This week’s developments point to customer analytics and intelligence moving decisively from retrospective reporting toward continuous, AI-assisted decision-making.
For CX leaders, the new priority is connecting customer data into a reliable view of context and translating that view into timely action.
With AI being used to identify emerging themes, resolve identity across channels, and predict outcomes, the market is also becoming more pragmatic.
Today enterprise buyers are looking beyond flashy generative AI demonstrations for governed data foundations, embedded business knowledge, clear permissions, auditable outputs and human oversight.
Furthermore, fragmented systems and operating silos remain the central obstacle to omnichannel intelligence.
As a result, the winners of this phase of CAI will be the organizations that turn customer signals into accountable workflows, measure whether interventions improve outcomes, and prioritize relevance over speed.
HappyOrNot Launches AI Tools to Turn Micro-Feedback Into CX Action
HappyOrNot have announced the expansion of its AI Feedback Analytics platform, designed for CX teams that cannot interpret micro feedback in line with demand.
As customer feedback only becomes intelligence when teams can see the story behind a score, these new AI capabilities are designed to close that gap.
Tim Waterton, CEO of HappyOrNot, said the company’s new AI feedback analytics capabilities are designed to help operational teams identify high-priority CX issues.
“Operational teams don’t need more data,” he said.
“They need to know what matters and where to focus.”
Many organizations collect abundant real-time ratings and comments, but frontline and operational managers rarely have time to reconcile this feedback, leading to delayed or missed actions and increasingly emerging issues.
When feedback programs become measurement exercises rather than engines for improvement, the value of the collected analytics decreases.
To solve this, HappyOrNot has introduced Insights and Themes as an expansion of its AI Feedback Analytics suite, producing a monthly performance narrative from HappyOrNot data.
Insights flags notable changes, comparisons, and observations, combining quantitative signals with open-text feedback and directing users to deeper analysis where necessary.
Themes enables AI to automatically group comments into topics and sub-topics, then drill into specific issues such as friendliness, speed, and cleanliness.
By explaining why the experience changed alongside what customers are discussing, these features complement HappyOrNot’s existing Open Feedback Summary.
“Insights surfaces the changes, patterns and issues that need attention, while Themes groups customer comments by topic so managers can quickly see what really matters,” Waterton explained.
“Together, they cut through the noise and help teams spend less time analyzing feedback and more time acting on it.”
For CX leaders, the tools could shorten the distance between customer signal, diagnosis and action.
To ensure analytics remain valuable, leaders should define escalation thresholds, connect feedback findings to operational measures, and track whether faster insight produces measurable service recovery and sustained experience improvements.
Forrester Raises the Bar for Customer Feedback and Analytics Providers
Customer feedback platforms will now need to prove they can turn fragmented customer signals into trusted, business-wide action.
In the Forrester Wave™ Customer Feedback Management and Analytics Solutions, Q3 2026 report, organizations increasingly expect platforms to do more than collect survey responses or summarize customer comments.
They now need to connect signals from previously ignored channels, such as digital journeys, social media, and outside channel-owned reviews, turning those into trusted actions across the business.
Rising technology trends such as generative AI is raising the bar, however an LLM alone is not often enough for enterprise-grade customer intelligence.
By combining generative AI and machine learning with embedded rules and knowledge allows organizations to produce more accurate insights while reducing the risk of inconsistent outputs.
The study also finds that omnichannel analytics remains difficult to achieve, largely because of fragmented legacy data, siloed teams, and complex integrations.
With many under the assumption they have a shortage of analytics tools, CX buyers should instead be looking for platforms that can unify data without excessive dependency, support identity resolution, provide effective governance and embed findings into internal and frontline decisions.
Furthermore, technology selection should follow the organization’s primary use case instead of following trending products, with Forrester urging buyers to weigh current capabilities, strategic direction, and customer feedback against their own operating model.
Leaders
Medallia: As the only leader in this year’s Forrester Wave for customer feedback management and analytics solution, this brand has been selected for its mature, comprehensive, well-rounded enterprise CX platform, coupled with its use of hybrid topic modeling, synthetic data, native conversation intelligence, governance, unified customer records, and strong omnichannel analytics.
Strong Performers
Qualtrics: This provider has been recognized for its optimization capabilities for medium to large organizations. Its platform offers self-service feedback collection, CX measurement, real-time service-recovery features, global SaaS scale, extensive integrations and low-/no-code extensibility.
CallMiner: Offering strong conversation intelligence, text mining, natural-language understanding, advanced analytics, and real-time action capabilities, Forrester recommends it for high-volume contact-center environments seeking to move beyond survey-led quality management.
Sprinklr: This organization was selected for combining social validation, reviews, and survey feedback with broad omnichannel analytics, flexible data architecture, and AI capabilities. For existing Sprinklr users and organizations prepared to invest internal resources in AI-led CX programs, Forrester recommends this platform.
Contenders
Cresta: This provider has been included for its AI-native conversation intelligence and predictive CSAT model, seen as an innovative option for organizations ready to reduce dependence on traditional surveys.
Alchemer: Recognized for its fast, accessible survey deployment, solid CX measurement and support for programmes with significant ratings and review volumes, Forrester recommends this for organizations prioritizing speed and a lower-friction survey start.
Salesforce: The CX giant has been included because of its customer identity-resolution strengths, AI investment, and workflow capabilities within its wider ecosystem. Forrester recommends it primarily for existing ecosystem customers with relatively straightforward feedback requirements.
Zoom Brings Conversation Intelligence and Forecasting Into One Revenue System
Zoom has launched its AI-powered revenue OS, a connected go-to-market platform that links buyer intelligence, customer conversations, sales engagement, and forecasting so revenue teams can act on signals throughout the lifecycle.
Announcing the release at Dreamforce 2026, it aims to turn its meeting and conversation data into a broader AI-powered revenue platform.
As organizations collect customer signals everywhere, many are rarely connecting them quickly enough to improve the next interaction.
Linda Lian, GM of Common Room and Zoom Revenue Accelerator at Zoom, explained that the launch reflects the need to connect every customer signal across the revenue lifecycle.
“Revenue isn’t built in a single sales call. It’s built across a relationship,” she said.
“Every signal a customer sends, every conversation a seller has, and every action that follows adds valuable context.”
Zoom argues that customer understanding should not sit separately in call recordings, systems, or channels, but should inform the people responsible for prospecting, selling, renewing, and expanding relationships.
Without that connection, teams work from fragmented views of the customer, causing repetitive outreach, poorly timed engagement, inconsistent handoffs, and forecasts that reflect internal optimism.
To solve this, Zoom’s revenue OS combines four layers:
Common Room by Zoom: This tool collects identity, activity, community, digital, and product signals to create a fuller account picture.
Zoom Revenue Accelerator: It adds conversation intelligence, identifying what customers care about, objections, stakeholders, commitments, and next steps.
Engage: Translates said intelligence into structured outreach across email, phone, and tasks.
Forecast: Uses deal-level and conversation signals to give leaders a live view of commitments, movement, and risk, with drill-down to individual opportunities and CRM write-back.
Treating customer intelligence as a continuous operational input across the lifecycle could help teams identify when customers need help, when an expansion is appropriate, and where outreach may add friction.
“Bringing more of that context together can give AI a deeper understanding of the customer, what they care about, what’s happened before, and where they are in their journey,” Lian continued.
“That understanding is what enables AI to help determine the right action to take next, so revenue teams can engage customers more effectively and drive growth.”
Whilst the platform may connect more context, CX leaders must recognize that better experiences still depend on teams acting on it consistently, appropriately, and with the customer’s interests in mind.
Oracle AI Usage Surges as Enterprises Move Agents Into Production
Oracle’s data platform is bringing governed, semantic AI analysis into Fusion Data Intelligence and Oracle Analytics Cloud as customers rapidly scale embedded AI and production agents.
Oracle’s Q1 FY2027 results position customer analytics and intelligence as an AI-led, governed data capability, with the aim of connecting data across hundreds of sources and enabling analysis grounded in business context, governance, and enterprise ontologies.
Mike Sicilia, Co-CEO of Oracle, said the scale of Fusion AI usage shows customers are moving beyond experimentation and incorporating embedded AI into everyday business workflows.
“Overall, AI production usage across Fusion alone consumed 900 billion tokens during the quarter,” he said.
“I think it is fair to say that customers are using our AI built into our Fusion applications and our application stack every day.”
Oracle’s results revealed its embedded AI features were used more than 150MN times in the quarter, up 42% sequentially; production AI-agent executions nearly doubled to more than 3.5MN; and more than 2,300 agents were live, up 90% QoQ.
In NetSuite, more than 10,000 customers now use its AI Connector Service to connect business data with third-party assistants such as ChatGPT and Claude.
However, the earnings call revealed concerns as to whether agents can securely access proprietary customer and operational data across fragmented, non-Oracle systems.
Whilst the provider said its platform is source-agnostic and supports hundreds of data sources, it did not offer detailed proof on cross-stack CX use cases.
In regard to governance, Oracle’s customers are asking for clarity on permissions, consent, auditability, accuracy, and human oversight when agents act on sensitive data.
Thirdly, Oracle claims its AI-assisted implementations can reduce timelines significantly, however, its customers still need evidence that those gains are repeatable alongside transparent pricing and cost of ownership.
Adobe Pushes Customer Intelligence From Insight to Agentic Action
Adobe is turning customer intelligence into action, using predictive analytics and agentic AI to help brands optimize CX before campaigns go live.
In its Q3 FY2026 earnings call, Anil Chakravarthy, CEO ( previously President of CX and Orchestration) at Adobe, said the company’s agentic platform is gaining early traction by connecting insights, AI agents, and workflows across the CX stack.
“CX Enterprise Coworker synthesizes insights from Adobe and third-party applications while coordinating AI agents and workflows across analytics, content creation, and journey orchestration,”
“Early customer interest is strong, with over 1,700 customers and early adopters of CX Enterprise Coworker.”
Adobe’s Q3 FY2026 results point to a shift in customer analytics and intelligence from reporting on customer behaviour to using AI agents to act on it, with Adobe Experience Platform and related applications growing, ending ARR by more than 20% YoY.
Adobe also expanded Brand Intelligence with a “simulate” feature, enabling marketers to predict how content may resonate with audiences before launch, and reported that paid Brand Visibility customers QoQ.
That offering combines Semrush intelligence, Adobe LLM Optimizer and a dataset of nearly 300MN AI-search prompts to track brand presence across generative-search platforms.
Despite these results, customers are now asking how they can make AI-driven insights more dependable, connected and commercially useful.
Adobe’s answer is an enterprise-data-grounded, agentic workflow model, but customers will still expect proof that these tools improve conversion, experience quality, and marketing efficiency at scale.
