impact.com, the global infrastructure for partnership-driven commerce, today announced new AI and creator capabilities at its annual iPX event, designed to help brands discover partners, drive conversion, scale creator content, and automate partnership workflows in one platform. The announcements reflect impact.com’s continued evolution into an AI-powered infrastructure layer for modern partnerships.

New innovations unveiled at iPX include new ask impact capabilities, autonomous partnership agents, Storefronts, in-platform social amplification, creator discovery integrations, conversion optimization capabilities, and faster creator payouts.

“Partnerships are becoming one of the most important growth engines for modern brands, but much of the infrastructure supporting them is still fragmented and reactive,” said Max Ciccotosto, Chief Product Officer at  impact.com. “That’s why we’re building the infrastructure layer that connects discovery, conversion, measurement, payouts, and AI-driven decision-making in one platform. We believe the category is moving from manual partnership management toward more intelligent, performance-driven partnership orchestration.”

At the center of the announcements are new ask impact capabilities now available, designed to move beyond question-and-answer interactions into contextual guidance, workflow navigation, and task execution across the platform.

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Built on a knowledge graph spanning hundreds of platform screens and thousands of documents, including Partnerships Experience Academy courses, ask impact V2 understands where users are in the platform, what actions they’ve already taken, and what they may need next. Users can ask live performance questions, resume previous conversations, draft outreach, pull partner profiles, and navigate directly into relevant workflows—all within a single conversational thread. 

impact.com also introduced autonomous partnership agents, beginning with a Recruitment Agent (currently in closed beta) capable of identifying, qualifying, and onboarding prospective partners while allowing brands to maintain approval controls and oversight. The company also previewed AI Search Visibility, available this summer to help brands understand which creators and publishers are shaping AI-generated recommendations. 

To support broader AI adoption, the company unveiled an expanded Integrations Hub and Application APIs, enabling partnership data and workflows to integrate directly into external AI ecosystems and large language models, including Anthropic’s Claude. impact.com also previewed a publicly available Model Context Protocol (MCP) connector, planned for release later this summer, that extends the platform’s reach into the growing ecosystem of AI-native tools and workflows.

Together, these capabilities position impact.com as an open AI infrastructure layer for partnerships, built to connect with the broader ecosystem of AI-native tools and workflows. impact.com also announced the general availability of Storefronts, persistent, creator-curated shopping destinations that allow brands and creators to build always-on commerce experiences with performance attribution.

Also Read: Brands Are Making ‘No AI’ Their Biggest Selling Point

Unlike traditional affiliate links that fragment across campaigns and channels, Storefronts provide a permanent destination where creators can curate products while brands maintain control over merchandising, brand presentation, and measurement.

“Our fans are incredibly passionate, and many of them are already acting as creators—curating and sharing the merchandise they love,” said Wade Tonkin, Director of Affiliate Marketing at Fanatics. “Giving creators the ability to build personalized storefronts allows them to reflect their own voice while staying true to our brand, creating a more authentic commerce experience for fans. Paired with impact.com’s performance capabilities, we can now connect that authentic fan-driven content directly to measurable outcomes, creating a more scalable and integrated partnership strategy.”

Additional creator innovations announced at iPX include:

• In-Platform Social Amplification, enabling brands to turn creator content into paid media campaigns directly within impact.com, available now with Instagram,  with additional major social platform integrations planned throughout the year
• Verified creator discovery integrations with Instagram’s Creator Marketplace, building on earlier integration work with YouTube, to bring first-party creator insights and audience data directly into the platform, with TikTok and Facebook integrations planned
• Single-Use Promo Code Links on the roadmap for Shopify and BigCommerce brands, automatically applying creator discounts at checkout while reducing attribution leakage and checkout friction
• Creator Benchmark Reporting, now providing brands with comparative creator program performance insights across key industry metrics
• Anytime Withdrawal, now available with PayPal, enabling creators and partners to access earnings faster, with Venmo and additional payment methods planned

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Together, these innovations reflect a broader shift from fragmented tools and passive reporting to infrastructure that helps brands and creators discover opportunities, optimize performance, and drive measurable growth. By combining creator commerce, paid media execution, first-party data, payouts, APIs, and AI-driven automation in one platform, impact.com is building the operating system for modern partnerships.

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Fast Simon introduced an AI personalization solution purpose-built for merchandisers at mid-market and enterprise e-commerce brands.

While most personalization technologies were originally built for massive marketplaces and ultra-high-traffic e-commerce environments such as AWS or Google, Fast Simon’s AI Personalization is architected for brands on Shopify. The model is optimized for the unique merchandising needs of mid-market and enterprise retailers, for which brand presentation, collection strategy, product launches, promotions, and inventory management are critical to conversion performance.

Early adopters of Fast Simon’s new AI Personalization for Merchandisers saw double-digit increases in revenue and conversion rates. 

Also Read: Are We Using AI to Help Customers or Avoid Them?

AI that Reduces the Operational Burdens of Merchandisers

Fast Simon’s AI Personalization for Merchandisers system uses behavioral signals to continuously adapt experiences across shopping surfaces while enabling merchandising teams to maintain strategic control over the customer experience. Without requiring any Personally Identifiable Information (PII), the model understands both shopping behaviors and merchandiser workflows, dramatically reducing manual curation, rule management, and other operational burdens while also significantly boosting shopper engagement, conversions, and revenue.

Latest Fast Simon AI Merchandising Breakthrough

Today’s news continues Fast Simon’s rapid delivery of no-code, retail-specific AI solutions specifically designed for merchandisers struggling with large product portfolios and catalogs, dynamic shopper behavior, and lackluster AI tools that continue to ignore or misunderstand their needs.

Also Read: Is AI About to Make Media Buying an Endless Experiment?

Earlier this year, Fast Simon enabled e-commerce merchandisers, for the first time, to quickly and accurately optimize products and collections for maximum conversion and revenue. The AI agent solution instantly shows which products are truly successful, overexposed, or hidden winners, and measures the short- and long-term opportunity costs of merchandising decisions across the portfolio.

“DTC brands don’t want generic AI personalization that ignores merchandising strategies like brand positioning and critical operational realities such as inventory,” said Zohar Gilad, CEO of Fast Simon, “While many AI commerce tools rely primarily on large language models and chat interfaces, Fast Simon’s AI personalization is built around a commerce-native operational model that continuously adapts to shopper behavior, merchandising strategy, and catalog dynamics in real time.”

Fast Simon AI Personalization for Merchandisers is now generally available for Shopify Plus merchants. 

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DoubleVerify, the leading software platform to verify media quality, optimize advertising performance and prove campaign outcomes, today announced the launch of AI-powered brand suitability reporting for YouTube Audio Ads campaigns, extending DV’s trusted post-bid measurement capabilities to this listening-first format.

As advertisers increasingly allocate dedicated budgets to audio environments, the need for transparency and control continues to grow. With brand suitability reporting for YouTube Audio Ads campaigns, DV helps customers confidently expand into this media category, applying consistent standards and gaining the visibility needed to drive stronger performance.

“As audio consumption continues to grow, advertisers need the same level of transparency and control they expect across the broader digital ecosystem,” said Steven Woolway, EVP of Business Development at DoubleVerify. “With AI-powered brand suitability measurement, we’re extending the same trusted insights advertisers rely on for video into audio-forward environments, giving our customers the transparency and control needed to protect brand equity while scaling investment with confidence.”

YouTube Audio Ads are designed for listening-first experiences, running as in-stream ads with lightweight creative, typically a static image or simple animation paired with high-quality audio. These ads appear across environments where users are more likely to listen than watch, including YouTube Music, podcast content on YouTube and other audio-forward inventory.

Also Read: Are We Using AI to Help Customers or Avoid Them?

As audio becomes a more intentional part of media plans, it introduces a new layer of complexity for brand suitability. Without proper post-bid measurement, advertisers risk limited visibility into potential misalignment between their brand and the audio content surrounding their ads.

DV’s brand suitability reporting for YouTube Audio Ads is powered by Universal Content Intelligence™, DV’s AI-powered classification engine that analyzes content across audio, video, text and image signals to deliver precise, scalable classification. In audio-forward environments, DV applies advanced AI models to interpret spoken language, sentiment, contextual cues and metadata. This multi-signal approach enables more accurate identification of nuanced content, ensuring brand suitability standards are consistently applied.

Also Read: Brands Are Making ‘No AI’ Their Biggest Selling Point

DV’s brand suitability offering is a key component of DV’s Media AdVantage Platform, which combines media verification, ad performance optimization and campaign outcomes measurement to maximize media effectiveness and return on ad spend. As part of media verification, DV’s brand suitability measurement provides advertisers with actionable insights into content alignment, strengthening confidence in campaign performance.

DV recently announced the launch of DV AI SlopStopper™ for Social, expanding protection against low-quality, AI-generated content and helping advertisers maximize media quality and campaign performance across social environments.

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Atento, one of the world’s leading providers of customer relationship management (CRM) and business transformation outsourcing (BTO) services, has expanded its portfolio of solutions for companies that choose to keep their customer experience (CX) operations in-house.

With more than 25 years of experience in customer relationship management and operational transformation, the company offers advanced technologies to improve efficiency, customer experience, and performance across organizations of all sizes and industries.

The initiative democratizes access to technological solutions that were previously reserved for outsourced operations. “Atento brings its expertise in BTO and democratizes access to advanced customer relationship management solutions for companies that keep their CX operations in-house,” says Pablo Sánchez, CMO at Atento.

The combination of CX consulting, AI, and automation applied to critical processes opens new opportunities for the company to expand its reach beyond its current customer base. This offering reinforces Atento’s position as a strategic partner for companies that view customer relationships as a key asset and prefer to manage them in-house.

Also Read: Is AI About to Make Media Buying an Endless Experiment?

Suite of solutions to boost the efficiency of internal operations

Focused on automation, data analytics, AI, and management tools, Atento’s solutions enhance the customer experience and optimize KPIs in large-scale operations. Highlights include:

AI Agent Assist

AI-powered agent support that provides real-time responses to streamline and enhance the customer experience. By delivering the right information at the right time, it reduces handling time, increases resolution rates, and ensures brand consistency. This integrated, scalable tool turns every agent into a high-performing professional by providing NBA (next best action) and NBO (next best offer) in every interaction.

AI Advanced Insights

A platform that transforms complex data into actionable insights to modernize operations, optimize performance, and strengthen a company’s competitive advantage. The solution directly improves NPS and employee retention, driving operational excellence in the customer experience.

Also Read: Are We Using AI to Help Customers or Avoid Them?

AI Agents

Technology that automates multichannel interactions—voice, text, and multimedia—while maintaining a high level of personalization. This solution reduces costs, optimizes workflows, and provides strategic insights that accelerate growth, strengthening customer engagement and loyalty without requiring human intervention.

“Thanks to streamlined processes, continuous investment in technology, and a people-centric approach, Atento is leading the evolution of customer experience operations and business transformation, influencing companies that view the customer relationship as fundamental and do not intend to outsource it,” Sánchez notes.

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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.

Also Read: Brands Are Making ‘No AI’ Their Biggest Selling Point

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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Sprinklr, the definitive, AI-native platform for Unified Customer Experience Management (Unified-CXM), today announced LLM Insights, a new AI-native capability within Sprinklr Insights that helps brands understand and shape how they are represented in large language model (LLM) search results.

Generative AI platforms are evolving how customers discover and evaluate brands. Visibility is no longer defined by rankings or clicks, but instead by how brands appear in AI-generated answers. Yet most organizations have little visibility into how they are represented, misrepresented, or even excluded in these platforms, exposing a new risk: a self-reinforcing visibility gap, where brands that aren’t surfaced generate no discovery, no conversation, and ultimately remain absent from future results.

Sprinklr’s LLM Insights helps enterprises address this gap by providing real-time insight into how their brand appears in AI-generated answers, analyzing visibility, sentiment, recommendations, and competitive positioning across LLM-driven queries. It enables brands to track and improve key outcomes, such as AI mention rate, share of voice, and sentiment, while connecting visibility into AI directly to downstream impacts like traffic, conversions, decisions, and customer experience.

“Your brand is already part of the AI conversation, and Generative AI platforms are compressing the traditional buyer journey,” said Sprinklr Chief Product and Corporate Strategy Officer, Karthik Suri. “Customers increasingly move from a single prompt to a synthesized recommendation, often without visiting brand websites or owned channels. Representation in these platforms is a critical driver of awareness and consideration. LLM Insights gives organizations the ability to understand that conversation, act on it, and be part of the answers that matter – all from the same unified platform they already use to monitor and manage their brand.”

In beta deployments this spring, early customers found that AI-generated answers were often incomplete or misrepresenting their brands at critical decision points. Competitors were surfaced more prominently, their offerings were positioned as higher-cost alternatives, and influential third-party domains were reinforcing inaccurate pricing and brand narratives – entirely without the brand’s knowledge.

Using Sprinklr’s unified architecture, these teams were able to trace how social and digital signals were shaping those outcomes — giving them a way to detect emerging distortions early and take action before they spread.

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Sprinklr’s LLM Insights is differentiated from other solutions in three key ways:

1. Real-world prompts, powered by the breadth of Sprinklr’s platform: Unlike tools that rely on keyword lists or synthetic prompts, Sprinklr generates queries from real customer conversations across its unified platform – spanning social, reviews, communities, and care – giving brands a more accurate view of how they appear in AI-generated answers and the signals shaping those outcomes.
2. Turn insights into action: LLM Insights connects directly to content, knowledge, and engagement workflows, enabling teams to quickly improve how their brand is represented and recommended by LLM Platforms.
3. Activated quickly within the platform brands already trust: LLM Insights is built into Sprinklr Insights, allowing teams to get started in minutes using the same unified environment they already rely on for brand monitoring and customer intelligence.

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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.

Also Read: Why the CMO Now Owns the Privacy Problem

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.

Enterprise systems are complex because the real world is complex. Every automation, workflow, data model, and report reflects a decision the business needed at the time. The real challenge now is to make that complexity legible enough for both people and AI agents to act on it safely, keeping the valuable parts and dropping the rest.

Sweep, the agentic layer for enterprise systems, announced the launch of its cross-platform agent for Salesforce, Snowflake, and Data 360. The new capability allows teams to reason across all three systems simultaneously through a continuously updated dependency graph, so they can see how enterprise data, workflows, metadata, permissions, and business logic connect before making any change.

Generic AI was built for clean, greenfield environments. But Enterprise AI has to work inside real systems that carry years of complexity, which often require first-hand context to understand and build upon. 

The gaps between systems, particularly, are where complexity compounds, explains Sweep CEO and co-founder, Ido Gaver.

Also Read: Your ERP Is Holding You Back. Here’s How to Fix It.

“Every platform wants to be the agentic layer for its own domain. None of them can see what happens in between,” says Gaver. “Enterprise system complexity grows with the business. Sweep gives teams a way to embrace that complexity, apply context, and use AI to discover, design, build, and monitor change across the systems that actually run the enterprise.”

When agents act with incomplete context, ordinary business logic can become operational risk. For example, an agent might approve an expedited order because they can see the customer record in Salesforce, but miss a credit hold stored downstream or a data-quality exception surfaced in Snowflake. Another team might change a picklist or workflow in Salesforce without seeing how that value feeds a Data 360 audience, a Snowflake report, or a downstream compliance process. In both cases, the issue is not the agent’s reasoning ability. It is the absence of cross-system context.

Sweep addresses that problem by making the connected stack understandable to both teams and agents. Instead of stitching together one-off queries, data catalogs, and platform-specific copilots, Sweep creates and maintains a live map of dependencies across Salesforce, Snowflake, and Data 360. Teams can ask plain-language questions, understand impact, and govern changes before they ship.

Also Read: Why the CMO Now Owns the Privacy Problem

How Sweep’s Cross-platform Agent Works

• Discover: Sweep automatically indexes metadata, schema, permissions, automations, and dependencies across connected platforms, creating a shared map of how revenue and data infrastructure actually operate.
• Design: Teams can use natural language to then ask cross-platform questions before making a change, such as which reports, data products, automations, audiences, or processes depend on a field, object, workflow, or dataset.
• Build: Sweep supports governed execution in Salesforce, where teams can turn approved plans into changes while preserving the context and impact analysis behind them.
• Monitor: Agentic Governance continuously scans connected systems for drift, permission inconsistencies, policy violations, and dependency risks, providing teams with a single source of truth across a distributed architecture.

Sweep’s internal benchmarks, derived from analysis of more than 12,000 anonymized chats between their customers and agents, show that what typically takes enterprise teams 11 hours of cross-system investigation is compressed to roughly 1 hour. That’s because dependencies are already mapped and traced, and the downstream impact is already visible before the change ships.

The post Sweep Launches AI Agent for Salesforce and Snowflake appeared first on MartechView.

Sitecore announced that it acquired Scrunch, an AI customer experience platform that helps brands understand and improve how they appear in AI search. The acquisition comes as large language models and AI-generated answers dominate the digital landscape, where buyers research solutions, compare options, and decide what deserves their attention and money. To compete, companies must continuously create, curate, and connect content that can be properly interpreted by these platforms.

AI systems influence brand perception: which offerings appear in answers, which competitors get mentioned, and which sources are cited. By the time buyers reach a brand – if they reach it at all – they have often already formed a point of view.

Brands need a scalable way to see where they appear in AI answers and to fix what AI is missing or getting wrong. Scrunch provides visibility into where brand messages are appearing, missing, or misrepresented in AI-driven discovery, working in concert with SitecoreAI to shape and manage content across AI ecosystems and other digital channels.

This transaction comes at a critical inflection point where brands must move from insight to action. It combines Scrunch’s insights, recommendation capabilities, and Agent Experience Platform (AXP) with Sitecore’s AI-powered digital experience platform (DXP). Together, Sitecore and Scrunch create a connected platform that publishes content across channels with brand integrity and message clarity, increasing traffic, improving conversion, and enabling organizations to measure performance. These capabilities help brands ensure that buyers find, trust, and choose them.

“AI has changed buyer behavior forever. We’re at a pivotal moment where companies must rethink traditional digital strategies and accept that the internet must be written for machines to understand if we want humans to experience it,” said Eric Stine, Chief Executive Officer of Sitecore. “With Scrunch, we enable our customers to understand how they are represented in AI-generated answers and automatically act on those insights, maximizing how they reach, engage, and serve their customers. We’re helping brands show up with greater clarity, authority, and relevance so they can build trust, increase share of voice, and influence decisions early in the buying journey when it matters most.”

Also Read: Your ERP Is Holding You Back. Here’s How to Fix It.

“Control of the brand narrative is shifting, and customers are increasingly trusting AI to define brands, rather than the organizations behind those brands,” said Chris Andrew, Chief Executive Officer of Scrunch, a Sitecore company. “By joining forces, we’re helping companies meet buyers where they are, moving beyond traditional SEO to win inside AI-generated answers. That’s where Scrunch’s AXP is a critical advantage, delivering content in a format AI agents can read and use, without disrupting the human experience, allowing brands to become the trusted sources that power those answers.”

Driving Traffic, Delivering Outcomes

Customer adoption demonstrates the potential of Scrunch’s approach. In customer case studies and analyses, organizations using Scrunch have seen measurable improvements in AI search visibility, citations, and business outcomes.

With Scrunch, Akamai evaluated AXP-enabled webpages against comparable non-AXP webpages across tracked AI search prompts and models. The analysis found that AXP-enabled pages achieved a 364% increase in brand presence for non-branded prompts and a 218% increase in citations in AI-generated results.

In a separate study, Runpod used the Scrunch platform to expand prompt tracking and identify rendering and indexing issues that were limiting AI discoverability. Runpod reported a 400% increase in paying customers associated with its AI search optimization efforts.

“At Perficient, we’re building an AI-first marketing engine focused on speed, relevance, and real impact,” said Carrie Grapenthin, Chief Marketing Officer of Perficient. “As a customer and partner of both Sitecore and Scrunch, this combination is an exciting opportunity for our team. Insight without action isn’t enough. Scrunch helps us see where our brand is showing up, where gaps exist, and what needs to improve. Sitecore helps us act on those insights across the content and experiences we already manage, so we can move faster and deliver differentiated content in our unique brand voice.”

Connecting AI Insights to Content, Engagement and Performance

The transaction addresses a growing enterprise need: helping brands understand how AI represents them and act on what needs to change. The issue is not simply more content. Most enterprises already have a lot of content, but it is often fragmented, outdated, or hard for AI systems to access and interpret. The opportunity is to make the right content clear, current, and contextualized for the machines that increasingly shape what buyers see before they ever reach a brand’s website. Scrunch provides visibility into buyer queries, brand representation, and competitive positioning, while SitecoreAI enables teams to manage and activate content across web, social, and other digital channels, helping organizations continuously improve performance and drive engagement.

Also Read: Why the CMO Now Owns the Privacy Problem

Advancing AI-Driven Content Optimization

Standalone Answer Engine Optimization (AEO) platforms typically show which questions a brand wins or loses, where competitors appear, and which sources shape the answer. Scrunch goes further by:

• Moving beyond the website: Scrunch surfaces valuable knowledge across the broader content estate, not just publicly available web content.
• Delivering actionable recommendations for brands: Scrunch gives next-level visibility across the broad landscape of Large Language Models (LLMs) – ChatGPT, Google Gemini, Perplexity, etc. – identifying the content, source, and delivery opportunities teams can act on to improve how their brand appears in AI answers. These recommendations will be automated within the SitecoreAI Content Management, Content Marketing, and Digital Asset Management solutions.
• Optimizing content for AI agents: Scrunch’s AXP delivers content formatted for LLMs in a way that AI agents can read and use, without disrupting the human experience.
• Driving execution: Unlike standalone tools, Scrunch connects directly to Sitecore workflows, enabling faster action and continuous improvement.

The post Sitecore Buys Scrunch to Help Brands Win AI Search appeared first on MartechView.

Modernization Is Now a Business Priority

Enterprise modernization has reached a point where delays carry business risk, and careless speed poses equal danger. Many organizations still depend on ERP landscapes shaped by years of local decisions, customizations, acquisitions, compliance needs, and short-term fixes. They run the business, yet often make it harder to see clearly, respond quickly, and scale.

Selective Transformation Offers a More Practical Path

The answer is not a reckless replacement of everything that exists. It is also not a technical conversion that carries yesterday’s complexity into a newer environment. A more responsible path sits between those extremes. Selective transformation gives leaders a way to protect what still has value while removing what has become a burden. Historical data, proven controls, and essential operating knowledge can be retained. Outdated code, fragmented processes, and avoidable variation can be reduced.

A Clean Core Creates Room for Progress

A clean core is not a technology slogan. It is a management discipline. It asks the enterprise to standardize common processes, limit unnecessary customization, govern integrations, and keep the business’s core ready for improvement. When the core is crowded with exceptions, every upgrade becomes harder. Every report becomes debatable. Every innovation effort starts with a cleanup.

For senior leaders, the clean core should be viewed in business terms. Finance needs trusted numbers. Operations need reliable signals. Supply chain teams need visibility across demand, inventory, suppliers, and plants. Compliance teams need evidence that controls are working. Employees need systems that do not force them into manual workarounds. Customers need commitments that can be met. None of this is possible when the foundation is unstable.

Also Read: Is AI About to Make Media Buying an Endless Experiment?

Public Cloud Readiness Requires Honest Assessment

Public cloud readiness belongs in the same conversation. Public cloud ERP models are attractive because they encourage alignment with standard thinking, shorter deployment cycles, predictable upgrades, and a lower infrastructure burden. They can help growing enterprises move away from fragmented systems and toward a more consistent operating platform. Yet, public cloud should be adopted honestly. It works best when the business is ready to accept standard ways of working.

That distinction matters. Some processes are truly differentiating. Others are merely familiar. A mature modernization program separates the two. The enterprise should not customize a new core simply because the old system carried a certain practice for years. At the same time, critical regulatory, manufacturing, quality, or industry-specific needs should not be dismissed casually. Good leadership wisely uses standardization to create speed, control, and scale.

Industry Needs Should Shape the Modernization Journey

Industry realities make this balance more important. Manufacturers may need better costing across products and plants. Utilities may need modernization without disruption to regulated asset operations. Life sciences companies may need validation discipline, audit readiness, and data integrity. Automotive suppliers may need faster carveout execution, partner integration, and production visibility. Food and dairy businesses may need traceability and recall readiness. Chemical companies may need batch insight, yield control, and margin protection. These challenges are different, but they point to the same principle. The enterprise core must be standardized enough to scale and flexible enough to respect real operating needs.

AI Readiness Begins with the Foundation

Artificial intelligence has made this discussion more urgent. Intelligent tools depend on reliable data and disciplined processes. When data definitions differ across functions, recommendations become questionable. When workflows are unclear, automation may introduce errors more quickly. When integrations are fragile, digital operations become difficult to trust. AI readiness begins long before a model is introduced. It begins with architecture, data, process ownership, and governance.

Also Read: Brands Are Making ‘No AI’ Their Biggest Selling Point

Digital Workers Need Governance and Accountability

The same thinking applies to software agents and digital workers. These capabilities may assist with monitoring, analysis, exception handling, and workflow execution. Their value will depend on control. Each digital worker should have a defined purpose, a clear owner, approved boundaries, escalation paths, and performance measures. Human oversight should remain visible where decisions affect customers, employees, financial results, safety, or compliance. Without that discipline, automation can create risk.

Transformation Must Also Support People

Modernization also has a human side. Employees experience transformation through daily tasks, not through board presentations. They will judge success by whether the new process is clearer, whether training is practical, whether leaders explain the reason for change, and whether the system helps them do better work. When people are left to interpret change on their own, uncertainty grows. When they are supported, adoption becomes more natural.

This requires leadership beyond the technology function. Finance, operations, supply chain, human resources, risk, and business unit leaders must be involved early. Process owners must be willing to make decisions. Governance must be active. Change management must be practical. A modern ERP program should not be handed to IT and reviewed only at milestones. It should be a business transformation enabled by technology.

Business Outcomes Should Define Success

The measure of success should also become more grounded. A successful program should improve cost visibility, working capital control, reporting confidence, inventory accuracy, customer response, compliance quality, productivity, and resilience. These outcomes matter more than the number of features launched. They also matter more than speed if speed comes at the expense of readiness.

The current moment should be treated as a chance to simplify with courage. Aging ERP environments have forced many organizations to make decisions that had been delayed too long. That pressure can be uncomfortable, but it can also be useful. It gives leadership a reason to remove complexity and build a cleaner foundation.

Also Read: Are Brands Losing Credibility in the AI Era?

The Future Belongs to Enterprises That Modernize with Discipline

The organizations that move wisely will not confuse modernization with migration. They will protect continuity while reducing debt. They will adopt public cloud where standardization creates value. They will assess specialized operations carefully. They will prepare data before scaling AI. They will govern digital workers before granting autonomy. They will bring employees through the change with clarity and respect.

Modern ERP modernization is ultimately about confidence. Confidence that the business can change without losing control. Confidence that data can be trusted. Confidence that people can adopt new ways of working. Confidence that intelligent operations can scale responsibly. When clean core discipline, public cloud readiness, industry awareness, AI governance, and business continuity are aligned, the enterprise gains more than a new system. It gains a stronger foundation for the future.

The post Your ERP Is Holding You Back. Here’s How to Fix It. appeared first on MartechView.