Interview

The Future of Digital Identity: How AI is Enhancing Authentication and Fraud Prevention

cybersecurity 18 Feb 2026

Whether you sell restricted products, impart banking services, or provide just verification services, digital identities are at the heart of all these. Everyone requires digital identity that is secure and verifiable across systems easily. In recent years, digital identity fraud is on the rise, and it is only going to increase as the attackers leverage latest technology and become smarter.

The best way to secure your digital identity verification and creation process is to leverage AI in the process. In this article, we will look at how AI is enhancing authentication and fraud prevention as well as the future of digital identity. But before that, let's understand what digital identity is.

What is Digital Identity?

As the name suggests, a digital identity is a collection of information that can help in verifying the identity of anyone in a digital world. A digital identity will contain personal information for the user, biometrics, and other such identification data that can help in uniquely identifying a user online.

Having known about digital identities, now is the right time to understand the issues with traditional verification systems and why we need digital identity with AI in the future.

Issues with Traditional Verification Systems

Weak Security

In traditional verification systems people often resort to small and easy to guess passwords which provide weak security. This makes the system vulnerable and easier to hack for attackers.

Poor User Experience

Traditional verification systems have a lot of friction points, and they often deliver a poor user experience due to complex verification workflows and frustrate users.

Centralized Data Storage

Traditional verification systems rely on centralized data storage which can easily become a target for hackers. Once the hacker gets into the system, they can get access to all the verification data and misuse it, which is really bad for users.

As we have already discussed that traditional systems are not safe and they provide poor user experience, it is perfect time to understand how digital identity is evolving with AI and what the future looks like for identity verification.

How AI Enhances Digital Identity Verification?

Biometric Authentication

AI models can help in biometric authentication by leveraging facial recognition technologies and liveness detection models. These models can verify whether a user is live through video feeds, and only grant access when the biometrics match and the liveness detection is passed. This way you can build systems which are highly secure and only accessed by real users.

Behavioral Analytics

Every real user has a different behavior, and when building digital identity verification this characteristic can be really helpful. AI models can be built and trained to analyze typing patterns, mouse movements, device data and other pointers to analyze behavioral data for any user. By building robust behavioral analytical models, you can ensure that the system also verifies user behavior and only gives access if it is a real user.

Continuous Verification

Traditional systems verify identity once, and then they trust the user, but modern times require better solutions. Today, attackers are smart and to combat them, we need to build systems that can do continuous verification. AI models can help in continuously analyzing user behavior and monitor actions on the platform, which can be then matched with behavioral data or model to check whether it is a legitimate user or not.

Risk-based Adaptive Authentication

AI models are smart, they can identify risky situations and help you make the authentication process stricter and adaptive for such situations. You can collect contextual data like device, location, time and user behavior to categorize whether an identity verification attempt is riskier or not. If the model thinks its risky, it can adapt the verification attempt to be stricter and perform deeper verification than usual to ensure only safe users can access your platform.

AI helps in enhancing authentication in modern systems in many different ways, but it also protects your systems from fraud. So, let’s look at how AI prevents fraud in modern digital identity verification systems.

How AI Prevents Fraud?

Real-time Anomaly Detection

As you collect and process behavioral and user data on your platforms, you can also develop machine learning models that are experts at identifying patterns and highlighting suspicious user behavior through real-time anomaly detection. These models can help you quickly find anomalous behavior on your platform, and take remediation actions to safeguard your platform.

Pattern Recognition

Every fraud transaction has a pattern, and when you give a large enough dataset to your machine learning and AI models to understand these patterns, it can help you with pattern recognition. The model can then try to find such patterns in real transactions and block them or route them through stricter processes to prevent fraud on your platform and ensure your platform is safer for everyone.

Synthetic Fraud Prevention

Synthetic fraud is rising rapidly and attackers are creating fake digital identities of users to perform this. While it is hard to detect and prevent such fraud manually, it is not impossible for AI models. AI models can prevent synthetic fraud by verifying data across multiple signals and creating a confidence score for each transaction. If the confidence score is below the threshold it can prevent the action and ensure safety on the platform.

As we have already discussed how AI is enhancing authentication and preventing fraud for modern platforms and digital identity, lets also look at the future of digital identity.

Future of Digital Identity

Passwordless Authentication

Passwordless authentication will become mainstream, and it will replace the need to enter password for every verification. Instead verification can be done through push notifications and biometric identity verification methods which are much faster.

Decentralized Identity

As attackers become smart and they try to target centralized identity stores, platforms and users will start leveraging decentralized identity storage systems that securely store identity data, and restrict damage or identity theft even when the system is compromised.

AI-powered Identity Wallets

AI powered identity wallets will also be on the rise in the future as they help users manage their identity data better and provide stricter security and usage guidelines around their data.

If you are unsure about the future of digital identity as AI advances, you should know that AI models will help in making digital identity verification systems smarter, faster and much more secure than traditional methods. By moving first, you can create a better user experience for your users, and beat your competitors in implementing the latest digital identity verification solutions powered with AI.

The Content Bottleneck Has Shifted from Production to Operations

marketing 17 Feb 2026

Q1: This is now the fourth year of Canto’s State of Digital Content report. What stood out to you most in this year’s findings compared to previous years?

Just how tangible the cost of fragmentation in brands’ content and creative operations has become. We’ve been seeing teams acknowledging the problem, saying “yeah, our digital assets are scattered, our workflows are messy.” But this year the data puts real business consequences behind that. The survey found 44% of folks reporting employee burnout tied directly to poor asset management. Wasted budget, duplicated work, missed revenue, and delayed launches are no longer hypothetical risks of fragmentation, they are measurable business outcomes.

The other thing that struck me is product content and information as a major theme. Previous reports focused heavily on creative assets, but this year we saw just how much brands are struggling to manage product information alongside their digital assets. 88% of teams can’t keep product content consistent across channels, and more than half are still managing product data completely separately from the assets used to actually market and sell those products. That disconnect is creating real friction, especially as e-commerce demands keep growing.

Q2: The report found that 82% of content teams saw volume increase in the past year, with three in four attributing at least some of that growth to AI. How are teams actually keeping up with that pace, and where are they falling short?

AI is driving the content volume up, but it’s also the thing helping teams manage the surge. 75% of content professionals told us AI has increased their output, and 30% said that increase was significant. At the same time, about half of teams are already using AI to accelerate creation, tagging, and organization. So the same technology pushing volume higher is also becoming the relief valve.

A critical part of the data, though, shows how teams are falling short on the operational side. The volume is growing but the underlying systems and workflows haven’t yet caught up. Only 43% of teams describe their digital content workflows as standardized and automated. The rest are still dealing with manual processes, inconsistencies, and fragmented tools that slow everything down. You can produce content faster with AI, but if your team can’t find the right asset, doesn’t know which version is current, or has to manually push updates across channels, you’re just creating more chaos. The content bottleneck has shifted from production to operations.

Q3: One of the more striking data points is that teams with full connectivity between their digital assets and product information are more than four times as likely to report significant ROI improvements. Why is that gap so wide?

That 4x multiplier surprised even us, but when you think about what connectivity actually enables, it makes sense. When your product information and digital assets live in the same environment, you eliminate an enormous amount of duplicated effort. Teams aren’t hunting across systems to match the right image with the right product description, nor manually updating the same information in five different places every time something changes.

The data backs this up across the board. Teams with fully connected systems told us that tasks like locating assets, maintaining brand consistency, and collaborating across teams were “extremely easy” at rates two to three times higher than everyone else. 67% of fully connected teams said locating assets was extremely easy, compared to 21% of those without full integration. That speed and confidence compound across every campaign, every channel update, every product launch. Just as importantly, it all shows up directly in revenue. 65% of teams that can make real-time content updates reported significant revenue increases, versus just 16% of those with slower timelines.

Q4: Only 35% of respondents said they feel very confident that employees are using the most current, approved version of brand assets. What’s driving that confidence gap, and what does it cost organizations?

That number reflects the reality of how most brands manage their content today. When assets are spread across cloud drives, local desktops, email threads, and multiple platforms, it becomes almost impossible to guarantee that everyone is working from the same source of truth. 62% of teams are using cloud file storage, 44% have content on local servers, and 41% still rely on individual hard drives. That’s a lot of places where an outdated logo or last quarter’s product spec can be sitting around, ready to get used by mistake.

The cost shows up in a few ways. 30% of respondents reported publishing off-brand or inconsistent content as a direct consequence of poor asset management. You also see it in the 30% who flagged legal or compliance risk. When you’re operating in regulated industries or across global markets, using the wrong version of an asset can have real legal and financial consequences.

Beyond those specific risks, there’s the broader drag on team productivity. People spend time second-guessing whether they have the right file, chasing down approvals, or recreating something that already exists somewhere in the organization.

Q5: The data shows that 51% of teams still rely on spreadsheets to manage product information, and 56% manage product content separately from digital assets. What needs to change operationally before those numbers start to shift?

I think a lot of brands have grown into this situation organically. Spreadsheets are familiar, they’re flexible, and when you only have a handful of products or channels, they work fine. But when you’re managing hundreds or thousands of SKUs across e-commerce platforms, retail partners, marketplaces, and your own website, spreadsheets stop scaling. You end up with version control nightmares, no clear ownership, and inconsistencies that erode customer trust.

The shift really starts with recognizing that product content and creative assets are two sides of the same coin. A product image, its description, its specifications, its pricing…those all need to move together when something changes. When 78% of teams are using two or more separate solutions just to manage product content, every update becomes a multi-system coordination exercise. Teams told us the improvements that would deliver the most benefit include making product data easier to access across teams, eliminating duplicate or outdated information, and managing product data alongside creative assets. Those are all fundamentally about bringing things together rather than continuing to manage them in silos.

Q6: AI adoption for content is nearly universal at 96%, but only 30% of teams describe their use of AI as widespread. What’s holding back deeper adoption?

The adoption curve is real, and I think it’s actually healthy that most teams are taking a measured approach. 47% are using AI in limited ways, and 16% are planning to adopt. That’s a lot of momentum. But moving from experimentation to deep integration requires trust, infrastructure, and governance, and those things take time.

On the trust side, the news is actually encouraging. 81% of content professionals expressed confidence in AI’s accuracy for tagging and organizing assets, and that confidence gets even stronger with hands-on experience. Among teams using AI most extensively, 77% reported high confidence. The hesitation seems to be more about the operational layer. When we asked about top worries over the next two years, integrating new technologies like AI tied for the number one concern alongside security and access control, both at 30%. Teams want to adopt AI more broadly, but they want to do it in a way that doesn’t compromise brand consistency or introduce new security risks. Brands seeing the biggest returns are embedding AI into a centralized, governed environment rather than layering it on top of fragmented systems.

Q7: Teams with advanced, standardized workflows were dramatically more likely to see significant ROI gains - 48% versus 0% among teams with ad hoc processes. What does workflow maturity actually look like in practice for content and creative teams?

That 48% to 0% gap is one of the most compelling findings in the report, and it really underscores that operational maturity isn’t merely a nice-to-have.

In practice, workflow maturity starts with processes for creating, reviewing, approving, and distributing content that are documented and consistent across teams rather than reinvented every time. On top of that, the repetitive work (like tagging, metadata generation, format conversions, routing assets for approval) is automated instead of eating up people’s time. Additionally, the tools are connected so that updates flow through the system rather than requiring someone to manually copy information from one platform to another.

The teams doing this well are also much more likely to have invested in AI, analytics, and template-driven approaches. When we looked at what high-ROI organizations are doing differently, they’re significantly more likely to be introducing automation to reduce manual work, expanding template and modular content approaches, and measuring content performance to refine their processes.

Q8: The report highlights security, AI integration, and brand consistency as the top concerns about managing content at scale over the next two years. How should content leaders be prioritizing those?

I’d say these concerns are actually more interconnected than they might appear at first. Security and access control, AI integration, and brand consistency all improve when you centralize how content is managed and governed. If your assets are scattered across disconnected systems with inconsistent permissions, you have a security problem, a brand consistency problem, and a much harder time rolling out AI in a controlled way.

The practical starting point is getting your foundation right. That means establishing a centralized, structured environment where assets are governed, versioned, and accessible to the right people with the right permissions. Once that’s in place, you can layer in AI capabilities, things like smart tagging, visual search, and content recommendations, with confidence that the AI is working within guardrails rather than amplifying existing chaos. And brand consistency becomes much more manageable when there’s one source of truth rather than dozens of repositories where outdated files can linger.

I think content leaders should also be paying attention to the product content dimension. Managing storage costs at 26% was one of the top concerns, and that’s only going to grow as content volume keeps climbing. The teams managing costs most effectively can reduce duplication, improve reuse, and avoid recreating assets that already exist somewhere in the organization.

Q9: What’s one thing you’d want a marketing or content operations leader to take away from this year’s report and act on immediately?

Audit your fragmentation! Take an honest look at how many systems, folders, drives, and platforms your team is using to manage content and product information today. The data is really clear that fragmentation is the single biggest drag on performance. Teams using two or more systems to manage digital assets are significantly more likely to experience delays, missed revenue, burnout, and wasted budget compared to those working from a unified approach.

You don’t have to solve everything at once, but understanding the full scope of the problem is the first step. Once you can see where assets and product information are scattered, you can start making intentional decisions about what to centralize, what to connect, and where to apply AI and automation to eliminate the most painful bottlenecks.

"First-Party Data Isn’t Enough Anymore”.

marketing 17 Feb 2026

By: Scott Kozub, VP, Product at Experian Marketing Services

For years, first-party data has been positioned as the answer to nearly every challenge in digital advertising. Lose cookies? Build first-party relationships. Privacy gets more complicated? Lean into owned data. Measurement becomes murky? Go direct to the source.

That logic still holds, but only up to a point.

What many marketers are discovering in practice is that first-party data alone creates depth without scale. It offers rich insight into customers a brand already knows, but far less visibility into the audiences it still needs to reach. In a fragmented, privacy-conscious ecosystem, relying exclusively on first-party signals often results in limited reach, frequency challenges, and diminishing returns on prospecting

The next phase of targeting will be defined by how well marketers combine first-party, third-party, contextual, and geographic signals to drive growth, improve efficiency, and strengthen customer relationships.

Why first-party and third-party data are better together

The biggest challenge facing modern targeting is not the loss of identifiers. It is the growing fragmentation of signals across devices, channels, and environments. In that reality, identity does not disappear. It becomes more important as the connective layer that brings different data sources together for planning, activation, and measurement

First-party data remains essential. It provides accuracy, consent, and a reliable foundation for personalization and measurement. But on its own, it reflects only a partial view of the market. Most first-party data sets skew toward existing customers, logged-in users, or known devices, leaving significant gaps in reach and understanding.

This is why third-party data is so valuable. Not as a standalone solution, but as a complementary layer that expands perspective beyond what first-party data can capture alone. Responsibly sourced third-party data adds demographic, behavioral, interest, and purchase context that helps marketers understand who they should be reaching next, especially in an environment shaped by privacy constraints and signal fragmentation.

First-party data on its own is limiting. Third-party data on its own is incomplete. The real power comes from connecting the two through identity, allowing marketers to plan, activate, and measure across fragmented environments with greater accuracy and confidence.

Contextual and geographic signals as privacy-safe extensions

Contextual and geographic targeting are not new tactics. They are proven approaches that have evolved alongside changes in technology, privacy expectations, and data availability.

Today, data-informed contextual targeting goes far beyond keywords or simple page adjacency. When contextual signals are combined with audience insights, they help marketers understand where high-indexing audiences naturally spend time, regardless of channel or environment. Certain content consistently attracts users with shared behaviors, demographics, or purchase intent. Identifying those patterns allows advertisers to reach relevant audiences in ways that are both effective and privacy-safe.

Geographic data functions in a similar way. People with similar lifestyles, needs, and behaviors often cluster in similar locations. When geographic signals are informed by behavioral and demographic data, rather than used as blunt radius targeting, they become a meaningful proxy for intent. This is especially important for categories like retail, CPG, and automotive, where location continues to influence decision-making.

These signals are not replacements for first- or third-party data. They are additional layers that strengthen a modern data strategy while supporting privacy-forward activation.

AI as decision intelligence in a fragmented ecosystem

Artificial intelligence plays an increasingly active role in making fragmented signals and multi-source data strategies manageable.

AI is not replacing targeting strategy. It is enabling it. By interpreting fragmented signals at scale, machine learning models help marketers connect identity, first-party data, third-party insights, contextual signals, and geographic information into actionable intelligence. Models trained on both structured and unstructured data can identify patterns across content, timing, device behavior, and location, then optimize delivery in real time.

This shift allows campaigns to move beyond static audience definitions and toward dynamic decisioning. As performance signals change, activation strategies can adapt accordingly, without relying on persistent identifiers or exposing sensitive personal data.

What this means for marketers in 2026

Marketers who want to create and activate campaigns more efficiently in 2026 will need integrated approaches that reflect how fragmented the ecosystem has become. Success will not come from betting on a single data type, but from building flexible systems that connect signals through identity and intelligence.

First-party data alone is no longer sufficient. Marketers who combine it with third-party, contextual, and geographic signals will be better positioned to plan, reach, and measure advertising in an environment defined by fragmentation, evolving privacy standards, and constant change.

How's this "Returns Shouldn’t Be Tolerated — They Should Be a Strategic Differentiator"

marketing 13 Feb 2026

Your research shows returns are now a routine part of shopping, not a seasonal issue. What does the data reveal about how frequently consumers are returning items, and why should CX leaders care?

It’s true, what we uncovered with our survey is that returns are no longer a seasonal anomaly, but a meaningful brand interaction, a routine part of commerce, and a stepping stone to building lasting relationships. When our survey was conducted in early January, 55% of respondents had already made or planned to make a post-holiday return, and 21% of shoppers said they return an item as frequently as once a month. This means returns are a recurring touchpoint that happens across the customer lifecycle, not just in peak holiday periods. Given the volume of returns, even small inefficiencies become points of real friction, and that’s tied directly to loyalty and CSAT. CX leaders in retail and ecommerce should recognize returns as a high-value touchpoint and focus on making the process an opportunity for brand affinity and trust, not frustration.

More than half of shoppers say a bad returns experience could impact future purchases. Why do returns have such an outsized effect on loyalty compared to other post-purchase moments?

Returns matter because they’re consequential and emotional. While purchase experiences are driven by anticipation and reward, a return is triggered by disappointment. How a brand handles that disappointment fundamentally shapes trust. 57% of consumers say a bad return experience would influence whether they buy from that brand again, regardless of previous loyalty. It’s a high-stakes moment. If brands can’t resolve a problem quickly, transparently, and with a bit of empathy, they risk turning a one-time issue into long-term disengagement.

More than 60% of consumers say they’d use an AI-powered agent to handle returns. What are shoppers actually hoping AI will fix at that moment?

Speed, clarity, and resolution are the top three things consumers expect from returns. While only a small percentage currently prefer chatbots (12%), 60% of respondents in our survey said they would use an AI-powered agent if it could instantly answer questions and process their return. This is customers signaling a desire for accurate, real-time assistance that gets the job done, with as little friction as possible. Only 36% of survey respondents say they are "very satisfied" with the returns process today, leaving significant room for improvement. AI, when done well, can eliminate many of the pain points consumers feel, including long wait times, confusing policies, and shipping hassles.

For retail leaders evaluating AI investments in 2026, why should returns be prioritized alongside acquisition and personalization efforts?

Trends in retail tech investment continue to focus on personalization and AI integrations to help the buyer build confidence. But what happens after the first purchase often determines whether the brand will get a second purchase, a third purchase, and so on. Returns are one of the few moments in the journey where customers are actively questioning their relationship with a brand, and that moment in time is where differentiation matters the most. AI investments in customer service are maturing quickly, proving that they can handle sensitive, complex situations with clarity and human-like empathy, all of which are critical to a successful returns process. But AI is not a “set and forget it” proposition. CX leaders must invest in training and empowering their teams to ensure their AI can grow, learn, and evolve alongside the needs of their customers. If a brand provides a strong purchase experience, but then loses the customer during a frustrating return experience, all those early investments in acquisition are at risk.

Trust remains a major concern with AI. According to your research, what conditions make consumers comfortable using AI for returns?

Earning consumer trust will be an ongoing challenge for brands as they continue to integrate AI into their practices. Our recent survey took a deeper look into why consumers lack trust in AI currently. It found that consumers worry AI will be less efficient than a human, will have difficulty understanding their issue, or will provide inaccurate information. All of these concerns can be addressed by ensuring that the AI agent is given accurate customer data and policy information from the brand, and is overseen by well-trained ACX managers and teams.

At Ada, we know this can be done well because our customers are seeing significant results from their AI investments today. One of our customers, IPSY, operates one of the largest beauty subscription networks in the world, serving more than 20 million community members across its brands. At that scale, customer experience isn’t just about support. It’s about relationship management, where every improvement compounds.

In just four months, IPSY, GenAI agent, Glam Bot, which is built and managed through Ada’s ACX Platform, unlocked:

→ a 41% lift in CSAT,

→ a 943% ROI on their generative AI investment,

→ 64% increase in autonomous resolution, and

→ It remains one of the largest AI deployments inside the company to date.

The key to ensuring consumers are comfortable with AI isn’t removing humans, but creating a seamless integration with humans, including transparent escalation paths.

Returns should no longer be an interaction that consumers tolerate, but a strategic differentiator for brands using AI to turn problems into opportunities.

Looking ahead, how do you expect AI to reshape post-purchase CX over the next 12–24 months, particularly around returns?

In the next 12-24 months, AI will become increasingly agentic. This means it will do more than answer simple queries – it will automate increasingly complex tasks end-to-end with context, accuracy, and even empathy. This would include checking inventory at nearby stores for pickup, processing payments, and making repurchases of the same products easy. We will see AI become more deeply capable in policy, status updates, logic, and personal preferences, which can make returns virtually frictionless by default. Brands will also increasingly measure the success of their ACX investments not simply in resolution rates, but in revenue generation, both from cross-sell/upsell opportunities and in reduced customer churn. But this requires a thoughtful approach to AI management and adoption, as well as a team that’s empowered to grow and evolve their own agents. Brands that win will understand AI success isn’t just a technology deployment, it’s a management discipline. You cannot delegate your transformation to a vendor.

What scaling AI reveals about governing personalisation

artificial intelligence 12 Feb 2026

By Mark Drasutis, Head of Value, APJ, Amplitude

As brands increasingly seek to understand and act on customer behavior, they need to continuously analyse user journeys, identify patterns and friction, and recommend or execute next steps in real time to deliver true personalisation. AI is accelerating this shift, redefining personalisation by moving brands beyond static journeys to experiences that adapt dynamically to customer behaviour.

Australia’s National AI Plan sends a clear message to marketing and product teams; AI can only scale if it is safe, transparent and responsibly governed. Yet, while AI capabilities are advancing rapidly toward greater autonomy, most organisational governance remains manual and fragmented.

With conversational AI and agentic AI becoming the primary interfaces for digital experiences, governance needs to operate at the same speed and complexity as the systems it oversees. Brands need capability uplift and accountability in equal measure or they risk falling behind.

The trust gap limiting AI-driven personalisation

AI-driven personalisation is being held back not by technology but by trust and transparency – a gap driven by weak governance, unclear accountability and a lack of workflows to manage AI safely. This matters because trust in AI remains fragile in Australia. A University of Melbourne-led study found that while half of Australians already use AI regularly, only one in three feel confident trusting it.

That trust gap is widening as personalisation evolves. Traditional rules based marketing, built on fixed segments, pre-defined journeys and manual triggers, is being replaced by real-time, generative personalisation where decisions are made continuously by AI. This shift demands new operating models, stronger governance frameworks and far greater visibility into how AI systems make decisions.

As agentic AI becomes more embedded in personalisation, teams are moving beyond static segmentation toward systems that can learn continuously from behaviour, test autonomously and adapt experiences in the moment. But even the most advanced systems will fail if customers don’t trust the intelligence behind them.

Australia’s National AI Plan reinforces that trust and transparency are not optional – they are the foundation for safe, scalable AI-driven personalisation. Done well, brands can deliver meaningful, adaptive experiences without compromising privacy, fairness or customer confidence.

AI governance needs to be built in, not bolted on

As AI takes on a bigger role in shaping personalised customer experiences, the governance behind those systems becomes just as important as the technology itself. The rise of employees using AI tools independently outside formal approval channels creates security and compliance risks. Organisations cannot rely on ad hoc controls anymore – they need transparent systems that formalise how AI is accessed, monitored and governed so teams can innovate without losing control. Boards and executives are accountable for AI strategy, governance and ethical application, emphasising that oversight must be enterprise grade, not experimental.

Effective guardrails start with visibility. As AI drives personalised decisions, brands need full clarity on how those decisions are being made. Brands need to trace which data an AI model uses to make a decision, understand the prompts, models and parameters behind an output and maintain clear logs that show how AI shapes the paths customers take and the outcomes they experience. Without transparency, it becomes impossible to spot bias, drift or unintended behaviour.

What matters in practice is real time visibility. When teams can see how AI driven decisions influence user behaviour, conversion and retention, they can assess whether those decisions are delivering value or creating unintended consequences. This kind of visibility is what allows personalisation to move from experimentation to something dependable.

Some early adopters are already putting this into practice. ZIP, an Australian fintech company, is already using AI agents on Amplitude’s MCP server to embed their domain knowledge directly into their LLM workflows, improving how personalised journeys are monitored and optimised. The result of this was a 60% increase in customers starting an additional repayment flow and the removal of more than 4,000 days of navigation friction.

This visibility makes it possible to intervene early, course correct when required and prevent minor issues from scaling into larger problems. For marketing and product teams, this means AI driven personalisation becomes safer, more predictable and more aligned with actual customer behaviour. AI governance cannot be patched on later. It must be embedded into the core of decisioning systems so AI operates safely, predictably and in line with both regulation and customer expectations.

Invest in continuous oversight for continuous experimentation

Echoed in the National AI Plan, real time personalisation means AI is constantly adapting, which requires continuous oversight rather than periodic manual checks.

When AI underpins the customer experience, these risks compound quickly. Automation without continuous oversight risks locking incorrect decisions at scale. Continuous oversight is what ensures experimentation remains safe, explainable and aligned with customer expectations on personalisation.

AI Agents are most effective when they work alongside humans, not in place of them. They can monitor customer behaviour, surface opportunities and support controlled experimentation at speed, while humans can remain responsible for setting strategy, defining guardrails and approving customer facing changes. A leading Australian bank currently using Amplitude’s AI Agents has advanced their data-driven experimentation, allowing them to uncover key customer behavioural patterns and traffic shifts with central human oversight. Autonomy can be adjusted over time as confidence grows, but accountability remains firmly with people.

This in-loop model ensures personalised experiences adapt based on real customer behaviour, while still reflecting brand intent, fairness standards and evolving privacy expectations. Products can optimise continuously, but only within approved parameters, keeping customer experience safety and performance aligned.

AI has the potential to fundamentally reshape personalisation, but only when trust, transparency, and governance scale alongside the technology. Without them, AI accelerates risk and limits growth. With them, it becomes a powerful and defensible competitive advantage.

The brands that succeed won’t be those that deploy the most AI, but those that govern it with intent and discipline. Now is the time to move beyond experimentation - strengthening oversight, embedding clear governance and building transparent data foundations that allow AI to scale safely and deliver personalised experiences customers genuinely trust.

How Mundial Media Uses AI to Decode Cultural Context

artificial intelligence 11 Feb 2026

Tony, there's a lot of talk about multicultural audiences being "important." Can you explain?

Multicultural audiences are no longer a segment; they’re the primary drivers of U.S. economic growth. Multicultural consumers are fueling most of the country's buying power. But reaching this deeply nuanced, diverse audience in a privacy-first ad technology environment has never been more difficult. Mainstream ad platforms weren’t built for this.

You've been vocal about mainstream ad platforms becoming "too automated." What's the problem with automation?

Automation is powerful for handling large volumes, but it falls short when it ignores cultural layers. Culture shapes everything from how people interpret signals to what motivates them. For instance, a Puerto Rican millennial in New York and a Mexican American Gen Z in Texas could show similar online patterns, yet their cultural influences create distinct needs. That's why tools like Mundial Media’s proprietary Cadmus AI technology are designed to decode those deeper contexts.

How does Mundial Media achieve that precision at scale?

It starts with processing hundreds of millions of signals daily and pinpointing where audience interests and cultural shifts overlap. This enables us to effectively reach over 50 million users, balancing broad scale with targeted accuracy.

Privacy regulations are tightening, and third-party cookies are disappearing. How is Mundial Media navigating this shift?

We've long prioritized first-party data and contextual cues over invasive tracking. Cadmus AI, trained on over three years of compounded AI learnings, delivers precise, real-time cultural understanding, privacy-safe scale, and high-performing contextual targeting, the “right ad at the right moment” without outdated cookies, IDs, or legacy identity signals.

Mundial Media emphasizes that your team embodies the diversity of the audiences you serve. Why does "lived experience" matter in the technical world of ad tech?

Technical tools alone can't capture bias and subtleties; that's where personal insights come in. Our diverse team brings an innate grasp of what makes messaging authentic, spotting resonant visuals, and avoiding stereotypes. This human element sharpens AI's effectiveness beyond raw data analysis.

You mentioned Cadmus AI has been trained on "over three years of compounded learnings." What does that continuous training look like?

It's an ongoing cycle where each campaign refines the system. Over time, this builds a smarter model for predicting what engages various segments and when to deliver messages for maximum relevance.

What does delivering "the right ad at the right moment" mean in a culturally nuanced context?

Delivering 'the right ad at the right moment' in a culturally nuanced context is about relevance rooted in understanding. It means knowing why a moment matters, who it matters to, and how a brand can show up in a way that feels natural and aligned with the audience's mindset.

With Cadmus AI, we know when brands want to target NFL football versus global football or soccer, and when Beyoncé has a major moment, it's a moment your brand should be part of. It's using cultural insight to match a message with the emotional and social context people are in at that exact moment, so the brand feels relevant to what they actually care about right then.

Can you give an example of how Mundial Media can help brands capitalize on major cultural moments?

The 2026 FIFA World Cup is the perfect example. We're talking about 6 billion viewers worldwide, over 29 million multicultural fans in the U.S. alone. This is arguably the decade's biggest multicultural marketing moment. The opportunity here goes beyond traditional sponsorship. What actually works is showing up authentically. Cadmus AI helps brands understand when and how to participate in ways that honor what these moments actually mean to different countries. Those are real emotional connections – hometown pride. Brands that respect that earn trust, and trust drives everything else.

What's the biggest misconception brands have about reaching multicultural audiences?

Many view it as a simple add-on, such as translating content or ticking diversity boxes, while seeing these groups as peripheral. In truth, they're central to modern culture, large consumer spending, innovating trends and adopting early, making them essential for any brand eyeing long-term growth.

Looking ahead, how do you see AI and cultural understanding evolving in advertising?

With AI democratizing data processing, the edge will come from embedding cultural depth to handle nuance and authenticity. We're advancing both tech and expertise to merge these, creating systems that target precisely while respecting human contexts in an increasingly complex landscape.

How Marketing Agencies Can Protect Client Data in an Era of AI-Powered Threats

artificial intelligence 11 Feb 2026

Marketing agencies are uniquely positioned as custodians of client data across dozens of platforms. How has this role evolved in terms of security responsibility, and why is 2026 a critical year for agencies to address this?

Marketing agencies have fundamentally transformed from service providers into data custodians, often holding the keys to their clients' most valuable digital assets. A typical agency today manages credentials for 50+ client accounts across advertising platforms, analytics tools, social media, CRMs, and content management systems. Each login represents a potential entry point not just to the agency's infrastructure, but directly into client operations.

2026 marks a critical inflection point for three reasons. First, AI-powered attacks have made credential harvesting exponentially more sophisticated; attackers can now analyze user behavior patterns and craft targeted phishing campaigns that are nearly indistinguishable from legitimate communications. Second, regulatory frameworks around data protection are tightening globally, with agencies increasingly held liable for breaches originating from their access points. Third, clients are becoming more security-conscious in their vendor selection process. We're seeing RFPs that explicitly require agencies to demonstrate robust security protocols, including how they manage shared credentials. Agencies that can't articulate their security posture are losing contracts to competitors who can.

How can agencies transform their security practices from a checkbox requirement into an actual competitive advantage during pitches and contract renewals?

The agencies that win in 2026 are those positioning security as a core competency, not an afterthought. During pitches, leading agencies now include dedicated sections on their security infrastructure, demonstrating their zero-knowledge password management system, showing how they can onboard and offboard team members to client accounts in minutes rather than days, and explaining their audit trail capabilities.

The competitive advantage comes from trust. When an agency can tell a prospective client, "We use enterprise-grade password management with military-grade AES-256 encryption, and no one, not even our leadership, can access your credentials without proper authorization," that's powerful differentiation. We're working with agencies that have made their security protocol a key selling point in proposals. It demonstrates professionalism and shows they take their custodian role seriously. In an industry where one breach can destroy years of client relationships, that message resonates.

AI-powered phishing attacks are becoming increasingly sophisticated. Can you describe what modern social engineering attacks targeting marketing agencies actually look like in 2026, and what makes agencies particularly vulnerable to these AI-driven threats compared to other industries?

Today's AI-powered attacks targeting agencies are remarkably sophisticated. We're seeing threat actors create fake emails that perfectly mimic client communication styles, analyzing previous email threads to replicate tone, terminology, and timing patterns. An account manager might receive what appears to be an urgent request from their client's CMO asking for immediate access to campaign data or credentials, using language and formatting that's virtually identical to legitimate requests.

Agencies are particularly vulnerable for several reasons. First, they operate in a high-velocity environment where urgent client requests are routine, and attackers exploit this culture of responsiveness. Second, agencies typically have multiple team members accessing the same client accounts, creating more potential entry points. Third, the creative nature of agency work means employees regularly click on links to review creative assets, making them more susceptible to malicious links disguised as client deliverables or campaign previews.

The most dangerous attacks we're seeing involve AI tools that harvest credentials while appearing to provide legitimate services. An employee might install what seems like a helpful SEO analysis tool or content optimization app, not realizing it's designed to capture login credentials and monitor user behavior.

Beyond technical solutions, what role does human awareness and training play in defending against these evolving threats?

Technology provides the foundation, but human awareness is your critical last line of defense. The most sophisticated password management system in the world can be undermined by an employee who falls for a convincing phishing email or shares credentials via an unsecured channel.

Effective training goes beyond annual compliance modules. Agencies need ongoing security awareness that addresses real-world scenarios; what does a credential harvesting attempt actually look like? How do you verify an urgent request is legitimate? What are the red flags in AI-generated phishing attempts? The key is making security awareness part of the agency culture, not just an IT department concern.

We also emphasize the importance of establishing clear protocols for credential sharing and verification. When someone requests access to a client account, what's the verification process? Training employees to pause and verify, even when requests seem urgent, can prevent the majority of social engineering attacks. It's about creating a security-conscious culture where asking "Can you verify this request through a secondary channel?" is encouraged, not viewed as slowing down work.

How should agencies think about credential management differently when they're not just protecting their own data, but serving as the gateway to client accounts across platforms?

Agencies need to shift from thinking about passwords as individual assets to viewing credential management as an enterprise-wide access control system. When you're managing keys to client kingdoms across dozens of platforms, you need infrastructure that provides visibility, control, and accountability.

This means implementing a zero-knowledge architecture where credentials are encrypted at the source and can only be decrypted by authorized users. It means having granular access controls so team members only access the specific client accounts relevant to their projects. It means maintaining detailed audit trails so you can track exactly who accessed which credentials and when, which is essential for both security and client trust.

The critical shift is moving from reactive to proactive management. Rather than manually hunting for passwords when someone needs access or scrambling to change credentials when someone leaves, you need systems that allow instant onboarding and one-click offboarding. When a client relationship ends or a team member transitions, you should be able to revoke access immediately without requiring manual password changes across multiple platforms. This isn't just about security; it's about operational efficiency and demonstrating to clients that their data is managed with enterprise-level rigor.

If you could recommend three immediate actions that agencies should take this quarter to strengthen their security posture, what would they be?

First, implement a business-grade password management solution immediately. This is your foundation; everything else builds from here. For less than $400 annually for a 20-person team, you eliminate the single biggest vulnerability in your security stack. Every day you continue managing client credentials through spreadsheets or browser-saved passwords is a day you're exposed to preventable breaches.

Second, conduct a Shadow IT audit. Require every team member to log every software tool and platform they're using into your password manager, sanctioned or otherwise. You cannot protect what you cannot see. This gives you a complete inventory of your software ecosystem and often reveals surprising security gaps where sensitive data is being stored in unapproved tools.

Third, establish and document your credential management protocols. Create clear written policies for how credentials are shared, how access is granted and revoked, and how urgent requests are verified. Make sure every team member understands these protocols and knows that following them isn't bureaucracy, it's protecting both the agency and your clients. Share these protocols with clients during onboarding and in annual reviews. It demonstrates professionalism and gives them confidence in your security practices.

For agencies that have historically viewed cybersecurity investments as cost centers, how should they reframe this thinking given the current threat landscape?

The calculation has fundamentally changed. A single credential breach can cost an agency a major client relationship, trigger regulatory penalties, and destroy years of reputation building. We've seen agencies lose six-figure accounts because they couldn't demonstrate adequate security controls. Conversely, agencies that position security as a strength are winning competitive pitches specifically because of their security infrastructure.

Consider the math: implementing enterprise-grade password management costs roughly $54 per user annually. Compare that to the cost of a single client breach: legal fees, notification requirements, lost business, reputation damage. Or consider the competitive advantage: if robust security protocols help you win just one additional mid-sized client per year, the ROI is exponential.

But beyond risk mitigation and competitive advantage, there's operational efficiency. How many hours does your team waste hunting for passwords, resetting forgotten credentials, or manually managing access when team members join or leave projects? Proper credential management eliminates this friction, making your team more productive and your operations more professional. This isn't a cost center, it's a revenue enabler and an efficiency multiplier.

For agencies managing multiple client accounts, security is not only about where passwords are stored. It is also about where and how those accounts are accessed. As an anti detect browser and cloud phone tool, MoreLogin helps agencies create separated browser profiles and cloud phone environments for different clients, projects, or team members, so cookies, sessions, fingerprints, and access conditions do not get mixed across accounts. With team permission controls, agencies can assign access to specific profiles without handing over raw account assets, making onboarding, daily operations, and offboarding more controlled. This adds another layer to credential management: instead of only protecting the password, agencies can also protect the operating environment around each client account.

Looking ahead through 2026, what emerging threats should agencies be preparing for now, even if they haven't fully materialized yet?

The intersection of AI and social engineering will become increasingly dangerous. We're already seeing early versions, but expect to see AI-powered attacks that can conduct real-time conversations, adapting their approach based on responses. Deepfake audio and video will make verification of urgent requests significantly more challenging. Imagine receiving a video call from a "client" requesting immediate credential access.

Watch for increased targeting of mobile devices. As remote work remains standard and team members access client accounts from personal devices, mobile endpoints become attractive targets. Agencies need to ensure their security infrastructure works seamlessly across devices without compromising security.

Finally, regulatory compliance will expand. More jurisdictions will implement data protection regulations that specifically address third-party access to client data. Agencies that can demonstrate compliance, showing encrypted credential management, detailed access logs, and clear data handling protocols, will have significant advantages in enterprise client relationships.

The agencies that thrive in 2026 won't be those that react to threats after they emerge, but those that build security into their operational DNA now. Password management as the first line of defense isn't just about protecting credentials, it's about demonstrating to clients that when they trust you with their digital assets, that trust is respected with enterprise-grade security at every level.

Turbo-Speed AI Saves Auto Sales: Silent Partner’s Contactter.ai Drives Buyer Engagement

sales 10 Feb 2026

Why has traditional sales automation failed to deliver true conversational intelligence in real customer interactions in the automotive retail industry, and what distinguishes conversational AI from rule-based automation in high-stakes sales environments like automotive retail?

Traditional sales automation in automotive was never designed to handle real conversations. It was built to trigger actions — send an email, fire a text, drop a voicemail — based on simple rules and timelines. That works fine for task management, but it breaks down in real customer interactions where intent shifts quickly, questions come out of sequence, and emotion plays a role in decision-making.

Conversational AI is different because it is built to interpret context, intent, and timing in real time. In automotive retail, where the stakes are high and buyers expect immediate, relevant responses, static automation simply can’t keep up. Conversational AI adapts to how people actually communicate instead of forcing customers into predefined workflows.

How can conversational AI tools act like a top-performing salesperson without replacing the human sales team?

Conversational AI can behave like a top-performing salesperson because it mirrors the habits that make great salespeople successful: speed, consistency, and the ability to ask the right questions at the right moment. What it does not do is replace the human element that closes deals.

At Contactter.ai, the AI handles the initial engagement, qualification, and follow-up at a speed no human team can match across every channel. That ensures no opportunity is lost due to delay. When the conversation reaches a point where judgment, negotiation, or relationship-building matters most, the human sales team steps in. The result is not replacement, but leverage. Salespeople spend more time selling and less time chasing leads that have already gone cold.

What makes sales-focused conversational AI fundamentally different from customer service chatbots, and what enables Contactter.ai to maintain context across text, email, and voice as a single continuous conversation for automotive buyers?

Sales-focused conversational AI is fundamentally different from customer service chatbots because the goal is entirely different. Customer service bots are designed to reduce workload and deflect inquiries. Sales-focused AI is designed to build momentum and move conversations forward.

Contactter.ai was built as a single conversation engine across text, email, and voice, rather than separate tools stitched together. That shared context allows the system to understand that a text reply, an unanswered call, and a follow-up email are part of one ongoing conversation. From the buyer’s perspective, the experience feels continuous and human rather than fragmented and repetitive.

What signals does Contactter.ai use to determine when a conversation should transition to a human salesperson?

The decision to transition a conversation to a human salesperson is based on intent signals rather than arbitrary rules. These signals include buying language, questions about pricing or availability, readiness to schedule an appointment, trade-in discussions, financing-related questions, or a clear request to speak with someone.

When those signals appear, the AI escalates the conversation with full context so the salesperson doesn’t have to start from scratch. That handoff is critical because it preserves momentum and ensures the human enters the conversation informed and prepared.

How does Contactter.ai’s direct integration with CRM and DMS systems enhance its real-time decision-making during sales conversations for auto dealerships?

Direct integration with CRM and DMS systems allows Contactter.ai to operate with real dealership data rather than assumptions. The AI can reference inventory availability, customer history, prior interactions, and dealership workflows while the conversation is happening.

This real-time access improves decision-making, prioritization, and handoffs. Instead of acting as a standalone chatbot, the AI becomes part of the dealership’s operating system, aligned with how the store actually sells and services customers.