Typeform, the AI engagement platform that turns forms into workflows, today launched Research Flow, a new AI-moderated research solution that helps teams design studies, recruit participants, run moderated surveys and interviews at scale, and synthesize findings in hours instead of weeks. Research Flow is the latest release in Typeform’s evolution from a form builder into an AI-powered platform for end-to-end workflows.

For years, teams have had to choose between two imperfect options: fast surveys that capture broad feedback but miss the deeper “why”, or traditional qualitative research that delivers rich insight but is expensive and often takes weeks. For small and growing teams, that trade-off can delay critical product, marketing, and customer decisions or base them on incomplete information.

Research Flow eliminates that tradeoff. Built directly into Typeform, it combines survey-scale reach with the conversational depth of one-on-one interviews, all without switching platforms or stitching together disconnected tools. The result is a faster way for teams to understand their audiences, validate decisions, and uncover the “why” behind customer behavior without the overhead of traditional research. Early studies using Research Flow have shown that participants provide 4.5x more feedback per question, generate 3x more emotional and contextual insights, and spend 2x longer engaging with studies than with traditional approaches.

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“Every business wants deeper insights, but traditional research has been too slow, too expensive, and too operationally heavy for most teams to run as often as they need,” said Aleks Bass, Chief Product and Technology Officer at Typeform. “Research Flow brings the speed and scale of AI to the depth of real human insight. We’re giving small and growing teams the ability to operate with the research intelligence of a much larger organization, without the headcount, the agency fees, or the six-week wait.”

Research Flow acts as an AI research assistant across the full workflow:

• Design rigorous studies with AI-assisted support for research objectives, discussion guides, question design, and study structure that help any team, including those without dedicated research expertise, turn a vague business question into a research-ready framework.
• Recruit the right participants through verified global panels or existing customer lists, with built-in screening, quota management, and incentive handling that eliminate the manual coordination tax of scheduling, emails, and no-shows.
• Run AI-moderated studies at survey scale across text, voice, and video, interviewing hundreds of participants simultaneously. Dynamic follow-up questions adapt in real time to capture tone, hesitation, and emotional nuance, surfacing the deeper context behind every response.
• Analyze results in minutes with automatic transcription, theme extraction, sentiment scoring, and direct querying of findings, replacing days of manual review with research-grade synthesis.
• Turn insights into decisions faster with video highlight reels and shareable reports that make research findings easy to review and act on.
• Preserve trust and brand consistency by giving participants the polished Typeform experience they already know, rather than a generic AI bot, helping protect response quality.

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Research Flow is built for any team that needs research-backed answers across customer, market, and public opinion research. For AI market research and other insight-gathering use cases, Research Flow helps users validate ideas, test messaging, understand buying behavior, evaluate pricing, and uncover the reasons customers hesitate, convert, or churn.

“Companies come to us when they need more than research—they need clarity on their customers, their market, and the decisions that will help them grow,” said Predrag Topić, Co-founder and Director at pokretach. “Our role is to understand the client’s business challenge, uncover valuable insights, and turn them into actionable business solutions. With Research Flow, my team can run customer and market research studies in a fraction of the time, whether we’re working from a client’s customer list or recruiting through a panel. We can capture the depth of a one-on-one conversation at a scale we could never staff manually, and turn that into recommendations with real business value.”

The launch marks the next phase of Typeform’s evolution from form builder to AI engagement platform. Growth Flow helps teams act on customer signals by turning every form submission into an automated workflow for capture, enrichment, and conversion. Research Flow helps teams understand their audiences before they act. Together, they turn every form response into a flow that drives clearer business outcomes.

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Customer experience has become one of the most overused phrases in business. Every company claims to put the customer first. Very few actually know what that means.

Rodrigo Aviles, Senior Manager, rpc – The Retail Performance Company, has spent his career inside that gap — across automotive, retail, and consulting — watching organizations confuse CX with a department, a survey score, or a line in a strategy deck. At Hyundai Motor Europe, he saw firsthand what happens when an industry obsessed with the thrill of the product forgets the moment after the sale. At RPC, he is now helping companies rethink the foundations.

A self-described Human-First AI advocate, Aviles is not anti-technology. He is anti-thoughtlessness. In this conversation, he makes the case that the real crisis in customer experience today is not a lack of AI — it is a lack of honesty about what problem you are actually trying to solve.

Excerpts from the interview; 

You’ve worked across automotive, retail, and consulting — three industries with very different relationships to the customer. Where did you see the biggest gap between what a company thought its customer experience was and what it actually was?

Three gaps keep coming up across industries.

The first is understanding. The definition of customer experience varies widely from one organization to the next. In some companies, CX was considered a function within customer service. In others, it sat inside marketing. In others still, people used customer experience and customer journey interchangeably, as if they were the same thing. They are not.

What I have come to believe and what I actively push in my work now is that customer experience is not a department or a team. It is a mindset. From the person at the reception desk to the C-suite, everyone has an impact on the customer, whether they realize it or not. Every touchpoint, every decision, every internal process eventually reaches the customer in some form.

The second gap is ownership. Because CX spans functions, nobody wants to claim it fully. I have been in organizations where the CX team was treated like the black sheep — where other departments would close the door on us because they assumed we were trying to take over their responsibilities. We were not. We were trying to orchestrate. There is a significant difference.

The third gap is measurement. NPS and CSAT have been the default metrics for years, and I will be honest, I am not a fan of NPS. Both metrics measure a moment. They capture how a customer feels at the end of an interaction, whether they are satisfied or not. Everything in between all the friction, the waiting, the confusion, the small recoveries goes unrecorded. And that is precisely where the real opportunities are hiding.

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You led CX at Hyundai Motor Europe during a period when the entire car industry was being turned upside down by EVs, software, and changing ownership models. What broke first — the product experience or the service experience?

The service experience. Without question.

I have seen a pattern across the automotive industry, and I wrote a white paper about it because I think it deserves more attention than it gets. I call it the unsexy part of the journey. Most of the resources, the energy, the excitement — all of it goes into pre-sales. Bring the customer in, get them excited, close the deal. The assumption that follows is: they bought the car, they are happy, they will stay.

But in automotive, some of the most significant revenue sits in after-sales — spare parts, insurance, and additional services. And that is exactly where the experience tends to collapse.

Think about what happens the first time a customer brings their car in for a service. Nobody knows why they are there. There is no communication while the car is being worked on. Days go by with no update. These are not dramatic failures — they are quiet ones. But they accumulate, and they erode trust.

What makes this worse is the benchmark customers are carrying in their heads. When someone brings their car in for a service, they are not comparing the experience to another dealership. They are comparing it to Amazon. They know the exact moment they press send on an order where their package is, when it will arrive, and what they will pay. No hidden fees, no surprises, constant visibility until it arrives at their door.

We cannot change that reference point. We can only decide what to do about it. And in the automotive industry, far too many OEMs have not yet asked that question seriously enough.

You call yourself a “Human-First AI” advocate. Tell me about a moment when you saw AI deployed in a customer experience that made you genuinely angry.

I was with a health insurance provider in Germany for almost six years. Then they redesigned their app — completely overhauled the UX — and everything I knew about how to navigate it disappeared overnight. I am a quick learner and reasonably tech-savvy. It still took me a significant amount of time to find basic functions.

Once I did, I submitted an invoice for a medical expense and heard almost nothing back. The app had a chatbot — marketed as AI-powered, though I remain sceptical there was much intelligence behind it. What followed was an exhausting back-and-forth across multiple channels, where I was asked the same questions repeatedly and gave the same answers repeatedly. Eventually, I left the provider.

A friend told me recently that the same company now claims to have an improved AI agent. His experience has been identical to mine.

This is the pattern I see most often. Companies encounter a problem, and instead of diagnosing it clearly, they reach for AI as the solution. But if you have not truly understood what you are trying to fix, and for whom, the technology will not save you. It will just create a faster, more scalable version of the same failure.

I use this analogy: it is like having a Ferrari in the garage when you do not know how to drive. Technology is not the problem. The lack of clarity about the problem is.

I had a manager early in my career who gave me a piece of paper the size of a business card and told me to write my idea on it. If it did not fit, I had not understood it well enough. The same discipline applies here. If someone needs half an hour to explain the problem their AI deployment is meant to solve, they are not ready to deploy it.

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Customer journey mapping sounds clinical on paper. Walk me through the most surprising thing you’ve ever discovered when you actually mapped one out for a real company.

The most consistent surprise is how little companies actually know about the full journey their customers go through — including the teams supposed to manage it.

Part of this is structural. When organizations work in silos, each function only sees its own piece. Marketing sees the awareness stage. Service sees the complaint. Nobody is watching the customer move from one to the next, experiencing the whole. What journey mapping forces you to confront is that the customer does not care about your org chart. They are moving through your brand as a single continuous experience.

What struck me most in several of these exercises was how much we were unconsciously mirroring our own internal complexity onto the customer. The difficulty, the confusion, the unclear ownership — we were essentially asking the customer to navigate our internal dysfunction alongside us.

But the most valuable realization came from pairing data with direct observation. I am a strong believer in data, but I also think we lean on it too heavily as a substitute for actually watching people. In one exercise, I challenged a team to spend three hours sitting in a dealership, not collecting data, not running surveys — just observing. Who comes in, what happens, where do things slow down, and what do people look confused by?

The gaps between what the data said and what the observation revealed were significant. There were friction points we never would have identified through surveys alone, and opportunities that no amount of voice-of-customer data had surfaced. That is what I mean when I say journey mapping should never just live on a wall or in a workshop deck. There are real people moving through that journey every day. You have to go and watch them.

You’re now based in Munich, having worked across Latin America and Europe. Does culture change what good customer experience looks like — or do people fundamentally want the same things everywhere?

Both things are true, and I think the tension between them is actually useful.

Culture absolutely shapes expectations. Coming from Mexico, warmth is embedded in how service works. You walk into a restaurant, and within seconds, someone greets you, makes you feel welcome, and treats you like you matter. That is not exceptional service in Mexico — it is the baseline. In Germany, that does not exist in the same way. My wife is German, and when we visited Mexico, she found it overwhelming. Too many people checking on her, too much attention. For her, that was not hospitality — it was intrusion.

Neither one is wrong. They are simply different cultural contracts around what a positive experience feels like.

But then there is the other layer, which is universal. Regardless of where someone is from, regardless of generation or background, when something goes wrong, people want it resolved quickly. The frustration of being ignored, passed between channels, asked to repeat yourself, or left waiting for a response that never comes — that is not cultural. That is human. And the tolerance for it is shrinking everywhere because the reference points keep improving. WhatsApp read receipts. Two-minute grocery delivery. Real-time tracking. Every one of these has raised the floor for what people consider acceptable.

The challenge for companies is navigating both. They need cultural sensitivity in how they design the emotional texture of an experience, and they need universal speed and clarity in how they resolve problems. Getting one right while neglecting the other is no longer enough.

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You speak publicly about CX. What’s the question nobody in the audience ever asks — but should?

Are we using AI to help our customers or to help ourselves avoid them?

I think about this a lot. When I look at many of the AI deployments happening in customer experience right now — the agents replacing call center teams, the chatbots standing between customers and resolution — the honest question is: who is this actually serving?

In some cases, the answer is genuinely the customer. Faster, available around the clock, consistent. In others, the technology is being used to reduce the cost and friction of human contact for the company, while the customer gets a worse experience dressed up in modern language.

Klarna is an interesting example. They made significant headlines for replacing a large number of customer service staff with AI. A year or so later, they were quietly hiring humans back because the experience had gaps that the technology could not cover.

I am not against AI in customer experience. I use it constantly in my own work, and it has changed how I operate. But there is a distinction that is not examined enough: are we deploying this because it genuinely helps the person on the other end, or because it helps us avoid dealing with them?

That question — asked honestly, before any deployment decision is made — would change a lot of what gets built.

The post Are We Using AI to Help Customers or Avoid Them? appeared first on MartechView.

Typeform, the AI engagement platform used by more than 150,000 businesses, has launched Growth Flow — a customer lifecycle solution that converts every form submission into the starting point of an automated workflow, connecting lead capture, data enrichment, nurturing, and conversion without requiring a separate tool for each step.

The launch targets a structural inefficiency familiar to most growing businesses: the gap between the moment a customer signals interest and the moment a business responds. That gap, typically the product of fragmented tools and manual handoffs, is where leads go cold and revenue opportunities quietly disappear.

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What Growth Flow Does

Growth Flow is built on a straightforward premise. A form submission is not the end of a customer interaction — it is the beginning of one. The platform uses AI to interpret each response in context and automatically trigger the appropriate next action, without delay or data loss between systems.

In practice, that means a prospect can book a meeting, sign an agreement, or complete a payment directly within the form, at the moment of peak intent, without being routed to a separate platform. Sales teams receive instant Slack alerts. Meetings are booked via Google Calendar. Multi-step nurture campaigns launch automatically across email, SMS, and integrated tools, including Klaviyo, from a single submission.

Every response also generates a dynamic customer profile, automatically enriched with third-party data on company size, industry, job title, and demographic signals. The enrichment layer operates within a security-first architecture aligned to GDPR and CCPA requirements, reaching match rates of up to 92 percent for business-to-business contacts and 71 percent for business-to-consumer. Teams gain immediate segmentation and routing context without manual research.

Typeform says its conversational form design already helps businesses collect up to 3.5 times more data than conventional form tools. Growth Flow extends that data advantage into automated action, surfacing real-time conversion signals and audience patterns that allow teams to adapt workflows as customer behavior evolves.

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The Broader Shift

“For decades, forms have been treated as the end of a conversation — a place where customer intent gets captured and then handed off, disconnected from everything that happens next,” said Aleks Bass, Chief Product and Technology Officer at Typeform. “What used to take ten tools and a lot of manual effort now takes one platform and a single prompt. Typeform is no longer just the form, but the engine that turns responses into revenue.”

The launch reflects a broader consolidation trend in marketing technology, as businesses under resource pressure look to reduce the number of point solutions required to manage the customer journey. Growth Flow’s pitch is that the form — historically a data collection endpoint — can serve as the operational hub for the entire early customer lifecycle, provided the automation and enrichment layers behind it are sufficiently capable.

For small and growing teams running lead generation, sales capture, or customer onboarding, the commercial argument is direct: fewer tools, less manual coordination, and a shorter path from customer intent to customer action.

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Each week, I speak with business leaders who have invested significantly in AI and CRM, yet still see customers waiting on hold, repeating information, and leaving frustrated. Our latest research quantifies this: poor customer service in EMEA costs consumers 9.2 hours annually, more than a full working day. While AI can address these issues, applying it to fragmented systems only worsens the experience. The real solution is to rethink the systems supporting service, not just the technology layered on top.

Frontline customer service agents often lack the necessary tools and information, with only a third of organizations reporting their agents are truly empowered, according to ServiceNow’s annual CX Shift report. Despite investments in technologies like AI, many businesses undermine these efforts by failing to unify their organizations within a single CRM system.

Addressing this challenge requires a fundamental shift in CRM usage toward a connected enterprise, where departments share information effectively and CRM functions as a system of action across the business. Moving away from siloed approaches reduces customer frustration and repetition. Every customer interaction, whether proactive or reactive, can either build or erode loyalty.

The Cost of Disconnection

The research surveyed 17,335 executives, service representatives, and consumers across EMEA. It found that 47% of customers would switch brands after a single poor or slow service experience. Even in sectors where quick resolution is critical, such as banking and telecoms, issues can take three to four days to resolve.

A critical gap remains between insight and action, as businesses struggle to translate AI investments into improved customer relationships. Too often, organizations layer AI onto fragmented CRM systems, expecting a transformation that does not materialize. Siloed workflows and disparate data only accelerate negative customer experiences.

Half of the surveyed executives report deploying end-to-end CRM platforms that support the entire customer lifecycle. However, what is needed is an AI-driven service powered by CRM data, enabling seamless, efficient customer escalation.

Connected Enterprise Is the Foundation, Not Just a Goal

Notably, 76% of EMEA organizations expect progress in the next three years to result from a connected enterprise approach, compared to 51% who expect it from AI adoption alone. A truly connected CRM is increasingly viewed as the operational foundation for enhanced customer experience.

Traditional CRM systems, which most organizations have used, were designed primarily for the front office. They capture customer requests but often do not manage fulfillment, resolution, or back-office actions, which are handled by separate systems. This fragmentation forces customers to repeat themselves, as no single system provides a complete view.

Current expectations require a new approach. Sellers, service agents, field technicians, and back-office teams should operate on a unified AI-powered platform, sharing a single customer record in real time. Customer issues raised through chat should be tracked, escalated, fulfilled, and resolved without requiring the customer to repeat information.

When CRM is implemented in this manner, businesses experience stronger customer loyalty, increased agent productivity, and improved cost efficiency.

The Agent Experience Is Part of the Customer Experience

It is essential that agents have access to the right tools and data, which requires rethinking how information is shared across the organization. In most organizations, agents are hindered by fragmented systems and disconnected tools. The research found that 79% of EMEA service agents must log into three to five separate systems to resolve customer queries, and inconsistent data across these systems remains a significant challenge.

Service agents prefer to focus on complex customer issues where human assistance is essential, rather than compensating for fragmented systems. By adopting integrated systems, agents are freed from switching between platforms, administrative tasks, and searching for information across departments. Instead of investing in automation without a strategy, organizations should reimagine CRM as a connected engine that unites teams, data, and processes, enhancing the service agents provide.

When organizations implement CRM effectively, the benefits extend beyond improved service interactions. A connected, service-led CRM strategy saves time for both customers and employees, transforming fragmented touchpoints into cohesive journeys that foster trust and long-term loyalty. Customers are more likely to deepen relationships when they feel recognized and understood at every stage. Internally, teams gain shared data visibility, enabling faster decisions, more effective AI use, and meaningful human engagement where it matters most.

AI Delivers When the Plumbing Is Right

However, few organizations have integrated customer data across touchpoints to create a unified view. Only 43% of EMEA organizations surveyed have achieved this, and just 23% have broken down silos to enable seamless AI strategies that connect departments.

This highlights the need for an approach that connects data across cloud environments, departmental tools, and legacy systems, providing AI with a complete customer view before taking action. In this model, AI agents act autonomously, coordinate across departments, and operate continuously without waiting for human input.

This is the difference between AI that resolves a billing dispute at 2:00 AM without human intervention and AI that simply accelerates the same flawed experience customers already face.

Service as a Growth Engine

Organizations that implement connected CRM effectively are discovering what the cost-center perspective overlooks: when service has full visibility into the customer relationship, it becomes a revenue driver.

In practice, this allows service agents to identify upsell and cross-sell opportunities in real time. They can create quotes, manage renewals, and escalate commercial opportunities on the same platform used for service requests. Service and sales become integrated, enabling the agent who resolves an issue to also strengthen the customer relationship.

A connected, service-led CRM strategy not only reduces the nine hours EMEA consumers lose annually to resolution friction but also builds trust and encourages customers to strengthen their relationships rather than switch brands.

Curbing Repetition, for Good

When repetition is the issue, the solution can be straightforward. By establishing a connected enterprise where customer information is shared across all channels and applications, agents are empowered to deliver effective service. A single interface with a comprehensive information streamlines work. In this model, service becomes a growth driver by enhancing productivity, insight, and competitive advantage across the organization.

Investing in AI to accelerate flawed processes will not yield results. Business leaders should instead reimagine CRM as a system of action across all channels, ensuring agents can deliver, and customers no longer waste valuable time with each interaction.

The post Is Your CRM Making Your Customer Service Worse? appeared first on MartechView.

Sprinklr is rolling out its Spring ’26 Release — a broad update to its AI-powered customer experience platform that touches service, marketing, insights, and platform governance. The throughline across nearly every new capability is the same: giving enterprise teams more visibility into what their AI is deciding, and more confidence that it is deciding correctly.

For a company that has staked its identity on AI-native customer experience management, the update reflects a maturing conversation in the market. Deploying AI agents is no longer the hard part. Trusting them at scale is.

The Trust Problem in AI-Powered Service

The most significant addition in this release is Autonomous Evaluation, a new capability within Sprinklr Service that provides explainable logs and test-backed validation for AI agents operating autonomously in contact center environments.

The logic is straightforward and overdue. As AI agents take on more customer interactions without human intervention, the organizations deploying them have limited visibility into why a particular decision was made, what went wrong in a failed interaction, or whether agent behavior is drifting from intended parameters. Autonomous Evaluation is designed to make that behavior legible — providing the audit trails and test infrastructure that allow teams to understand, refine, and scale agents with some degree of confidence.

Alongside this, Agent Copilot has been upgraded to operate proactively, surfacing real-time guidance during live customer interactions rather than waiting to be consulted. The focus is on core service metrics: first call resolution and average handle time — two indicators that contact center leaders actually manage to.

Operational improvements round out the service updates, including automatic shift bidding for agent scheduling and unified KPI dashboards that aim to shorten the distance between insight and action.

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Listening With Less Noise

On the insights side, Sprinklr is addressing a problem that has quietly undermined social listening for years: the signal-to-noise ratio.

AI Topics now use generative AI enrichments to filter out irrelevant mentions and surface only the conversations that are genuinely meaningful to a brand. The distinction between a brand being mentioned and a brand being discussed in a way that matters is one most listening tools have struggled to make reliably. The update is an attempt to close that gap.

Customer profiles are also being consolidated across channels, giving teams a single, coherent view of each customer rather than fragmented data spread across disconnected dashboards. Global Web Survey support has been expanded with one-click localization, stronger governance controls, and intelligent sampling — improvements aimed at producing cleaner, more representative feedback at scale.

Action Plans, previously limited to parts of the platform, now extend across the full Insights suite, allowing teams to assign tasks, set owners, and track progress on insights without leaving Sprinklr.

Marketing and Social Updates

For marketing teams, the release adds access to TikTok’s Commercial Music Library, enabling compliant video content creation without the licensing friction that has complicated branded content on the platform. Integration with Canva’s Digital Asset Management system streamlines how creative assets move into Sprinklr workflows while maintaining brand governance.

Performance analytics have also been upgraded. Automated root-cause analysis now flags and explains sudden shifts in campaign results, and a unified dashboard compares pre- and post-boost metrics in a single view. LinkedIn seller profile tracking gives social selling teams visibility into performance data that was previously difficult to surface in one place.

Also Read: CES 2026: The Year “Physical AI” Claimed the Real World

Platform and Governance

At the infrastructure level, Sprinklr is introducing bulk testing and AI telemetry within AI+ Studio, allowing enterprises to evaluate AI performance across large volumes of interactions rather than sampling spot-checks. Additional updates include unified integration management through the Sprinklr Marketplace, automated data ingestion via CRON-based scheduling, and an updated compliance framework under DRP 2.0.

“As AI Agents resolve more customer issues autonomously, we’re giving teams the transparent, test-backed validation they need to trust and scale them,” said Karthik Suri, Chief Product and Corporate Strategy Officer at Sprinklr. “These advancements help brands turn automation into measurable impact — and ultimately into more seamless, personalized moments of customer delight.”

The Spring ’26 Release is available now.

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TELUS Digital said it will use this year’s Mobile World Congress in Barcelona to make a pointed case to telecommunications operators: artificial intelligence must move beyond pilots and into production.

At MWC 2026, the company plans to showcase AI-driven customer experience and network optimization systems it says are already operating at enterprise scale. The message is aimed at an industry that has invested heavily in AI experimentation but struggled to translate pilots into measurable returns.

According to NVIDIA’s State of AI in Telecommunications: 2025 Trends report, 44 percent of telecom operators cite customer experience optimization as their top AI priority, while 40 percent focus on network planning and operations. Yet many communications service providers remain in what some executives call “pilot purgatory” — testing tools without scaling them into production systems.

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TELUS Digital argues it has moved past that phase. In 2025, the company said it processed more than 2 trillion tokens across dozens of AI models through its Fuel iX generative AI engine, deployed both internally and for external customers. The scale, executives say, reflects enterprise-grade AI embedded across live telecommunications environments.

“In the AI world, tokens represent the currency of experience,” said Bret Kinsella, general manager and senior vice president of Fuel iX at TELUS Digital. “They distinguish organizations experimenting with AI from those operating it at scale.”

At MWC, the company plans a 90-minute session detailing more than 20 production use cases, alongside representatives from NVIDIA and F3 Networks. The discussion will focus on how operators can shift from isolated proofs of concept to AI-driven automation integrated across customer service and network operations.

TELUS Digital’s positioning is strengthened by its relationship with TELUS, the Canadian telecommunications provider that serves as what executives describe as a “living laboratory.” By deploying AI tools within its parent company’s global operations before offering them to clients, TELUS Digital says it can validate performance under real-world conditions.

Among the solutions on display at MWC are Fuel iX Agent Trainer, which uses AI-driven voice and chat simulations to accelerate agent onboarding; Fuel iX Fortify, a platform for automated AI application testing and monitoring; and AI-powered network design services intended to modernize legacy telecom infrastructure into cloud-native environments.

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The broader ambition is to reframe AI not as a speculative investment but as operational infrastructure — one capable of reducing costs, improving service and generating measurable returns.

As telecom operators confront intensifying competition and rising infrastructure demands, the challenge is no longer whether to experiment with AI. It is whether they can industrialize it.

The post TELUS Digital Pushes Telecom AI Beyond Pilots appeared first on MartechView.

Talkdesk on Monday unveiled new automation capabilities designed to extend its agentic AI deeper into enterprise workflows, introducing a cross-system orchestration engine and expanding autonomous resolution to email.

The announcements, made at CCW Berlin, include the launch of Automation Flows — a code-free orchestration engine — and an update to Talkdesk Autopilot that brings its AI-driven decision-making to the email channel.

Together, the enhancements are intended to strengthen what the company calls its Customer Experience Automation (CXA) platform, linking customer-facing interactions directly to backend systems and enabling AI agents to carry requests through to completion across channels.

Automation Flows is designed to connect customer interactions with third-party systems, allowing multi-step processes to execute across platforms without custom development. The company said the tool supports complex, long-running workflows — such as mortgage refinancing — in which customer inquiries can trigger document collection, compliance validation, underwriting decisions and coordinated updates across customer relationship management and loan systems, all within a single governed workflow.

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The extension of Autopilot to email targets one of the most operationally complex customer service channels. Unlike rule-based email bots, the company said, Autopilot uses AI reasoning to interpret intent, analyze message content and determine appropriate next steps. The system can verify customer data, update accounts and issue final resolutions without human intervention.

“Some of the hardest problems in customer experience happen in the inbox,” said Tiago Paiva, chief executive and founder of Talkdesk. “Email is high-volume, high-context and deeply operational.” By bringing agentic AI to email, he said, the company aims to deliver autonomous resolution in a channel where scale and complexity often intersect.

Paiva added that Automation Flows allows organizations to design and deploy cross-system workflows without custom coding, reducing the friction that has historically slowed enterprise automation initiatives.

With the latest updates, Talkdesk now extends automation across voice, chat, SMS and email, integrating cross-system orchestration into the broader customer journey.

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The company’s CXA platform coordinates multiple AI agents across service, sales and support functions, bridging contact center operations with back-office systems. Underpinning the system is the Talkdesk Data Cloud, which provides real-time contextual data intended to guide agent decision-making and improve performance over time.

The expansion reflects a broader push among enterprise software providers to move beyond conversational AI and toward fully executable, end-to-end automation — where customer requests are not only understood, but completed.

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Nearly two-thirds of consumers believe brands value new customers more than the ones who have already proven their loyalty, according to a new survey from GetResponse.

The perception is particularly strong among frequent buyers: about 70 percent of customers who make monthly purchases say brands prioritize acquisition over retention.

Yet the imbalance may be more emotional than financial. Nearly seven in 10 brands surveyed said they split their budgets evenly between customer acquisition and retention. The study, which surveyed 2,400 consumers and 600 direct-to-consumer and e-commerce brands across the United States and Europe, suggests a widening perception gap between how companies allocate resources and how customers experience them.

Larger brands appear more focused on retention. Forty percent of companies with more than $50 million in revenue ranked retention as their top growth priority, compared with 29 percent of companies generating less than $5 million in revenue.

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For customers, however, the issue is not simply about spending — it is about recognition.

“I think most of us have had that moment when we see a great introductory offer and realize it’s only for new customers,” said Patricia Camden, managing director and customer experience and loyalty leader at EY. “Meanwhile, we’ve been loyal for years, and there’s no acknowledgment of that history. That creates a perception problem very quickly.”

Camden said research at EY consistently shows that customers want to be recognized in meaningful ways, not merely enticed with discounts. Loyalty programs that embed recognition into everyday interactions — rather than limiting rewards to promotional campaigns — are more likely to shift perceptions.

“When loyalty programs are built around customer insight and specific triggers for relevance, they shift the perception from ‘brands only care about new customers’ to ‘this brand values my long-term relationship,’” she said.

Consumers appear willing to meet brands halfway. According to GetResponse, 91 percent of respondents said they would share personal preferences in exchange for more tailored rewards or offers.

Recognition, however, is not one-size-fits-all. Camden noted that a recent EY loyalty study found differences in how customers value certain gestures. Many women, for example, said birthday recognition carried meaning, while milestone recognition resonated less strongly with men. Frequency also matters: some customers prefer regular, predictable rewards, while others respond more strongly to less frequent but more personalized gestures.

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Across demographics, one theme remains consistent: customers seek value beyond discounts.

“Exclusive services, member events, unique experiences and benefits with partner brands can resonate more than just a coupon or an offer,” Camden said.

The appetite for exclusivity appears strong. Ninety-three percent of consumers surveyed said they would be likely to join an exclusive experience or recognition program available only to loyal customers if their preferred brands offered one.

The findings underscore a challenge for marketers: loyalty is not simply earned through points or price cuts. It is sustained through recognition — and the perception that long-term relationships matter as much as new sign-ups.

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ServiceNow and OpenAI on Tuesday announced an expanded, multi-year strategic collaboration aimed at accelerating enterprise AI outcomes and delivering more advanced agentic AI experiences for global businesses.

Under the agreement, OpenAI models will become a preferred intelligence capability for ServiceNow customers, providing direct access to frontier model capabilities without the need for bespoke development. The collaboration brings together OpenAI’s technical advisors and ServiceNow engineers to deliver custom AI solutions aligned with customer roadmaps, while increasing speed and scalability across enterprise deployments.

ServiceNow said it will use OpenAI models to build direct speech-to-speech and native voice technologies, enabling more natural interactions and helping enterprises overcome language barriers. With access to OpenAI’s latest frontier models, including GPT-5.2, ServiceNow aims to unlock a new class of AI-powered automation across complex enterprise environments.

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“ServiceNow leads the market in AI-powered workflows, setting the enterprise standard for real-world AI outcomes,” said Amit Zavery, president, chief operating officer, and chief product officer at ServiceNow. “As companies move from experimenting with AI to deploying it at scale, they need multiple AI leaders working together to deliver faster, better outcomes.”

Brad Lightcap, chief operating officer at OpenAI, said the partnership would help enterprises embed agentic AI into workflows that are secure, scalable, and designed to deliver measurable impact across industries.

The collaboration is designed to simplify adoption as AI model innovation accelerates. By embedding OpenAI models directly into the ServiceNow AI Platform, customers gain native access to advanced intelligence that complements ServiceNow’s configuration management database, while the company’s AI Control Tower provides governance, orchestration, and visibility into how AI models are used across workflows.

Among the capabilities under development are real-time speech-to-speech AI agents that can listen, reason, and act without text-based intermediaries—allowing users to open cases, trigger approvals, and orchestrate next steps in their preferred language. The partnership also introduces computer-use models that extend automation to unstructured data and legacy systems, enabling more autonomous orchestration across IT environments.

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The agreement builds on ServiceNow’s existing use of OpenAI models for natural language assistance, AI-powered summarization and content generation, intelligent search, and developer tools that translate intent into workflows and automation.

ServiceNow said it powers more than 80 billion workflows each year, and that the expanded collaboration with OpenAI positions the company to deliver even more advanced automation and AI-driven experiences across industries.

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Mercuryo has partnered with Visa to offer near real-time crypto-to-fiat off-ramping through Visa Direct, the payments network’s real-time money movement platform.

The partnership expands Mercuryo’s off-ramp services globally, allowing eligible users to convert digital token holdings into fiat currency and receive funds directly on Visa debit or credit cards. The companies said the service is designed to be fast, low-cost, and accessible across borders.

By extending Visa Direct coverage, Mercuryo will integrate its network of non-custodial wallets, exchanges, and payment platforms with Visa’s global payments infrastructure. The result is a streamlined crypto-to-fiat conversion experience that allows users to cash out without leaving the platforms they already use and trust.

Executives said the collaboration supports broader Web3 adoption by reducing friction in cross-border payments and shortening the time it takes for users to access funds in their local currencies.

“This partnership with Visa further enhances our ability to deliver a fast, low-cost user experience,” said Petr Kozyakov, co-founder and chief executive of Mercuryo. He noted that the integration enables users to convert digital assets into fiat that can be spent at more than 150 million Visa-accepting merchant locations worldwide.

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Anastasia Serikova, head of Visa Direct for Europe, said the integration helps bridge crypto and traditional finance by making conversion to fiat “faster, simpler, and more accessible,” while enabling near real-time settlement.

Mercuryo positions itself as an early innovator in the Web3 payments space, offering on-chain and off-chain payment solutions designed to simplify access to digital assets. The company said the Visa partnership strengthens its mission to improve capital flow across the decentralized finance ecosystem.

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