Supply chains are no longer just about moving goods efficiently—they’re about orchestrating data, decisions, and partners across increasingly complex ecosystems. That shift is reflected in TraceLink’s latest recognition. The company has been named a Leader in the IDC MarketScape: Worldwide Multi-Enterprise Supply Chain Commerce Network 2025 Vendor Assessment, a nod to its growing influence in how large, regulated industries connect and collaborate digitally.

For TraceLink, the designation validates a long-term strategy centered on building an open, industrial-grade digital network rather than another point solution. At the heart of that strategy is OPUS, its Orchestration Platform for Universal Solutions, which IDC highlights as a foundational enabler for multi-enterprise collaboration.

Why OPUS matters now

Traditional supply chain systems were designed for internal optimization. They struggle when processes span dozens—or hundreds—of trading partners, each with different systems, standards, and regulatory requirements. IDC’s assessment points to OPUS as a response to that limitation.

According to the report, OPUS is an open platform that supports low-code application development, allowing both TraceLink and third parties to build multi-enterprise applications. In practice, that means companies can create digital networks that connect organizations, people, processes, and systems around shared business outcomes, rather than stitching together brittle integrations.

This approach aligns with a broader industry trend: enterprises are moving away from linear supply chains toward network-based operating models. In life sciences and healthcare especially, compliance, traceability, and real-time coordination are no longer optional—they are operational requirements.

From standalone solutions to network effects

TraceLink’s portfolio includes established offerings such as MINT, POET, and track-and-trace solutions, each delivering value on its own. What IDC’s recognition underscores is how those tools gain disproportionate impact when unified on OPUS.

When deployed together, these solutions enable faster issue resolution, higher data quality, and stronger compliance across global partner networks. Instead of managing fragmented workflows and disconnected data, organizations can operate on a shared digital foundation that scales across partners and geographies.

This “network effect” is increasingly important as supply chains face persistent disruption—from regulatory changes and geopolitical pressure to labor shortages and demand volatility.

Agentic orchestration enters the supply chain

TraceLink is also pushing OPUS beyond connectivity into orchestration. The platform is evolving to support agentic automation, allowing companies to deploy AI-powered digital teammates using no-code tools.

These agents are designed to monitor processes, reconcile data, and manage exceptions in real time, while keeping humans in the loop for oversight and accountability. For industries governed by GxP and other regulatory frameworks, that balance between automation and control is critical.

Shabbir Dahod, President and CEO of TraceLink, framed OPUS as a shared digital foundation built on trust and clarity. He argues that with agentic orchestration, organizations can link data, decisions, and partners in ways that fundamentally change how supply chains operate—moving from reactive coordination to proactive, network-wide intelligence.

Integrate once, collaborate everywhere

IDC also called out TraceLink’s Business-to-Network Integrate-Once™ architecture, which addresses one of the most persistent friction points in multi-enterprise systems: integration overhead.

Rather than requiring separate, point-to-point integrations for every trading partner, TraceLink’s model allows companies to integrate once and interoperate across the entire network. That dramatically reduces onboarding time, improves interoperability, and enables real-time visibility across shared processes.

In an environment where speed and responsiveness are competitive advantages, this model stands in contrast to legacy approaches that scale complexity faster than value.

Analyst perspective: orchestration over optimization

IDC analyst Reid Paquin noted that as organizations accelerate toward digitally connected supply networks, orchestration at scale has become essential. TraceLink’s platform approach—combining no-code tools with multi-enterprise process capabilities—maps closely to what enterprises now need: modern collaboration, shared visibility, and faster response across ecosystems.

That framing reflects a subtle but important shift in how supply chain technology is evaluated. The question is no longer just how well a system optimizes internal operations, but how effectively it enables collaboration across company boundaries.

The bigger picture

TraceLink’s placement as a Leader in the IDC MarketScape highlights a broader evolution in enterprise platforms. Supply chain networks are becoming programmable, intelligent, and increasingly autonomous—yet still governed and auditable.

By positioning OPUS as an open, no-code, agent-ready platform, TraceLink is signaling where it believes the market is headed: toward shared digital infrastructure that supports continuous improvement across entire ecosystems, not just individual enterprises.

For life sciences and healthcare organizations navigating regulatory pressure, operational complexity, and the push for resilience, that vision may be less about innovation for its own sake—and more about survival at scale.

Get in touch with our MarTech Experts.

Integral Ad Science is making a clear statement about where ad verification and optimization are headed. The company has announced IAS Agent, a new AI-powered assistant designed to help marketers activate campaigns faster, uncover deeper insights, and optimize performance at scale—without surrendering control to a black box.

Set to debut publicly at CES 2026, IAS Agent will roll out globally in early Q1 2026 at no additional cost to customers. That pricing decision alone signals how seriously IAS views AI assistance as a baseline expectation rather than a premium upsell.

An AI assistant built for marketers, not data scientists

IAS Agent is positioned as a natural-language interface layered directly into the IAS platform, allowing marketers to interact with campaign intelligence conversationally. Users can chat with the agent to streamline pre-campaign setup, adjust brand safety and suitability settings, and surface insights without needing technical expertise or manual dashboard analysis.

What differentiates IAS Agent from many AI tools flooding the ad tech market is its foundation: more than 15 years of proprietary IAS data across viewability, fraud, brand safety, and suitability, applied at omnichannel scale. Rather than relying on narrow or synthetic training sets, the assistant draws from what IAS describes as the industry’s most comprehensive dataset.

That matters in a market where AI recommendations often feel disconnected from real-world media complexity. IAS Agent’s outputs are grounded in historical patterns across publishers, platforms, and formats—not just recent signals.

Explainable AI, not opaque automation

The most pointed critique of AI in advertising has been its opacity. IAS is leaning directly into that concern with what it calls “explainable AI.”

Every recommendation surfaced by IAS Agent includes transparent self-reporting. Marketers can hover over suggestions inside the IAS UI to see what’s being recommended, why it’s being proposed, and what data signals informed the guidance. Users retain full control: they can customize, override, or adopt recommendations based on their own judgment and client requirements.

This design choice reflects a broader industry shift. As AI systems increasingly influence media spend, advertisers need to justify decisions internally—to legal teams, brand leaders, and regulators. Tools that can’t explain themselves are becoming liabilities rather than advantages.

Faster insights, fewer manual workflows

Beyond transparency, IAS Agent is built to reduce one of the most persistent drains on media teams: time.

According to IAS, early tests show efficiency gains of up to 50 percent in areas like brand safety and suitability configuration. The agent can recommend protection settings with minimal user input, allowing teams to scale governance across all investments without rebuilding rules for every campaign.

IAS Agent also continuously scans data across IAS dashboards to detect trends and patterns automatically. Instead of analysts hunting for signals across multiple reports, the agent surfaces what’s working—and what isn’t—up to five times faster than manual analysis.

In an environment where campaigns are increasingly fluid and omnichannel by default, that speed advantage could be decisive.

From campaign setup to real-time optimization

IAS Agent’s utility spans the full campaign lifecycle. During activation, marketers can use natural language prompts to get AI-assisted guidance on settings and configurations. Once campaigns are live, the agent highlights performance drivers, surfaces risk signals, and suggests optimizations in real time.

Crucially, IAS frames the tool not as a replacement for human decision-making, but as an advertising compass—guiding teams through complexity rather than automating judgment away.

Srishti Gupta, Chief Product Officer at Integral Ad Science, emphasized that IAS Agent is only the beginning. Future iterations are expected to expand agentic capabilities across supply path insights, tagging activation, and campaign settings assistance, further reducing friction across the media workflow.

Why agencies are paying attention

For agencies managing large, distributed media buys, the appeal is immediate. Jeff Omoregie, EVP of Unified TAAG at Publicis Media, highlighted the tool’s potential to reduce ad waste and speed action across complex environments.

That endorsement underscores a critical point: verification and optimization are no longer separate steps. They’re converging into a single intelligence layer that informs planning, activation, and optimization simultaneously.

IAS Agent positions IAS closer to that role—less a post-bid watchdog, more an always-on decision engine.

Responsible AI as a competitive differentiator

IAS is also using the launch to reinforce its stance on responsible AI. The agent is built using Databricks Agent Bricks, enabling enterprise-grade governance and observability—two requirements that are quickly becoming non-negotiable for large advertisers.

IAS notes it is the only company to hold all three major AI certifications relevant to the industry: TrustArc Responsible AI, ISO 42001, and Ethical AI certification from the Alliance for Audited Media. In a landscape where AI claims often outpace accountability, those credentials are meant to signal credibility.

A glimpse at the future of ad verification platforms

IAS Agent reflects a broader transformation underway in MarTech and AdTech. Verification platforms are evolving from compliance tools into intelligence systems—ones that don’t just flag problems, but actively guide better outcomes.

As AI assistants become embedded across marketing stacks, the winners will be those that combine scale, transparency, and trust. IAS is betting that explainability—not just automation—will be the feature that determines adoption.

For marketers facing tighter budgets, higher scrutiny, and increasing complexity, an AI assistant that can explain itself may be exactly what the industry has been waiting for.

Get in touch with our MarTech Experts.

As AI reshapes how people search, discover, and decide, marketing agencies are being forced to rethink both their tools and their footprint. Interact Marketing is making a bet on both. The agency has opened a new office in Jamestown, New York, expanding its regional presence while sharpening its focus on AI-driven search and performance-led media strategies.

The new office is located in the top-floor suite of the historic Fenton Building, a space with an unusual pedigree: it once housed the office of U.S. Supreme Court Justice Robert H. Jackson. The move coincides with Interact Marketing entering its 19th year in business—a milestone that reflects both longevity and adaptation in an industry defined by constant change.

A physical expansion tied to digital disruption

At first glance, opening a new office might seem like a traditional growth move. In Interact Marketing’s case, it’s tightly linked to how search and media are evolving.

The Jamestown location supports the agency’s expanded capabilities around AI-driven search platforms, including Google AI Overviews, Google AI Mode, ChatGPT, and other emerging AI-powered discovery tools. These offerings are not experimental add-ons; they are the result of technology and workflow investments the agency has been building over the past two years.

As search engines increasingly answer queries directly—and as large language models become part of the discovery journey—brands face a new challenge: visibility is no longer just about ranking blue links. Interact is positioning itself to help clients adapt to that shift, optimizing for how AI systems interpret, summarize, and surface content.

Why SEO still sits at the center

Despite the expansion into new media formats, Interact Marketing is clear about what anchors its strategy: search engine optimization.

The agency continues to treat SEO as more than a traffic channel. Instead, it uses search data to map purchase intent, inform media planning, and improve efficiency across paid and owned channels. In a fragmented media environment, search insight acts as connective tissue—linking awareness, consideration, and conversion.

That philosophy explains why SEO remains central even as the agency expands into geofence advertising, streaming TV, podcast advertising, and social media retargeting. These channels benefit from search intelligence, particularly when budgets are under pressure and marketers need clearer signals about intent and timing.

This approach reflects a broader MarTech trend: SEO is increasingly being used as a planning layer, not just a performance metric.

Expanding reach across the Great Lakes region

The Jamestown office also strengthens Interact Marketing’s regional footprint across Western and Central New York and nearby markets, including Buffalo, Erie, Pittsburgh, and Cleveland. While the agency has served national clients since its founding, the expansion reflects ongoing demand for local accessibility.

For many mid-market and enterprise brands, proximity still matters—especially for strategy sessions, quarterly planning, and complex integrations that benefit from in-person collaboration. Interact’s move suggests that even as marketing becomes more digital, relationships remain stubbornly human.

“This expansion opens up significant new market opportunities for us across Western New York and the Great Lakes region,” said CEO Joe Beccalori. His comments point to a reality many agencies are rediscovering: hybrid models—national scale combined with local presence—can be a competitive advantage rather than a contradiction.

An agency model built for what comes next

Founded in 2007, Interact Marketing has spent nearly two decades navigating algorithm updates, platform shifts, and now, the rapid rise of AI in search and advertising. Its reputation has been built on technical SEO, data-driven strategy, and performance-focused media planning—disciplines that are being tested, but not replaced, by AI.

The Jamestown expansion signals confidence that the next phase of growth won’t come from chasing every new channel, but from integrating emerging technologies into a coherent strategy. AI-powered search, connected TV, and location-based advertising are not standalone tactics; they’re components of a broader system informed by intent, data, and long-term client relationships.

In an industry often obsessed with speed and scale, Interact Marketing’s move suggests a different kind of maturity: investing in infrastructure, deepening expertise, and expanding thoughtfully—both digitally and geographically.

Get in touch with our MarTech Experts.

As the gambling and iGaming market matures—and competition intensifies—visibility alone is no longer enough. Brands now have to balance growth with trust, regulation, and reputation across an increasingly fragmented digital landscape. That’s the opportunity 5W is aiming to capture with the expansion of its gambling PR and digital marketing services.

The independently owned U.S. PR firm announced it is broadening its offering for iGaming operators, sports betting platforms, casinos, esports brands, and gaming studios, positioning itself as a full-spectrum partner as the industry looks toward 2026. The move reflects a wider shift in the sector: gambling brands are investing less in isolated campaigns and more in integrated strategies that combine PR, performance marketing, and brand safety.

A bundled approach to growth and credibility

At the core of 5W’s expanded offering is integration. Rather than treating PR, digital marketing, and reputation management as separate disciplines, the firm is packaging them into a single, coordinated strategy designed to drive both awareness and measurable growth.

The services span traditional media relations and influencer partnerships alongside digital-first tactics such as SEO, content creation, social media campaigns, email marketing, and event promotion. Online reputation management and crisis communications are also central to the offering—a critical component for gambling brands operating under regulatory scrutiny and public trust concerns.

For iGaming and betting companies, this approach addresses a long-standing challenge: how to grow aggressively without triggering backlash from regulators, platforms, or consumers. By aligning earned media with search visibility and social credibility, 5W is betting that trust will become as important a performance metric as acquisition.

Digital PR takes center stage

One notable emphasis in the expansion is digital PR, which 5W positions as a way to strengthen credibility across both search and social platforms. As Google continues to reward authoritative, trustworthy content—and social platforms tighten policies around gambling promotion—earned coverage and high-quality backlinks have become increasingly valuable.

For gambling brands, digital PR also plays a defensive role. Strong brand narratives and consistent visibility can help offset sudden policy changes, ad restrictions, or algorithm updates that often disrupt paid acquisition channels. In that sense, PR is no longer just about headlines; it’s part of the growth stack.

This aligns with a broader MarTech trend: brands are blending PR and SEO more closely, treating media coverage as both a reputation asset and a performance lever.

Preparing for a tougher 2026

The timing of the expansion is telling. As the industry moves toward 2026, operators face tightening regulations in several markets, higher customer acquisition costs, and a crowded competitive field that includes global sportsbooks, local operators, and digital-native gaming studios.

Standing out now requires more than promotional spend. It requires consistent messaging, strong partnerships, and the ability to respond quickly when issues arise—whether that’s a compliance concern, a platform crackdown, or a public relations crisis.

“Our expanded gambling and gaming PR and digital marketing services are designed to help brands enter 2026 with momentum,” said Ronn Torossian, Founder and Chairman of 5W. His emphasis on integration reflects a growing realization across the sector: fragmented marketing efforts are no match for an ecosystem where trust, transparency, and brand perception directly impact revenue.

Why this matters for MarTech leaders

For marketers in gambling and gaming, 5W’s move underscores a larger shift in how growth is being approached. Performance marketing alone is becoming less predictable, while brand-led strategies—supported by data, content, and reputation management—are gaining renewed importance.

It also highlights how PR firms are evolving. No longer confined to press releases and media outreach, agencies like 5W are repositioning themselves as hybrid partners that sit at the intersection of communications, MarTech, and growth strategy.

As gambling, esports, and iGaming brands look ahead, the firms that can combine reach with responsibility—and visibility with trust—are likely to be the ones that endure.

Get in touch with our MarTech Experts.

As digital platforms flood advertisers with more video, more creators, and more ambiguity, brand suitability has quietly become one of marketing’s hardest technical problems. Zefr thinks it has found a better way to solve it—and the U.S. Patent and Trademark Office agrees.

The brand suitability and media intelligence company has been granted a new U.S. patent for its AI-driven approach to content annotation and model distillation, a system designed to dramatically improve how digital content is analyzed, classified, and ultimately deemed safe (or risky) for advertisers.

This is not just another incremental AI filing. The patent formalizes how Zefr combines large language models (LLMs), AI agents, and targeted human review to tackle one of the industry’s most persistent challenges: understanding context at internet scale without sacrificing nuance.

Why content annotation still breaks at scale

Most content classification systems today fall into one of two camps. On one side are heavily manual operations, where large reviewer teams label content with human judgment—but at a cost that doesn’t scale with YouTube, TikTok, or emerging video platforms. On the other side are fully automated systems that scale beautifully, right up until they misclassify satire as harm, fiction as reality, or cultural references as violations.

Zefr’s newly patented approach aims to close that gap.

Instead of using humans to annotate everything—or machines to decide everything—the company deploys AI agents to scan massive video datasets and actively look for uncertainty. Ambiguous cases, underspecified scenarios, or content that sits at the edge of policy definitions are flagged and escalated for human review. Clear-cut cases are handled automatically.

The result is a system that focuses human expertise where it matters most, rather than wasting it on obvious calls.

Teaching machines when they don’t know enough

At the core of the patent is the idea that AI shouldn’t just classify content—it should understand when its own confidence breaks down.

Zefr’s system uses LLMs to query and explore large volumes of video content, surfacing examples that challenge existing policy boundaries. These edge cases are then reviewed by human experts, whose decisions don’t just resolve individual annotations but are fed back into the models through a process known as model distillation.

In practical terms, this means the AI gets smarter over time—not by brute-force labeling, but by learning from the hardest, most instructive examples.

It’s a sharp contrast to traditional annotation pipelines that rely on volume rather than insight, and it reflects a broader shift across enterprise AI toward more deliberate, human-guided learning loops.

Context over keywords—and why advertisers care

One of the most compelling aspects of Zefr’s approach is its ability to distinguish between content that looks similar on the surface but means something very different in context.

A fictional crime scene in a TV show trailer is not the same as footage of real-world criminal activity. A news report discussing extremism is not extremist propaganda. For advertisers, those distinctions determine whether campaigns appear next to content that aligns with brand values—or sparks backlash.

By combining automated discovery with human policy guidance, Zefr’s system can make these finer distinctions consistently, at scale. That translates into more confident media buying decisions, fewer false positives, and less blunt exclusion of entire content categories.

In an era where advertisers are demanding both reach and responsibility, that balance is increasingly non-negotiable.

A signal of where brand safety tech is heading

Zefr’s patent arrives at a moment when brand safety and suitability are being reshaped by three converging forces: the explosion of short-form video, the growing use of generative AI, and increased scrutiny from regulators and brand leaders alike.

Competitors across the ad verification and media intelligence landscape are racing to incorporate AI, but many still rely on opaque models or legacy taxonomies that struggle with modern content formats. Zefr’s emphasis on transparency, explainability, and peer-reviewed research positions it differently—closer to an AI lab with commercial instincts than a traditional verification vendor.

“This patent represents another major step forward in our mission to bring transparency and trust to the digital ecosystem,” said Jon Moora, Chief AI Officer at Zefr, pointing to the company’s focus on accountability as much as automation.

That framing matters. As AI increasingly governs where ads appear, advertisers are asking tougher questions about how decisions are made—and who is responsible when systems get it wrong.

Building an IP moat around responsible AI

The newly granted patent is Zefr’s eighth overall and its second specifically focused on AI, adding to a growing intellectual property portfolio that spans content understanding, brand suitability, and machine learning systems.

More importantly, it signals a strategic commitment to defensible, responsible AI development at a time when many ad tech players are bolting generative models onto existing workflows without rethinking the fundamentals.

Zefr’s approach suggests that the future of brand suitability won’t be fully automated or fully manual, but intentionally hybrid—machines handling scale, humans providing judgment, and systems designed to know the difference.

For marketers navigating an increasingly complex media landscape, that may be less flashy than pure automation, but it’s far more useful.

Get in touch with our MarTech Experts.

Malaysia is stepping onto the global AI infrastructure map—and GIBO Holdings wants to help lay the groundwork. The Nasdaq-listed company announced a strategic collaboration with E Total Technology Sdn Bhd to plan, site, and deploy next-generation AI compute centers across Malaysia, designed to handle the surging demand for large-scale AI training and inference.

The partnership signals more than a routine data center build-out. By anchoring the project around NVIDIA’s latest high-performance AI chips and GPU architectures, GIBO is targeting the kind of dense, high-throughput compute environments typically reserved for hyperscalers and top-tier research institutions.

Why This Is a Big Deal

AI ambition increasingly hinges on compute access. As enterprises push beyond pilots into production—training larger models, running inference at scale, and supporting real-time applications—regional shortages of advanced compute have become a bottleneck. Southeast Asia, in particular, has relied heavily on offshore infrastructure.

This project aims to change that. By building AI-first compute centers locally, GIBO and E Total Technology plan to provide enterprises, research institutions, and digital economy players with scalable, high-performance resources closer to home—reducing latency, improving data sovereignty, and increasing regional competitiveness.

E Total Technology Takes the Lead on the Ground

Under the collaboration, E Total Technology Sdn Bhd will act as the primary local execution partner, overseeing everything from site sourcing to regulatory approvals. Its remit includes:

• Identifying and evaluating sites suitable for AI compute and data center facilities

• Conducting technical, commercial, and operational feasibility studies

• Managing local coordination, compliance, and approvals

• Supporting infrastructure planning and deployment

That local expertise matters. AI data centers aren’t just power-hungry—they’re regulation-heavy. Land use, energy availability, cooling, and compliance all influence whether projects move fast or stall. E Total’s experience navigating Malaysia’s infrastructure and regulatory landscape could significantly shorten time-to-deployment.

NVIDIA-Class Hardware, Built for Scale

While specific SKUs weren’t disclosed, the compute centers are expected to deploy NVIDIA’s most advanced AI chips and GPU platforms, optimized for high-density workloads. That positions the facilities to support:

• Large-scale foundation model training

• Advanced inference pipelines

• Multi-industry AI applications spanning finance, healthcare, manufacturing, and logistics

The emphasis isn’t just raw performance. The architecture is designed for efficiency—delivering improved energy utilization and internationally competitive compute density, while allowing room to scale as AI workloads continue to grow.

A Play for Regional AI Leadership

As governments and enterprises race to secure AI capacity, compute infrastructure has become a strategic asset. Countries that can host reliable, high-performance AI platforms stand to attract investment, talent, and innovation ecosystems.

By introducing globally benchmarked AI compute infrastructure, the GIBO–E Total collaboration aims to strengthen Malaysia’s position as a regional AI compute hub in Asia-Pacific—complementing national digital economy initiatives and making the country more attractive to AI-driven businesses.

The partners say they will continue evaluating opportunities to expand capacity as demand grows. Given the trajectory of AI adoption, that expansion may come sooner rather than later.

Get in touch with our MarTech Experts.

Legacy IVR systems have long been a bottleneck for healthcare organizations—rigid call flows, poor caller experiences, and change cycles that stretch into months. AudioCodes says it just proved that doesn’t have to be the case.

The communications software vendor announced that its Voca Conversational Interaction Center (Voca CIC), working with Go2Uno and global BPO Atento, completed a large-scale AI-powered Voice Agent and Conversational IVR modernization for a major healthcare organization in a matter of weeks—far faster than typical enterprise IVR projects of similar complexity.

For an industry where call volumes are massive, systems are fragmented, and downtime is not an option, the deployment serves as a real-world example of AI moving from experimentation to operational backbone.

From Legacy IVR to AI Voice Agents—Fast

According to AudioCodes, the project replaced multiple legacy IVR systems with a modern, AI-driven conversational IVR capable of supporting more than 500 concurrent voice agents. That scale alone would normally put the project into a multi-quarter timeline.

Instead, the joint team completed the rollout in roughly 30 days, a timeframe Atento says others estimated at three to six months.

The new setup supports complex voice networking, integrates multiple carriers, and introduces intelligent, AI-powered call routing—critical for healthcare environments where calls range from appointment scheduling to sensitive patient inquiries.

Why This Matters in Healthcare CX

Healthcare contact centers face a uniquely difficult mix of challenges:

• Extremely high call volumes

• Seasonal and event-driven spikes

• Strict reliability and compliance requirements

• A growing expectation for natural, conversational self-service

Traditional IVRs struggle under that weight. They’re expensive to modify, brittle when scaled, and often frustrate callers with rigid menu trees.

By contrast, AudioCodes’ Voca CIC platform uses conversational AI to interpret intent, route calls intelligently, and contain more interactions without human intervention—all while maintaining the resilience required for mission-critical environments.

Inside the Deployment: What Changed

The modernization effort wasn’t a simple overlay. AudioCodes, Go2Uno, and Atento reworked the organization’s voice infrastructure end to end:

• Complex call flows were redesigned to support conversational interactions rather than menu-driven logic

• Multiple carrier systems were integrated, reducing dependency on siloed networks

• AI-powered routing was implemented to improve containment and reduce handling times

• AudioCodes SBC infrastructure was deployed to ensure continuity and reliability across a highly complex environment

• Advanced reporting and analytics were added to give Atento and the healthcare organization real-time visibility into voice agent performance and customer behavior

The result is a platform that doesn’t just automate calls, but actively improves how patients and members move through the system.

Azure Conversational AI at Enterprise Scale

One notable detail: the deployment included a seamless integration of Azure Conversational AI, something Atento says is often underestimated in terms of effort.

“Go2Uno and AudioCodes accomplished in just 30 days what others projected would take three to six months,” said Gustavo Samaniego, Senior IT Service and Deliveries Manager at Atento. He highlighted the Azure integration as a particular challenge that was executed “flawlessly.”

That matters because many AI IVR projects stall at integration—especially when cloud AI services meet legacy telephony environments. This deployment suggests those barriers are becoming more manageable with the right architecture and partners.

Not a Pilot—Production AI at Scale

AudioCodes is keen to position this project as something more than a proof of concept.

“This is real AI in action,” said Gidi Adlersberg, Head of the Voca CIC Business Line at AudioCodes. “Not a pilot, not a demo—transforming complex operations quickly and improving customer experience where it truly matters.”

That distinction is important. While conversational AI has been widely marketed, many enterprises remain stuck in pilot mode, hesitant to deploy at scale due to reliability concerns. A 500-agent healthcare rollout challenges the notion that AI voice systems are still experimental.

The Business Impact: Efficiency and Resilience

Beyond speed, the deployment delivered tangible operational benefits:

• Improved call containment, reducing the load on live agents

• Shorter average handling times, improving efficiency

• Greater resiliency, with SBC-backed infrastructure ensuring continuity

• A scalable foundation for future AI-driven enhancements

For Atento, the project also created a reusable, future-ready platform that can support new clients and evolving use cases without rebuilding from scratch.

A Broader Trend: IVR Modernization Gets Practical

The announcement reflects a broader shift in enterprise CX: IVR modernization is no longer about incremental upgrades. It’s about replacing rigid systems with AI-native voice platforms that can adapt quickly.

What’s notable here is the emphasis on speed and predictability—two areas where AI projects often fall short. By delivering a complex healthcare deployment in weeks, AudioCodes and its partners are making a case that conversational IVR can now meet enterprise timelines and expectations.

Availability and Access

AudioCodes says Voca CIC is available as a 30-day free trial through its website, the Microsoft Marketplace, and the Microsoft Teams Store. New customers can spin up a conversational contact center with AI and omnichannel capabilities in minutes, including a free phone number for evaluation.

That low-friction entry point suggests AudioCodes is targeting both large enterprises and organizations earlier in their IVR modernization journey.

Bottom Line

Healthcare IVR projects are notorious for running long, going over budget, and underdelivering on experience. This deployment shows that with mature conversational AI, strong infrastructure, and the right partners, those assumptions may finally be outdated.

For AudioCodes, the win reinforces its position in AI-powered voice and contact center modernization. For the industry, it’s another signal that AI voice agents are moving decisively from promise to production.

Get in touch with our MarTech Experts.

New Jersey is making a deliberate play to become a serious AI gravity well. The New Jersey Artificial Intelligence Hub announced it will launch a dedicated AI Accelerator in early 2026, powered by global innovation heavyweight Plug and Play. The move is less about hype and more about infrastructure—connecting startups, researchers, and enterprises into a single pipeline designed to move AI ideas from lab bench to market faster.

At its core, the accelerator aims to remove friction. New Jersey–based AI startups and university-affiliated founders will get direct access to mentors, investors, and industry partners, while top-tier AI startups from outside the state will be actively recruited to build and scale locally. The program will run out of the NJ AI Hub’s 6,500-square-foot facility in West Windsor, anchoring AI development in a region already dense with research institutions and enterprise buyers.

Why This Matters Now

AI accelerators aren’t new—but timing and execution matter. As enterprise AI adoption accelerates, startups face a familiar bottleneck: access to compute, customers, and credible validation. New Jersey’s play is to bundle all three.

The AI Accelerator builds on a series of deliberate steps by the NJ AI Hub, including its recent designation as one of only two global sites to host Microsoft Discovery, an agentic AI and cloud platform aimed at accelerating scientific research. That puts New Jersey in rare company—and signals an intent to compete not just with regional peers, but with global AI clusters.

“This partnership with Plug and Play will unleash new technologies, foster powerful cross-sector collaborations, and speed AI innovations from concept to impact,” said Liat Krawczyk, executive director of the NJ AI Hub.

Plug and Play Brings the Global Network

Plug and Play’s role is the accelerant. The Silicon Valley–born innovation platform runs more than 60 innovation hubs across 25+ countries and connects over 100,000 startups with 550+ corporate partners. Its model is proven: structured cohorts, hands-on mentorship, enterprise pilots, and investor access—all tuned to help startups scale, not just pitch.

Michael Olmstead, Plug and Play’s CRO, is leading the expansion with the NJ AI Hub, positioning the program as a gateway between New Jersey’s research depth and Plug and Play’s global commercialization engine.

For founders, the offering goes beyond demo days. The accelerator will provide business model refinement, technical workshops, funding access, and curated introductions to enterprise partners—often the missing link for AI startups stuck between proof-of-concept and revenue.

Built Around New Jersey’s Industry Strengths

Unlike generic accelerators, this one is explicitly sector-driven. Cohorts will tap into New Jersey’s established strengths in healthcare, pharmaceuticals, advanced manufacturing, financial services, energy, telecommunications, logistics, and smart infrastructure.

That focus matters. AI startups increasingly need real-world data, regulated environments, and industry partners willing to pilot solutions. New Jersey’s proximity to Fortune 500 companies, major hospital systems, and global manufacturers gives the accelerator a practical edge over more abstract innovation hubs.

Princeton University, a founding partner of the NJ AI Hub, sees the accelerator as a commercialization bridge for academic innovation. “This partnership will enable faculty and students to turn their novel ideas into successful products and companies,” said Princeton Provost Jennifer Rexford.

A Growing Public–Private AI Stack

The accelerator doesn’t exist in isolation. The NJ AI Hub itself was founded by Princeton University, the State of New Jersey, Microsoft, and CoreWeave—an unusually strong coalition spanning academia, government, hyperscale cloud, and AI infrastructure.

CoreWeave, founded in New Jersey, brings deep GPU infrastructure expertise at a moment when compute access can make or break an AI startup. Microsoft’s involvement, via TechSpark and the Discovery platform, adds enterprise credibility and cloud-scale tooling. The New Jersey Economic Development Authority provides policy alignment and economic incentives to keep innovation—and jobs—local.

“Innovation flourishes when talented people are empowered with mentors and programs that help unlock their full potential,” said Mike Egan, general manager of Microsoft TechSpark.

The Bigger Picture

States are increasingly competing not just on tax incentives, but on AI ecosystems. Texas is courting data centers. New York is leaning into fintech AI. California still dominates research and venture capital—but it’s expensive and crowded. New Jersey’s bet is that a tightly integrated accelerator, anchored by real industry demand and global networks, can punch above its weight.

If executed well, the NJ AI Hub Accelerator could become a model for regional AI development—one where startups don’t just build impressive models, but deploy them into regulated, revenue-generating environments.

Early next year will show whether New Jersey can turn that ambition into sustained momentum. The pieces are in place. Now comes the hard part: execution.

Get in touch with our MarTech Experts.