector may not grab headlines like e-commerce or AI, but it remains a critical battleground for brands competing for shopper attention.

With a seasoned CPG executive leading North American sales and marketing—and leadership putting more skin in the game—SPAR is signaling that it intends to play a more influential role in how merchandising evolves over the next several years.

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