Unified endpoint management is increasingly judged not just by device coverage, but by how reliably it supports mission-critical hardware in the real world. ProMobi Technologies is leaning into that reality with a new milestone for its flagship platform, Scalefusion.

The company announced that Scalefusion is now Zebra validated, following successful completion of Zebra Technologies’ Enterprise Testing Program. The validation confirms Scalefusion’s interoperability with Zebra’s thermal printer portfolio, clearing the way for enterprises to confidently manage Zebra printing devices within a unified management environment.

For organizations that rely on Zebra printers for essential labeling, tracking, and workflow automation—particularly in retail, logistics, manufacturing, and healthcare—the validation removes a key friction point: uncertainty around compatibility, performance, and long-term manageability.

Why Zebra Validation Matters

Zebra’s Enterprise Testing Program is designed to rigorously assess how third-party platforms perform with Zebra’s hardware ecosystem, which spans mobile computers, scanners, printers, and RFID technologies. Earning validation signals that a solution meets Zebra’s benchmarks for reliability, scalability, and performance in demanding operational environments.

For Scalefusion customers, the outcome is practical rather than symbolic. Zebra validation means Zebra thermal printers can now be brought under the same Unified Endpoint Management (UEM) umbrella as smartphones, tablets, kiosks, and rugged devices—without workarounds or custom integrations.

“Validation from Zebra underscores the strength of our platform and its ability to support customers operating in highly demanding environments,” said Sriram Kakarala, Chief Product Officer at Scalefusion.

He added that enterprises using Zebra printers for critical workflows can now manage those devices through Scalefusion with confidence in performance, security, and compliance.

Extending Unified Management to the Front Line

Scalefusion has positioned itself as a platform for managing diverse device fleets across distributed locations. With Zebra validation, that scope now extends more deeply into front-line operations where thermal printers play a central role.

Through Scalefusion, IT teams can remotely configure, monitor, and secure Zebra printers alongside other endpoints. Capabilities such as Firmware Over-the-Air (FOTA) updates help ensure devices remain aligned with the latest releases and security standards—an increasingly important requirement as printers become more connected and exposed within enterprise networks.

From an operational perspective, this reduces overhead for IT and operations teams that would otherwise juggle separate tools for endpoint management and printing infrastructure. A single console for governance and visibility becomes especially valuable in high-volume environments where uptime and consistency directly affect revenue and service quality.

A Broader Signal in Enterprise Device Management

The announcement also reflects a broader trend in UEM and enterprise IT: validation and ecosystem alignment are becoming competitive differentiators. As device fleets grow more heterogeneous, enterprises are prioritizing platforms that are formally tested and endorsed by hardware leaders, rather than relying on “best effort” compatibility.

Scalefusion’s status as a member of Zebra Technologies’ PartnerConnect program reinforces that positioning. The program brings together technology partners focused on delivering integrated, high-performance solutions for front-line workers—an audience that increasingly depends on seamless coordination between hardware and software.

What This Means for Enterprises

For businesses already standardized on Zebra printers, Scalefusion’s validation lowers the barrier to consolidating device management. For others evaluating UEM platforms, it strengthens Scalefusion’s case as a solution capable of handling not just mobile endpoints, but the specialized hardware that underpins day-to-day operations.

As enterprises continue to modernize workflows at the edge—where printing, scanning, and real-time data capture intersect—validated integrations like this are likely to matter more than feature checklists alone.

In that context, Scalefusion’s Zebra validation is less about a badge and more about trust: assurance that unified management can extend all the way to the devices keeping front-line operations running.

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Global technology group e& and IBM are moving beyond AI experiments and into operational reality. At the World Economic Forum Annual Meeting in Davos, the two companies announced a strategic collaboration to build an enterprise-grade agentic AI foundation at e&, starting with one of the most regulation-heavy areas of the business: policy, risk, and compliance.

The initiative marks a clear shift away from traditional NLP-driven chatbots toward governed, action-oriented AI agents that are embedded directly into core enterprise systems. In practical terms, this means AI that doesn’t just answer questions—but can reason, orchestrate tasks, and support decision-making while remaining auditable, explainable, and compliant by design.

From Chatbots to Action-Oriented AI

For many enterprises, AI adoption has stalled at conversational interfaces—useful for surface-level queries, but limited when it comes to executing real work under regulatory constraints. e&’s collaboration with IBM aims to address that gap head-on.

At the center of the initiative is an agentic AI solution built on IBM watsonx Orchestrate, which offers access to more than 500 tools and customizable, domain-specific agents developed by IBM and its partners. Integrated with IBM OpenPages and the broader watsonx portfolio, the solution allows employees and auditors to quickly access, interpret, and act on legal, regulatory, and compliance information—while maintaining traceability and governance.

Instead of searching through policy documents or escalating questions manually, users can rely on AI agents that understand context, reason through requirements, and deliver responses aligned with enterprise governance standards.

A Proof of Concept in Eight Weeks

The collaboration isn’t theoretical. A joint proof of concept (PoC) delivered by IBM, Gulf Business Machines (GBM), and e& was completed in just eight weeks, demonstrating that agentic AI can operate at enterprise scale under real-world conditions.

IBM’s Client Engineering team led the design and system integration, while GBM supported delivery with project coordination and deep expertise in e&’s existing OpenPages and watsonx Assistant environment. Together, they showcased AI capabilities that go beyond question-and-answer tools—enabling reasoning and action while staying aligned with e&’s governance, risk, and compliance (GRC) framework.

This rapid execution is notable in an industry where enterprise AI pilots often take months just to get off the ground.

Why Risk and Compliance Comes First

Starting with risk and compliance is a strategic choice. These functions are document-heavy, process-driven, and highly regulated—making them both a challenge and an ideal proving ground for agentic AI.

For e&, embedding AI directly into GRC workflows means faster policy interpretation, more consistent decision-making, and reduced response times across the organization. It also enables 24/7 self-service access to compliance information, reducing bottlenecks without compromising oversight.

“Our ambition is to move beyond isolated AI use cases toward enterprise-scale agentic AI that is trusted, governed, and deeply integrated into how the organization operates,” said Hatem Dowidar, Group CEO of e&. “By collaborating with IBM, we are embedding intelligence directly into our risk and compliance processes, enabling faster decisions, consistent policy interpretation, and a foundation for broader agentic AI adoption across the enterprise.”

Governance by Design, Not Afterthought

A recurring concern with AI agents is governance—particularly when systems are empowered to take action. This collaboration addresses that issue by aligning natively with watsonx.governance, which is already in use at e&.

By embedding agentic AI directly into the OpenPages GRC platform, the solution ensures explainability, accountability, and compliance are built in from the start. Every response and action is traceable, supporting audit requirements and regulatory scrutiny.

This approach positions the deployment as one of the early enterprise-grade agentic AI implementations in the region, offering a practical example of how AI can support human-led decisions rather than replace them.

Hybrid AI, Enterprise Control

Another key element of the initiative is flexibility. IBM’s AI and model gateway approach allows large language models to run across hybrid environments, including customer-managed infrastructure, while remaining governed under enterprise controls.

This matters for organizations like e&, where data sovereignty, security, and regulatory compliance often limit the use of fully cloud-hosted AI services. The architecture enables innovation without forcing compromises on control or compliance.

A Signal to the Market

For the broader MarTech and enterprise AI ecosystem, the e&–IBM collaboration reflects a maturing market. Enterprises are no longer asking whether AI works; they’re asking whether it can be trusted, governed, and scaled across mission-critical systems.

“As organizations move from experimenting with AI to embedding it into the fabric of how they operate, governance and accountability become just as important as intelligence,” said Ana Paula Assis, SVP and Chair for Europe, the Middle East, Africa, and Asia Pacific at IBM. “This proof of concept demonstrates how agentic AI can be designed and validated for enterprise-scale use—deeply integrated into core systems and trusted to support human-led decisions.”

What Comes Next

While the initial focus is on risk and compliance, the foundation being built is designed to scale. Once validated, the same agentic AI framework can extend into other enterprise domains, from operations to customer engagement.

For e&, this collaboration represents more than a single deployment—it’s a strategic milestone in its enterprise AI journey. By embedding action-oriented AI into core governance workflows, the company is setting a benchmark for responsible, enterprise-grade agentic AI in the region.

As enterprises worldwide grapple with how to move from AI pilots to production systems, the message from Davos is clear: the future belongs to AI that is not just intelligent, but governed, integrated, and trusted.

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As WhatsApp cements itself as a primary customer communication channel, a new entrant is betting that automation doesn’t have to mean rigid scripts or sales-only bots. 247Rep has officially launched its AI-powered WhatsApp marketing and customer engagement platform, positioning it as an all-in-one system for sales, customer support, and service delivery—not just lead conversion.

The platform is available immediately and starts with a notable hook: 20 free AI credits for every user, no credit card, no trial clock, and no forced upgrade path. According to the company, early adopters are already seeing tangible results. One organization reported a 60% reduction in customer support calls within three weeks, alongside higher customer satisfaction compared to its previous manual support setup.

In a crowded automation market, 247Rep’s pitch is straightforward: AI that works across the entire customer lifecycle and actually sounds like the business using it.

Beyond Sales Bots: A Broader Take on WhatsApp Automation

Most WhatsApp automation platforms are designed with one goal in mind—driving conversions. 247Rep takes a broader view. At launch, the platform includes two core tools: AI-driven WhatsApp automation and a fully customizable web widget.

Rather than stopping at pre-sale interactions, 247Rep is built to manage everything from initial inquiries to post-sale support and ongoing service. That distinction matters as businesses increasingly use messaging apps not just to sell, but to handle order updates, troubleshooting, onboarding, and repeat engagement.

“Businesses don’t just need to sell—they need to support customers after the sale,” said Tobi, founder of 247Rep. “Our AI handles everything from answering pre-sale questions to helping existing customers resolve issues, all with the same intelligence and consistency.”

This lifecycle-first approach aligns with a broader MarTech trend: customer experience is now measured end-to-end, not funnel-by-funnel.

AI That Learns Your Voice, Not Just Your FAQs

One of 247Rep’s more differentiated features is how its AI is trained. Instead of forcing businesses to write scripts, define flows, or build decision trees, the platform allows users to train the AI naturally—by typing or speaking to it.

The system analyzes speech patterns, vocabulary, tone, and phrasing to create a communication model that mirrors the user’s voice. Businesses can also train the AI by uploading or connecting past customer conversations, allowing it to learn from real interactions rather than hypothetical scenarios.

The result is an AI that doesn’t sound like a generic chatbot. Responses are designed to feel consistent with how the business already communicates—an increasingly important factor as customers grow more sensitive to automated interactions.

WhatsApp Features Designed for Day-to-Day Operations

247Rep’s WhatsApp automation isn’t limited to answering questions. The platform includes a set of features aimed at reducing operational friction:

Visual Product Catalog

Businesses can upload product images, service details, pricing, and variants directly into WhatsApp conversations. Customers can browse and make purchase decisions without leaving the chat, reducing drop-offs caused by external links or redirects.

Advanced Message Scheduling

Messages can be scheduled to send at specific times, even when the business owner’s phone or computer is completely offline. Follow-ups, reminders, and announcements go out reliably, eliminating a common gap in manual WhatsApp workflows.

Order Management Dashboard

When the AI completes a sale, orders are automatically logged in a centralized dashboard with full customer details. This removes the need to scroll through chat histories to track purchases or fulfillment status.

Intelligent Escalation

The AI continuously monitors conversation complexity and customer sentiment. When it detects that a human response would improve the outcome, it flags the interaction. Users can also manually take over a conversation with one click and return control to the AI just as easily.

Together, these features reflect a shift from “chatbot” to operational assistant—AI that actively supports daily business processes.

A Web Widget That Learns on Its Own

Alongside WhatsApp automation, 247Rep includes a fully customizable web widget designed to mirror the intelligence of its messaging AI.

From branding elements like colors, fonts, and logos to interaction styles, the widget can be tailored to match a company’s website experience. But the more interesting capabilities sit under the hood.

Automatic Knowledge Base Creation

Once embedded on a website, the widget automatically analyzes product pages, FAQs, policies, and published content. Even without manually uploading documentation, the AI gains an understanding of what the business does and how it operates.

Persistent Conversation Memory

Unlike typical website chat widgets that reset on refresh, 247Rep maintains conversation history across devices and sessions. Customers can start a conversation on desktop, continue it on mobile hours later, and return days afterward without losing context.

Voice Input and File Sharing

Customers can interact using voice instead of text and share files such as images, videos, or PDFs. The AI analyzes these inputs contextually and responds in real time—an increasingly important capability for support and service-heavy businesses.

The “General Manager”: AI That Oversees the Big Picture

Perhaps the most distinctive element of 247Rep is what it calls the “General Manager.” This isn’t a chatbot interacting with customers directly, but an oversight AI that monitors all conversations across WhatsApp and the web widget.

The General Manager identifies patterns, flags high-value sales opportunities, and suggests improvements to the knowledge base. It also functions as an intelligent memory layer, allowing users to retrieve specific conversations instantly.

Questions like “Who was the customer with the password issue?” or “Summarize my conversation with James” can be answered on demand, without manual searching.

“Think of it as having an experienced manager who remembers every customer interaction and can recall any detail instantly,” Tobi said. “You never have to dig through chat history again.”

This kind of meta-level AI reflects a growing trend in MarTech: systems that don’t just automate interactions, but analyze them for strategic insight.

A Pay-As-You-Go Model Aimed at Accessibility

247Rep’s pricing strategy is deliberately lightweight. Users start with 20 free credits, enough to test the platform’s core capabilities without restrictions. Once those credits are used, additional credits can be purchased starting at $1, with no subscriptions or long-term commitments.

This usage-based approach lowers the barrier for small and mid-sized businesses while still allowing the platform to scale with demand—an alternative to the seat-based or monthly pricing common in automation tools.

Available Now, With More Channels Planned

247Rep is available immediately at 247rep.app, with no waitlist or mandatory sales calls. Users can sign up, train their AI, and begin automating interactions within minutes.

At launch, the platform supports WhatsApp Business API and web widget integrations. Support for Instagram, Telegram, and Facebook Messenger is on the roadmap, signaling ambitions beyond a single-channel solution.

Early Results Signal Real-World Impact

Early adopters suggest the platform is already delivering measurable value. One operations manager reported handling over 200 daily support calls—mostly for basic questions—before deploying 247Rep.

“The AI handles those instantly now,” the manager said. “Our team focuses on complex issues, and customers get help immediately instead of waiting in queue.”

For a market increasingly focused on efficiency, consistency, and experience, 247Rep’s launch highlights how AI-driven messaging is evolving from novelty to necessity.

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Automation Anywhere is making a clear statement about where enterprise automation is headed—and it’s not just faster bots. The company today unveiled a new generation of AI-native agentic solutions, developed in collaboration with OpenAI, aimed at helping enterprises move from scripted automation to autonomous, reasoning-driven operations.

At the heart of the announcement is a tighter integration between Automation Anywhere’s Process Reasoning Engine (PRE) and OpenAI’s advanced reasoning models. Together, the two technologies create what the company calls a full reasoning-to-action loop: OpenAI models interpret context and intent, while PRE determines the next best enterprise action and executes it securely across systems.

For enterprises struggling to translate AI pilots into production-grade impact, the message is direct: agentic AI needs governance, orchestration, and context—not just smarter language models.

From Rule-Based Automation to Agentic Operations

Traditional robotic process automation (RPA) systems were designed to follow predefined steps. They work well when processes are stable, but quickly break down when inputs change, exceptions arise, or context is missing. Automation Anywhere’s leadership argues that this rigidity is fundamentally misaligned with how AI agents operate.

“Traditional solutions automate work by following rigid steps, much like humans would, which often makes them brittle when things change,” said Mihir Shukla, CEO and Chairman of Automation Anywhere. “Our agentic solutions are fundamentally different; they’re designed for how AI agents work, enabling them to autonomously reason, solve problems, and adapt to changes.”

The collaboration with OpenAI is intended to address that gap. Instead of using large language models as conversational overlays, Automation Anywhere is embedding them directly into decision-making and execution flows, where reasoning leads directly to action.

The Reasoning-to-Action Loop Explained

The technical crux of the announcement is the combination of OpenAI’s reasoning models with Automation Anywhere’s Process Reasoning Engine.

OpenAI’s models handle interpretation—understanding unstructured inputs, intent, and context across documents, conversations, and systems. PRE then evaluates what should happen next, applying enterprise rules, security policies, and compliance controls before orchestrating work across applications.

This separation of reasoning and execution is intentional. It allows enterprises to benefit from advanced AI reasoning while keeping governed, auditable control over what actions are actually taken inside critical systems.

In practice, this means AI agents can assess a situation—such as a financial exception, IT incident, or HR request—decide on a course of action, and execute it end-to-end, with human oversight where required.

A Faster On-Ramp to Agentic AI

One of the biggest barriers to agentic AI adoption has been implementation complexity. Many enterprises find themselves stuck between proof-of-concept demos and production-ready systems.

Automation Anywhere says its new agentic solutions are designed to shorten that gap. The offerings are pre-built, deeply contextual, and deployable in weeks rather than months, providing what the company describes as a “fast on-ramp” to agentic operations.

Rather than asking customers to assemble agents from scratch, the platform delivers production-ready capabilities that can be tailored and extended without code. This approach reflects a broader trend in enterprise AI: moving away from bespoke experimentation toward standardized, repeatable deployments.

OpenAI’s Enterprise Push Beyond Chat

For OpenAI, the partnership underscores a growing emphasis on enterprise-grade, action-oriented AI, not just conversational use cases.

“Automation Anywhere is showing what’s next—AI that goes beyond automating tasks to redefining how work actually happens,” said Giancarlo “GC” Lionetti, Chief Commercial Officer at OpenAI. “Together, we’re embedding intelligence directly into core workflows so enterprises can move faster, work smarter, and drive meaningful outcomes.”

This aligns with a broader market shift. Enterprises are increasingly asking how generative and reasoning models can drive outcomes, not just answer questions. Agentic frameworks—where AI can reason, decide, and act under governance—are emerging as the next logical step.Balancing Autonomy, Control, and Trust

Agentic AI has also raised concerns about over-autonomy, risk, and reliability. Automation Anywhere is positioning its architecture as a middle ground between fully autonomous agents and overly constrained automation.

The company’s agentic architecture integrates reasoning, enterprise context, human-in-the-loop controls, and orchestrated action into a single framework. The goal is to ensure AI agents can adapt and operate independently where appropriate, while still deferring to human judgment in sensitive or high-risk scenarios.

“Most agentic initiatives fail because they’re either too autonomous or too constrained,” said Dustin Snell, SVP of Agentic Solutions at Automation Anywhere. “What’s different here is that we deliberately blend agentic reasoning, deterministic execution, and human judgment into a single, governed flow.”

This emphasis on balance reflects lessons learned from earlier AI deployments, where lack of oversight or excessive rigidity undermined trust and adoption.

Where the Solutions Are Aimed

Automation Anywhere says its growing portfolio of agentic solutions targets high-value business processes across finance, HR, IT, and customer service. These are domains where processes are complex, exception-heavy, and often span multiple systems—conditions where traditional automation struggles.

Examples include resolving finance exceptions, handling employee lifecycle events, managing IT incidents, or responding to customer service escalations. In each case, agentic AI can assess context, reason through options, and take coordinated action across systems, rather than triggering isolated tasks.

For MarTech and RevTech leaders, the implications are notable. As customer journeys, revenue operations, and campaign execution become more complex, agentic automation could play a growing role in orchestrating workflows across CRM, marketing automation, analytics, and support platforms.

Market Context: The Rise of Agentic Automation

The announcement comes as “agentic AI” rapidly becomes one of the most discussed—and least clearly defined—terms in enterprise technology. Vendors across automation, analytics, and SaaS are racing to position themselves as platforms for AI agents.

Automation Anywhere’s strategy stands out by anchoring agentic capabilities in process orchestration and governance, rather than treating agents as standalone tools. By pairing OpenAI’s reasoning models with a deterministic execution engine, the company is betting that enterprises will prioritize control and reliability as much as intelligence.

If successful, this approach could help move agentic AI from experimental labs into everyday enterprise operations—a shift many CIOs and COOs are eager to see.

The Bottom Line

Automation Anywhere’s collaboration with OpenAI signals a maturation of the agentic AI conversation. This isn’t about chatbots with more autonomy; it’s about re-architecting how work happens, with AI reasoning directly connected to governed execution.

For enterprises under pressure to deliver faster ROI from AI investments, the promise is compelling: deploy agentic solutions in weeks, not months, and let AI reason through complexity without losing human control.

Whether this model becomes the dominant blueprint for enterprise agentic AI remains to be seen. But with OpenAI and Automation Anywhere aligning around reasoning, orchestration, and governance, the market now has a clearer picture of what production-ready agentic automation might actually look like.

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As unified communications and collaboration (UC&C) platforms mature, the real battleground is no longer features—it’s execution. That was the clear message from Wildix’s annual virtual UC&C Summit, where the AI-powered communications vendor gathered partners and industry stakeholders to show how channel-led delivery is redefining what modern communications platforms are expected to do.

With economic pressure tightening budgets and service expectations climbing, organizations are reassessing communications not as a convenience layer, but as a core operational system—one that must deliver reliability, visibility, and accountability at scale. Wildix argues that this shift fundamentally changes the role of vendors and, more importantly, the partners who deploy their technology.

From resale to responsibility

Wildix used the Summit to reinforce its long-standing 100 percent channel-only model, but with a sharper edge. The company made it clear that partner differentiation is no longer about reselling licenses or competing on price. Instead, partners are increasingly expected to act as advisors—designing, governing, and continuously optimizing communication workflows that underpin daily business operations.

“The UC&C market has matured,” said Emiliano Tomasoni, CMO at Wildix. “Customers are no longer evaluating platforms in isolation; they are looking for partners who can translate communication into operational value.”

To underline that philosophy, Wildix announced the launch of its new Spokesperson Program, selecting a single partner each year to act as a global ambassador for the brand. The initiative is designed to give the channel a visible, credible voice—positioning partners not as extensions of the vendor, but as central protagonists in the Wildix ecosystem.

AI moves from vision to live deployment

AI wasn’t new at this year’s Summit—but the conversation had clearly shifted. After outlining its agentic AI strategy in 2024 and launching Wilma AI, the embedded AI layer across its platform, Wildix focused this year on execution.

Rather than demos or roadmap promises, the company highlighted live customer deployments where AI-driven automation and assistance are already embedded into voice, messaging, and meeting workflows. Crucially, these deployments are governed through partner-led frameworks, reinforcing the idea that AI success depends as much on implementation and oversight as on algorithms.

“Wildix has demonstrated incredible technological vision and agility, making it seamless to integrate complex AI into real-world business environments,” said Carlos Estrela, CEO of Leader Redes y Comunicaciones. “Generative and agentic AI—especially for voice—is no longer just innovation. It’s the true differentiator.”

That emphasis on voice is notable. While much of the AI hype has centered on chat and analytics, Wildix is betting that intelligent voice workflows—where speed, accuracy, and context matter most—will separate operational platforms from feature-rich tools.

A channel inflection point

Wildix framed the current moment as a turning point for the channel. As AI-native vendors and point solutions flood the market, relevance is increasingly tied to outcomes, not capabilities. According to industry forecasts, more than 80 percent of UC&C sales will be indirect by 2026, reinforcing the strategic importance of partners who can deliver measurable results.

To support that shift, Wildix highlighted early results from its Sales Academy, a partner-first sales methodology launched to address increasingly complex UC&C buying behavior. Unlike traditional training programs, Sales Academy applies structured frameworks directly to live opportunities.

The results, at least so far, are tangible. In its first year, participating partners generated more than $40,000 in new monthly recurring revenue and achieved 23 percent year-over-year growth. The program has also earned external validation, receiving recognition from UC Today through the UC Awards for partner enablement.

Real-world impact: healthcare, retail, and beyond

The Summit also showcased concrete examples of partner-led execution across industries including healthcare, professional services, and retail. One standout was RoboReception, an AI-embedded healthcare solution co-developed by RoboReception and Wildix, and delivered through U.K.-based MSP Focus Group.

Originally created by a dentist to solve front-desk bottlenecks and missed calls, RoboReception automates inbound patient interactions and reduces administrative workload without adding staff. According to Wildix, the solution generated more than $9 million in measurable ROI within its first six months, deployed across 65 U.K. dental clinics—all while maintaining service levels.

It’s a case study that neatly reinforces Wildix’s thesis: AI becomes valuable when it is embedded into workflows, governed properly, and delivered by partners who understand the operational context.

Looking to 2026: communications as infrastructure

Looking ahead, Wildix positioned 2026 as a year defined by operational depth rather than experimentation. The roadmap includes continued investment in AI-driven coaching and insights, tighter governance across voice, messaging, and mobile environments, and expanded partner control through capabilities like fixed-mobile convergence and emerging messaging standards.

Together, these priorities reflect a broader reframing of unified communications—not as a standalone platform, but as business infrastructure.

“As customer expectations rise, AI is the opportunity for our partners to deliver value and stay relevant,” said Steve Osler, CEO of Wildix. “We provide the full AI stack to turn the channels they control into intelligence, making them indispensable architects of customer growth.”

For a UC&C market crowded with features and promises, Wildix’s message is clear: the future belongs to vendors—and partners—who can prove that communications actually work.

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As generative AI tools multiply, so does a quiet frustration among creators and marketers: too many models, too many tabs, and too little clarity about which AI actually performs best for a given task. Seela is betting that the next wave of AI adoption won’t be driven by more models—but by better ways to compare and use them.

Today, Seela announced its all-in-one AI-powered creative platform, bringing together text-to-image, image-to-image generation, and side-by-side AI chatbot comparisons into a single workspace. The goal is simple but ambitious: help creators, designers, marketers, and AI-curious teams explore, evaluate, and create without constantly switching tools or guessing which model to trust.

Rather than positioning itself as another standalone AI model, Seela acts more like an AI command center—one designed to make differences between models visible, actionable, and useful in real creative workflows.

Side-by-side AI chat: comparing models in real time

At the core of Seela’s platform is its multi-model AI chat comparison feature, which addresses a common pain point for professionals using generative AI at scale.

Instead of submitting the same prompt separately to ChatGPT, Claude, Grok, DeepSeek, or other large language models, users can enter a single prompt and receive responses from multiple models simultaneously. Each response appears side-by-side in the same interface, allowing users to instantly compare tone, reasoning depth, creativity, and accuracy.

For content teams, researchers, and strategists, the value isn’t just convenience—it’s insight. Differences in how models interpret the same prompt become immediately obvious, helping users decide which AI is best suited for a specific task, whether that’s long-form writing, ideation, analysis, or creative experimentation.

In practice, this reduces both friction and bias. Users no longer default to a single model out of habit or availability; instead, they can make informed choices based on observable output quality.

A practical approach to AI decision-making

Seela’s comparison-first design reflects a broader shift in how businesses are using AI. As generative models become more capable—and more numerous—the challenge has moved from access to evaluation.

For marketers and creative professionals, choosing the wrong model can mean weaker messaging, inconsistent brand voice, or more time spent rewriting outputs. Seela’s side-by-side approach turns model selection into a visible, repeatable process rather than a guessing game.

By treating AI models as interchangeable tools rather than black boxes, Seela positions itself as a decision-support layer—one that helps users understand not just what AI can do, but how different AIs behave.

Flexible image generation for real-world creative teams

Beyond text, Seela AI also brings visual creation into the same workspace. The platform supports both text-to-image and image-to-image workflows, allowing users to generate new visuals, refine existing ones, or transform images using AI.

What stands out is the platform’s emphasis on practical use cases. Alongside generation, Seela includes commonly used image utilities such as background removal, watermark removal, and access to popular art styles—all without requiring third-party tools.

For social media teams, designers, and growth marketers producing high volumes of visual content, this consolidation matters. Instead of bouncing between design software, AI generators, and utility tools, users can handle much of the workflow in one place.

The result is less tool sprawl and faster turnaround—two priorities that increasingly define modern content operations.

Designed for speed without sacrificing control

Seela is clearly not targeting AI researchers or engineers as its primary audience. Instead, the platform emphasizes usability, clarity, and speed, with a visual-first interface that lowers the learning curve for non-technical users.

At the same time, Seela hasn’t stripped away control. Power users still have access to advanced options, allowing them to fine-tune outputs without overwhelming less experienced users.

“As AI models multiply, users shouldn’t have to,” the Seela team said. “Our goal is to give users control and visibility—so they can focus on creating, not managing tools.”

That philosophy reflects a growing demand for AI platforms that prioritize workflow design over raw capability. In many organizations, the bottleneck isn’t AI performance—it’s adoption, trust, and ease of use.

Positioning in a crowded AI landscape

Seela enters a market crowded with both specialized AI tools and broad creative platforms. What differentiates it is not a proprietary model, but orchestration.

Rather than competing directly with OpenAI, Anthropic, or other model providers, Seela treats them as components within a larger creative system. This model-agnostic stance could prove advantageous as enterprises increasingly want flexibility, portability, and transparency in their AI stacks.

It also aligns with a broader industry trend: AI platforms evolving into hubs rather than destinations. As organizations experiment with multiple models, tools that help compare, govern, and operationalize AI outputs are becoming just as valuable as the models themselves.

What’s next for Seela

Seela is currently in its MVP stage, with a clear focus on two core capabilities: multi-model AI chat comparison and AI-powered image generation. According to the company, future iterations will expand into video model support and additional creative scenarios.

Over time, Seela aims to position itself as a centralized hub for AI-driven creation and experimentation—one that grows alongside the rapidly evolving AI ecosystem rather than locking users into a single approach.

If successful, Seela could appeal to a growing segment of professionals who don’t want “another AI tool,” but instead want a clearer way to navigate the AI tools they already use.

In an era where generative AI is everywhere, Seela’s pitch is refreshingly grounded: better visibility, better decisions, and better creative outcomes.

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For years, marketing personas have been built on a shaky foundation: surveys, assumptions, and generalized demographic models that often look good in presentations but fall apart in execution. Wiland is aiming to change that equation.

The data-driven marketing intelligence provider today announced MarketSignals™ Custom Personas, a new segmentation solution designed to help brands and agencies identify, understand, and activate their most valuable customers using real-world spending behavior—not inferred intent or self-reported preferences.

The launch signals a broader shift in marketing intelligence, as brands increasingly demand segmentation that doesn’t just inform strategy, but directly powers personalization, acquisition, and retention across channels.

Moving beyond “one-size-fits-all” personas

Traditional personas have long been a blunt instrument. Built largely on survey responses or third-party demographic groupings, they often fail to capture how customers actually behave—especially when it comes to purchasing decisions.

MarketSignals Custom Personas take a different approach. Wiland combines a client’s first-party customer data with its proprietary transactional spend dataset to create behaviorally rich audience segments grounded in what consumers actually buy, how often they buy, and where they spend.

Instead of relying on assumed interests or stated preferences, marketers get personas based on verified purchase activity. The result is a more accurate, more actionable view of customers—one that reflects reality rather than aspiration.

“MarketSignals Custom Personas give marketers the missing piece in their personalization and growth strategies,” said Mike Gingell, CEO of Wiland. “Our clients want more than just insights—they want segmentation they can actually use.”

Turning insight into activation

A key differentiator of MarketSignals Custom Personas is that they’re not designed to live in a slide deck. Wiland has positioned the product squarely around execution.

Persona attributes are appended directly to a client’s customer file, allowing them to be activated immediately across marketing platforms. That makes the personas usable for real-time personalization, retention campaigns, and acquisition strategies—without requiring complex translation between strategy and execution teams.

According to Wiland, the personas support multiple use cases, including:

• Tailored personas built on actual consumer spending behavior

• Direct integration into first-party customer datasets

• Use across personalization, loyalty, and retention initiatives

• Expansion audiences for prospecting across digital and programmatic channels

This approach reflects growing pressure on marketing teams to prove ROI. As budgets tighten and expectations rise, segmentation needs to directly improve performance—not just inform messaging.

Why spend data matters now

The emphasis on transaction-level data comes at a critical moment for marketers. With signal loss accelerating due to privacy changes, cookie deprecation, and platform restrictions, brands are leaning more heavily on first-party data and durable behavioral signals.

Spend data, in particular, offers a level of clarity that interest-based or survey-driven models struggle to match. What people buy—and where they consistently spend—is often a stronger predictor of future behavior than what they say they like.

By grounding personas in purchase activity, Wiland is betting that brands can reduce wasted spend, improve targeting accuracy, and better identify high-lifetime-value customers before competitors do.

It also positions MarketSignals Custom Personas as a bridge between analytics and activation—connecting customer intelligence directly to media, CRM, and personalization systems.

Designed for brands, agencies, and nonprofits

Wiland says the new personas are designed to be industry-agnostic, supporting businesses and nonprofits alike. That flexibility matters in a market where segmentation needs vary widely—from retail and financial services to healthcare, education, and advocacy organizations.

For agencies, the product offers a way to move beyond generic segmentation frameworks and deliver differentiated value to clients. Instead of reusing the same persona templates across accounts, agencies can build custom, data-backed segments that reflect each client’s actual customer base.

For brands, the appeal lies in precision. Rather than marketing to broad categories, teams can focus on customers who already demonstrate the behaviors they want to scale—whether that’s repeat purchasing, premium spend, or category loyalty.

A sharper answer to mediocre results

Wiland is direct in its critique of traditional segmentation. Generic personas, the company argues, lead to generic results—especially in an environment where consumers expect relevance and personalization as table stakes.

“Don’t settle for generic segmentation that gives you mediocre results in your marketing efforts,” Gingell said. “Our MarketSignals Custom Personas are built specifically for you and provide unmatched performance.”

That positioning aligns with a wider industry trend: marketing intelligence tools are being judged less on theoretical sophistication and more on their ability to drive measurable outcomes.

The bigger picture: segmentation as a growth lever

The launch of MarketSignals Custom Personas reflects a broader evolution in how segmentation is viewed. Once considered a planning exercise, it’s increasingly seen as a core growth lever—one that influences everything from media efficiency to customer lifetime value.

By anchoring personas in spend behavior and integrating them directly into activation workflows, Wiland is pushing segmentation closer to revenue operations. In doing so, it’s challenging marketers to rethink personas not as static profiles, but as dynamic, data-driven assets.

For brands struggling with fragmented data, declining signal quality, and rising acquisition costs, that shift could make the difference between personalization that sounds good—and personalization that actually works.

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Qualitative research has long struggled with a familiar trade-off: depth versus scale. Traditional methods deliver rich human insight, but slowly and expensively. Automated tools promise speed, but often strip away nuance. Hootology believes it has found a middle ground—and the market appears to be responding.

Since the July launch of HOOQZ, its AI-powered platform designed to host dynamic discussions in simulated environments, Hootology has seen accelerating momentum across customers, product development, and talent acquisition. The company says interest has surged as brands seek ways to preserve human-centered insights while operating at a scale and speed that modern decision-making demands.

Reframing qualitative research with AI

HOOQZ is built to replicate and expand qualitative research conversations—without relying solely on small focus groups or one-off interviews. The platform enables moderated, dynamic discussions in AI-powered simulated environments, allowing researchers to explore attitudes, motivations, and emotional drivers with far more participants than traditional methods allow.

The pitch is not automation for automation’s sake. Hootology positions HOOQZ as a way to preserve the psychological rigor of qualitative research while extending it to a quantitative scale—something that has historically been difficult, if not impossible.

That framing has resonated at a time when marketers, strategists, and product teams are under pressure to make faster decisions without sacrificing insight quality.

Enterprise clients signal growing trust

In the six months since launch, Hootology has expanded its client roster with several high-profile additions, including a Fortune 100 financial services company, a Fortune 200 healthcare brand, and an NFL team.

These wins suggest more than early curiosity. Enterprise buyers tend to be cautious with research methodologies, particularly when insights inform high-stakes decisions around brand, customer experience, and product development. Hootology argues that the adoption of HOOQZ reflects growing confidence in AI-enabled qualitative research—when it is grounded in established research principles.

The company notes that demand has also validated a core assumption behind HOOQZ: that human-driven insights remain essential, even as organizations scale research through AI.

Funding fuels faster iteration

Hootology announced $1.1 million in pre-seed funding in July, earmarked for accelerating its product roadmap. According to the company, those investments are already paying off.

Since launch, HOOQZ has improved both the speed at which studies can be run and the depth of insight they generate. Expanded capabilities have increased the platform’s ability to support more complex research designs, while maintaining a participant experience that feels conversational rather than transactional.

That balance—speed without flattening nuance—has become a key differentiator as AI research tools proliferate.

Senior talent joins to scale the vision

The technological promise of HOOQZ has also attracted experienced industry leaders to Hootology’s team, a signal that the platform’s ambitions extend beyond experimentation.

Recent hires include:

• Jennifer Holland, Head of Growth, who brings more than 30 years of experience in business development for brand and marketing organizations. She will lead Hootology’s marketing and sales strategy.

• Katrina Noelle, Strategic Growth Director, a well-known industry thought leader who transitioned from an advisory role at her own insights agency to join Hootology. She described Hootology as “the only player in this space building exactly what’s needed to take this industry forward.”

• Anirban Ghosh, PhD, Data Scientist, tasked with advancing HOOQZ’s analytical depth and insight generation.

• Sangdi Chen, Client Strategist, a former Bain strategist with a master’s degree in social psychology from the University of Chicago, focused on translating research findings into actionable client strategies.

Hootology also plans to add another researcher to meet growing demand, underscoring the company’s emphasis on pairing AI capabilities with human research expertise.

Modernizing insights without losing psychology

Founder and CEO Stefanie Francis frames the recent hires as both validation and acceleration.

“To have true industry insiders get as excited about HOOQZ as we’ve been from day one—and who can see the power of what we’ve built from an outside lens—is as meaningful an endorsement as the new client wins,” Francis said.

She emphasized that the team shares a commitment to the psychology underlying human insights, as well as to improving the participant experience—an often-overlooked factor in research quality.

That focus may prove critical as AI becomes more deeply embedded in research workflows. Tools that optimize efficiency but ignore participant engagement risk degrading the very insights they aim to scale.

A broader shift in the insights industry

Hootology’s momentum reflects a wider transformation underway in the insights and market research space. As AI tools mature, the question is no longer whether AI can support research, but how it should be applied without undermining methodological integrity.

HOOQZ’s simulated discussion environments offer one answer: use AI to expand reach and speed, while keeping human reasoning, emotion, and context at the center of analysis.

This hybrid approach is increasingly attractive to strategists and marketers navigating fast-changing markets, fragmented audiences, and rising expectations for evidence-backed decision-making.

What’s next for Hootology

Hootology says development of HOOQZ is ongoing, with additional features tailored to evolving needs across marketing, strategy, and product development. A larger product update is expected later this year, signaling that the platform’s current capabilities are only a starting point.

Looking toward 2026, the company appears focused on scaling responsibly—growing its client base, deepening analytical sophistication, and continuing to invest in talent that bridges research rigor with modern technology.

If Hootology succeeds, it may help redefine how qualitative insights are gathered in an AI-first era—not by replacing human understanding, but by finally giving it the scale the industry has long promised.

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