Enterprise AI is booming, messy, and—more often than many leaders admit—dangerously inaccurate. OpenText thinks it knows why: organizations have unleashed AI on oceans of unstructured, unlabeled, poorly governed data, then act surprised when the models hallucinate, misinterpret, or leak sensitive information.

This week at OpenText World 2025, the company revealed its counterstrategy: the OpenText AI Data Platform (AIDP), an open, governed data layer engineered to give enterprise AI the one thing it consistently struggles with—context.

Where other vendors chase bigger models or flashier agents, OpenText is doubling down on its heritage: decades of document management, metadata discipline, and enterprise-grade information governance. In an era where half of AI-using organizations report at least one serious accuracy or risk failure (McKinsey’s numbers, not OpenText’s), the pitch hits close to home.

OpenText’s message is blunt: if the data is wrong, the AI will be wrong—no matter how impressive the model is.

Why Contextual AI Is the Hill OpenText Wants to Die On

OpenText has spent more than 30 years holding, securing, and classifying some of the world’s largest enterprise datasets. That experience underpins its thesis: AI agents only become useful when they understand where they are, what they’re allowed to see, and why a task matters.

Documents. Tickets. Commerce records. Security logs. Machine outputs. Human inputs.
All tagged, secured, governed, versioned, and compliant.

OpenText says enterprises must treat AI less like a chatbot experiment and more like a discipline rooted in data lineage, identity access control, retention policies, and contextual metadata. Otherwise, even the smartest models become highly efficient generators of confusion.

This foundation feeds directly into OpenText Aviator, the company’s enterprise AI engine, which can now orchestrate workflows through domain-aware agents.

The Three Pillars of Aviator’s Architecture

OpenText insists it’s not building another AI walled garden. Aviator’s architecture leans heavily into openness:

1. Multi-cloud
   Works across on-prem, cloud, hybrid, or multi-cloud deployments.

2. Multi-model
   Compatible with any LLM or SLM—including “bring your own model.”

3. Multi-application
   Built for deep integration with ERP, CRM, ITSM, security suites, and more.

In reality, this means OpenText wants its AI agents to plug into the daily arteries of enterprise work—from SAP order flows to Salesforce deals to Oracle records to Microsoft infrastructure.

“Everyone is chasing the mega-agent. But enterprises need armies of domain-specific agents,” said Savinay Berry, CPO & CTO at OpenText. “Accuracy through trusted data isn’t an IT feature—it’s a C-level mandate.”

The Databricks Partnership: A Strategic Data Power Play

A major announcement embedded in the platform launch is OpenText’s expanded partnership with Databricks. The companies will co-innovate on AIDP with deeper technical integrations, Delta Sharing, and a unified governance path.

OpenText already ran Threat Detection and Response on the Databricks Data Intelligence Platform. Now the partnership widens into joint engineering.

The intent is clear:
Combine Databricks’ analytics engine with OpenText’s governed data fabric to deliver trustworthy, enterprise-ready AI.

If successful, this pairing could become a serious contender against Microsoft’s Fabric, Google’s Vertex-BigQuery pipeline, and Snowflake’s AI-ready enterprise stack.

Inside the 18-Month Roadmap: OT 26.1 to OT 27.2

At OpenText World, the company revealed a surprisingly detailed roadmap for the next six releases:

OpenText AI Data Platform (AIDP)

A unified data and AI framework with governance orchestration. Think of it as a control tower for every agent decision.

OpenText Aviator Studio

A no-code environment for building and governing enterprise AI agents—without requiring data scientists to hand-craft pipelines.

OpenText Knowledge Discovery

A metadata-first ingestion engine that transforms structured and unstructured data into AI-ready context.

OpenText Data Compliance

A suite spanning privacy, tokenization, encryption, PII controls, redaction, AI readiness checks, and threat detection.

OpenText Aviator AI Services

A professional services track to help enterprises move from AI experiments to production-grade agent deployments.

This aggressive roadmap signals OpenText’s belief that the battle for enterprise AI will be fought not in the model layer, but in the data and governance layer.

What Enterprises Can Use Today

OpenText emphasized that Aviator is already live for real-world use cases like:

• fraud detection

• claims management

• predictive maintenance

• customer service automation

• IT operations workflows

The company also announced that the Aviator entry-tier package will be included at no extra cost with upgrades to OT 26.1 for Content Management, Service Management, and Communications Management.

Better yet for risk-averse industries, Aviator will become fully available on-premises starting with OT 26.1 across multiple modules, including DevOps and Application Security.

For global enterprises navigating sovereignty laws, this on-prem push is a quiet but important differentiator.

What This Means for the Enterprise AI Landscape

OpenText is staking out a clear and contrarian position:
AI models do not matter unless the data behind them is governed, contextual, and trustworthy.

This philosophy diverges sharply from model-first players—hugging the foundational layers of enterprise information instead of competing in the model arms race. With model commoditization accelerating, that may prove to be a winning angle.

AIDP also signals a broader industry shift toward:

• governed AI pipelines

• enterprise-grade agent orchestration

• model-agnostic architectures

• contextual knowledge layers

• compliance-integrated design

In short, OpenText is rewriting AI around the data source, not the model endpoint.

If other vendors follow, the next generation of enterprise AI may finally behave less like an unpredictable intern and more like a dependable colleague.

Get in touch with our MarTech Experts.

When it comes to communication, federal agencies operate under an impossible paradox: they must modernize fast—without making a single mistake. Messaging apps like Teams, SMS, and WhatsApp have become the backbone of everyday collaboration, yet government environments remain bound by some of the strictest security and compliance rules in the industry.

That's the gap LeapXpert and Iron Bow Technologies are now aiming to close.

The two companies have announced a partnership to bring secure, compliant, audit-ready messaging solutions across U.S. government agencies—a move that feels less like a nice-to-have and more like long-overdue modernization.

The Federal Messaging Problem No One Likes to Talk About

Most agencies have already embraced modern collaboration platforms, but using them securely is a different challenge altogether. Communications need to be encrypted, discoverable, logged, and retained according to frameworks like NIST 800-53 and the increasingly important CMMC.

For federal IT leaders, the mandate is simple:
Modernize communication, but don’t break any laws while doing it.

LeapXpert’s Pitch: Flexibility Without the Risk

LeapXpert’s platform is purpose-built for environments where messaging must be both convenient and controlled. It provides:

• Secure communication across Teams, WhatsApp, SMS, and other channels

• Full audit trails and message capture

• Encryption and policy-driven retention

• Compliance alignment for NIST, CMMC, and federal cybersecurity standards

In other words, the real-time flexibility employees want, with the accountability government regulators demand.

“Government agencies need the same communication flexibility as the private sector, but with far greater accountability,” said Avi Pardo, Co-founder and CBO at LeapXpert. His point lands: the government can’t simply adopt consumer-grade tools and hope for the best.

Why Iron Bow Is the Right Delivery Vehicle

Iron Bow Technologies isn’t new to federal modernization. The company has long been embedded in federal IT procurement, cybersecurity implementation, and mission-critical digital transformation initiatives.

Which is why the pairing makes sense. Iron Bow knows the compliance terrain; LeapXpert knows secure communication. Together, they remove one of the last barriers to complete digital collaboration inside agencies.

“LeapXpert stood out because they address one of the most urgent and often overlooked challenges in federal IT: enabling secure, modern messaging without sacrificing control or compliance,” said Rachel Murphy, General Manager for Federal Civilian Sales at Iron Bow.

What This Means for the Federal Landscape

With cloud adoption surging and agencies accelerating their cybersecurity modernization plans, the partnership arrives at a critical moment.

Agencies have been under pressure—political, operational, and regulatory—to digitize faster. But messaging has remained a stubborn blind spot with significant security implications.

This collaboration signals a broader industry shift:
Modern communication tools are no longer optional in government—they’re becoming core infrastructure.

Expect ripple effects. Rival collaboration providers will need to demonstrate similarly airtight compliance. Legacy communication setups that rely on rigid, siloed systems will face scrutiny. And as more agencies shift to multi-channel messaging, platforms that can secure every interaction—across every device—will have the upper hand.

The Bottom Line

LeapXpert and Iron Bow are providing agencies with something they haven’t had until now: a safe on-ramp to modern communication. The combination of LeapXpert’s compliance-driven tech and Iron Bow’s federal deployment expertise gives agencies a clear path to embrace messaging without compromising accountability or cybersecurity.

It’s modernization with guardrails—and in the federal world, that’s exactly the point.

Get in touch with our MarTech Experts.

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In the financial world, data may be abundant, but usable data—the kind analysts can actually trust—is another matter entirely. That’s where Rimes has staked its reputation. And now, it’s plugging that data expertise directly into Databricks, one of the fastest-rising players in enterprise AI.

Rimes has partnered with Databricks to make its Managed Data Services available natively on the Databricks Data Intelligence Platform, using Delta Sharing, the open-source protocol designed for secure data exchange. For investment teams that have been juggling data pipelines, governance obstacles, and latency headaches, this move could be a meaningful shift.

Cutting Out Replication, Latency, and Complexity

Traditionally, investment firms have had to replicate or manually pipe their structured datasets into analytics platforms—a costly endeavor that introduces delay and governance risk. By delivering Rimes’ curated datasets via Delta Sharing, clients can now connect directly to governed data without replication, eliminating a major bottleneck.

The benefits feel tailor-made for today’s AI-driven investment workflows:

• Faster time-to-insight with low-latency access

• A single governed source of truth

• AI-ready data powering modeling, automation, and workflow optimization

• Direct integration into Databricks notebooks, dashboards, and AI agents (including Agent Bricks)

In other words: the plumbing just got a lot smarter.

“Rimes has built a reputation for delivering the highest quality managed data and data governance capabilities,” said Vijay Mayadas, CEO of Rimes. “Through our partnership with Databricks, we’re enabling clients to accelerate their time to insight and unlock the full potential of their investment data.”

Databricks: AI Runs on Good Data

Databricks, fresh off its own acceleration in the enterprise AI race, sees the partnership as essential infrastructure for financial institutions hoping to build scalable AI applications.

“Enterprises are looking for ways to scale high-quality, trusted AI apps and agents on their own data,” said Dael Williamson, Field CTO, EMEA at Databricks. “By making Rimes’ Managed Data Services available via Delta Sharing, financial institutions can now access clean, curated, and timely investment data directly within their Databricks workspaces.”

Databricks’ pitch to Wall Street is clear: AI isn’t magic—it’s data quality, governance, and explainability. With Rimes feeding its platform, the data layer just got significantly more robust.

A Bigger Strategy Is Taking Shape

The partnership also marks an early milestone in Rimes’ post-Five Arrows investment expansion. As part of its long-term strategy, Rimes plans to add more datasets, broaden availability, and introduce AI-driven use cases built on top of its unified data layer.

The vision:
A seamless, interoperable data foundation that can power analytics, automation, compliance, and next-generation intelligent workflows across the financial ecosystem.

If the industry trend holds, investment firms are increasingly turning away from fragmented data estates and toward unified, governed platforms that can feed AI systems responsibly. With Databricks gaining momentum as the go-to open AI stack, Rimes’ deep domain expertise lands at exactly the right moment.

The Bottom Line

Rimes and Databricks aren’t just aligning technologies—they’re aligning philosophies: open, governed, trustworthy data as the backbone of financial innovation.

For financial institutions wrestling with AI adoption, messy data estates, and governance challenges, this partnership offers a cleaner, faster path forward. The combination of Rimes’ investment data pedigree and Databricks’ AI capabilities could reshape how firms build intelligence into their workflows.

Get in touch with our MarTech Experts.

Mitel is doubling down on the future of customer experience, and this time it’s taking aim at one of the most persistent enterprise problems: fragmented, aging communications stacks. With the launch of Mitel CX 2.0, the company is rolling out what it calls an “AI-embedded, hybrid communications engine” designed to unify agents, supervisors, and back-office teams on a single workspace. Think of it as a modern contact center, stretched across the entire organization—minus the usual tangle of disconnected apps and clunky interfaces.

And if Mitel gets its way, the customer journey won’t just live inside the contact center anymore; it will live anywhere an employee interacts with a customer.

A Modern CX Stack for Hybrid Reality

CX 2.0 expands on Mitel’s multi-cloud hybrid communications portfolio, blending private cloud control with modern AI workflows. It’s a response to a market that has clearly shifted: IDC data shows that two-thirds of enterprises now prefer hybrid communications for resiliency and flexibility, while Techaisle points to customer engagement as the leading driver behind communications investments.

The pitch? Enterprises shouldn’t have to choose between innovation and compliance, scalability and control. CX 2.0 tries to offer all of it at once.

“Mitel CX 2.0 gives enterprises the freedom to innovate without sacrificing control,” said Martin Bitzinger, SVP of Product Management. “We’re extending customer engagement beyond the walls of the contact center and giving every employee the AI tools to influence the customer journey.”

Riding a Wave of Momentum

Mitel’s timing is convenient—and strategic. The company has been gaining traction in the CX market, earning recognition from Aragon Research as a Leader in the Intelligent Contact Center category, and scoring high marks from The Eastern Management Group in large enterprise evaluations. According to the firm, Mitel beats several competitors on reliability and management tools—two factors CIOs weigh heavily when modernizing CX.

“Mitel has consistently ranked among the top vendors,” noted John Malone, President and CEO at The Eastern Management Group. “Enterprises want flexibility and control, and Mitel delivers both.”

CX 2.0 builds directly on this momentum, adding AI depth, hybrid resiliency, and more enterprise-grade integration options.

One AI Workspace to Rule the Contact Center

CX 2.0’s biggest upgrade is its unified, AI-powered workspace. Instead of juggling separate tools for voice, messaging, digital channels, analytics, and coaching, employees can now manage everything in one place. Supervisors get real-time insights and performance tools, while agents can move fluidly across channels.

Behind the scenes, Mitel’s AI assistants work quietly but aggressively—summarizing interactions, suggesting responses, routing customers, and even taking autonomous actions.

The City of Baltimore is already seeing the benefits. “Our 458 agents can now work from anywhere, and our workflows have become dramatically simpler,” said Ron Gross, Deputy Director of Communications. “The GenAI automation built into Workflow Studio is a game-changer.”

Where the AI Gets Interesting

Much of the real differentiation lies in Mitel’s deeper integration with Workflow Studio, its AI-ready orchestration platform. CX 2.0 ties directly into this layer, which lets enterprises build agentic workflows, automate actions, and connect communication data to business processes.

Key capabilities include:

Industry-Tailored AI Virtual Agents
Built via Workflow Studio, these agents can resolve routine inquiries, escalate complex cases, and tap both front-line and back-office teams.

Voice AI with Smart Handoff
When calls move from bots to humans, transcripts, context, and suggested responses travel with them, eliminating repetitive pre-amble and improving resolution speed.

Agentic AI Workflows
These mini-agents automate actions—placing orders, generating tickets, sending alerts, processing approvals—reducing human workload and cutting delays.

Low-Code/No-Code Design Tools
Workflow Studio and the MCX Bot Builder let teams build GenAI-driven workflows without specialized development knowledge.

A Strategic Push Toward Enterprise-Wide Engagement

Ultimately, Mitel CX 2.0 isn’t just a new contact center release—it’s a shot at redefining enterprise engagement. The company’s approach is less about replacing agents and more about giving every team member access to AI-driven insights, automation, and communication tools.

In a market where competitors like Genesys, NICE, and Cisco are aggressively layering AI into their CX stacks, Mitel’s hybrid-first, workflow-oriented model stands out—especially for customers with complex compliance or on-prem requirements.

CX 2.0 positions Mitel not just as a contact center vendor, but as an enterprise-wide engagement orchestrator. And for businesses betting on hybrid operations for the long haul, that’s a compelling pitch.

Get in touch with our MarTech Experts.

ScaleOut Software, known for its powerful enterprise caching and in-memory data grid solutions, has announced a major upgrade to its product line: the “Gen AI Release” of its ScaleOut Product Suite. At its core, this release injects generative AI into ScaleOut Active Caching™, allowing users—especially non-technical ones—to transform live, fast-moving data into real-time insights with natural-language prompts.

This isn’t just a UI facelift. ScaleOut is betting big on its distributed cache—not just as a place to store data, but as a live engine for operational intelligence. By embedding an LLM (OpenAI’s models, specifically) directly into the cache management layer, the platform now supports real-time analytics, charting, queries, and geospatial visualizations, all generated by users through plain English.

The Power of AI + In-Memory Data

Traditionally, analytics on frequently changing data streams—like transactions, user behavior, or operational signals—has required complex ETL (extract, transform, load) pipelines, streaming frameworks, or even micro-batch systems. ScaleOut’s innovation flips that model: instead of moving data out, you analyze it where it lives.

With Active Caching now paired with generative AI, business users can ask questions like, “Show me a chart of order volume over the past hour”, or “Map customer clicks in our southeastern region”, and get immediate visual feedback. That means no waiting on data scientists to build dashboards, no painful BI setup, and far fewer handoffs.

For companies operating in sectors where real-time context matters—such as e-commerce, financial services, logistics, gaming, or cybersecurity—this is a potential game-changer. ScaleOut CEO Dr. William Bain frames it well: “Organizations of all sizes face the same need to respond quickly as conditions change… a combination of active caching with Gen AI-powered analytics enables customers to strengthen their operational intelligence, increase efficiency, and respond to changing conditions in real time.”

Democratizing Real-Time Insights

One of the most compelling aspects of this release is how ScaleOut lowers the technical bar for real-time analytics. Rather than requiring SQL knowledge, data modeling, or BI tool mastery, non-technical users can prompt the system in natural language.

Behind the scenes, the LLM parses these prompts and translates them into precise queries against JSON-encoded objects in ScaleOut’s cache. Then it generates chart specifications or map visualizations as needed—all on the fly.

This democratization has notable implications:

• Faster decision-making: Business leaders don’t have to wait for data teams to build dashboards.

• Lower friction: Analytics becomes accessible across roles, not just to data scientists or BI specialists.

• Real-time responsiveness: As live data changes, so do the visualizations and insights, keeping everyone aligned with current conditions.

In effect, ScaleOut is turning its distributed cache into an AI-powered front door for real-time operational intelligence.

Smarter Management Console

Alongside the Gen AI features, ScaleOut has revamped its management UI. A redesigned object browser now allows administrators and users to search and filter cached objects more easily, tailored to modern usability expectations.

This is more than aesthetic—it addresses a real enterprise pain point: large in-memory caches can store millions of complex objects, and managing or exploring them can be tedious. With improved filtering, search, and navigation, users can jump directly to the data they care about, inspect it, and even tweak their analytics modules from within the same interface.

Bringing Messaging Into the Mix

ScaleOut didn’t stop at analytics. The Gen AI Release also introduces support for Amazon Simple Queuing Service (SQS). This means ScaleOut’s distributed cache can directly subscribe to SQS message streams—making it possible to process queued events in real time. This is especially valuable for architectures where decoupling via message queues is common, like microservices, event-driven systems, or cloud-native pipelines.

By listening to SQS, ScaleOut can keep its cache fresh, respond to events instantly, and feed its AI-powered analytics engine with up-to-date data without additional glue code.

Why It Matters: The Competitive Landscape

ScaleOut’s move comes in an era where real-time analytics and operational intelligence are increasingly prerequisites, not luxuries. Competitors like Redis (with RedisAI) and Hazelcast tout in-memory speed, but often rely on separate analytics or streaming platforms.

ScaleOut, on the other hand, aims to collapse that stack: caching, computation, LLM-based query interpretation, and analytics all live together. That unified model could deliver lower latency, simpler architecture, and fewer moving parts. For enterprises with high-speed workloads—fraud detection, live personalization, logistics optimization—this integrated approach could offer a smoother, more performant path forward.

Real-World Use Cases: Putting Gen AI to Work

Here are some concrete scenarios where ScaleOut’s new features could shine:

1. E-commerce Flash Sales
   Retailers can monitor live customer behavior during flash sales—who’s hitting what product, where drop-offs are happening, and how demand is evolving—all through live visualizations. They can then tweak pricing, inventory, or messaging in real-time.

2. Financial Market Trading
   Trade desks or quant teams can query for patterns in transactional data, streaming orders, or credit risk signals without waiting for batch jobs or overnight ETL runs.

3. Logistics & Operations
   Supply chain operators can map real-time vehicle locations, process inventory updates as they arrive, and visualize geospatial trends dynamically.

4. Gaming & Online Services
   Gaming platforms can track user engagement, in-game events, or server performance in real time and make automated adjustments or trigger alerts.

5. Security & Monitoring
   Security teams can track anomaly detection outputs, suspicious events, or threat indicators as they're cached, and immediately visualize or escalate via automated workflows.

Implications for Non-Technical Teams

One of the biggest hurdles in real-time systems has always been making insights accessible to non-engineering teams. ScaleOut's Gen AI Release tackles this by bringing real-time data into the hands of business analysts, operations professionals, and domain leaders—not just engineers.

• Ops leaders can spot and correct trends fast.

• Business analysts can ask “what just changed?” without opening a BI tool.

• Service managers can chart performance metrics on-the-fly.

• Product teams can monitor usage behavior in real time and pivot quickly.

By reducing the friction between data and decision-makers, ScaleOut gives organizations a powerful lever to act fast—not just with data, but with understanding.

Risks, Adoption, and Considerations

Naturally, injecting an LLM into fast-moving data systems isn’t without challenges:

• Cost: Running LLM-backed analytics on high-throughput caches may be expensive, depending on scale.

• Latency: While caching reduces data-access latency, prompt processing and LLM inference could introduce new delays.

• Security and Privacy: Live data may contain sensitive information; ensuring secure prompt handling, encryption, and auditing becomes critical.

• Accuracy: Generative AI systems can misinterpret prompts or mis-generate query syntax. Users will need guardrails, validation, and possibly human oversight.

Despite these risks, ScaleOut's architecture—bringing the AI directly into the cache rather than sitting downstream—positions it to mitigate some of them. Caching ensures speed, but the platform design still requires governance and thoughtful implementation.

The Strategic Move

ScaleOut’s Gen AI Release reflects a broader trend in enterprise IT: bringing intelligence closer to the data. Rather than shipping data off to dedicated analytics clusters, more organizations are embedding compute—and now, generative AI—into wherever data lives.

This shift has several implications:

• Simplified architecture: fewer systems to integrate, less data movement.

• Better performance: faster insights and lower operational latency.

• Greater democratization: business users can self-serve, reducing demand on data teams.

• Competitive differentiation: companies that act on real-time data gain a leg-up in responsiveness and agility.

ScaleOut is positioning itself as a pioneer in this space, not just as a cache vendor, but as a platform for real-time operational intelligence powered by AI.

What’s Next for ScaleOut

Looking ahead, the company may push into other areas:

• More LLM integrations: support for other models or private LLMs.

• Expanded visualizations: richer dashboards, more chart types, custom layouts.

• Workflow automation: coupling analytics with automated actions—alerts, triggers, business processes.

• Deeper cloud integrations: beyond SQS, support for more message queues, event buses, and cloud-native services.

As real-time demands mount across industries—particularly in financial trading, e-commerce, and cybersecurity—ScaleOut's Gen AI Release could become a cornerstone for architecture designs that prioritise speed, insight, and action.

The Bottom Line

ScaleOut Software’s Gen AI Release for Active Caching isn’t just an incremental upgrade—it’s a shift in how enterprises think about in-memory data. By embedding generative AI directly into the cache, the company bridges the gap between raw, fast-changing data and actionable insight, all while making it accessible to non-technical users.

For organizations seeking real-time responsiveness and intelligence, particularly in high-velocity industries, this could be the nudge that pushes them from being data-rich to insight-rich. And in today’s world, that might be what defines competitive advantage.

Get in touch with our MarTech Experts.

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Every industry likes to talk about customer experience innovation — AI assistants, omnichannel orchestration, automated journeys, frictionless moments, and all the other shiny buzzwords crowding conference stages. But consumers? They’re not buying it.

Broadridge’s latest CX and Communications Consumer Insights report — its seventh annual edition — delivers a tough-love reality check: 71% of consumers now believe companies need to improve their customer experience, an all-time high and roughly double the dissatisfaction seen in 2019.

That’s not a dip; that’s a systemic failure.

For brands that spend millions optimizing funnels, redesigning interfaces, and deploying AI copilots, the findings land like a bucket of cold water: the simplest part of the customer journey — communication — is where trust is breaking.

“Customer communications aren’t just touchpoints — they’re the heartbeat,” says Christoph Stehmann, President of Broadridge Customer Communications. His point is direct: clarity builds trust; friction erodes it.

And right now? Friction is winning.

A Trust Recession, Fueled by Communication Failures

Broadridge polled more than 4,000 consumers across the U.S. and Canada, and the message is unmistakable: bad communication destroys loyalty.

• 59% have lost trust in a company because of a poor experience or unclear communication.

• Nearly 40% want brands to honor their preferred communication channels.

• 38% expect seamless engagement across channels — no more digital dead ends.

• 33% simply want companies to make interactions easier.

In other words, the bar is lower than most enterprises assume. Consumers aren’t asking for hyper-realistic AI agents or sci-fi personalization. They want communication that is clear, consistent, and coherent.

Yet brands continue to overinvest in technical complexity while underinvesting in the fundamentals.

Why CX Efforts Are Falling Short (Hint: It’s Not the Tech)

Companies have poured billions into digital transformation, and yet customer dissatisfaction keeps rising. Why? Because modern CX systems often prioritize automation over comprehension.

Think of typical customer journeys:

• Push consumers into apps they didn’t ask to use

• Send emails packed with jargon and legalese

• Deliver inconsistent information between channels

• Require customers to hunt for answers

This is the communication equivalent of a scavenger hunt. Consumers aren’t amused.

Broadridge’s research shows that customers punish brands for complexity. Whether it’s a financial statement no one can decipher or a customer service flow that feels like a maze, the outcome is the same: frustration.

The most dangerous part? Frustration quietly snowballs into distrust.

The Personas Shaping Tomorrow’s CX: Explorers vs. Optimizers

To help companies understand these shifting dynamics, Broadridge identifies two dominant customer personas emerging in 2025 — both influential, both demanding, and both underserved.

1. Engaged Explorers

The researchers call them “proactive,” but a better description might be “CX detectives.”

These consumers:

• Seek context

• Read deeper

• Evaluate before they act

• Prefer interactive emails (84%)

• Want digital bills and statements consolidated in one place (87%)

Yet only 15% believe brands deliver a quality experience. Explorers want transparency and substance, not marketing fluff — and the market rarely meets them halfway.

2. Practical Optimizers

This group wants one thing above all else: efficiency.

They’re less interested in bells and whistles and more focused on:

• Clear communication (44% rank it as the top priority)

• Tools that are intuitive

• Fast resolutions with minimal complexity

Interestingly, 41% say companies do an “okay” job — higher than Explorers but still far from a vote of confidence.

The takeaway?

Brands that think demographic segmentation is enough are missing the point.
In 2025, behavioral mindsets drive loyalty, not age, income, or device preference.

Companies that build communication strategies tailored to how people think, not just who they are, will win.

AI: Popular in Strategy Decks, Unimpressive in Reality

AI dominates marketing conversations, but consumers remain unconvinced. Broadridge’s study shows:

• Only 37% say AI has improved their experience — barely up from last year’s 33%.

• The divide between personas is significant:

  • 70% of Explorers think AI helps

  • Only 33% of Optimizers agree

AI promises speed and convenience, but consumers are still encountering scripted dead ends, inaccurate responses, or robotic interactions that don’t resolve anything.

Still, the data points to one bright spot: people are increasingly willing to share data — but only if they trust the company.

• 62% engage more with brands that deploy strong security measures

• 52% will share personal data if it tangibly improves their experience

The mandate is clear:
Security builds confidence.
Confidence unlocks data.
Data powers AI.
AI improves experience — if implemented thoughtfully.

Shortcuts don’t work.

Paper: Not Dead, Just Misunderstood

One of the most surprising insights from the study: the stubborn resilience of paper.

• 55% of consumers still receive paper communications

• Nearly half would switch to digital if platforms were intuitive and secure

Paper isn’t disappearing — it’s evolving into a complementary channel. Consumers want choice, not ultimatums. Mandated “go paperless” campaigns may save companies money, but they undermine autonomy — and autonomy drives adoption.

Digital transformation works best when customers feel in control, not cornered.

The Real Problem: Companies Don’t Think Like Their Customers

The report surfaces a deeper pattern: corporations look at communication as an operational pain point; consumers look at it as a relationship signal.

Businesses want:

• Efficiency

• Cost savings

• Automation

• Consistency

• Compliance

Consumers want:

• Clarity

• Simplicity

• Empathy

• Predictability

• Choice

The mismatch is widening.

Broadridge's findings expose the underlying truth of modern CX: you can deploy every tool in the martech stack, but if your communication is unclear, fragmented, or frustrating, everything else collapses.

Brands don’t lose customers because of one terrible email. They lose them because communication feels like an obstacle instead of a service.

The Next Era of CX Will Belong to Companies That Master Communication

The study’s message is urgent: CX is no longer defined by shiny digital features — it's defined by how effectively companies communicate in an age of information overload.

Companies that will win in the next decade are those that:

• Strip away complexity

• Deliver personalized, relevant communications

• Use AI responsibly and transparently

• Offer seamless, omnichannel experiences without forcing channel dependency

• Honor customer preferences

• Treat communication as a strategic differentiator

The brands that make life easier — not more digital, not more automated, but easier — will own the future of customer loyalty.

And as Broadridge hints, ignoring this shift isn’t just risky. It’s expensive.

Get in touch with our MarTech Experts.

 As AI accelerates its takeover of creative workflows—from image generation to personalized marketing copy—the industry has been sprinting toward efficiency while sprinting past responsibility. Marketers want speed. Creators want protection. Consumers want transparency. Regulators want clarity. And the technology itself? It never stops moving.

Santa Cruz Software believes the industry desperately needs a reset.

Today, the company announced Santa Cruz Software Labs, a dedicated initiative built not to chase the AI hype cycle but to shape what comes after it: a foundation for ethical, transparent, evidence-based AI in creative and marketing technology.

The goal isn’t to slow innovation. Instead, it’s to make sure innovation doesn’t bulldoze over authorship, data privacy, creative ownership, or human judgment.

“Responsible AI in marketing is not just about what technology can do — it's about what it should do,” said Luis Mendes, Innovative Solutions Expert at Santa Cruz Software Labs. It’s a pointed reminder in an era where AI tools routinely generate art without attribution and analyze customer data without always asking permission.

Santa Cruz Software Labs aims to create a space where marketers, agencies, technologists, and creators can experiment with AI — but with guardrails.

Why an Ethical AI Lab — and Why Now?

AI in creative technology is evolving faster than any previous marketing innovation. That speed means two things:

1. Brands are adopting tools they don’t fully understand

2. Creators worry their work is being absorbed into datasets with no recourse

The tension is real. Generative AI is transforming content creation, but the ethics behind training data, model transparency, and authorship rights remain murky.

Santa Cruz Software Labs enters the scene at a moment when:

• AI-generated creative is flooding digital platforms

• Consumer trust in AI-driven experiences is under scrutiny

• Copyright lawsuits are reshaping expectations for AI training data

• Marketing teams are under pressure to adopt AI but fear unintended consequences

• Regulators globally are sketching the first outlines of AI governance

In short: the market needs practical standards and transparent tools, not marketing gloss.

Inside Santa Cruz Software Labs: A Framework for Ethical AI

The lab anchors itself around three pillars that form a feedback loop between research, experimentation, and governance.

1. Research & Surveys: Evidence Over Assumptions

Santa Cruz Software Labs will publish ongoing studies exploring:

• How teams actually use AI in creative workflows

• Where they struggle (ethics, bias, reliability, quality control)

• How fast-paced AI adoption affects creators, agencies, and enterprise marketing teams

• What makes users trust — or distrust — AI tools

• Whether consumers understand when content is AI-generated

These findings will be crucial for marketers who want to defend AI investments with data, not storytelling.

Right now, most teams are operating on instinct, vendor claims, or competitive pressure. Santa Cruz wants to pivot the industry toward evidence-based adoption.

2. Tech Experiments & Early-Access Prototypes: Hands-On Innovation

The lab will offer access to early-stage prototypes built by Santa Cruz Software’s engineering team. These are not polished commercial tools — they’re experimental playgrounds.

Marketers and creators will get to:

• Test new AI-driven creative concepts

• Explore efficiency gains without sacrificing control

• Validate what workflows are enhanced — and which ones break

• Provide direct feedback that shapes real product direction

• Influence AI features before they reach the mainstream

Think of it as a wind tunnel for AI ideas: test, refine, stress-test, validate.

3. AI Code of Ethics: Guardrails for the Future of Creative Tech

The Lab’s AI Code of Ethics is its most consequential component — and the one likely to resonate widely across the industry.

Built around three commitments:

• Ethical data stewardship

• Human-centered intelligence

• Transparent, accountable innovation

The code aims to answer questions the industry is still wrestling with:

• How do you ensure AI models respect copyright?

• How do you prevent “black box” creative decisions?

• How do you design AI that augments human creativity rather than replace it?

• How do you ensure consumers know when AI is being used?

• How do you prevent marketing AI from becoming surveillance AI?

These aren’t academic questions. They define the next decade of digital marketing.

Marketing Teams Are Hungry for AI — But Fear Its Consequences

Santa Cruz Software’s timing is strategic. Most marketing organizations are:

• Increasing their AI budgets

• Testing new generative tools

• Integrating AI into design platforms

• Experimenting with personalized content at scale

But they also express major concerns:

• Dataset transparency

• Creative ownership

• The potential loss of originality

• Overreliance on machine-generated content

• Difficulty validating AI outputs

• Fear of brand risk from ungoverned AI usage

AI skepticism is rising at the same time AI adoption is accelerating — which is precisely the gap the Lab aims to close.

The Industry Has Been Moving Too Fast — and It Shows

One reason this initiative stands out is because most AI innovation in marketing has followed a different playbook:

• Release tool

• Market benefits

• Add features

• Scale adoption

• Then think about ethics

Santa Cruz Software Labs flips that script.

Here, ethics isn’t a compliance afterthought; it’s the foundation.

This is a sharp contrast to companies that launch AI features first and worry about safeguards later — a pattern that has already led to:

• Accidental copyright violations

• Unintended data exposure

• Questionable algorithmic decisions

• Consumer backlash

There’s an opportunity for Santa Cruz Software Labs to set a precedent for the industry.

A New Hub for Collaboration, Community, and Accountability

The Lab is not positioning itself as the sole authority on AI ethics — instead, it wants to be a collaborative hub.

The initiative plans to:

• Publish regular research reports

• Release public demos

• Invite community participation

• Host discussions across marketing, design, and tech

• Spotlight best practices from across the industry

• Encourage shared standards rather than proprietary definitions

This matters because AI is reshaping marketing faster than any single organization can control. The industry needs consensus, not isolated guidelines.

Ethical AI Is Becoming a Competitive Advantage

Marketers have long competed on channels, creative, data, and performance metrics. In the next phase of digital marketing, they’ll compete on:

• Trust

• Transparency

• Provenance

• Creative integrity

• Data respect

• Authenticity

AI will help brands create more content than ever — but ethical AI will help them create content consumers believe in.

Santa Cruz Software Labs is betting big on a simple truth: the future of marketing belongs to brands that innovate responsibly.

And as generative AI continues blurring the line between inspiration and imitation, that bet looks increasingly smart.

The Bottom Line

The launch of Santa Cruz Software Labs signals a shift from reactive AI adoption to principled, proactive innovation.

For marketers and creative teams navigating a world where AI is powerful, unpredictable, and often misunderstood, the Lab offers something the industry has lacked: a structured, transparent place to explore AI without compromising ethics or creativity.

As AI continues to redefine digital marketing, Santa Cruz Software Labs is urging the industry to slow down — not to stop progress, but to ensure we’re building something worth accelerating.

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It’s no secret that marketing leaders are under mounting pressure to “do something with AI.” Boards want faster growth. Executives want efficiency. Teams want clarity. Vendors want budget. And the industry at large is drowning in lofty promises, abstract frameworks, and slide decks that over-index on possibility rather than practicality.

MatrixPoint, a digital strategy consultancy, believes marketers need less hype and more direction. Today, the firm introduced the Marketing AI Accelerator, a structured program designed to help organizations identify high-impact AI opportunities — not by evaluating technology in the abstract, but by grounding decisions in real-world, proven use cases.

The premise is simple but refreshing: instead of spending months analyzing readiness, capability maturity, or theoretical ROI models, start with what already works. Then decide how — and whether — those solutions can map onto real marketing priorities.

Why Use Cases Matter More Than AI Theory

For years, the marketing ecosystem has been captivated by AI frameworks, vendor demos, and increasingly technical roadmaps. But in reality, most marketing teams struggle with a much more basic question:
Where, exactly, should we apply AI first?

MatrixPoint argues that the industry’s obsession with capability assessments often creates analysis paralysis. Leaders want action; teams want clarity; and no one wants to be the company that experimented for 12 months only to realize they never solved a real business problem.

“Brand leaders are under pressure to demonstrate AI progress but face competing priorities and unclear paths forward,” said Eran Goren, Managing Principal at MatrixPoint. “The Marketing AI Accelerator gives senior leaders a clear framework for deciding where AI can drive impact quickly and effectively.”

Put simply: less theoretical alignment sessions, more practical ROI.

Inside the Marketing AI Accelerator: A Pragmatic Playbook

MatrixPoint’s Accelerator is built around a three-phased methodology designed to compress the time between curiosity and implementation — while avoiding the common pitfall of chasing AI for AI’s sake.

1. Discovery & Prioritization Workshop

MatrixPoint engages cross-functional teams with a curated library of proven marketing AI use cases — spanning personalization, content automation, audience modeling, predictive intelligence, and more. The workshop isn’t just an idea dump; it’s structured to identify which use cases align with strategic priorities and operational constraints.

2. Return & Readiness Measurement

Traditionally, organizations start with a readiness assessment, spending months diagnosing systems, data hygiene, governance maturity, and resource availability — only to discover that some initiatives were never viable to begin with.

MatrixPoint flips this order.
Use cases come first.
Feasibility comes second.

By working backward from validated use cases, the Accelerator determines whether the organization is actually equipped to implement the ideas that matter — not the ones that merely sound innovative.

3. Implementation Roadmap for High-Potential AI Opportunities

Once priorities and feasibility align, the program delivers a detailed roadmap for execution. This includes:

• Required data sources

• Technology considerations

• Workflow implications

• Cross-team dependencies

• Talent and training needs

• Time-to-value projections

Senior leaders get a practical, actionable blueprint — not a 150-slide maturity assessment.

Why This Approach Lands With CMOs Right Now

Marketing AI is reaching its consolidation phase. Generative engines aren’t novelties anymore; they’re standard tools. Predictive models aren’t special; they’re expected. And personalization isn’t an experiment; it’s table stakes.

What’s actually rare is clarity.

MatrixPoint’s approach mirrors what’s happening in finance, supply chain, and operations: a shift toward use-case-first AI evaluation, where organizations focus on outcomes before infrastructure. It’s a methodology already embraced by leaders in AI-heavy sectors — but comparatively new in marketing.

Steve King, Principal at MatrixPoint Consulting, underscores this point:
“Most organizations spend months on readiness assessments without validating whether their AI initiatives will actually solve business problems. We reverse that.”

The Accelerator pushes CMOs toward prioritizing business alignment instead of technical aspiration — something the marketing industry has needed for years.

A Growing Trend: Pragmatism Over Possibility

Across the broader market, the AI conversation is maturing:

• Brands are shifting from experimentation to measurable impact

• Boards are demanding cost savings and efficiency, not prototypes

• Teams want AI that integrates into existing workflows

• Vendor fatigue is real — and rising

• Risk, governance, and data privacy concerns continue to increase

MatrixPoint’s use-case-first model reflects a shift away from experimental AI toward operational AI: solutions that are ready, proven, and context-specific.

This is also a subtle but important contrast to many consulting approaches, which often sell AI strategies but deliver frameworks with limited execution value. MatrixPoint positions the Accelerator as a shortcut through that fog.

Supporting the Launch: A White Paper for CMOs

To broaden the impact of the Accelerator, MatrixPoint also released a white paper titled:
“The Use Case Advantage: How Leading CMOs Prioritize Marketing AI Initiatives.”

The white paper outlines:

• Why traditional AI planning processes break down

• Common pitfalls that derail AI adoption

• A framework for evaluating impact, feasibility, and readiness

• Real examples of marketing use cases delivering measurable ROI

• How top CMOs build AI portfolios aligned with business strategy

It’s written for senior marketing executives who need defensible, business-aligned logic behind their AI decisions — not just inspiration.

The Bigger Picture: Making AI Practical, Not Theoretical

MatrixPoint’s Marketing AI Accelerator arrives as marketing enters a phase where AI isn’t optional but neither is unstructured experimentation.

Organizations know they need to adopt AI, but they’re unsure:

• Where to start

• What problems AI should solve

• What to prioritize

• How to avoid wasted investment

• How to measure outcomes

• How to build internal confidence

The Accelerator aims to remove that uncertainty by anchoring decision-making in use cases that are already delivering results across the industry — making AI less mysterious, less abstract, and far more actionable.

Bottom Line

MatrixPoint isn’t launching another AI framework or high-level consulting model — it’s launching a pragmatic system for identifying, validating, and implementing AI where it actually matters.

For CMOs and marketing teams struggling to translate AI ambition into executive-ready plans, the Marketing AI Accelerator offers something the industry has been missing: speed, clarity, and grounded decision-making.

AI adoption may be complex, but prioritization doesn’t have to be. And MatrixPoint is betting that the fastest path to enterprise AI success starts not with assessing maturity — but with understanding what already works.

Get in touch with our MarTech Experts.