Customer analytics is moving into a new phase as AI changes how organizations collect and act on customer signals.
From producing insights for periodic reviews to making customer intelligence available in everyday decisions, this shift underscores the importance of data foundations, identity resolution, governance, and context, particularly as AI systems increasingly combine customer-centric data.
The convergence of customer intelligence with AI management is also shifting as organizations deploy more customer-facing agents and copilots.
Because of this, analytics teams will increasingly need to understand not only what customers are doing, but why AI systems are producing particular outcomes, where those decisions fail, and how they affect resolution, satisfaction, and cost.
At the same time, conversational access to customer data is reducing the role of traditional dashboards and static reporting.
CX leaders should therefore watch out for how vendors differentiate their intelligence capabilities as AI makes basic analysis faster and cheaper.
Remaining competitive will be decided by whether platforms can connect trusted customer signals to real-time decisions, explain AI-driven outcomes, and demonstrate measurable impact. AI search visibility, journey orchestration, feedback integration, and observability are also becoming connected parts of this wider customer intelligence landscape.
Adobe and Jet2 Partner to Bring Together Agentic AI and Travel Orchestration
On Tuesday, Jet2 and Adobe reportedly signed a multi-year strategic partnership to improve its existing customer-service reputation with personalized travel and destination experiences.
With roughly 10 million myJet2 customers, the airline plans to use Adobe’s CX Enterprise platform and agentic AI to make holiday planning, service, and in-destination experiences more catered to the customer.
By combining a unified customer-data capability, AI-driven journey orchestration, content production tools, custom brand-aware AI models, and an embedded delivery team, this partnership aims to turn the existing customer-service reputation into a more scalable, real-time, individually relevant experience model.
Nathan Hancock, Vice President and Managing Director, UK, Ireland, Middle East and Africa at Adobe, explained that the partnership is intended to build on existing customer loyalty by making experiences more relevant, responsive and personalized at scale.
“Jet2 has built one of the most trusted customer communities in UK travel and our partnership is about building on that foundation,” he said.
“Every day, customers expect more relevance and faster responses, whether that’s a recommendation, a personal offer, or a change to their plans. Together, Adobe and Jet2 are creating something that will transform travel experiences.”
After the recent NATS failures across the UK, roughly 7,200 Jet2 customers required additional assistance or repatriation, as well as arranging a dozen extra flights.
With delayed or cancelled flights becoming increasingly common, this requires airlines to introduce additional measures into their frontline teams when complex problems increase.
Furthermore, this partnership addresses the fragmentation when each stage of the customer journey involves different systems collecting separate data, introducing a unified customer view to turn understanding into timely action.
The Jet2 x Adobe Experience Lab also includes several other capabilities, including:
- Journey orchestration: to deliver offers and messages across supported channels at the appropriate point in the holiday journey.
- AI-powered recommendation and planning: Travellers can receive a curated daily itinerary recommendation while on holiday.
- Content supply-chain automation: Enables teams to create approved, personalized holiday content at greater speed and consistency.
- Custom AI models through Firefly Foundry: Informed by Jet2’s own approved brand assets and content.
- Brand Intelligence controls: Designed to validate brand standards across AI-supported content workflows.
- LLM visibility optimization: Reflects the growing role of AI search in trip research and discovery.
David Hills, Chief Customer Officer at Jet2, highlights that strong customer satisfaction is built on making customers feel genuinely valued.
“At Jet2, our customers are at the heart of everything we do. Customers know they will always receive a VIP experience when they holiday with us, reflected in our industry-leading customer satisfaction scores, which are significantly ahead of the market,” he said.
“Working with Adobe will enable us to serve our customers even better, using the power of AI to make every interaction more personal, more relevant, more timely and always helpful, strengthening what makes Jet2 different and bringing us even closer to customers.”
For CX leaders, this partnership reveals that AI personalization depends on more than segmentation, requiring trusted identity resolution, current customer context, operational data, decisioning, relevant content whilst fulfilling the promised experience.
This means focusing on high-value moments where relevance reduces effort or increases confidence, and ensuring that recommendations reflect real customer eligibility, policies and service capacity.
Forrester Launches AI Disruption Model to Reshape CX Analytics
Forrester has launched its AI Disruption Model, a framework designed to help technology and service providers assess how AI impact the markets.
Applied to more than 200 markets across 17 technology and service categories, the model identifies a widening divide between AI winners and markets whose existing value is vulnerable to replacement.
Craig Le Clair, Vice President and Principal Analyst at Forrester, explained that AI is forcing providers to adapt their products, operating models and value propositions as automation changes what customers need and expect.
“Our research shows that AI’s benefits will not be distributed evenly across technology markets. Only markets in three categories — infrastructure; data and AI; and identity, access, and network security — are broadly positioned for clear growth. Technologies in the other categories will be forced to adapt.”
This release reveals that basic insight production will eventually become less differentiated as AI increasingly summarises interactions, identifies themes, analyzes sentiment, generates reports, and automates routine quality assurance.
Platforms and services whose primary value is dashboarding, manual analysis or periodic reporting will face pressure on both cost and relevance.
CX leaders should be assessing where their current operating model is dependent on manual analysis or fragmented reporting, moving from retrospective measurement toward intelligence that can support in-the-moment service.
Successful organizations provide the trusted intelligence layer around AI-enabled customer decisions will integrate first-party customer and interaction data, resolve identity, and apply governed definitions whilst delivering real-time insight to support next-best actions and proving outcomes.
CX platforms and teams will now need to retain strategic value when AI makes routine insight work cheaper and faster.
AskNicely Launches Claude Connector for Enhanced Customer Feedback Review
AskNicely has launched a Claude Connector that gives its customers direct access to NPS, CSAT and review data inside Anthropic’s AI assistant.
This move will allow teams to query feedback alongside hidden customer-centric data to reduce dependency on separate feedback dashboards, manual CSV exports and analyst-led reporting.
Tony Ward, CEO at AskNicely, warns that feedback is often trapped in separate systems and accessed too late for decision-making.
“Feedback shouldn’t live in a silo that only gets opened when someone remembers to ask for a report,” he said.
“With the Claude Connector, customer sentiment is right there next to the rest of the business data, in the moment a decision is being made – even live in a meeting.”
This enables sentiment data to become part of day-to-day decision-making, enabling leaders to explore questions in Claude, refine them as they learn more, and connect feedback with other business signals to uncover previously hidden patterns.
With feedback data often sitting in a silo, this causes slow, analyst-dependent access to insight and weak root-cause analysis.
A falling NPS score matters less than understanding whether it is connected to a product problem, a support failure, an operational change or a specific customer segment.
Customer intelligence is becoming more conversational and cross-functional, and the value of feedback platforms now depends on their ability to make customer signals available in the wider enterprise intelligence environment.
CX leaders should ensure that their data is ready and consistent for reliable cross-system analysis, including strong governance around permissions and interpretations of AI-generated findings.
Feedback can become an active operational input, but self-service insight still requires analytical discipline.
GoodData.AI Launches AI Extension for Enterprise Visibility
On Wednesday, GoodData.AI announced the launch of AI Observability, a capability that combines organization-wide AI performance analytics, interaction-level tracing and recurring-issue analysis.
This extension is designed to show enterprises not only whether their AI is being adopted, performing effectively or becoming costly, but also why a particular AI response was produced and what should be improved.
Rosta Striz, Principal Product Manager at GoodData.AI, argues that dashboards do not give teams enough evidence to understand AI cause or determine how to improve performance.
“Enterprise teams need more than a dashboard telling them that AI quality or cost changed,” he explained.
“AI Observability connects those layers so teams can improve AI with evidence rather than intuition.”
The product enables teams to monitor AI usage, active users, quality signals, token consumption and cost across agents and skills.
When an individual response requires investigation, they can trace the underlying execution path, analyze interaction history to identify repeat issues, then recommend changes to knowledge, semantic models or configuration.
Whilst traditional monitoring can show that chatbot satisfaction has declined, it rarely explains what the exact cause was.
This distinction is important for CX leaders embedding AI as it increasingly handles conversations, assists employees and influences next-best actions, meaning teams need evidence behind each answer.
A wrong response can create unnecessary repeat contacts and lost trust, while observability enables teams to trace the source of the failure rather than simply measure its outcome.
To ensure this, CX leaders should connect technical AI metrics to resolution and sentiment-based.
With AI observability is becoming the evidence layer needed to operate customer-facing AI safely, CX leaders must act now for continuous improvement and scale it with confidence.
