Automotive dealerships have no shortage of data. What they lack is coherence. Today, Data Axle and Tealium announced a partnership designed to fix that problem by unifying fragmented dealership systems and turning scattered records into actionable intelligence. For an industry long plagued by inconsistent communications and conflicting customer profiles, the timing is ideal.

The collaboration blends Data Axle’s verified consumer and vehicle datasets with Tealium’s real-time customer data platform. Together, the companies aim to help dealers and OEMs merge disjointed customer information, eliminate duplicate records, and guide marketing, sales, and service decisions with far greater accuracy.

The need is clear. A new survey commissioned by Data Axle shows consumers are tired of mixed signals from dealerships. Nearly half of respondents reported receiving duplicate or contradictory messages from the same dealer. Even worse, 68 percent said dealership outreach often feels inconsistent or irrelevant. Yet consumers are willing to share core data—emails, phone numbers, and vehicle history—so long as they receive real value in exchange, such as accurate trade-in estimates or relevant service offers.

According to Chris McTague, managing director of automotive at Data Axle, the issue isn’t a shortage of information. “Dealerships are sitting on a wealth of data that rarely works in concert. Cars have become interchangeable, but data isn’t. What separates one dealer from the next is how intelligently they use their information,” he said. For McTague, the partnership offers a foundation that turns verified insights into better decision-making and more trustworthy customer experiences.

At the core of the integration is Data Axle’s deterministic identity graph, which feeds directly into Tealium’s CDP to clean and enrich customer profiles in real time. Dealers gain a unified view of each customer, allowing them to refine audience models, tailor outreach, and strengthen service engagement without relying on guesswork. It’s a shift from fragmented databases to measurable impact.

Stephen Smith, RVP of Partnerships at Tealium, emphasized that automotive data challenges run deeper than outdated CRMs. “The auto industry faces unique challenges and often navigates fragmented data systems, making it difficult for dealers to fully understand and engage their customers,” he said. By combining Tealium’s orchestration engine with Data Axle’s verified datasets, the partnership brings “clarity, control, and connected data” into the dealer’s workflow.

With pressure mounting for dealerships to modernize how they identify and reach buyers, the partnership arrives at a strategic moment. As digital-first shoppers expect relevant and timely interactions, the dealer that masters its data—not just its inventory—wins. Data Axle and Tealium’s solution offers a path toward more credible, efficient, and intelligent customer engagement.

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Thanks, the customer-first native ad network known for reimagining post-purchase experiences, is expanding into live events through a new partnership with Oztix, Australia’s largest independent ticketing company. The collaboration brings Thanks’ AI-powered media monetisation platform directly into Oztix’s digital ecosystem, turning the high-intent moment after a ticket purchase into a smarter, more curated discovery experience for fans.

Instead of generic ads or irrelevant offers, buyers will now see contextually aligned recommendations—travel options, local dining, merch, and experience upgrades—delivered at the precise moment their excitement peaks. It’s a strategy already trusted by brands like eBay, Linktree, and Booking.com, but this marks its first major expansion into the live-events sector.

The partnership extends beyond commerce. As part of the rollout, Thanks and Oztix will jointly support Make-A-Wish Australia, dedicating a portion of proceeds each quarter to help grant wishes for children with critical illnesses. The initiative brings an added layer of purpose to every ticket sale, allowing fans to contribute to a national cause simply by completing their purchase.

“Oztix is a cornerstone of Australia’s live-event scene, connecting millions of fans to the experiences they love,” said Steve Tesoriero, Founder and Co-CEO of Thanks. He noted that Oztix’s audience-first ethos made the partnership a natural fit. The goal, he added, is to “add more meaning to every ticket purchase” while expanding the same AI-driven platform used by global brands into a new industry.

Oztix has spent more than two decades building partnerships across promoters, venues, artists, and community organisations. Its reputation for strengthening the live-events ecosystem makes the collaboration especially aligned with its mission. The integration with Thanks introduces a new way for fans to both receive value and give back—connecting entertainment with meaningful social impact.

Stuart Field, Co-Founder and Managing Director of Oztix, emphasized the human side of the initiative. “Together, we’re showing how retail media and community can work side by side—creating rewarding experiences for fans while giving back,” he said. For many on the Oztix team, the partnership with Make-A-Wish is personal. “It’s about spreading the same joy and connection we see at live events to children and families who need it most.”

For Thanks, the collaboration marks another milestone in its Australian expansion and reinforces its mission to bring more relevance, humanity, and value to the moments brands often overlook. As retail media continues its shift toward high-intent, first-party environments, the post-purchase audience is becoming one of the most strategically important surfaces in digital marketing. By pairing real-time contextual intelligence with charitable impact, Thanks and Oztix are betting that the future of advertising isn’t just more efficient—it’s more meaningful.

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Live events have never struggled to attract people—they’ve struggled to measure what actually matters. ROME Insights, a new analytics and AI startup, wants to fix that. Today, the company unveiled ROME (Return on Memorable Experiences), a measurement framework built to capture human attention, emotional engagement, and the lasting impact of in-person experiences. For an industry built on connection, it’s a shift that feels long overdue.

Co-founded by event-technology veteran Justin Zebell and marketing strategist and AI researcher Bob Hutchins, ROME Insights was created in response to a long-standing issue: traditional event metrics don’t tell the full story. Attendance numbers, badge scans, and booth visits offer surface-level signals. What they miss is the depth—the moments that spark memory, influence behavior, and justify sponsorship spend.

“We built ROME because the events industry has been measuring the wrong things,” Zebell said. His argument is simple: event organizers, sponsors, and internal stakeholders need proof of value grounded in real human experience, not just headcounts. ROME provides that proof.

The ROME framework blends multiple data sources—behavioral attention tracking, qualitative feedback, and quantitative analytics—to produce a composite engagement score. It captures both immediate reactions and longer-term recall, offering what the company calls a clearer, more defensible metric for understanding event impact.

Hutchins sees ROME as a tool that protects the value of live gatherings in an increasingly digital world. “Live events are one of the few places where people still gather in person to learn, connect, and be moved by ideas,” he said. “ROME helps organizers protect and prove the value of that experience in a world that demands measurable outcomes.”

ROME Insights will work directly with conference organizers, trade show producers, and corporate event teams to integrate the framework across different formats. The company also provides consulting services to help teams interpret findings and use those insights to shape future events—whether that means adjusting programming, reallocating budget, or refining sponsor packages.

Early adopters are already seeing results. Event organizers using ROME have reported higher sponsor renewal rates, smoother budget approvals, and a sharper understanding of which moments drive the most value. For a sector where gut instinct has often outrun measurement, ROME’s data-backed approach could become a competitive advantage.

In an era defined by metrics, the company is betting that the most important event KPIs aren’t clicks or counts—they’re the moments people remember.

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AI has already transformed market research, but according to the new 2026 Market Research Trends report from Qualtrics, the divide between teams using basic AI and those embracing purpose-built capabilities is widening rapidly. The stakes aren’t small: research groups relying only on generic tools are four times more likely to lose influence inside their organizations.

Meanwhile, 72% of teams using synthetic responses, agentic AI, and AI-native workflows say their organizations rely on research far more than they did last year—momentum that’s translating directly into higher budgets. Traditional teams, however, are almost twice as likely to face stagnant or shrinking demand for their work.

“In today’s fast-moving economies, rapid access to consumer insights is a huge advantage,” said Ali Henriques, Executive Director of Edge at Qualtrics. “The teams embracing AI are reimagining what research looks like, asking bigger questions, and moving earlier in the innovation cycle.”

Purpose-Built AI Becomes the New Standard

AI adoption has crossed a maturity threshold. More than half of researchers now use AI regularly, and nearly nine in ten have experimented with it, but the report shows a clear shift away from generic chatbots toward AI embedded directly in research platforms. Purpose-built capabilities grew from 62% to 66% adoption, while usage of general-purpose tools dropped by nearly ten points.

The teams gaining the most traction are those leaning into specialist functions. Conversational analytics and visual content analysis—both at 49% adoption—give researchers deeper qualitative insight at a fraction of the time. What once took weeks can now be processed in hours.

Synthetic data is driving an even more dramatic evolution. Researchers using synthetic datasets are:

• 11% more likely to engage in early-stage innovation

• 7% more likely to run go-to-market studies

• 5% more likely to perform final product testing

Among those who’ve adopted it, 45% now consider synthetic data their most reliable source, surpassing traditional online panels—a remarkable shift for an industry built on human surveys.

Brands like Gabb are already using Qualtrics’ purpose-built synthetic model to reduce fielding costs, accelerate discovery, and test messaging against emerging trends. As Research Director Garred Sheppard described it, “Synthetic data became our cultural radar—cutting timelines from a week to hours while letting us validate high-stakes decisions with human panels.”

AI Agents Push Research Toward End-to-End Automation

Another major shift is the rise of agentic AI. While only 15% of researchers use AI agents today, nearly 80% expect that these tools will handle more than half of research projects end-to-end within the next three years.

Efficiency gains are already visible. Among teams using agentic AI, 84% report significantly higher efficiency, compared with 68% of those who haven’t tried it.

Henriques said the biggest unlock isn’t workload reduction—it’s democratization. Product teams can test ideas without submitting requests. Marketing can evaluate sentiment without waiting on insights teams. Executives can explore new markets directly. “The barrier to insights is no longer specialist knowledge,” she said. “It’s simply asking the right question.”

Leadership vs. Frontline: A Growing Execution Gap

Despite major investments in AI, many organizations aren’t seeing the full return. The report highlights a sharp misalignment between research leaders and individual contributors:

• 39% of leaders say AI has revolutionized their processes vs. 19% of frontline researchers

• Only 5% of leaders fear layoffs due to AI vs. 15% of individual contributors

• 68% of leaders consider themselves synthetic data experts vs. 41% of contributors

• 79% of leaders trust synthetic data quality vs. 61% of contributors

This mismatch results in underused tools, wasted budget, and slower execution—while competitors with tighter alignment surge ahead.

“When frontline teams don’t buy in, expensive AI tools go unused,” Henriques warned. “Organizations need shared definitions of success, real hands-on training, and clarity across levels about the practical applications of new AI capabilities.”

A Global Snapshot of a Rapidly Changing Industry

The findings come from a global Qualtrics study conducted in Q3 2025 with more than 3,000 research professionals across 14 countries. The data reveals a sector in transition—from manual workflows and traditional surveys to hybrid human-synthetic models, autonomous research agents, and a new definition of what strategic research teams look like.

The message from Qualtrics is clear: the teams that invest in purpose-built AI now will set the pace for the next decade of research. The ones that don’t risk being left behind.

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ON24 just added another weapon to its engagement stack. The company secured U.S. Patent No. 12,445,698 B2 for its AI engine that finds and extracts “Key Moments” from long-form video content. The system works across webinars, virtual events, digital conferences, and recorded demos.

The feature solves a long-standing marketing headache. Teams often run events that generate hours of footage but struggle to reuse that content. ON24’s patented technology tackles that gap by automating the discovery of highlight moments. The platform then packages them into short clips that can slip into nurture streams, partner enablement programs, landing pages, and social feeds.

Turning Engagement Behavior Into Short-Form Assets

The AI engine does more than trim videos. It examines engagement signals from the event itself. That includes viewing behavior, interactions, and content consumption patterns. The model then ranks moments that matter and extracts them into short, high-value videos.

This helps B2B marketers extend the shelf life of every event. Instead of a single live session, teams can quickly launch follow-ups, repurposed assets, and targeted campaigns—without manual editing or guesswork.

According to ON24 CEO Sharat Sharan, the core of the company’s strategy is AI-driven engagement. He says the new patent strengthens ON24’s position as an AI-enabled platform that understands audience behavior in real time. It also helps the company scale personalized content across channels.

Built by Engineers and Data Scientists for Demand Teams

The “Key Moments” engine is the product of ON24 developers and machine learning experts. The goal was simple: pull out the most engaging sections from any event. That includes product walkthroughs, customer stories, expert analysis, or Q&A sessions.

ON24 says the tech benefits a wide range of teams—marketing, customer success, sales, partner management, and training groups. Each can reuse “Key Moments” to reinforce messaging or support education workflows.

CTO Jayesh Sahasi says the patent advances ON24’s vision of connected, AI-led engagement. The system ties together behavioral data, automation, and content analysis. It lets teams transform a single event into continuous, data-backed campaigns.

Why This Matters for the B2B Engagement Market

Short-form content is the new currency in B2B marketing. Marketers want rapid content delivery, personalized outreach, and scalable video workflows. ON24’s patent supports that shift by automating one of the slowest steps in the process—manual video review.

Competitors offer clip-generation tools, but ON24’s differentiator is its use of engagement data tied to event behavior. It’s not guessing what content works; it’s identifying what audiences respond to.

As AI becomes central to B2B engagement platforms, this patent strengthens ON24’s position in a crowded market. It also signals the company’s larger push toward AI-powered orchestration across the full customer journey.

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Alembic Technologies just pulled off one of the biggest jumps in enterprise AI valuation this year. The Causal AI startup secured $145 million in Series B and growth funding, marking a 15.7x valuation increase since its last round. The investment was led by Prysm Capital and Accenture, signaling strong institutional confidence in a company aiming to redefine how enterprises understand cause and effect.

The move comes at a moment when every B2B vendor claims AI leadership. Yet Alembic’s pitch is sharper: while competitors rely on generic models, it focuses on proprietary data, causal inference, and a compute layer powerful enough to keep up with Fortune 500 demand. That combination has drawn interest from companies such as Delta Air Lines, Mars, NVIDIA, and others seeking clarity in a messy measurement landscape.

A Data Flywheel With Real Competitive Weight

At the core of Alembic’s strategy is a simple idea with major implications: better insights create better decisions, which then generate better data. This closed loop forms what CEO Tomás Puig calls a compounding data flywheel, and it’s difficult for rivals to replicate.

Many enterprises are drowning in dashboards and correlation-based reporting. However, Alembic claims its Causal AI identifies what actually drives outcomes. That advantage positions the company as a key intelligence layer rather than another analytics feature.

Accenture’s involvement reinforces that view. The consulting firm will integrate Alembic’s Causal engine into its enterprise transformation work, giving clients tools that move beyond pattern recognition toward evidence-based decisions. As Accenture CEO Julie Sweet put it, large companies need “verifiable, cause-and-effect insights” to act quickly and safely.

Big Customers, Bigger Impact

Enterprise customers are already using the platform to quantify decisions that were previously guesswork. Delta Air Lines linked Olympic sponsorship activations to ticket sales within days. Mars measured the exact dollar impact of viral brand moments. North Sails optimized digital spend for record returns. One Fortune 500 company grew its sales pipeline by 37% with precise attribution.

These examples illustrate why Alembic is drawing attention. Most organizations are not short on data; they are short on certainty. Causal AI promises to close that gap.

A Supercomputer Strategy Built for the Long Game

Alembic is pairing its funding with serious infrastructure investment. The company will deploy a new NVIDIA NVL72 superPOD cluster at Equinix’s SV11 data center, running NVIDIA AI Enterprise across its stack. This system is engineered for spiking neural networks, high-speed graph processing, and continuous-learning workloads.

This will be the company’s second dedicated supercomputing cluster, creating bi-coastal redundancy and ensuring the compute headroom needed for real-time causal analysis. Alembic positions this as a strategic moat. Instead of relying on shared cloud resources, it will operate a private AI fleet optimized for its unique workloads.

The deeper tie to NVIDIA also reflects a broader trend: enterprise AI platforms increasingly require custom compute infrastructure to maintain speed, security, and differentiation.

Defining the Next Layer of Enterprise Intelligence

Investors see Alembic as more than a vertical analytics solution. Many describe it as an emerging foundational model for enterprise decision-making. Instead of generating text or images, it generates causal truth, and brands are paying attention.

Prysm Capital’s team sees the company as a “mission-critical intelligence layer,” while WndrCo partners highlight the platform’s ability to deliver what marketers have sought for decades: clear, quantifiable attribution that informs where every dollar should go.

The company is also building a reputation for marrying deep research with commercial relevance. Its approach combines spiking neural networks, advanced graph modeling, real-time simulation, and high-performance compute into a system that updates continuously as new data enters the ecosystem.

The Market Impact

With this round, Alembic is positioned to influence several fast-shifting categories:

• Marketing measurement, where correlation-based models are losing credibility

• AI-driven budgeting, as brands face pressure for provable ROI

• Enterprise intelligence platforms, which increasingly compete on proprietary data

• Custom AI infrastructure, especially among companies needing guaranteed compute

Causal AI sits at the intersection of all four. That gives Alembic a strategic lane with few direct rivals and strong tailwinds as enterprises rebuild their data strategies around reliability rather than volume.

The Bottom Line

Alembic now has the capital, compute, and customer base to accelerate its push into enterprise AI. The company’s focus on Causal intelligence sets it apart in a market dominated by generative hype. With a new superPOD, deeper ties to Accenture, and accelerating Fortune 500 adoption, it is shaping a category that could define the next era of decision intelligence.

If Alembic delivers on its promise, the industry may soon shift from asking what happened to understanding why—and acting with far more confidence.

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Marketing measurement has never been easy, but it’s about to get a serious upgrade. Accenture has invested in Alembic, an AI-powered causal intelligence platform built to show which marketing efforts actually generate revenue. The investment comes through Accenture Ventures and includes a strategic partnership designed to push Causal AI deeper into the enterprise stack.

The timing is ideal. According to recent Gartner research, two-thirds of marketing leaders struggle to prove campaign impact. Traditional attribution tools often rely on siloed datasets, lagging models, or incomplete signals. Alembic says it can fix that by grounding measurement in cause-and-effect logic instead of correlation.

A Causal Engine Built for Real-Time Answers

Alembic’s platform ingests data from broadcast channels, social media, site traffic, and direct-to-consumer communications. It then merges those signals with sales data and runs causal analysis to determine what actions drive outcomes. The system assigns an impact score to each channel or marketing event, giving executives a clear view of what moved revenue and why.

The appeal is clear. Marketers want real attribution. Finance teams want accountability. Executives want decisions backed by evidence rather than dashboards that contradict each other.

Accenture CEO Julie Sweet framed the partnership as essential for enterprise transformation. Companies are no longer deploying AI in isolation. They need trusted intelligence at the core of their operations, and Causal AI offers a more reliable foundation than traditional measurement.

Breaking the Limits of Traditional Attribution

Most measurement platforms struggle with data fragmentation. Many cannot handle channels like brand campaigns, event sponsorships, or quick-moving organic social content. Alembic claims its software can analyze those unstructured signals and map the downstream impact even as customer data expands rapidly.

The platform can also model external factors—such as policy changes or unexpected market events—to show how they influence performance. This helps brands adjust spend in real time and stay ahead of shifting conditions.

Alembic CEO Tomás Puig attributes this capability to the company’s NVIDIA SuperPOD compute backbone. The infrastructure gives the platform enough power to run continuous causal calculations and surface insights with minimal delay. “Most companies aren’t short on data,” Puig said. “They’re short on answers.”

A New Measurement Paradigm for the Enterprise

Accenture Song sees the partnership as a turning point for performance measurement. According to Arun Kumar, global customer AI and data lead, Alembic complements methods such as marketing mix modeling but adds the ability to analyze far more variables. Instead of viewing measurement as a post-campaign autopsy, Causal AI turns analytics into a live operational tool.

The partnership also joins a growing ecosystem of AI tools within Accenture Song. Aaru supports strategic planning; Writer enhances content creation; AI Refinery accelerates campaign execution. Alembic slots into the final stage—proving what worked, how it worked, and how to scale it.

Accenture is already piloting Alembic’s technology internally to assess its own marketing initiatives. This early integration signals confidence in the platform and sets the stage for wider client adoption.

Part of a Larger Funding Wave

This investment follows Alembic’s recent Series B round, which was led by Prysm Capital and Accenture. Other participants included Silver Lake Waterman, Liquid 2 Ventures, NextEquity, Friends & Family Capital, and WndrCo. The funding will help Alembic expand its Causal AI engine, enhance its infrastructure footprint, and support a growing roster of enterprise customers.

With demand rising for reliable, real-time attribution, the partnership positions Alembic as a key player in the next phase of AI-driven marketing intelligence. As enterprises look for clarity in a noisy market, Causal AI may prove to be the missing link between massive datasets and actionable decisions.

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Deepgram has added another milestone to its rapid rise in Voice AI. The company’s enterprise-grade text-to-speech model, Aura-2, has been named a 2025 Customer Experience Innovation Award winner by TMC’s CUSTOMER magazine.

The award highlights companies pushing customer experience forward across every touchpoint—including social channels, automated workflows, and AI-powered agents. And this year, Aura-2 stood out for one reason: it sounds great, but more importantly, it works great.

Built for Work, Not Entertainment

Most TTS models chase entertainment-quality voices. Aura-2 targets the enterprise instead. It is engineered to sound human in the places that matter most—contact centers, regulated workflows, and real-time digital agents.

It provides:

• Domain-specific pronunciation for complex vocabulary
  (drug names, legal terms, identifiers, structured data)

• Sub-200ms TTFB latency, crucial for live voice agents

• Human-like clarity and accuracy

• Pricing that scales for production workloads

The model is powered by Deepgram Enterprise Runtime (DER), which supports deployments across cloud, VPC, and on-prem environments. DER also enables model hot-swapping and real-time optimization, both rare capabilities in the TTS market.

Industry Recognition

TMC CEO Rich Tehrani praised Deepgram for raising the bar on customer experience technology. He highlighted Aura-2 as a model that delivers performance across all customer engagement channels, not just synthetic voice demos.

Deepgram CMO Praveen Rangnath framed Aura-2 as a turning point in enterprise TTS. According to him, the model redefines what production-ready voice AI must deliver—speed, accuracy, consistency, and reliability.

Why Aura-2 Matters

Enterprises are adopting real-time AI agents at unprecedented speed, but most TTS tools still struggle with latency, scaling, and proper pronunciation under load.

Aura-2 directly targets those gaps. Its performance profile makes it suitable for industries where every millisecond and every mispronounced value matters, from customer support to healthcare, fintech, and logistics.

Try Aura-2

Developers can test Aura-2 through a self-serve API, complete with documentation and a real-time playground.

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