WhatsApp is no longer just where customers ask questions—it’s where discovery, engagement, and transactions increasingly converge. That was the clear message from Omnichat’s “Social CRM and AI” conference, a large-scale industry event that brought together leaders from Meta, Maxim’s Group Hong Kong, and MEDILASE to unpack how conversational commerce is reshaping customer experience across Asia.

At the center of the discussion was Omnichat’s latest product announcement: Omni AI Agent Studio, a new platform designed to make agentic AI a practical, deployable reality for businesses using WhatsApp as a core customer channel. The launch reflects a broader shift in MarTech and CX platforms—away from fragmented engagement tools and toward unified, data-driven social CRM systems built directly into messaging environments.

From Messaging App to Social CRM Engine

Omnichat, a Meta WhatsApp Business Solution Provider, used the event to position WhatsApp as the connective tissue of modern customer journeys. What once functioned primarily as a support inbox has evolved into a channel where brands can acquire, convert, and retain customers in a single conversational flow.

That evolution is being driven by both consumer behavior and platform capability. According to Kantar research cited at the event, 71% of Hong Kong consumers message a business on WhatsApp at least once a week—a signal that messaging is no longer peripheral to the buying journey.

“Customers no longer just expect WhatsApp to be a customer service channel,” said Silvie Lam, Business Director, Greater China at Meta. “They now expect to reach a brand immediately after discovering it through social ads.”

Meta’s roadmap reflects that expectation. Tools like Ads that Click to WhatsApp, Website-to-WhatsApp ads, WhatsApp Catalog, and WhatsApp Flows are designed to help businesses manage the full funnel—from discovery to transaction—without forcing customers to jump between platforms. In effect, WhatsApp is becoming a commerce-enabled front door rather than a post-sale support line.

Omni AI Agent Studio: Making Agentic AI Practical

Omnichat’s response to this shift is Omni AI Agent Studio, a new environment that allows businesses to deploy and orchestrate multiple AI agents across conversational workflows. The studio builds on Omnichat’s existing native AI agents, including:

• AI Customer Service Agent

• AI Marketing Campaign Agent

• AI Shopping Agent

What’s new is the ability to integrate these agents seamlessly into any customer interaction—without requiring businesses to rebuild workflows from scratch.

Founder and CEO Alan Chan described the launch as a strategic inflection point. “This signifies a pivotal moment in our commitment to democratising Agentic AI for businesses,” he said. “When combined with our advanced WhatsApp functionalities—our Social Data Customer Platform and WhatsApp loyalty program—we’re enabling brands to build a truly powerful social CRM.”

In practice, that means consolidating customer profiles, analyzing conversational data across CRM and social channels, and delivering experiences aligned with real customer behavior throughout the lifecycle—not just at isolated touchpoints.

Maxim’s Group: Turning WhatsApp Into a Revenue Channel

One of the most compelling case studies came from Maxim’s Group, one of Hong Kong’s largest F&B operators. Rather than treating WhatsApp as a support add-on, Maxim’s has positioned it as a high-performing e-commerce channel that complements its Eatizen membership app.

Using Omnichat’s platform, enhanced with Omni AI, Maxim’s engages both existing Eatizen members and new customers through WhatsApp. AI-powered chatbots streamline day-to-day operations, while segmented broadcast campaigns drive targeted promotions based on customer behavior.

The results, according to Eileen Tang, Head of Digital Business at Maxim’s Caterers Limited, have been striking. During a recent Mid-Autumn Festival campaign, Maxim’s built a fully end-to-end WhatsApp buying journey—from discovery to payment—inside the messaging app.

“The outcome was immediate,” Tang said. “We achieved a 2–3x higher Average Transaction Value compared to our traditional online marketplace channels.”

She attributed the uplift to two key factors: a frictionless customer experience that allowed users to order and pay directly within WhatsApp, and significantly lower costs compared to building new features inside a proprietary app. Maxim’s is now scaling WhatsApp commerce as a core growth pillar rather than a seasonal experiment.

MEDILASE: Automating Care Without Losing the Human Touch

While Maxim’s showcased commerce scale, MEDILASE demonstrated how conversational platforms can improve service quality and operational efficiency in high-consideration industries like aesthetic medicine.

By integrating Omnichat’s WhatsApp API solutions, MEDILASE automated key touchpoints across the customer journey—from initial inquiries to appointment confirmations and treatment reminders. The centralized platform unified marketing, customer service, and sales workflows, improving collaboration across teams.

“By leveraging the WhatsApp API solution, we’ve deployed 24/7 chatbots and achieved seamless cross-team collaboration,” said KC Ng, CEO of MEDILASE. “This has significantly elevated our service standards and operational transparency.”

Beyond automation, MEDILASE is using conversational data to anticipate customer needs and improve consultation quality. In one recent campaign, roughly 40% of customers participated in an interactive WhatsApp game, resulting in a 5% conversion rate—a notable outcome in a category where trust and personalization are critical.

Why This Matters for MarTech and CX Leaders

Taken together, the announcements and case studies at Omnichat’s event highlight a clear industry inflection point. Social messaging is no longer an edge channel—it’s becoming the backbone of customer engagement, especially in mobile-first markets across Asia.

What’s changing isn’t just where conversations happen, but how intelligently they’re managed. Platforms like Omnichat are moving beyond routing messages to using AI agents, conversational data, and unified customer profiles to guide decisions in real time.

For MarTech leaders, the implication is clear: CRM strategies that ignore messaging platforms risk becoming blind to where customers actually engage. For CX and commerce teams, WhatsApp is emerging as a place where convenience, personalization, and conversion can coexist—without forcing customers through fragmented digital experiences.

Conversation and Commerce, Fully Aligned

The collaboration between Meta, Omnichat, Maxim’s Group, and MEDILASE sends a unified signal to the market. The convergence of conversation and commerce is no longer a future-state vision—it’s already delivering measurable business outcomes today.

As brands look for sustainable competitive advantage in crowded digital markets, the winners may be those that stop treating messaging as a support tool and start building it into the core of their customer strategy. Omnichat’s bet on agentic AI and social CRM suggests that the next phase of customer experience will be conversational by default—and intelligently orchestrated behind the scenes.

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The race to monetize AI is entering a more structural phase. While much of the industry’s attention has been fixed on OpenAI’s experiments with consumer ad testing and Google’s recent rollout of the Universal Commerce Protocol (UCP), a deeper shift is underway—one that redefines how commerce itself is discovered, evaluated, and executed.

That shift took a concrete step forward today as Deeplumen, an AI infrastructure company focused on next-generation commerce, announced the release of its UCP SDK for Java. The open-source launch follows Google’s introduction of UCP as an open standard meant to give AI agents a shared “commercial language” for discovering products and completing transactions directly within AI-powered experiences.

For enterprises running large-scale commerce systems, Deeplumen’s move addresses a practical bottleneck: how to participate in agentic commerce without rebuilding existing technology stacks from scratch.

UCP and the Rise of Agentic Commerce

Google’s Universal Commerce Protocol is designed to support native discovery and direct checkout across its AI surfaces, enabling brands to capture intent at the moment it emerges. Rather than routing users through traditional funnels, UCP allows AI agents to understand product data, validate availability, and execute transactions programmatically.

That model reflects a growing reality in commerce: decision-making is increasingly delegated to AI agents. Shopping workflows are no longer limited to humans scrolling, comparing, and clicking. Instead, autonomous agents evaluate options based on structured inputs—price, availability, fulfillment terms, and reliability—often faster and more objectively than human buyers.

Deeplumen’s Java SDK brings that future into reach for enterprise brands. By implementing UCP in Java, the company is enabling organizations with deeply embedded commerce infrastructure to connect directly to agent-driven ecosystems without abandoning proven systems.

From Persuading Humans to Informing Machines

The implications extend beyond technology integration. The shift to agentic commerce challenges decades of marketing orthodoxy.

“Traditional marketing is about optimizing for perception,” said Joy Wu, COO of Deeplumen. “AI agents optimize for parameters.”

In what Deeplumen describes as the M2AI (Marketing to AI) era, emotional storytelling and brand symbolism matter less to the point of transaction. What matters instead is high-fidelity, structured, and verifiable data—information that AI agents can trust, compare, and act upon with minimal ambiguity.

Rather than “selling” in the conventional sense, brands must now inform AI systems with precise product definitions, consistent availability signals, and reliable transaction logic. In this model, clarity becomes a competitive advantage.

Why Java Matters to the Enterprise

While much of modern AI development is Python-centric, global commerce infrastructure tells a different story. Core systems powering ERP platforms, large retailers, and order management engines are overwhelmingly built on Java.

Deeplumen’s decision to prioritize Java is a direct response to that reality. The UCP SDK for Java is designed to slot into existing enterprise environments, reducing friction at a time when many organizations are already stretched thin by digital transformation demands.

Key capabilities of the SDK include:

• Structured Identity: Tools that help brands define products and offers in ways AI agents can accurately interpret

• Seamless Integration: A plug-and-play library for Java environments, minimizing architectural disruption

• Transaction Readiness: Support for full-loop commerce, moving beyond discovery into fulfillment within AI interfaces

The result is a practical on-ramp to agentic commerce for enterprises that can’t afford wholesale rewrites of mission-critical systems.

Competing in the M2AI Era

Deeplumen positions the Java SDK as part of a broader infrastructure strategy aimed at helping brands compete on clarity, availability, and reliability rather than pure brand awareness. As AI agents become more influential buyers, these attributes increasingly determine which products surface—and which are ignored.

In this environment, the quality of a brand’s structured commerce data may matter more than its creative campaigns. Products that are easy for AI agents to discover, verify, and transact against will have a structural advantage, regardless of traditional marketing spend.

The company sees UCP for Java as an early building block in a longer-term roadmap focused on decentralized protocols and AI-to-AI commerce infrastructure. The goal is to enable transactions that happen autonomously between systems, with minimal human intervention, across interoperable standards.

A Signal of Where Commerce Is Headed

Deeplumen’s announcement lands at a moment when the industry is actively redefining what “commerce readiness” means. As Google, OpenAI, and others experiment with AI-native buying experiences, standards like UCP are emerging as the connective tissue between intent and execution.

By open-sourcing its Java SDK, Deeplumen is betting that adoption—not control—will determine who shapes this next layer of the commerce stack. For enterprise brands, the message is equally clear: preparing for AI buyers is no longer theoretical.

The transition from persuading humans to informing machines is already underway. And as agentic commerce accelerates, the brands that adapt early may find themselves becoming the default choice—not for people, but for the AI agents making decisions on their behalf.

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As factories return home and reshoring gathers pace, a hard truth is resurfacing alongside production lines: machines can be shipped, but know-how cannot. For decades, American manufacturing quietly outsourced not just labor, but institutional memory. Now, with geopolitical pressure mounting and experienced workers retiring, that loss is becoming painfully visible on shop floors.

Dirac, a manufacturing software company, believes it has found a way to rebuild that missing layer. Earlier this year, the company announced the general availability of BuildOS, which it calls the first automated platform for creating and managing model-based work instructions. The launch coincides with a surge of momentum for Dirac, including $10.7 million in funding and a strategic partnership with Siemens aimed at accelerating digital transformation across global manufacturing.

“Dirac has built the first and only automated work instruction platform,” said Tomás Klausing, Director for Technology Partnerships at Siemens, underscoring how central the technology could become to modern production environments.

Manufacturing’s Quiet Crisis: Lost Context

Across aerospace, defense, shipbuilding, and heavy industry, frontline operations still depend heavily on static PDFs, outdated SOPs, and informal shadowing to train workers and execute tasks. The most critical details—how to torque a joint, how to adjust a setup mid-process, what sequence to follow when something goes wrong—often live only in the minds of senior technicians.

As those workers retire and production demands increase, manufacturers face a bottleneck that can’t be solved by adding capacity alone. Engineering files may exist, but without context, they are unusable on the floor without weeks or months of onboarding.

Dirac CEO and founder Filip Aronshtein frames this as a structural problem, not a staffing one. “We don’t have a labor crisis; we have a context crisis,” he said. “You can’t reshore production if nobody remembers how to build.”

The stakes extend well beyond efficiency. While the U.S. produces roughly 1.5 submarines per year, China produces more than 10 per month. In that context, modernizing manufacturing processes is not just an economic concern—it’s a national security issue.

From Engineering Intent to Repeatable Execution

BuildOS is designed to replace static documentation with automated, animated, physics-aware, and interactive work instructions. Instead of manually creating step-by-step guides, the platform automatically translates CAD files and bills of materials into visual, shareable instructions complete with 3D models, tools, and embedded tribal knowledge.

In effect, Dirac is trying to do for manufacturing engineers what CAD once did for mechanical engineers: give them a way to encode processes once and scale them reliably across teams, facilities, and generations of workers.

“BuildOS turns engineering intent into repeatable execution and tribal knowledge into institutional knowledge—automatically,” Aronshtein said.

The platform’s impact is already visible among early adopters. Ancra Aircraft, which supplies cargo loading systems to more than 60% of the global freighter fleet, reported that work instruction creation time dropped from three days to two hours, a 95% reduction. The company also saw fewer quality errors thanks to tighter process standardization, while capturing tacit knowledge from senior technicians in a single session.

Why AI Alone Isn’t Enough

Dirac is careful not to position BuildOS as just another AI tool layered onto existing workflows. Instead, the company argues that intelligence must be embedded directly into how work is defined and executed.

Artificial intelligence helps automate translation from design to instruction, but the real value lies in context preservation—making sure every worker understands not just what to do, but why and how it fits into the broader process. That context becomes increasingly critical as factories move toward low-volume, high-mix production with rising compliance requirements.

“America is investing billions in industrial capacity,” said Trae Stephens, Partner at Founders Fund, “but without platforms like Dirac’s BuildOS, we’re just recreating the same bottlenecks that made offshoring attractive in the first place.”

Stephens added that Dirac’s advantage lies in helping manufacturers compete on intelligence, not incentives. “They’re re-architecting American manufacturing from the ground up.”

Built for the Shop Floor, Not the Slide Deck

Part of Dirac’s credibility comes from its team. The company’s founders and engineers come from hardware-heavy industries—planes, cars, submarines—where the cost of miscommunication is measured in delays, defects, and safety risks. BuildOS wasn’t designed for a theoretical factory of the future, but for the realities of today’s shop floors.

That practical focus has helped drive adoption across aerospace, defense, automotive, agriculture, and heavy equipment manufacturing, particularly among companies managing complex products with small batch sizes.

Dirac and Siemens: Closing the Digital Loop

The partnership with Siemens adds another layer to Dirac’s ambitions. Together, the companies aim to close the gap between product design and physical production, creating a tighter feedback loop between PLM and CAD systems on one side and ERP and MES platforms on the other.

“The partnership with Dirac enables us to provide additional efficiency to our customers,” Klausing said. Early integrations have already improved work instruction generation, manufacturability, and production planning. Siemens plans to deepen the integration as part of its broader digital manufacturing portfolio.

For Siemens, the collaboration supports a strategic goal: bringing emerging technologies to market faster through flexible partnerships rather than monolithic platforms.

Toward Smart Labor, Not Cheap Labor

As manufacturing shifts from mass labor to smart labor, BuildOS positions itself as a bridge between generations of workers. The platform allows companies to capture the expertise of experienced operators and make it immediately available to new hires—reducing ramp-up time while maintaining consistency.

“Our edge isn’t just speed,” Aronshtein said. “It’s repeatability. BuildOS gives every engineer, technician, and team the ability to build something once, and then build it right every time after that.”

In an era defined by reshoring, workforce transition, and geopolitical pressure, Dirac is betting that the most valuable asset a factory can protect isn’t machinery—it’s memory. And as American manufacturing attempts to relight an industrial engine long deprived of its wiring diagram, platforms like BuildOS may determine whether that engine runs smoothly or stalls again.

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Dealership CRMs have spent years getting better at conversations—but far less time thinking about what happens when it’s time to get paid. DriveCentric is betting that gap is no longer acceptable.

The dealership-focused CRM and engagement platform has announced a strategic partnership with Dealer Pay, a payments platform purpose-built for automotive dealers, to enable compliant payment collection directly inside DriveCentric. The move is designed to let dealerships complete the full workflow—from customer interaction to revenue—without jumping between disconnected systems.

Crucially, DriveCentric isn’t becoming a payments company. Dealer Pay supplies the regulated payments infrastructure, while DriveCentric embeds the capability into the CRM experience on both desktop and mobile.

Why Payments Are Moving Front and Center

Payment collection in dealerships has traditionally lived in the back office, separated from sales, service, and customer engagement tools. That separation increasingly feels outdated. Customers expect frictionless digital experiences, regulators expect airtight compliance, and dealers want fewer manual handoffs that slow deals and introduce risk.

By integrating Dealer Pay, DriveCentric aims to make payments a natural extension of engagement rather than a separate operational step.

“As the premier customer-centric engagement platform, we believe CRMs should orchestrate the entire customer journey across every touchpoint and every department within the dealership,” said Matt Leone, CEO of DriveCentric. “By embedding payments directly into DriveCentric, we’re extending the CRM engagement lifecycle through the point of revenue collection.”

The implication is clear: engagement that stops short of revenue is incomplete.

Dealer Pay Handles the Hard Parts

For DriveCentric, the partnership is as much about focus as expansion. Rather than building payments in-house, the company is leaning on Dealer Pay’s domain expertise in dealership-specific compliance, accounting workflows, and regulatory requirements.

“Engagement alone isn’t enough anymore,” said Julie Douglas, Founder and CEO of Dealer Pay. “Dealers expect systems to deliver outcomes. This partnership brings payments expertise and compliance into the CRM experience—so engagement doesn’t stop short of revenue.”

That distinction matters in automotive retail, where payment handling is tightly regulated and deeply intertwined with dealership accounting systems. A generic payments layer wouldn’t cut it; Dealer Pay is designed specifically for dealership realities.

A Broader Platform Strategy

The announcement fits neatly into DriveCentric’s broader platform strategy: remain laser-focused on customer engagement while expanding its ecosystem through bi-directional partner integrations. Instead of turning the CRM into an all-in-one monolith, DriveCentric is positioning itself as the orchestration layer—where conversations, data, and now payments converge.

This approach mirrors a wider MarTech and AutoTech trend. Leading platforms are increasingly choosing partnerships over vertical integration, allowing specialists to handle complex domains like payments while the core platform concentrates on experience and workflow.

For dealerships, that could mean fewer tools, fewer logins, and fewer dropped balls between departments.

What Dealers Should Expect Next

The integrated payments capability is expected to be available in Q1 2026, and both companies plan to outline the operational implications during a keynote-style session at the DriveCentric booth at NADA 2026.

If successful, the partnership could signal a shift in how dealership CRMs are evaluated. The next generation of platforms may not be judged solely on lead management or messaging features, but on how effectively they help dealers move from conversation to cash—securely, compliantly, and without friction.

In that sense, DriveCentric’s move isn’t just about payments. It’s about redefining where the CRM’s responsibility ends—and increasingly, that endpoint looks a lot like revenue.

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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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