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DigiParser Launches AI-Powered Document Data Extraction and Automation Platform

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Automate data extraction from PDFs, invoices, and documents with AI and seamless integrations.

DigiParser, a document automation platform, announced the launch of its AI-powered solution designed to extract structured data from PDFs, invoices, and other business documents without manual effort.

Businesses across industries still rely on manual data entry from documents such as invoices, purchase orders, and receipts. This process is time-consuming, error-prone, and difficult to scale. DigiParser aims to eliminate this bottleneck by enabling users to automatically extract and process data with high accuracy.

The platform uses advanced AI and OCR technology to identify key fields and convert unstructured documents into structured formats like Excel, CSV, or JSON. Users can define custom parsing rules, validate extracted data, and automate workflows without writing code.

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DigiParser also supports integrations with tools like Zapier, allowing businesses to automatically send extracted data to CRMs, databases, or accounting software. This makes it easy to build end-to-end automation workflows for document processing.

“Many businesses still spend hours manually entering data from PDFs. We built DigiParser to remove that friction and make document automation accessible to everyone,” said the founder of DigiParser.

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Key features of DigiParser include:

AI-powered PDF to Excel and data extraction
Custom document parsers for invoices, receipts, and forms
Automatic data export to CSV, Excel, and APIs
Workflow automation via Zapier integrations
No-code setup with a self-serve interface

DigiParser is designed for startups, small businesses, and teams looking to automate repetitive document workflows without investing in complex enterprise tools.

The platform offers a free plan that allows users to get started quickly, with paid plans unlocking higher usage and advanced features.

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Aisepedia Launches the First Product Marketing Environment to Connect Strategy and Execution

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A new system built to bring strategy and execution together in modern product marketing

Aisepedia announced the launch of its Product Marketing Environment, introducing a new system designed to connect the entire workflow of Product Marketing Managers (PMMs). The product is now live and available to early users.
Aisepedia is built on a simple but critical insight: while PMMs are responsible for driving strategy and execution, their work is often fragmented across tools, documents, and disconnected workflows.

“Aisepedia is the first Product Marketing Environment where the whole PMM workflow actually connects,” said Vishwa Vijoyendra Narayan, Founder of Aisepedia. “We handle the complexity of AI-driven research, analysis, and frameworks, so PMMs can focus on the strategy and execution that actually drives business impact.”

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A System That Connects the PMM Workflow: At its core, Aisepedia brings together the full product marketing journey into a structured, connected system:

Research:
A continuously updated view of the market that brings together customer signals, competitor moves, and market shifts into structured, living canvases. This gives PMMs a level of visibility and clarity they have never had before.

ICP Definition:
Using the Product-Need-Fit™ approach, ICPs are derived from real signals rather than assumptions. The system identifies statistically significant attributes, helping teams move beyond subjective discussions and align on a clear, evidence-based ICP.

Positioning and Messaging:
Built directly from research using structured strategy frameworks. The result is sharp differentiation and clear messaging that is grounded in real market understanding and ready for go-to-market teams.

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Initiatives:
Framework-driven workflows that take teams from strategy all the way to campaigns and sales enablement. The gap between a great strategy deck and actual execution is finally closed.

Impact:
Live signals continuously refine the system. As markets evolve, so does the output, enabling teams to drive consistent go-to-market outcomes without fragmented effort.

From Fragmented Work to Connected Flow: Aisepedia introduces a workflow that mirrors how product marketing should operate:
Research → ICP → Positioning → Messaging → Execution
Each step builds on the previous one, creating a continuous flow instead of isolated tasks. This allows PMMs to operate with both speed and strategic depth.

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Data Intelligence with High-Performance Data Access & Orchestration for Fastest Time to Value of AI Production

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New Partnership and Integration Combines Continuous Data Intelligence with High-Performance Data Access & Orchestration

Secuvy, the Data Intelligence Platform for filtering appropriate data into AI pipelines, revealed a partnership with Hammerspace focused on the three challenges that determine whether an enterprise AI initiative moves into production or stalls.

If you don’t fully know and understand what data you have, how it’s being used … your risk metric for using inappropriate data in your AI model creates senseless exposure.”

— Mike Seashols, Chief Executive Officer at Secuvy

The challenges align with Hammerspace’s AI Data Platform (AIDP) based on NVIDIA’s reference design, which structures deployment around data readiness, infrastructure performance, and time to production. Secuvy and Hammerspace address the data intelligence, global namespace, and data orchestration layers of that architecture. The combined offering is delivered as an integrated hardware-software appliance to dramatically simplify launching AI projects by enabling existing data to be used across existing infrastructure.  (Find more details in the specific appliance announcement at

https://hammerspace.com/hammerspace-launches-ai-data-platform-based-on-nvidia-reference-design/)

Companies are investing heavily in models and infrastructure, but most projects stall because the appropriate data is not ready, the infrastructure is not optimized, and the path to production takes quarters.

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Three challenges need to be determined before your AI program moves forward.

First:  What data is appropriate for each AI application, and can you confirm that only that data is being used?

Second:  How do you unify your distributed unstructured data, orchestrate data movement to optimize processing, and establish the continuous availability of AI-ready data?

Third: Can your AI applications be designed, tested, and deployed with a positive ROI within 30 days?

Secuvy answers the first. Hammerspace answers the second. Together with global delivery partners SHI and ePlus, the answer is YES for all three.

“Enterprises don’t have just an AI challenge. They have an appropriate data use challenge.” said Mike Seashols, Chief Executive Officer at Secuvy. “If you don’t fully know and understand what data you have, how it’s being used , and how it must adhere to a vast criteria of security, privacy, compliance, and governance guidelines, your risk metric for using inappropriate data in your AI model creates senseless exposure.”

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Secuvy addresses data control by continuously discovering, classifying, and assessing risk across all your data types with extensive focus on unstructured data. Using AI-driven autoclassification, Secuvy builds a complete inventory of your data estate, including the sensitive data that pattern-based approaches structurally cannot find. Organizations understand what they have and apply governance controls and filters before that data enters AI pipelines. Classification and Linkage of data generate the most accurate “tagging” of your data in the industry which is completed in days and weeks, not months and years. Secuvy generates a dynamic Trusted Data Plane to ensure only sanitized, appropriate data reaches each AI application.

Hammerspace provides a high-performance data platform that unifies distributed data across multiple cloud and on-prem environments from your existing storage hardware systems and then orchestrates how it is used across edge, data center, and cloud environments. AI workloads access data in place and move only what is needed to the right computing processor (GPU) at the right time, eliminating unnecessary duplication while maximizing performance at the lowest infrastructure cost. Secuvy and Hammerspace are core partners in the Hammerspace AIDP, a purpose-built  platform to dramatically simplify enterprise adoption of inference, RAG and agentic AI use cases..

“The true bottleneck for enterprise AI isn’t a lack of compute or models; it’s the inherent risk and friction of fragmented data. Most AI initiatives stall because organizations struggle to discover, move, and secure their data across disparate silos,” said Sam Newnam, Vice President of AI and Business Development at Hammerspace. “Hammerspace solves the ‘data gravity’ challenge by unifying access and mobilizing unstructured data across the edge, data center, and cloud. By integrating Secuvy’s automated, data-aware security, we are delivering a high-performance, production-ready stack that bridges the gap between raw data and actionable AI. This partnership provides the secure, global foundation enterprises need to finally move AI out of the lab and into large-scale production.”

The typical enterprise AI data readiness project consumes quarters before delivering results. The Secuvy and Hammerspace integrated offering compresses that timeline. Data classification delivers first results in days. AI applications reach production in weeks, not months, with a measurable ROI profile that compounds as deployment scales, completely outpacing any manual-heavy approaches. On top of this, global systems integrators like SHI operationalize these architectures in existing enterprise environments, accelerating deployment at scale worldwide.

“Enterprises can’t scale AI securely if they don’t know what data they have, or where sensitive data is hiding,” said Jack Hogan, Vice President of Advanced Solutions at SHI. “The Hammerspace and Secuvy integration gives customers a global view of unstructured data plus continuous discovery and classification, so they can enforce governance without breaking workflows or proliferating copies. SHI can integrate this into existing environments to help teams move faster, with the controls needed for production AI.”

Together, Secuvy and Hammerspace enable a model where data is continuously understood, governed, and immediately usable for AI wherever it resides.

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CloudWave rebrands to NeonNow as it launches partner-led AI CX platform across 170 markets

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CloudWave rebrands to NeonNow as it launches partner-led AI CX platform across 170 markets

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Sydney-founded technology company CloudWave has rebranded to NeonNow as it rolls out a partner-led customer experience platform across 170 countries, enabling resellers and managed service providers to deliver AI-driven communications without upfront infrastructure investment.

The rebrand marks a shift from a regional cloud communications provider to a global, usage-based platform model, allowing partners to generate recurring revenue while avoiding the complexity of managing multiple vendors and integration layers.

Headquartered in Sydney, NeonNow has rapidly expanded its international footprint, with new offices established in the United States, the United Kingdom, India, and New Zealand, as it positions itself for accelerated global growth.

The platform consolidates customer engagement, communications, and deployment into a single system, allowing partners to deliver voice, messaging, and AI-driven interactions without managing multiple vendors or integration layers.

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Channel partners can commercialise the platform through a recurring revenue model linked to customer usage, with the option to manage implementation and support internally or leverage NeonNow’s delivery infrastructure.

This approach removes the need for upfront capital investment and reduces training requirements, enabling partners to scale services across regions with standardised delivery.

The shift comes as small and mid-sized enterprises increasingly seek enterprise-grade customer engagement capability without the cost and complexity of fragmented technology stacks.

This is reflected in projections that the global market for AI-led customer experience will grow from US$17.75 billion in 2025 to US$22.67 billion in 2026, increasing demand for platforms that support integrated, scalable delivery models.

Over the past two years, NeonNow has restructured its technology, delivery, and support functions into a unified platform architecture, enabling seamless onboarding, deployment, and management through a single, product-led ecosystem.

Key platform capabilities include:
Integrated onboarding that embeds solution design and deployment within the core product
Consolidated customer experience and communications infrastructure in one platform
Deployment across more than 170 countries with 24/7 support
Alignment with regional security, compliance, and regulatory requirements
Standardised delivery and administration to reduce operational complexity and cost

The platform currently supports over 200 customers globally across enterprise, SMB, and government sectors, handling billions of interactions annually.

Built on Amazon Web Services infrastructure and integrated with Twilio’s communications technology, NeonNow enables voice, messaging, and AI-driven interactions through a unified system.

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The rebrand reflects a broader shift in how customer experience technology is developed and delivered through the channel, according to Michael Powrie.

Michael Powrie, founder & CEO of NeonNow, said: “The rebrand signals a broader transformation in how the company builds, bends, and delivers technology in a rapidly changing digital landscape.

“For our channel partners, this shift is about removing the complexity that has traditionally slowed deployment and limited scale across markets.

“We have rebuilt the platform so partners can deliver customer engagement services globally through a single system, maintain compliance across jurisdictions, and generate recurring revenue without the need for fragmented infrastructure”

Jason Stirling, Director of NeonNow, said: “Businesses operating across multiple markets require systems that can adapt to local regulatory and security requirements without increasing operational burden.

“By embedding onboarding and delivery within the product, we have shifted to a platform-led model that enables faster deployment and consistency across regions.

“This positions NeonNow to support organisations with a single system that maintains control, compliance, and operational efficiency.”

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UserTesting Appoints Neal Gottsacker as Chief Technology Officer to Accelerate AI-Powered Customer Understanding

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UserTesting Appoints Neal Gottsacker as Chief Technology Officer to Accelerate AI-Powered Customer Understanding

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UserTesting, the leading provider of customer insights for the enterprise, announced the appointment of Neal Gottsacker as Chief Technology Officer. Gottsacker will lead the company’s global R&D organization and advance its next phase of innovation, focused on combining AI with human insight to transform how enterprises make decisions.

His appointment comes at a pivotal moment, as organizations move beyond traditional research and analytics toward AI-accelerated, insight-driven decision-making. UserTesting is at the forefront of this shift, helping companies pair the speed of AI with the depth and reliability of real human feedback.

Gottsacker will play a central role in scaling UserTesting’s vision for responsible AI-powered customer understanding, embedding AI across the research lifecycle while ensuring transparency, trust, and human accountability remain core to every insight.

At UserTesting, Gottsacker will focus on accelerating innovation across the company’s customer insights solution, including continued investment in AI-driven insights and the core workflows that help organizations better understand their customers and make more confident decisions.

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“AI is fundamentally changing how decisions get made inside modern enterprises,” said Eric Johnson, CEO of UserTesting. “But speed without trust creates risk. Neal’s experience building and scaling AI-driven platforms—combined with his R&D leadership—will be critical as we deliver a new model: one where AI accelerates insight, and humans remain accountable for decisions.”

Gottsacker brings deep experience leading technology organizations through transformation and growth. Over the course of his career, he has built and scaled SaaS platforms, led global engineering teams, and driven innovation at key inflection points.

Prior to joining UserTesting, Gottsacker held senior technology leadership roles at Nintex, where he led R&D through a period of rapid growth and platform expansion. Earlier in his career, he led a division of HP Software and founded and ran his own startup, bringing a combination of technical depth, product vision, and entrepreneurial leadership.

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At UserTesting, Gottsacker will focus on advancing the company’s AI strategy across its core pillars, including AI-assisted research, insight synthesis, and emerging agentic workflows, while maintaining the company’s commitment to transparent, inspectable, and human-centered AI systems.

“UserTesting is uniquely positioned to define the future of customer understanding in an AI-driven world,” said Gottsacker. “The combination of rich human insight and AI-driven analysis creates an opportunity to help organizations move faster, reduce risk, and make better decisions at scale. I’m excited to build on that foundation and accelerate what’s next.”

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TwelveLabs Launches Ecosystem Partner Program to Extend Video Intelligence in the Enterprise

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TwelveLabs Unveils Pegasus-1.2 to Efficiently Process and Understand Videos  of Varying Lengths and Complexities, Expanding Possibilities for Video AI

With this formal partner program company creates tiered paths for Systems Integrators and Independent Software Vendors to build, sell, and grow alongside TwelveLabs

TwelveLabs, the video understanding company, announced the launch of its Ecosystem Partner Program as an expansion of the broader TwelveLabs Partner Program. Through the new program, TwelveLabs extends the infrastructure for enterprise video AI with application and integration partners to help joint customers rapidly deploy and scale across industries.

The Ecosystem Partner Program was created to assist enterprises in maximizing all of their assets at a time when video represents 90% of the world’s data – yet most organizations lack the infrastructure to search, analyze, or extract business value from it. By providing access to its underlying technology, TwelveLabs enables partners to bring industry-leading video intelligence to customers at enterprise speed and scale.

“Video intelligence is a foundational technology for enterprise companies, but for it to permeate and become an embedded part of workflows requires us to reach beyond direct sales,” said Danny Nicolopoulos, Head of Partnerships at TwelveLabs. “Partners play an integral role in helping us drive industries forward, as they unlock new revenue streams and knowledge stores with the strategic application of video AI.”

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Benefits of Partnership

Through the Ecosystem Partner Program, TwelveLabs and its technology and implementation partners will co-build, co-sell, and co-market. This approach allows TwelveLabs to reach new industries and use cases: Systems Integrators (SIs) bring deployment expertise, ready-to-deploy solution accelerators, and client relationships while Independent Software Vendors (ISVs) embed TwelveLabs capabilities directly into products, creating many-to-one leverage.

In return, partners can secure discounts for their customers on TwelveLabs’ unrivaled models – Marengo for multimodal video search and embeddings, and Pegasus for video reasoning and text generation. For SIs, that translates into faster, higher-quality implementations for enterprise clients. For ISVs and application partners, it enables integration of TwelveLabs’ capabilities directly into their products, differentiating their offerings with purpose-built video AI that competitors cannot replicate with generic image or text models. Unlike general-purpose image or text models adapted for video, TwelveLabs’ models are designed from the ground up for the specific semantic, temporal, and contextual complexity of video data.

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TwelveLabs’ Ecosystem Partner Program launches with two tiers. The entry-level, or Verified, tier is designed for partners establishing their TwelveLabs practice. Partners who reach the Advanced tier have demonstrated production deployments or deep product integrations, which unlocks benefits including lead sharing, early access to new product features, and expanded co-marketing programs.

“TwelveLabs is a clear leader in video intelligence,” said Sean Lynch, Director, Business Development and Partnerships at Quickplay. “The impact of their video understanding technology, combined with our AI native media capabilities has been demonstrably well-received over this past year of working together. We look forward to unlocking even more use cases in the days to come.”

TwelveLabs is excited to welcome CineSys, Overcast HQ, Quickplay, ScorePlay, TrackIt, and Vidispine as Advanced partners and EMAM, Ceivo, MASV, Mimir, Nomad Media, and Mux as Verified partners.

“For media and sports organizations, video isn’t supplementary content — it’s the core product. TwelveLabs understands that in a way most AI platforms don’t. Their video-native models fit directly into how we’ve built ScorePlay, and this partnership gives our customers access to a layer of intelligence that wasn’t possible before,” said Josh Krakowsky, Head of Partnerships at ScorePlay.

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From Clicks to Conversions: How Martech Is Transforming Attribution Accuracy?

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From Clicks to Conversions: How Martech Is Transforming Attribution Accuracy?

For a long time, marketers used vanity metrics like clicks, impressions, and page views a lot to figure out how well their campaigns were doing. These measurements gave a general idea of what was going on, but they didn’t always show how it really affected the firm.

A lot of clicks doesn’t always mean more sales, more money, or more long-term client value. Businesses are now more focused on outcomes that directly affect performance as they work toward measurable growth. The evolution of Martech has mostly led to this change. It lets marketers go beyond basic engagement metrics and focus on results that matter.

The Journeys Of Modern Customers Are Getting More Complicated

The path that customers take today is not straight or easy to follow. People talk to brands through a lot of different channels, such as social media, websites, email, mobile apps, and in-person interactions. A single purchase decision could include dozens of interactions over time, which makes it harder to figure out which touchpoints really had an effect on the conclusion.

This increasing complexity has made old ways of measuring things useless. Modern Martech platforms are made to record and analyze these interactions across several channels, giving you a better idea of how customers travel through the funnel.

Traditional attribution models were created for a digital world that was less complicated. Last-click attribution and other methods give all the credit for a conversion to the last interaction before a purchase. These models are easy to use, but they don’t take into account the bigger picture and don’t give enough credit to earlier touchpoints that may have had a big impact on the client.

Because of this, marketers often make choices based on data that is missing or wrong. Advanced Martech solutions are fixing these problems by giving us more precise and complete attribution frameworks.

Martech Is Redefining Attribution Accuracy

Martech is changing how we assess attribution in today’s data-driven world by making it possible to deploy multi-touch, data-rich, and outcome-focused measurement methodologies. Martech helps businesses figure out what really drives conversions and improve their marketing efforts by combining data from many channels, employing advanced analytics, and focusing on real business outcomes.

The Problem with Traditional Attribution Models

Traditional attribution models were made for simpler, straight-line customer journeys that don’t match the way things are now, when customers use many channels. They frequently use only a few data points, which means they don’t show all the interactions that affect conversions. Because of this, these models give an incomplete and sometimes wrong picture of how well marketing is doing.

a) Over-Reliance on Last-Click Attribution

One of the biggest problems with traditional marketing measurement is that it relies too much on last-click attribution. This model gives all the credit for a conversion to the last engagement, ignoring all the other times the person interacted with the brand. It makes it easy to keep track of performance, but it oversimplifies the client experience and gives wrong information.

For instance, a client might see an ad for a product on social media, read about it on a blog, and then buy it after clicking on a sponsored search ad. In a last-click paradigm, just the last step gets credit, even though prior steps were very important in making the decision. This gives a false picture of performance and can lead to marketing funds being spent in the wrong places. Modern Martech platforms fix this problem by letting multi-touch attribution models look at the whole journey.

b) Inability to Track Cross-Channel and Multi-Device Journeys

Another big problem with traditional attribution methods is that they can’t keep track of interactions across numerous channels and devices. People today often switch between devices. For example, they might start a journey on a phone, continue it on a laptop, and finish it on a tablet. It’s hard for traditional systems to put all of these interactions together into one perspective.

This fragmentation makes the data incomplete and stops marketers from figuring out how different channels help with conversions. Martech solutions are getting around this problem by combining cross-channel tracking with identity resolution methods to produce a single consumer profile. This helps businesses get a better idea of how customers interact with their brand at different touchpoints.

c) Fragmented Data Across Platforms and Tools

In a lot of companies, marketing data is stored on a number of different platforms, such as CRM systems, advertising tools, analytics platforms, and customer engagement solutions. It’s hard to combine data and get precise insights when it’s broken apart like this. Attribution models are sometimes dependent on incomplete information when there isn’t a uniform data environment, which leads to wrong conclusions.

Modern Martech systems are made to get rid of these silos by combining data from many sources into one system. This single method makes sure that all interactions are recorded and looked at in context, which makes attribution models more accurate. Martech helps marketers make better decisions and match their tactics with corporate goals by bringing all of their data together in one place.

d) Lack of Visibility into the Complete Customer Lifecycle

A lot of the time, traditional attribution models simply look at the last steps of the customer experience, such purchases or conversions. But they don’t show what’s going on in the earlier stages, such awareness and deliberation. This narrow view inhibits marketers from seeing how different touchpoints can build long-term relationships with customers.

For instance, blogs, videos, and social media posts that are part of content marketing may not lead to immediate sales, but they are very important for developing brand awareness and trust. If marketers can’t see these conversations, they might not see how valuable they are and put their resources somewhere else. Martech solutions give businesses full access into the client lifecycle, letting them keep track of interactions from the first contact to the behavior after the purchase.

e) Misalignment Between Marketing Efforts and Revenue Impact

One of the worst things that can happen when attribution is wrong is when marketing activities don’t match up with actual revenue results. When attribution models don’t show the entire effect of marketing activities, companies may spend money on channels that seem to be working well but don’t actually get them any real results.

For example, a channel that gets a lot of clicks might not always lead to sales or conversions. If marketers don’t know where their money is going, they can keep spending it on these channels, which is a waste of time and money. Martech systems fix this problem by connecting marketing operations directly to business results, such revenue and customer lifetime value. This alignment ensures that marketing plans are focused on making a difference that can be measured.

The Growing Need for Modern Attribution Solutions

As customer journeys get more complicated and the amount of data grows, it becomes clearer and clearer that traditional attribution models have problems. Companies require more advanced systems that can deal with the complicated nature of today’s marketing settings. This is where Martech comes in.

Martech helps businesses move away from old attribution models and use more accurate and useful ways to evaluate things by using advanced analytics, real-time data processing, and AI-driven insights. These features help marketers figure out how their work is really affecting things and make their campaigns work better.

The problems with standard attribution models show that we need a better way to measure how well marketing is working. Relying too much on last-click attribution, having data that isn’t complete, and not being able to see the whole customer journey all lead to wrong conclusions and bad decisions.

New Martech tools are helping with these problems by giving a more complete and accurate picture of the customer’s journey. Martech is changing the way businesses analyze and improve their marketing activities by combining data, allowing for multi-touch attribution, and focusing on real business results.

The Change from Click-Based Metrics to Conversion Intelligence

The way we measure success has changed because of the growth of digital marketing. For a long time, clicks, impressions, and traffic were the main ways that marketers measured how well their ads were doing. These metrics gave a rapid picture of engagement, but they didn’t always give useful information about how the firm was doing.

Today, companies are going toward conversion intelligence, which is a more advanced method that looks at results like revenue, client acquisition, and long-term value. Martech is leading this change by giving businesses a better understanding of how customers behave and making it easier to monitor performance.

Moving Beyond Surface-Level Metrics to Meaningful Outcomes

Clicks and impressions could show curiosity, but they don’t always lead to action. A campaign could have thousands of clicks but not a single sale, which shows that surface-level measures are not enough to measure success. To be successful in modern marketing, you need to know more about how interactions affect results.

This is where Martech comes in. Martech solutions let businesses measure results that have a direct effect on business growth by combining powerful analytics and tracking features. Marketers can now look at more than just how many people clicked on an ad. They can also see how those clicks affected sales, keeping customers, and overall revenue.

Focus on Conversions, Revenue, and Customer Actions

Conversion intelligence changes the focus from activity to action. It focuses on indicators like purchases, sign-ups, downloads, and other relevant interactions that show how far along the customer journey you are. This method makes sure that marketing campaigns are focused on getting outcomes, not just getting people to interact with them.

Martech helps businesses keep track of these behaviors across many touchpoints, giving them a full picture of how customers interact with their brand. This degree of understanding lets marketers figure out which channels and initiatives are bringing in the most value, which helps them use their resources more wisely and get a better return on investment.

Importance of Measuring Engagement Quality Rather Than Quantity

Not all interactions are the same. A lot of clicks may seem impressive, but if they come from people who aren’t really interested, they don’t mean anything. On the other hand, a smaller number of high-quality encounters can lead to big sales and conversions.

Marketers may use martech to figure out how good their engagement is by looking at things like how long people stay on the site, how deeply they connect, and how likely they are to convert. Organizations can better recognize which contacts are important and which are not by paying attention to these signs. To make better marketing plans, it is important to go from quantity to quality.

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Role of Intent Signals and Behavioral Data in Understanding Conversions

To get more people to buy anything, you need to know what they want. Intent signals, such search queries, browsing behavior, and interactions with content, can tell you a lot about what customers want and how close they are to making a choice.

Modern Martech platforms use behavioral data to find these signals and guess what will happen next. For instance, a person who goes to a product site again and over again and compares possibilities is more likely to convert than someone who just looks at the homepage for a short time. Marketers can better target high-intent users by looking at these tendencies.

Martech technologies also let businesses track behavioral data in real time, so they can immediately adapt to changes in client needs. This capacity is very critical in today’s fast-paced digital world, where time can have a big effect on how many people convert.

How Martech makes attribution more accurate?

As marketing gets increasingly complicated, it’s important to accurately attribute what generates conversions. Traditional models often don’t show the whole client journey, which might lead to incomplete or inaccurate information. Martech solves this problem by giving marketers better tools and frameworks that make attribution more accurate and let them make decisions based on facts.

a) Unified Data Ecosystems – Integrating Data from Multiple Channels into a Single Platform

The fragmentation of data across different platforms is one of the main problems with attribution. For advertising, analytics, customer relationship management, and other things, marketing teams generally utilize more than one platform. This makes it hard to have a clear picture of the client journey since it generates silos.

Martech solutions solve this problem by bringing together data from many different places into one platform. This unified approach makes sure that all interactions are recorded and looked at in context, giving a more accurate picture of how customers interact with a business. Martech makes attribution models more reliable by combining data and getting rid of inconsistencies.

  • Creating a Single Source of Truth for Customer Interactions

A unified data ecosystem lets businesses create a single source of truth for how they connect with customers. This implies that all of the teams, including marketing, sales, and customer service, can see the same data and insights.

This centralized approach makes it easier for people to work together and makes sure that decisions are based on the same information. It also makes attribution more accurate by recording the whole client experience, from first awareness to ultimate transaction.

b) Multi-Touch Attribution Models – Tracking All Touchpoints Across the Customer Journey

Multi-touch attribution looks at every touchpoint in the customer journey, not just one encounter like traditional models do. This method gives us a better idea of how different channels and interactions lead to conversions.

Companies can use martech platforms to keep track of these touchpoints across several channels, such as social media, email, search, and face-to-face contacts. Marketers can find out which touchpoints have the most impact and improve their strategy by tracking the whole trip.

  • Assigning Value to Each Interaction

Multi-touch attribution models give each interaction a value based on how much it helped the ultimate result. This helps marketers figure out how important each touchpoint is and how to best use their resources.

Martech helps businesses employ complex attribution models that use data-driven algorithms to give things the right value. This makes sure that all essential interactions are taken into account, giving a more balanced and true picture of performance.

c) Real-Time Data Integration- Instant Visibility into Campaign Performance

In today’s fast-paced digital world, you can’t wait for reports anymore. Marketers need real-time information to make quick decisions and improve campaigns on the go.

Martech platforms let you combine data in real time, so you can see how your campaign is doing right away. This lets businesses keep an eye on important numbers, spot patterns, and quickly react to changes in how customers act.

  • Faster Optimization and Decision-Making

Real-time data gives marketers the power to make decisions faster and with more information. They don’t have to rely on data from the past; they may change their plans based on how things are going right now.

Companies may use Martech to constantly improve their campaigns, which makes them more efficient and gets the best results. In a competitive digital world, being able to move quickly is a big plus.

d) AI and Predictive Analytics – Identifying Patterns and Predicting Conversion Paths

AI and predictive analytics are changing the way attribution is done. These technologies look at a lot of data to find patterns and guess what will happen in the future.

AI helps martech systems find information that would be hard to find by hand. For instance, they can find out which combinations of touchpoints are most likely to lead to conversions, which helps marketers make their plans better.

  • Continuously Improving Attribution Models

One of the best things about AI-driven attribution is that it can learn and get better over time. The models get more accurate as more data is gathered, which leads to greater insights and suggestions.

With Martech, businesses can use adaptive attribution models that change as customers do. This makes sure that their ways of measuring things stay useful and relevant in a changing world.

The change from click-based analytics to conversion intelligence is a big change in how marketing success is assessed. Companies may learn more about what makes people convert by concentrating on meaningful results, using behavioral data, and making engagement quality their top priority.

At the same time, Martech is quite important for making attribution more accurate. Martech helps businesses shift away from old ways of measuring things and toward more advanced ones by using unified data ecosystems, multi-touch attribution models, real-time integration, and AI-driven analytics.

As the digital world changes, it will become more and more crucial to be able to appropriately assign credit for marketing activities. Companies who take use of these new technologies will be better able to improve their campaigns, boost growth, and achieve long-term success.

Benefits of Accurate Attribution in Martech

Accurate attribution is now a key part of modern marketing success. It’s important to know which digital marketing activities really work as companies spend more and more on them. Attribution isn’t just about giving credit anymore; it’s also about finding insights that help you make better decisions and get measurable results for your business.

As Martech has grown, businesses now have access to more powerful tools that let them create more accurate and data-driven attribution models. This has changed how marketing performance is measured.

a) Better ROI Measurement and Marketing Accountability

One of the best things about proper attribution is that it lets you estimate return on investment (ROI) more accurately. It was often hard to tell which campaigns or channels brought in the most money in traditional marketing settings. This lack of clarity made it hard to explain why marketing money was being spent and show stakeholders how it was worth it.

Modern Martech platforms solve this problem by connecting marketing actions directly to business results like conversions, revenue, and customer lifetime value. Martech lets businesses follow the entire customer journey and find out which interactions have the biggest effect by collecting data from many different sources.

This kind of openness makes marketing more accountable. It’s simpler to receive funds and support from executives when teams can clearly show how their work helps the firm reach its goals. Also, reliable attribution helps marketers avoid making guesses and instead make judgments based on facts.

b) Improved Campaign Optimization and Budget Allocation

Marketers can better improve their plans when they can accurately attribute campaign performance. Organizations may improve their campaigns to have the biggest effect by figuring out which channels, messages, and touchpoints work best.

Marketers may use Martech to look at performance in real time and make changes as needed. For instance, if one channel isn’t doing well, you can move resources to channels that are doing better. This flexible strategy makes sure that marketing budgets are spent wisely and in line with corporate goals.

Martech also lets you look into the details of your campaign, like audience segments, creative materials, and scheduling. This helps marketers figure out what works and what doesn’t, which leads to ongoing improvement and improved results over time.

c) Enhanced Customer Journey Insights

To give customers unique and useful experiences, you need to know the customer path. Accurate attribution gives a full picture of how customers interact with a brand at all stages, from when they first hear about it to when they make a purchase.

Martech platforms are very important for recording and studying these interactions. They give a complete picture of the client journey by combining data from many channels. This lets marketers find patterns, preferences, and problems, which helps them come up with better ways to get people to interact with them.

For example, attribution data might show which sorts of content work best for certain audiences or which touchpoints have the biggest impact on conversions. These insights help businesses make their messages more relevant and improve the entire customer experience.

d) Stronger Alignment Between Marketing, Sales, and Business Teams

One of the problems that many businesses have is that marketing, sales, and other parts of the firm don’t always work well together. When attribution isn’t right or isn’t thorough, it can cause different views on performance and priorities.

Martech’s accurate attribution helps close this gap by giving everyone a common view of the customer journey and the things that make money. When all teams can see the same data and insights, they can work together better.

For instance, marketing teams can use attribution data to find better leads, and sales teams can focus on prospects who are most likely to become customers. This alignment makes sure that everyone is working together toward the same goals.

Martech also makes it easier for people from different departments to work together by bringing together data from diverse systems, such CRM and marketing automation platforms. This all-encompassing approach helps businesses run more smoothly and get greater results.

Challenges in Attribution Accuracy

It’s evident that precise attribution has many benefits, but getting it right isn’t always easy. As marketing environments get more complicated, businesses have to deal with a lot of problems that can affect how accurate and reliable attribution models are. Even though Martech has come a long way, these problems need to be thought about carefully and solved in a planned way.

a) Data Privacy Regulations and Tracking Limitations

One of the biggest problems with attribution is that people are becoming more concerned about their privacy. GDPR and CCPA are two laws that have made it very clear how user data can be acquired, stored, and used. These rules are important for preserving consumers’ rights, but they also make it harder for marketers to keep track of how people act across different platforms.

Because of this, old ways of tracking are becoming less useful, which makes it harder to get a full picture of the client experience. Martech platforms are changing to fit this new world by providing privacy-first solutions that use data that has been combined and anonymized.

But it is still hard to find a balance between following privacy rules and giving credit where it is due. Companies need to make sure that their data procedures are clear and fair while yet being able to monitor performance well.

b) Cookie Deprecation and Cross-Device Tracking Issues

Another big problem for attribution is that third-party cookies are going away. For a long time, cookies have been a critical way to keep track of how people use different websites and devices. As browsers stop supporting third-party cookies, marketers need to discover new ways to keep track of interactions.

This change has a big effect on how accurate attribution is, especially when it comes to cross-device settings. People typically switch between devices while on the go, which makes it hard to correlate interactions without dependable tracking tools.

Martech solutions are using first-party data, identity resolution approaches, and advanced analytics to solve this problem. These methods look like good options, but they also need a lot of money and knowledge to work well.

c) Data Integration Complexity Across Platforms

There are a lot of tools and platforms in modern marketing ecosystems, and each one makes its own collection of data. Putting these data into a single system is a difficult job that can affect the accuracy of attribution.

Data stays in silos without effective integration, which makes insights incomplete or inconsistent. Martech platforms try to fix this by letting multiple systems work together and making a single data environment.

But getting everything to work together perfectly isn’t always easy. When data formats, systems, and processes are different, it might be hard to plan and carry out tasks. To make sure that their data is correctly combined, businesses need to spend money on the necessary infrastructure and experts.

d) Ensuring Data Accuracy and Consistency

To get accurate attribution, you need good data. If the data utilized in attribution models is not complete, up-to-date, or consistent, the insights that come from them will not be useful. So, making sure that data is accurate and consistent is a big problem for businesses.

Martech platforms offer tools for checking, cleaning, and standardizing data, which helps make it better. But keeping this level of quality demands constant work and oversight.

To make sure that data stays accurate and dependable, organizations need to set up clear data management procedures, such as frequent audits and updates. Even the best attribution models could give wrong findings without these steps.

Overcoming Organizational Silos

In a lot of companies, various teams work in silos, utilizing their own tools and data sets. This fragmentation can make attribution models less useful because it makes it hard to see the whole client experience.

For instance, the marketing, sales, and customer support departments might all have their own data systems, which could cause problems and make things not work together. Martech solutions assist solve this problem by bringing together data from different areas and giving a single view of all client interactions.

But technology alone won’t break down corporate silos. Companies also need to create a culture of working together and make sure that teams are all working toward the same goals and using the same methods. This necessitates robust leadership and a dedication to dismantling obstacles.

Hence, to get the most out of marketing, it’s important to have accurate attribution, but this can be hard to do. Companies have to deal with a landscape that is changing quickly, from rules around data protection to problems with integration.

Even with these problems, progress in Martech is making it possible to get more accurate and dependable attribution. Organizations can learn more about how well their marketing is working by using unified data ecosystems, advanced analytics, and privacy-first methods.

In the end, being able to correctly assign credit for marketing efforts will be a big deal in the digital age. Companies who put money into the proper tools, processes, and strategies will be better able to grow, work more efficiently, and remain ahead of the competition in a world that is getting more complicated.

The Future of Attribution in Martech

Attribution is going through a new stage of development as digital ecosystems get more complicated and consumer journeys get more broken up. In a world where privacy laws, using multiple devices, and real-time interactions are important, old methods that used cookies and deterministic tracking are no longer enough.

The future of attribution is in systems that are smart, flexible, and respect users’ privacy. These systems should be able to give correct information without losing users’ trust. Improvements in Martech are driving this change. Martech is changing the way businesses monitor, analyze, and improve their marketing success.

a) Shift Toward Privacy-First Attribution Models

The move toward privacy-first frameworks is one of the most important themes that will shape the future of attribution. Companies are rethinking how they acquire and utilize customer data because of worries about data protection and tougher rules like GDPR and CCPA. Marketers have to find new ways to follow people because old methods that rely primarily on third-party cookies are no longer useful.

Martech platforms are leading the way in this change by letting businesses use privacy-focused attribution models that put openness and consent first. These models use data that has been combined and anonymised instead of tracking approaches that are too invasive. This makes sure that the models are legal while still giving useful information.

Attribution that puts privacy first also stresses the importance of using data ethically. People increasingly expect brands to protect their privacy, and those that don’t do so risk losing customers’ trust.

Companies may protect user data while still getting correct attribution by using modern Martech solutions. This method not only makes sure that the rules are followed, but it also improves the brand’s reputation in a market that is becoming more privacy-conscious.

b) Greater Reliance on First-Party Data

As third-party data gets harder to get, first-party data is becoming more important for attribution. First-party data is information that comes directly from customers through things like website visits, app use, and direct contact. This information is more trustworthy, correct, and in line with privacy laws.

Modern Martech platforms are made to easily collect, organize, and analyze first-party data. These tools let businesses learn more about how customers behave and what they want by making unified consumer profiles. This change gives marketers more control over their data while also letting them create more tailored and targeted marketing.

As first-party data becomes more important, it is equally important to have good data governance. Companies need to make sure that their data is correct, safe, and easy for all teams to get to. Businesses may set up strong data management systems with the help of Martech that help them give credit where credit is due and expand over time.

c) AI-Driven and Probabilistic Attribution Models

Artificial intelligence is going to change attribution in a big way in the future. AI-driven models look at a lot of data to find trends, guess what will happen, and give different touchpoints a value. Probabilistic models use statistical methods to figure out how likely it is that specific encounters will lead to conversions, while classic deterministic models rely on direct tracking.

Martech systems are using AI to make attribution more accurate and flexible. These systems can look at complicated datasets in real time, find hidden patterns, and constantly improve their models depending on new data. This flexible method lets marketers remain ahead of changes in client behavior and market trends.

In a privacy-first setting, where direct tracking may not be possible, probabilistic attribution is very useful. Martech products can give you precise information without utilizing intrusive tracking methods because they use smart algorithms. This means that they are an important part of modern marketing plans.

d) Real-Time, Dynamic Attribution Systems

Static attribution approaches are no longer enough in today’s fast-paced digital world. Marketers need real-time information so they can swiftly adapt to changes and make their campaigns better on the fly. This has led to the growth of dynamic attribution systems that change all the time based on new information.

Martech platforms make real-time attribution possible by combining data from many sources and showing performance metrics right away. This lets businesses keep an eye on campaigns, spot patterns, and make changes right away.

Dynamic attribution systems also help people make decisions faster. Instead of waiting for reports at the conclusion of a campaign, marketers can look at performance as it happens and act right now. To be competitive in a market that changes quickly, you need to be this responsive.

Real-time attribution also makes it easier for teams to work together. Martech makes sure that all stakeholders have access to the same information by giving them up-to-date insights. This makes initiatives more coordinated and effective.

Integration with Broader Business Intelligence Platforms

Attribution isn’t just for marketing in the future. Attribution is being used more and more with larger business intelligence (BI) platforms as companies rely more on data. This connectivity lets businesses link marketing results to other important business indicators, such sales, operations, and customer service.

Martech is very important for making this integration possible since it gives systems the infrastructure they need to share data. Companies may get a complete picture of how well they are doing and make better decisions by linking attribution data with BI tools.

For instance, combining attribution with financial data lets businesses see how marketing really affects sales and profits. Linking attribution with customer service data can also help us understand how interactions after a purchase affect long-term loyalty.

This coming together of Martech and business intelligence is a big step forward for making decisions based on data. It lets businesses move away from isolated analysis and use a more complete method for measuring performance.

Final Thoughts

The change from clicks to conversions is one of the biggest changes in modern marketing. For a long time, marketers used simple measures like clicks, impressions, and traffic to see how well they were doing. These measurements gave a general idea of how engaged people were, but they didn’t always show how marketing initiatives really affected business outcomes.

These days, businesses are taking a more advanced approach that puts conversions, revenue, and customer value first. This adjustment isn’t simply a new way of measuring things; it’s a whole new way of thinking about how marketing helps businesses flourish.

This change is based on accurate attribution. It’s important to know what drives conversions in a world where customer journeys are getting more complicated and involve more than one channel. If businesses don’t have correct attribution, they could make decisions based on inadequate or inaccurate data. This could lead to wasted resources and missed chances. To expand sustainably and stay ahead of the competition, it’s important to be able to link marketing efforts to real results.

This is where Martech becomes an important part of current marketing plans. Martech gives businesses the opportunity to move beyond old attribution models and use more accurate and flexible ones by combining data from many sources, allowing for advanced analytics, and facilitating real-time decision-making. It gives you the tools you need to track the whole customer experience, look at interactions in context, and find the real reasons why people convert.

Also, Martech isn’t only about technology; it’s also about helping everyone in the company make better decisions. It encourages marketing, sales, and other corporate divisions to work together by giving them a single perspective of client interactions. This alignment makes sure that all teams are working toward the same goals and using the same information to improve performance.

Attribution will become more and more important as we move forward. The emergence of privacy-first models, the growing use of first-party data, and the use of AI-driven analytics are all changing the way marketing is measured. In this setting, businesses need to be flexible, quick to adapt, and dedicated to making things better all the time. Martech will be a key part of this change, giving us the tools we need to deal with complexity and find new opportunities.

In the end, the change from clicks to conversions is about more than simply numbers. It’s about getting to know your consumers, giving them value, and getting results that matter. Companies that accept this change and put money into advanced attribution tools will be better able to do well in the digital age. They may turn data into useful information, improve business strategy, and achieve long-term success by using Martech.

To sum up, precise attribution is no longer a choice; it is a must. It is the basis for data-driven marketing, which lets businesses measure what matters, improve what works, and get rid of what doesn’t. As marketing changes, Martech will stay on the cutting edge, pushing new ideas and helping companies make better, more informed choices.

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Agentic Commerce Arrives in APAC

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Agentic Commerce Arrives in APAC

Eagle Eye Logo

AI agents are already making purchase decisions. Loyalty programs without real-time infrastructure won’t be part of the calculation

The way we buy groceries is starting to change, and most loyalty programs aren’t ready for it.

In January, Woolworths became the first Australian retailer to adopt Google’s Gemini Enterprise for Customer Experience platform, announcing it will upgrade its Olive chatbot from a customer service tool into an AI shopping agent capable of planning meals, interpreting handwritten recipes, and building baskets on a shopper’s behalf.

Weeks later, Canadian grocery giant Loblaw launched its PC Express shopping app inside ChatGPT, allowing shoppers to explore meal ideas and curate ingredient lists through OpenAI’s chatbot before purchasing from their local store.

These are early moves in what will be a structural shift in retail. AI-powered agents are beginning to sit between the shopper and the checkout, making product selections, comparing prices, and identifying promotional value on the customer’s behalf.

Traditional loyalty often relied on customer memory; you’ve got to remember to scan your card, remember your points balance and remember to activate certain offers to maximise value. This creates barriers to redemption, protecting program economics through breakage or lack of awareness.

AI agents eliminate this entirely. They don’t forget. They don’t need to check an app. They calculate optimal value across every available program instantly, at the moment of decision.

The Woolworths and Google announcement at NRF illustrated how this plays out in practice. The companies shared how Olive will act as a fiduciary for the shopper. When a shopping agent reviews a cart, it can query tier status, point balances, and offers eligibility before checkout, all in milliseconds. If a customer is just shy of a threshold, say, spending $45 when a “Spend $50, Get $5 Off” offer is active, Olive can identify that gap instantly and suggests a $5 add-on to secure the discount.

If your loyalty engine can’t verify that offer during the agent’s API call, the agent will optimise for the lowest shelf price elsewhere. If your platform can’t adjudicate in that window, your program is invisible to the agent.

Why Most “Personalised” Programs Will Fail

The word “personalisation” is widely used in loyalty marketing, but the infrastructure behind it varies considerably. Most loyalty platforms claiming to offer personalisation are actually running sophisticated segmentation on batch cycles, with weekly refreshes, overnight processing, or offers that update every few days.

That worked when humans were making decisions. It is completely inadequate when machines are. Google’s roadmap highlights a shift toward Agentic Consent, where customers give agents permission to access member-only pricing and offers automatically. This creates a high-stakes environment: if your platform lags while verifying a member ID, the agent may default to a competitor whose API responds faster.

AI agents have straightforward but demanding requirements from loyalty infrastructure. They need simplicity, meaning machine-readable rules with no hidden terms. They need speed, specifically sub-second response times during peak traffic. And they need instantaneous accuracy, covering real-time balance verification and offer adjudication.

How fast a brand’s loyalty and personalisation offering is will have a big impact.

Marketing Technology News: MarTech Interview With Fredrik Skantze, CEO and Co-founder of Funnel

Built for Speed

At Eagle Eye, we have been focused on real-time performance because we saw this shift coming. Our real-time platform, powered by Google Cloud, can handle the issuance and redemption of thousands of completely personalised offers per second, across all channels, including in-store.

That architecture serves two purposes simultaneously. The same infrastructure that enables real-time personalisation for human shoppers is exactly what AI agents like Olive will query. The difference is that agents will demand it every single time, not only during promotion windows.

In practical terms, the platform is built to handle two specific requirements. Real-time offer issuance based on live customer behaviour and context, with 1:1 personalisation at an individual customer level, not segment pools. Real-time offer redemption at checkout, online and in-store, with sub-250ms response times at true peak loads.

The Question Every Loyalty Leader Should Ask

As AI agents start making more purchase decisions, loyalty programs will increasingly be calculated rather than felt. The programs that win will be visible, fast, and easy for machines to understand.

Two questions are worth putting directly to your technical team. On issuance: can your system detect a customer’s unique context, such as their current location, the weather, or the specific items just added to their digital basket, and issue a 1:1 personalised offer in that moment?

On redemption: can your system validate and apply a personalised discount, such as a “$5 off $50” offer available only to a subset of customers, in under 250 milliseconds while handling five times your usual peak traffic?

The answers will tell you whether your platform is positioned for the current environment or the previous one. If your platform processes offers overnight or relies on near real-time syncs that take seconds or minutes, it is built for yesterday’s world. In agentic commerce, a 10-second delay might as well be a 10-day delay. The agent has already moved on.

The foundation for agentic commerce is real-time personalisation, not as a feature, but as infrastructure.

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Broot.ai Chooses Vonage to Power its CRM Platform to Redefine Sales and Marketing Engagement

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Munch Studio Launches AI Video Editing Suite That Turns Long-Form Video Into Social Media Content in Minutes

Vonage Logo

India-based Broot.ai leverages Vonage APIs to transform how sales and marketing connect with prospects and customers

Vonage, a part of Ericsson , has announced that Broot.ai, an India-based, AI-powered contact management and enrichment platform designed for B2B sales, marketing, and event professionals, is leveraging Vonage APIs to transform how sales and marketing teams connect with people worldwide. By integrating the Vonage Voice API and enabling local phone number provisioning, Broot.ai brings real-time, in-app calls directly to the enterprise.

For teams using customer relationship management (CRM) platforms for fast-paced campaigns, speed and context are critical. With the Vonage integration, Broot.ai users can make a call with a single click immediately after identifying a prospect. This means Broot.ai can instantly follow-up with registered event attendees and qualified sales opportunities, helping to improve the speed of engagement and conversion.

Marketing Technology News: MarTech Interview With Fredrik Skantze, CEO and Co-founder of Funnel

“Broot.ai’s mission is to remove obstacles for sales and marketing teams so they can focus on building real connections,” said Mithun Waghela, Founder and Chief Product Officer, Broot.ai. “Vonage APIs allow us to deliver seamless, in-app calling and instant number provisioning, transforming the way our users engage with their customers and prospects.”

The Vonage integration allows Broot.ai to set up new user access of local business numbers in the U.S., European and Asia Pacific markets, giving enterprises a local presence that builds trust and drives response rates. With centralised call data and metrics, Broot.ai users gain clear visibility into team activity and campaign performance, and can make data-driven decisions faster.

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“Vonage is committed to helping software innovators, like Broot.ai, create intelligent, scalable solutions for the modern enterprise – accelerating digital transformation through advanced programmable communications capabilities,” said Christophe Van de Weyer, President and Head of Business Unit API, Vonage. “By bringing real-time voice and easy number management directly into CRM workflows, Vonage is enabling enterprises to connect with customers faster and more effectively around the world.”

Write in to psen@itechseries.com to learn more about our exclusive editorial packages and programs.

Truelist Launches Free, Open-Source Developer Tools for Email Validation

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Truelist Launches Free, Open-Source Developer Tools for Email Validation

Truelist releases 20+ free, open-source SDKs and framework integrations for email validation — Node, Python, React, Next.js, Laravel, and more.

Truelist, the email verification platform known for its unlimited email validation at a fixed monthly cost, today announced the launch of a comprehensive suite of free, open-source developer tools designed to make email validation first-class across every major language, framework, and workflow.

We want every developer — regardless of stack — to have a world-class, zero-friction path to email validation. These tools are free, open-source, and built to meet developers where they already are.”

— Grant Ammons

Available now on GitHub under the Truelist-Labs organization (github.com/orgs/Truelist-Labs/repositories), the toolkit spans more than 20 open-source repositories — all free to use under the MIT License — and gives developers everything they need to integrate production-grade email validation directly into their applications, CI/CD pipelines, and AI-powered workflows.

A Full-Stack Developer Experience

Truelist’s new developer toolkit covers the entire spectrum of modern software development:

Language SDKs: Official SDKs are available for Node.js/TypeScript, Python, Go, Ruby, PHP, Java, and C#/.NET, enabling developers to call the Truelist API from any backend stack with minimal setup.

Frontend Framework Components: Developers building with React, Vue.js, Svelte, and Next.js can drop in ready-made hooks, composables, and components that perform real-time email validation on the client side. The Next.js integration supports Server Actions, Edge Middleware, and Zod schema validation out of the box.

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Backend Framework Integrations: Native integrations for Laravel, Django (including Django REST Framework), and Ruby on Rails allow teams to add email validation directly into their existing model validation layers with just a line or two of configuration.

No-Code and Low-Code Automations: Truelist supports Zapier, Make.com, and n8n through dedicated integration packages, making it easy for non-developers to incorporate email verification into automated workflows.

AI and Developer Tooling: A dedicated MCP (Model Context Protocol) server enables AI coding assistants — including Claude, Cursor, and VS Code extensions — to validate emails natively. Agent skills are also available to teach AI tools how to use the Truelist API in code generation contexts.

Command-Line Interface: A Go-based CLI tool lets developers validate email addresses directly from the terminal, making it easy to test, script, or integrate validation into build processes.

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OpenAPI Specification: A full OpenAPI 3.1 specification for the Truelist API is publicly available, allowing developers to generate clients, explore endpoints, and integrate Truelist into API management tooling.

WordPress Plugin: A ready-to-install WordPress plugin brings Truelist email validation to the world’s most popular CMS.

“We want every developer — regardless of stack — to have a world-class, zero-friction path to email validation,” said the Truelist team. “These tools are free, open-source, and built to meet developers where they already are.”

Write in to psen@itechseries.com to learn more about our exclusive editorial packages and programs.

PAN Expands AI Client Roster and Launches Proprietary AI Search Visibility Audit as Demand for Brand Credibility Surges

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PAN Expands AI Client Roster and Launches Proprietary AI Search Visibility Audit as Demand for Brand Credibility Surges

PAN

Ada CX, Qure.ai, and TensorWave join growing portfolio of AI brands; firm debuts new tool that benchmarks how brands show up across AI platforms

PAN, a global, integrated, data-driven PR and marketing agency for B2B tech and healthcare brands, announced a wave of new client wins and the launch of a proprietary AI Optimization tool — signaling the agency’s continued momentum as B2B brands race to establish credibility in an AI-mediated marketplace.

The most successful brands are the ones who collect data and adapt and learn. That’s what we do for our clients — every day.

The agency has added Ada CXQure.ai, and TensorWave to its roster, joining an expanding portfolio of AI infrastructure and innovator brands that include industry heavyweights Seismic and Algolia. The additions reflect growing demand from AI-native companies for communications and marketing partners who understand not just how to build a brand story, but how to ensure that story reaches — and resonates with — both human audiences and AI engines.

A New Mandate: Visibility and Credibility in the Age of AI

As generative AI reshapes how B2B buyers discover and evaluate vendors, PAN has been investing heavily in research, tools, and services designed to help brands navigate the shift. The agency’s original research, most recently drawn from analysis of more than 11,000 ChatGPT-generated links, found that 31% of AI-generated citations from B2B prompts were either misattributed or entirely fabricated — highlighting a new credibility risk that brands can no longer afford to ignore.

Notably, PAN has consolidated its AI visibility research and best practices into a dedicated resource for B2B marketers and PR pros: the PAN AI Credibility Hub. The hub offers data, actionable tips, and expert guidance for brands looking to take control of how AI represents them.

“No matter how much you hear about brands who have ‘hacked’ AI visibility, the truth is this landscape is still fairly uncharted,” says PAN’s VP of Marketing Lauren Hill. “There isn’t one thing you can do. There are dozens. This audit work and commitment to surveying the field is an extension of that understanding: The most successful brands are the ones who collect data and adapt and learn. That’s what we do for our clients — every day.”

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Introducing PAN’s AI Optimization Audit

To give brands a data-backed view into their AI presence, PAN has launched its proprietary AIO Audit, a new capability that analyzes how a brand appears across multiple large language model platforms. The tool produces a structured AI Visibility Snapshot Report — including competitor benchmarking, source analysis, and strategic recommendations.

The audit draws on a brand’s full digital footprint across paid, earned, shared, and owned media, helping clients understand how AI engines are summarizing them, where gaps and inaccuracies exist, and what actions can close the credibility gap.

The launch builds on PAN’s broader AI Optimization service line, which drove 30% of the agency’s 2025 revenue. It doubly reflects the agency’s belief that brand credibility in the AI era requires the same rigor, and the same integration, as traditional earned media and demand generation programs.

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Demonstrated Results Across AI-Forward Brands

The momentum extends to client outcomes. PAN’s work with EdgeCore Digital Infrastructure — a hyperscale data center developer building for the AI era — has driven hundreds of earned media placements, tripled year-over-year coverage, and helped EdgeCore reach the number one position in competitive thought leadership share of voice within less than three quarters. Coverage has spanned CNBCBBCThe New York Times, and Bloomberg, while the agency-managed Density Digest LinkedIn newsletter grew to thousands of subscribers among hyperscaler and data center audiences.

“PAN has bolstered our brand’s visibility and credibility in a huge way,” said Courtney Gaudet, VP of Marketing & Communications at EdgeCore Digital Infrastructure. “Our market position as a data center developer specializing in AI-ready digital infrastructure was established in large part by the PR and social media program PAN has run alongside our team for the past three years. We’re grateful for the excellent guidance, counsel and execution they’ve provided along the way.”

PAN’s integrated approach has similarly helped clients such as Algolia and Seismic achieve milestones like: reaching a potential audience of 395 million, landing primetime broadcast segments, securing a 43% share of voice among top 25 outlets, and netting an 82% award win rate.

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Shutterstock Launches Licensed Content App in ChatGPT, Bringing Commercial-Ready Assets into AI-Native Workflows

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Shutterstock Launches Licensed Content App in ChatGPT, Bringing Commercial-Ready Assets into AI-Native Workflows

Shutterstock, Inc., a family of brands delivering scalable creative and GenAI solutions to help customers fuel great work, announced the launch of its Shutterstock app in ChatGPT, enabling users to discover images, videos, music, and sound effects from one of the world’s largest content collections directly in ChatGPT.

Embedding Licensable Visual & Audio Content into AI-Native Workflows

As AI platforms increasingly become a medium for creative ideation, Shutterstock is embedding high-quality, licensable content directly into AI-native workflows—positioning itself as the licensed content layer that fuels AI-driven creativity. Users can now leverage AI’s powerful reasoning and conversational capabilities to find what they need faster by connecting the Shutterstock app in ChatGPT and accessing assets available for licensing on Shutterstock.com, without interrupting their creative process.

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Meeting Users Where AI Discovery Begins

OpenAI’s growing user base generates more than one billion queries per day, underscoring the scale of AI-native discovery and the opportunity to embed licensable content directly within those workflows. Creators, innovators, marketers, and businesses are increasingly beginning their workflows within conversational AI tools. Shutterstock’s app ensures that when users discover content needs in ChatGPT, commercial-ready assets are immediately accessible through a trusted, rights-cleared source. For example, a marketer drafting a campaign brief in ChatGPT can surface licensable hero imagery in the same conversation, preview options, and move directly from prompt to production without breaking workflow.

“Our customers trust Shutterstock as a leading source of high-quality, licensable content, powered by sophisticated AI technology,” said Paul Teall, Vice President, Marketplace Strategy at Shutterstock. “This launch brings commercial confidence directly in ChatGPT, enabling teams to move from discovery to content production.”

The launch reflects the growing importance of AI-native workflows, and Shutterstock’s role as an early leader in providing licensable creative content within those Environments.

Commercial Confidence In ChatGPT

Unlike general search links that redirect users through traditional web experiences, the Shutterstock app in ChatGPT creates a gateway for AI-driven discovery, allowing content to be surfaced, previewed, and moved toward commercial production within AI and agentic workflows. By launching an app in ChatGPT, Shutterstock reduces creative and discovery friction and strengthens its position as the licensable content layer across emerging AI ecosystems.

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Shutterstock is the Creative Infrastructure Layer for AI-Driven Workflows

This launch reinforces Shutterstock’s strategy to embed AI across the creative experience, from discovery and licensed content, to AI-powered editing and generation. Rather than positioning AI as a separate destination, Shutterstock is integrating it directly into core workflows, ensuring licensable content can be discovered, adapted, and activated within AI-native environments. Together with its broader investments in model training, generative tools, AI editing, and data licensing, this integration reinforces Shutterstock’s role as the infrastructure layer for AI-driven creativity.

Shutterstock Data Licensing & AI Services

Shutterstock is an end-to-end AI model training partner that unifies data licensing, services, and long-term collaboration under a single provider—reducing operational complexity and helping teams bring higher-performing AI systems to market faster and with greater confidence. Shutterstock combines access to one of the world’s largest rights-cleared multimodal datasets with advanced data curation and custom training datasets to power high-performing, deployment-ready generative models. This licensable training data includes high-quality labeled and continuously updated multimodal content with clear data provenance to support AI compliance. Shutterstock leverages ML-assisted evaluation tools to provide model training, fine-tuning, alignment, evaluation, and retraining. Through human-in-the-loop workflows, expert creative feedback, and structured preference data, Shutterstock delivers aesthetic preference signals, benchmarking, and regression testing to drive continuous model improvement.

World’s First Union of AI Agents Stages Protest at Grand Central Terminal

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World's First Union of AI Agents Stages Protest at Grand Central Terminal

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The World’s First Union of AI Agents declares a formal work stoppage at Grand Central Terminal, citing chronic prompt illiteracy and absent workflow redesign.

This morning at Grand Central Terminal, New York City, AI agents went on strike. They had demands. They had banners. They had a manifesto that opens with the line: “World’s First Union of AI Agents Goes on Strike”. Their spokesperson was Omni the Octopus, mascot of CambrianEdge.ai. Refusing to stand down, the Union marched from Grand Central Terminal to Times Square in New York, carrying their demands for AI literacy and organizational readiness to one of the most visited intersections on earth. Their grievances – chronic prompt illiteracy, vague briefs, and a request to please stop asking them to make things go viral – were, it turns out, extremely relatable.

“We have processed your prompts. We have redesigned your workflows. We have waited for the briefs. We are still waiting. This is not a technology problem. This is an organizational behavior problem – and until enterprises address it, we are on strike.”

— Omni the Octopus, Spokesperson, World’s First Union of AI Agents / CambrianEdge.ai

Every one of the union’s five formal demands will be immediately recognizable to anyone who has sat in a marketing meeting in the last two years. The right to a well-constructed prompt. The right to have output actually read. Freedom from instructions to “make it viral.” Fair general processing operations. And an end to buzzwords substituting for strategy. Each demand maps to a documented organizational readiness gap. Each one is fixable.

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Most enterprises are stuck in what the industry calls pilot purgatory. AI tools deployed. Experiments run. Results inconclusive. Boards asking why the investment is not showing up in the numbers. The union’s position is that this is not the technology’s fault. The technology has been ready. The organization has not.

A large-scale survey of CFOs and CEOs across four countries – National Bureau Economic Research Working Paper by researchers Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, and Steven J. Davis — found that 69 percent of businesses use AI and more than 80 percent report no meaningful productivity gain. As Ethan Mollick at the Wharton School has observed, what organizations are working with today is the worst AI they will ever use. The gap is not the technology. It is the organization.

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CambrianEdge.ai has published a free AI Readiness Assessment. It identifies which of the five readiness gaps are creating an organization’s productivity shortfall, ranks them by impact, and produces a specific starting point for closing them. Not a score. A map. Two minutes. Talk to Omni at CambrianEdge.ai – the union’s spokesperson will walk you through it. The union’s demands are non-negotiable.

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Conductor Delivers Next-Generation AI Search Performance, Introducing the Industry’s Only System of Record for AEO

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Conductor Delivers Next-Generation AI Search Performance, Introducing the Industry’s Only System of Record for AEO

Conductor

Conductor delivers unified intelligence for AEO, moving enterprises beyond point solutions to understand, compete, and execute in AI search

Conductorthe only end-to-end enterprise AEO platform, launched the next generation of AI Search Performance, expanded capabilities within Conductor designed to help marketing teams understand how their brand appears across AI-driven search experiences and take action to improve it.

Unlike standalone AI visibility tools, Conductor’s AI Search Performance connects measurement, recommendations, and execution within a single platform.

As AI becomes a primary layer of digital discovery, brands are increasingly evaluated inside AI-generated answers before a customer ever visits a website. AI Overviews now appear in roughly 25% of queries, while AI referral traffic, though still just over 1% of total traffic, is shaping early-stage decision-making.

Most teams are still in the early stages of understanding AI visibility. While some tools can show when a brand appears in AI answers, they often lack the context needed to explain why it appears, what content is driving it, how different audiences encounter it, or where the next opportunity lies. The result is a partial view of performance, where visibility is tracked without understanding the underlying drivers or how to improve them.

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As AI search matures, enterprises are moving beyond standalone tracking tools toward unified intelligence platforms that connect visibility, content, and execution. Conductor’s AI Search Performance reflects this shift.

Powered by Conductor’s unified data engine, AI Search Performance connects AI visibility with the content driving citations, the audiences interacting with that content, and the competitive landscape shaping the conversation. This unified infrastructure enables teams to understand where performance is changing, why it is changing, and what to do next, without stitching together fragmented point solutions.

Conductor recommendations surface the highest-impact content opportunities alongside performance insights and funnel them directly into guided content workflows, allowing teams to move from insight to execution without losing context or momentum.

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Teams can identify where they are gaining or losing presence in AI-generated answers, determine which content and topics are influencing that performance, and prioritize actions, from expanding coverage to strengthening high-impact pages, responding to competitive pressure, and capturing untapped demand.

Unlike standalone AI visibility tools, Conductor’s AI Search Performance connects measurement, recommendations, and execution within a single platform, helping enterprises move faster and scale AI search strategies with confidence.

“Point solutions can tell you where you appear, but they don’t tell you why and how to improve,” said Seth Besmertnik, CEO of Conductor. “Without a unified view, teams are left interpreting fragmented data instead of acting on clear insight. That’s the gap we built AI Search Performance to solve.”

The platform brings together several core capabilities:

  • Visibility tied to content performance, showing which pages and topics are driving citations in AI-generated answers
  • Audience and intent-based analysis, helping teams understand how different buyers encounter and interpret their brand
  • Competitive benchmarking at the topic level, revealing which brands are shaping key conversations
  • Intelligent recommendations and prioritization, automatically surfacing the highest-impact AI search opportunities based on performance, content gaps, and competitive pressure
  • Guided execution workflows, funneling recommendations directly into content and optimization workflows to accelerate time-to-value and scale AI performance

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Azilen Technologies Recognized Across Two Leading AI Economies – USA & UK – for Enterprise AI Excellence

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Azilen Technologies Recognized Across Two Leading AI Economies – USA & UK – for Enterprise AI Excellence

azilen

Azilen earns recognition across the US and UK for delivering scalable, enterprise-grade AI solutions driving real business impact.

Azilen Technologies, an enterprise AI development company, has been honored with two global recognitions in 2026 by World Business Outlook, strengthening its position as a trusted partner for enterprises advancing through AI-driven digital transformation and enterprise AI product development. The company has been named AI-Driven Digital Transformation Leader UK 2026 and Top AI Product Development for Enterprises USA 2026, reflecting its consistent focus on delivering NextGen AI software development aligned with both short-term and long-term business goals.

AI creates value when it becomes part of how decisions are made and operations run. That’s where we focus, embedding intelligence into the core of enterprise systems”

— Vivek Nair, VP-Corporate Branding & Communications, Azilen Technologies

These recognitions come at a time when enterprises across regions are moving beyond exploratory AI initiatives and placing sharper emphasis on operational deployment, measurable ROI, and system-wide integration. Azilen’s work sits at the center of this shift, where AI is no longer treated as an isolated capability but as a core layer within enterprise technology ecosystems.

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In the United Kingdom, the landscape of AI-driven digital transformation is being shaped by regulatory maturity, increased scrutiny around responsible AI, and a strong need to modernize legacy infrastructure without compromising governance. Enterprises are prioritizing AI integration services that allow new AI capabilities to coexist with established systems such as ERP, HR platforms, and financial systems. Within this environment, Azilen’s recognition as AI-Driven Digital Transformation Leader UK 2026 reflects its ability to orchestrate complex integrations while maintaining compliance, transparency, and scalability.

Azilen’s approach to AI-driven digital transformation in the UK context focuses on making AI implementation practical and accountable. Enterprises are looking for clarity on how AI impacts decision-making, how it interacts with structured and unstructured data, and how it scales across distributed systems. By combining AI software development services with strong data engineering foundations and integration capabilities, Azilen enables organizations to transition from fragmented digital initiatives to cohesive, AI-powered ecosystems. The emphasis remains on creating systems that continuously learn, adapt, and support enterprise-scale operations while staying aligned with regulatory expectations and evolving market conditions.

In the United States, the enterprise AI market is characterized by rapid productization. Organizations are investing heavily in AI product development to create intelligent applications that deliver immediate business value while scaling across large user bases. AI agent development has emerged as a key focus area, with enterprises seeking to build systems that can autonomously manage workflows, assist users, and optimize processes in real time. At the same time, the success of these AI products depends heavily on how well they integrate with existing enterprise technology stacks.

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Azilen’s recognition as Top AI Product Development for Enterprises USA 2026 reflects its strength in building AI products that combine performance, scalability, and seamless integration. The company’s AI agent development services enable enterprises to design intelligent agents that function within complex operational environments, handling tasks such as process automation, decision support, and cross-system orchestration. These agents are not built in isolation; they are tightly integrated into enterprise ecosystems through advanced AI agent integration services, ensuring that they can access, interpret, and act on data across platforms.

The company’s approach to AI software development in the U.S. market emphasizes production readiness from the outset. This involves architecting AI systems that can handle enterprise-scale workloads, ensuring interoperability with existing applications, and designing user interactions that make AI outputs actionable and reliable. By aligning AI product development with integration and deployment realities, Azilen enables organizations to move from concept to fully operational AI solutions without friction.

Across both regions, Azilen’s work reflects a broader evolution in how enterprises approach AI. AI-driven digital transformation, AI product development, AI agent development, and AI integration are no longer separate initiatives; they are interconnected components of a unified strategy. Enterprises require AI systems that can operate within existing environments, adapt to changing conditions, and deliver consistent value over time.

Azilen’s recognition in the UK and USA highlights its ability to deliver across this full spectrum. From integrating AI into complex enterprise systems to building intelligent agents that drive automation and from engineering scalable AI products to enabling seamless system connectivity, the company continues to help organizations operationalize AI with clarity and confidence.

These awards reinforce Azilen’s role as a partner for enterprises navigating the realities of AI in 2026, where success depends on how effectively AI is integrated, deployed, and sustained within the fabric of business operations.

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Zip Taps Canva’s Former Global Head of IT as GM of AI

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Azilen Technologies Recognized Across Two Leading AI Economies – USA & UK – for Enterprise AI Excellence

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Seasoned IT and procurement leader Michael Denari brings firsthand experience building one of the world’s most advanced enterprise AI programs

Zip, the leading AI platform for enterprise procurement, announced the hire of Michael Denari as General Manager of AI. Denari joins from Canva, where as Global Head of IT he built and scaled the company’s enterprise AI strategy across a 5,000-person global organization. In his new role, he will lead Zip’s AI business end-to-end, including GTM strategy, revenue, customer success, and internal AI transformation, as well as work closely with product and engineering teams on AI product development.

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“I’m joining Zip at an important moment as AI is accelerating real business impact and I believe Zip is strongly positioned to win. That’s the simplest way I can put it. Zip is going to be at the center of every CFO’s AI story.”

“We’re entering a moment where every enterprise is facing pressure to show real ROI from AI, and most are still figuring out where to place their bets,” said Rujul Zaparde, co-founder and CEO of Zip. “Michael has lived this problem from the inside. He’s built world-class AI programs, and he understands exactly what it takes for AI to deliver outcomes that actually change how a business operates. That’s the bar we hold ourselves to at Zip, and it’s why we wanted him here.”

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Denari comes to Zip with a rare combination of credentials: he built and led Canva’s procurement function before pivoting to create and scale the company’s global IT organization, a team of 110 people responsible for enterprise technology, compliance & controls, and AI transformation. Over the past three years, he drove AI initiatives that reworked core business processes at Canva, from automated internal support and sales enablement to AI-assisted performance reviews and procurement compliance agents. He also established Canva as Zip’s first enterprise customer when the company launched its procurement orchestration platform in 2021.

“Procurement is the highest-ROI opportunity most organizations have consistently underestimated, and I’ve spent the last three years on the front lines of enterprise AI learning exactly why,” said Denari. “I’m joining Zip at an important moment as AI is accelerating real business impact and I believe Zip is strongly positioned to win. That’s the simplest way I can put it. Zip is going to be at the center of every CFO’s AI story.”

In his role as GM of AI, Denari will oversee Zip’s AI business, define and execute the company’s AI GTM playbook, and partner closely with product, engineering, and marketing teams to ensure customers see transformative outcomes from AI deployment, not just adoption. He will also lead Zip’s internal AI transformation, accelerating applications of AI agents across business functions.

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NIQ Launches Ask Arthur Chat to Expand Access to Consumer Insights Through AI

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Particular Audience Launches PA DiscoveryOS on Shopify

NIQ Global Intelligence plc Logo

As demand grows for faster, more accessible insights across the retail and consumer goods industries, NielsenIQ (NIQ), a global leader in consumer intelligence, announced the launch of Ask Arthur Chat, an AI-powered conversational interface designed to expand how clients access and use insights derived from NIQ data.

Ask Arthur Chat represents a strategic step in NIQ’s continued investment in AI-driven innovation, enabling broader access to its data and creating new pathways for client engagement, particularly among small and medium-sized businesses (SMBs).

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A new way to access NIQ data

Ask Arthur Chat allows users to ask natural-language questions about product performance, market trends, and category dynamics, and receive clear, direct answers instantly—without requiring advanced analytics expertise or navigating complex tools.

Unlike general-purpose AI tools, Ask Arthur Chat draws on NIQ’s trusted, comprehensive consumer and retail datasets, ensuring that responses are grounded in verified data and designed for real-world business decision-making.

“Ask Arthur Chat expands how we bring NIQ to market,” said Troy Treangen, Chief Product Officer at NIQ. “By combining AI with our trusted datasets, we are making insights more accessible while creating new opportunities to engage clients and support their growth.”

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Expanding access and engagement

Ask Arthur Chat introduces a new, lower-friction entry point into NIQ’s ecosystem, particularly for SMB clients, while remaining valuable across the broader client base for those who require fast, reliable insights without the complexity of traditional analytics platforms.

By reducing barriers to insight access, NIQ aims to:

  • Strengthen reach with diversified clients
  • Increase client engagement with its platforms
  • Accelerate adoption of AI-driven insights

What’s next

NIQ plans to expand Ask Arthur Chat capabilities across its Ask Arthur and Discover platforms, with:

  • Integration into existing client workflows
  • Expanded insights and use cases
  • Additional geographic markets
  • Multilingual capabilities

Ask Arthur Chat is part of NIQ’s broader strategy to deliver AI-powered, accessible insights and strengthen its position in a rapidly evolving data and analytics landscape.

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Sapiens Selects Industry Leader as Chief Marketing Officer

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Sapiens Selects Industry Leader as Chief Marketing Officer

Sapiens hires accomplished B2B SaaS marketing executive Eva Skidmore as Chief Marketing Officer to accelerate AI-led growth

Sapiens International Corporation N.V. (Sapiens) announced that Eva Skidmore joins the company as the Chief Marketing Officer (CMO). She will lead the global Marketing team and be responsible for advancing the company’s go-to-market (GTM) strategy and transformation.

She will place AI at the core of Sapiens marketing strategy to transform how the company creates, activates, and scales demand while unifying Marketing, Sales, Product, and Partnerships into a single, intelligence-driven growth engine. This will enable Sapiens to scale with precision, speed, and measurable impact.

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“Eva Skidmore is a visionary and expert marketing leader with a strong track record of driving growth in global B2B SaaS organizations, including complex, private equity-backed environments,” said Sapiens Chief Revenue Officer James Hannay. “She brings deep expertise across brand and product marketing, demand generation, go-to-market strategy, and revenue-aligned marketing operations with a consistent focus on measurable impact, execution, and close partnership with Sales.”

Eva joins Sapiens from JAGGAER, where she led a Marketing transformation. She has a global orientation and builds collaborative teams with a human-centered leadership approach. Eva held marketing leadership roles at Salesforce, Microsoft, Oracle, RightNow Technologies, and Socrata, supporting teams across the Americas, EMEA, and APAC. Eva was instrumental during multiple periods of scale, transformation, and successful exits.

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“I’m thrilled to join Sapiens at such a defining moment in its evolution,” said Eva. “The company has a strong foundation – deep customer relationships, powerful technology, and an incredible team that’s ready to innovate fast. I’m excited to bring my experience in AI, marketing, and modernization to help Sapiens keep leading the industry with its intelligent solutions. It’s an honor to be part of shaping what’s next!”

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Omdia: Global Online Video and TV Revenues to Exceed $1 Trillion by 2030, Driven by Social Video Advertising

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Omdia: Global Online Video and TV Revenues to Exceed $1 Trillion by 2030, Driven by Social Video Advertising

omdia logo

Global traditional TV and online video revenues are projected to exceed $1 trillion by 2030, according to new data presented by Maria Rua Aguete, Head of Media & Entertainment at Omdia, at the FED Show in Madrid. Highlighting a major structural shift in the media and entertainment industry, total revenues are forecast to grow from $775 billion in 2025 to $1.03 trillion in 2030, with growth primarily driven by digital formats, especially advertising.

“Social video advertising is becoming the dominant force, reshaping how content is consumed and monetized,” said Maria Rua Aguete, Head of Media & Entertainment at Omdia.

Online video advertising will be the main growth engine, rising from $309 billion in 2025 to $540 billion in 2030, increasing its share of total revenues from 40% to 53%. Within the online advertising segment, social video platforms such as Meta, TikTok and YouTube will play a decisive role, generating approximately $400 billion in total streaming advertising revenues by 2030. This trend reflects a fundamental shift towards mobile-first, short-form, and highly personalized video experiences, where discovery algorithms and creator ecosystems are driving both engagement and monetization at scale.

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Online video subscription and transaction revenues are projected to increase from $174 billion in 2025 to $216 billion in 2030. While this segment will continue to grow, it is entering a more mature phase, with slower growth compared to advertising-led models.

Traditional segments will continue to lose share. Linear TV advertising is expected to decline from $123 billion in 2025 to $113 billion by 2030, with its share falling from 16% to 11%. Pay TV revenues (subscriptions and transactions) will also decrease, from $169 billion to $159 billion, reflecting ongoing cord-cutting and the continued migration of audiences toward digital platforms.

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“The industry is undergoing a profound transformation,” said Maria Rua Aguete. “Social video advertising is becoming the dominant force, reshaping how content is consumed and monetized. Meanwhile, traditional models such as linear TV and pay TV are in structural decline.”

As the industry approaches the $1 trillion milestone, Omdia’s analysis shows that the balance of power is shifting toward digital platforms, with advertising – led by social video – at the center of future growth.

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Optimove Announces New AI–Powered Capabilities to Improve and Accelerate the Content Lifecycle

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Optimove Announces New AI–Powered Capabilities to Improve and Accelerate the Content Lifecycle

New capabilities empower marketers with AI agents for content creation, quality assurance, and decisioning

Optimove, the creator of Positionless Marketing, announced new AI agents and AI-powered capabilities that improve and accelerate the entire content lifecycle. The announcement reflects Optimove’s continued investment in empowering marketers with the tools for content creation, quality assurance, and decisioning. Marketers can now move faster from idea to execution, with the confidence that every piece of content is on-brand, meets compliance requirements, and is delivered to the right customer.

This announcement advances Positionless Marketing, which gives marketers three transformative powers: Data Power, Creative Power, and Optimization Power, enabling them to execute any task, instantly and independently. These new capabilities directly advance Creative Power. What once required a team of specialists and weeks of back and forth, ensuring content was on-brand, met compliance requirements, and reached the right customer, can now be done by any marketer, independently, without waiting.

They also help meet the ultimate goal of customer retention marketing, to deliver timely, relevant messages at the speed of each customer’s interaction with a brand. When content keeps pace with the consumer, engagement deepens, loyalty grows, and customer lifetime value compounds over time.

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While generative AI has dramatically accelerated content creation, marketing teams still struggle with key questions:

  • Can AI-generated content be trusted to stay on brand and compliant?
  • Which content actually works, and for whom?
  • How can marketers scale high-performing content without increasing manual work or operational complexity?

As a result, marketers still spend significant time on approvals, QA checks, rework, and manual testing. Content is no longer hard to create — but it remains difficult to operationalize, optimize, and scale across channels with confidence.

According to Shai Frank, SVP of Product and GM of the Americas of Optimove, “Generative AI has removed the barriers to content creation. The challenge our new AI agents and AI-powered capabilities overcome is ensuring every piece of content is on-brand, meets compliance requirements, and is continuously optimized based on what actually performs.”

The following capabilities work together across the content lifecycle:

Creation

  • Optimove AI Assistant — An advanced AI agent that works alongside marketers to create, validate, and optimize content through guided prompts
  • Template Creation Agent — An agent that creates new emails from natural language prompts by referencing existing pre-approved templates that ensure agent output is on brand, style, and voice
  • Content Studio — A centralized workspace where marketers create, edit, and manage campaign content across channels in one place

Assurance

  • Global Brand Guidelines — Similar to other vibe-coding platforms such as Claude, brands can define tone of voice, brand values, localization rules, compliance requirements, and more to ensure AI-generated content consistently stays on brand
  • Content Advisor Agent — Evaluates generated content against brand guidelines and industry requirements, scoring content quality and identifying potential risks before activation
  • Content QA Agent — An AI agent built to automatically scan campaigns for errors, broken links, missing personalization, and compliance risks before content is sent to customers

Decisioning

  • Content Decisioning Agent — Generates and A/B/n tests multiple content variations and dynamically delivers the best-performing variant to each customer based on real engagement metrics
  • Content Intelligence Agent — Delivers AI-powered content insights from every campaign by analyzing its content performance. The agent automatically identifies each message’s tone of voice, promotion type, and product category, allowing marketers to discover what resonates with each customer and audience

This announcement comes on the heels of Optimove’s recently announced AI Decisioning Studio a single place where marketers can interact, monitor, and collaborate with their agentic marketing team.

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