Infovista: Simplicity in network complexity with AI

Infovista integrates AI to improve network experience

• At MWC Barcelona, Infovista presented advances in VistAI, its agentic AI framework for autonomous operations, and VistaOne, its AI-powered platform for network intelligence and customer experience (CX).
• The company aims to unify four domains—planning, testing, network management/observability, and CX—into an AI-driven layer.
• AI makes it possible to correlate network KPIs with CX metrics, bringing together two worlds that historically operated separately.
• The challenge is no longer “forcing” integrations: the promise is to extract and crystallize data according to the use case, at the right time.
• Infovista anticipates a shift in the sector from one-off purchases toward ecosystems and partnerships, with more cooperation among competitors.

AI for decisions and outcomes
This announcement is set within MWC 2026 (Barcelona) and a public conversation between Rick Hamilton (CEO of Infovista) and Sean Kinney (principal analyst at RCRTech). The central idea is not “more AI,” but using it to connect network decisions with customer outcomes operationally: what’s happening in the network, who it affects, where, and with what impact.

Infovista’s AI innovations

At the Mobile World Congress (MWC) in Barcelona, Infovista focused on an idea that has been circulating in the telco sector for years: practically connecting what happens in the network with what the customer perceives. The difference, the company argues, is that AI now makes that convergence viable at scale.

The approach was shared in a conversation between Rick Hamilton (CEO of Infovista) and Sean Kinney (principal analyst at RCRTech), as part of MWC 2026.

Rick Hamilton, CEO of Infovista, summed up the moment as an opportunity to “derive simplicity from complexity”: accelerate decisions and provide clarity on how network performance impacts the user experience, and how customer signals can influence network decisions.

VistAI: AI framework for autonomous operations

VistAI is Infovista’s agentic AI framework aimed at autonomous network operations. The proposition is based on agents capable of acting on operational workflows—from detection to recommendation and, in defined scenarios, execution—with the goal of reducing friction and response time.

According to Hamilton, the differentiating value lies in enabling a cross-domain reading: for example, overlaying business data (BSS) with network telemetry and performance to answer operational questions with commercial impact, such as identifying the highest-value customers, their real experience and geographic distribution, and observing how that experience is reflected in purchasing behavior.

In this context, BSS refers to business data and systems that complement the purely technical view of the network.

From data to action
1) Ingestion and context: the agent takes network signals (telemetry, events, KPIs) and, where applicable, business signals (BSS) to understand “what” is happening and “who” it affects.
2) Detection / correlation: identifies patterns (degradation, anomalies, changes) and relates them to services, areas, and customer segments.
3) Actionable recommendation: proposes prioritized actions (for example, mitigation, reconfiguration, escalation) with the “why” and the expected impact.
4) Execution (only in defined scenarios): automates repeatable tasks under operational rules/guardrails; non-deterministic or high-risk items are routed for human approval.
5) Verification: checks whether the technical KPI and the experience signal improve; if not, it iterates with a new hypothesis.
Practical control points:
– Define what can be automated vs. what requires approval.
– Agree on success metrics (technical and CX) before enabling automation.
– Maintain traceability: what data triggered the action and what change was executed.

VistaOne: Network and CX intelligence platform

VistaOne is presented as an AI-enabled platform for network andcustomer experience.

At MWC Barcelona, Infovista positioned VistaOne together with VistAI as its two main announcements aimed at bringing network intelligence and CX intelligence together in a single AI-powered layer. In Infovista’s vision, it is the meeting point where data from the following converge and are consumed:

• Planning (how to design and dimension the network),
• Testing (how to validate performance and changes),
• Management/observability (how to view and operate the network in production),
• CX (how all of the above translates into customer perception and outcomes).

The goal is for the platform to enable building use cases ranging from optimization and monetization to experience improvement, without forcing each domain to operate as a silo.

Intelligent Domain Connection
“4-domain” framework and how they connect in an AI layer
– Planning → defines the “target design” (capacity, coverage, investment) and generates hypotheses: where to grow, what to optimize.
– Testing → validates changes before production (new configurations, releases, parameters) to reduce regressions.
– Management/observability → detects and explains what happens live (events, degradations, performance by service/area).
– CX → translates the above into impact: perception, complaints, usage, churn, customer value.
With AI, the promise is that these domains are not consulted as “four screens,” but as a chain: what is planned is tested, what is deployed is observed, and what is observed is prioritized by impact on CX.

Integration of network intelligence and customer experience

The industry has long talked about bringing “network experience intelligence” together with “customer experience intelligence.” Infovista argues that the historical blocker was not a lack of intent, but of feasibility: they were universes with data structures and ways of deriving information that were too different.

The current thesis is that AI allows integration to stop being a rigid exercise in total normalization. Instead, different systems can leverage specific data for specific use cases: the important thing is not to mix everything, but to extract what is needed to answer what the customer—internal or external—wants to know, whether in real time or over a time window.

Total integration vs use-case-based
“Monolithic” integration vs extraction by use case
– Monolithic integration (unify everything first)
– Pros: model consistency, centralized governance, a “single source of truth.”
– Cons: long timelines, high cost of

standardization, with the risk that the project stalls before delivering value.
– Extraction by use case (crystallize what’s necessary)
– Pros: faster delivery, focus on concrete decisions, allows “mix and match” domains depending on the question.
– Cons: if there isn’t good governance, it can create multiple metric definitions; it requires discipline for traceability and quality.
Rule of thumb: start with 1–2 use cases where the impact is visible (for example, degradation in an area + high-value segment) and scale when there are clear metrics and ownership.

Challenges in data integration

Hamilton attributes previous failures to a “fundamental” difference in structure and information derivation: the network has traditionally been measured with availability, capacity, cost, and efficiency metrics; the customer, with satisfaction, behavior, and business outcome signals.

That clash is compounded by typical telco fragmentation: different tools for planning, assurance, testing, observability, and CX, each with its own data model and priorities. Infovista’s bet is that AI will reduce the cost of that fragmentation by enabling composing views and answers by use case, instead of requiring prior monolithic integration.

Frictions When Bringing Network and CX Together
Quick self-assessment: typical frictions when combining network + CX data
– ☐ Tool silos: planning/testing/observability/CX don’t share common identifiers (service, cell, area, customer).
– ☐ Incompatible models: the same KPI is calculated differently depending on the domain or the vendor.
– ☐ Latency: the CX signal arrives late (or without granularity) to operate in near real time.
– ☐ Quality and completeness: events without reliable timestamps, missing data, or noise that triggers false positives.
– ☐ Diffuse ownership: no one “owns” the definition of cross-domain metrics (for example, technical KPI → impact on churn).
– ☐ Insufficient traceability: it’s hard to explain what data led to what decision and what change was applied.
If you checked 3 or more, it’s usually more effective to start with extraction by use case and strengthen governance/identifiers in parallel.

The role of AI in KPI correlation

The most ambitious point—and the most tangible if it works—is the correlation between the KPIs that matter to engineering and what matters to the customer. Hamilton framed it as a paradigm shift: the network is no longer evaluated only by “whether it works” and “how much it costs,” but by its direct impact on experience.

In that logic, AI acts as a bridge so that performance and CX are seen “as the same thing”: not two separate dashboards, buta connected readout that accelerates decisions. The promise is pragmatic: operational clarity to prioritize actions where the impact on customer and business is greatest.

Future of the industry: from procurement to ecosystems

Looking two or three years ahead, Infovista expects use cases to continue expanding and the sector to move from an approach centered on procurement (buying tools) toward one of ecosystems (collaboration among players).

Hamilton noted that the pressure to realize value with AI pushes greater cooperation, even among competitors, and cited as an example Infovista’s alliance with CSG, combining network and BSS expertise to help customers extract more value from their data.

He also drew a line: AI is not a universal solution. The emphasis, he said, must be on being specific about how data is used in “pragmatic and meaningful” use cases, avoiding getting lost in generic promises of capabilities.

Pragmatic shift toward ecosystems
Concrete signals (not theory) that support the shift toward “ecosystems” in this narrative:
– The message was presented as two specific announcements (VistAI and VistaOne) at MWC 2026, not as aabstract vision.
– The public conversation between Rick Hamilton (CEO) and Sean Kinney (principal analyst at RCRTech) focused on use cases and on how to “crystallize” data depending on the question.
– A specific partnership with CSG was cited to combine network and BSS expertise, as an example of cooperation to extract value from data.
– An operational limit was made explicit: “AI will not solve everything for everyone”, reinforcing that value depends on pragmatic use cases.

The Transformation of Customer Service in Telecommunications

The Importance of Customer Experience

In telecommunications, customer experience has become an indicator of competitiveness: it influences churn, service adoption, and brand perception. The convergence between network intelligence and CX seeks to ensure that technical operations translate into visible outcomes for the end user.

Current Challenges in the Telecommunications Sector

The sector carries structural complexity: multiple technical domains, heterogeneous tools, and scattered data. Added to this is the need to respond faster to incidents and degradations that, although “small” in network metrics, can be critical for high-value customer segments.

Innovative Solutions to Improve CX

The approach Infovista is driving is based on two ideas: an AI framework to automate and orchestrate operations (VistAI) and a platform to unify data and analytics (VistaOne). The goal is to enable decisions that connect cause (network) and effect (customer) with less friction and more context.

The Future of Artificial Intelligence in Telecommunications

The direction taking shape is less “AI for AI’s sake” and more AI as a layer to make complex data usable in concrete scenarios: planning, optimization, monetization, and experience. If the shift toward ecosystems consolidates, interoperability and partnerships will be as important as the models.

Conclusions and Final Reflections

Infovista arrives at MWC with a clear narrative: AI finally makes it possible to operationally unite the network view and the customer view. The challenge is not only technical, but also one of focus: moving from integrating to activating data for specific, measurable, and repeatable decisions.

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Leverage artificial intelligence to optimize your operations

Suricata Cx proposes applying AI to reduce operational friction, speed up responses, and prioritize actions with customer impact.

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Automation and better correlation between operational and experience signals can cut handling times and improve service consistency.

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Unifying channels aims to avoid breaks in the customer journey and improve the traceability of each interaction.

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Scalability requires repeatable processes, connected data, and the ability to incorporate new use cases without redesigning the entire operation.

This idea reinforces a key point: when AI connects network and CX, decisions stop being “siloed” and become actionable. At Suricata Cx we share that pragmatic approach, bringing that correlation into omnichannel operations to prioritize and resolve interactions with the real context of the network and the customer, without losing human control.

From the perspective of an omnichannel CX platform for ISPs and telcos, the value of these types of initiatives usually materializes when network–customer correlation is translated into concrete operational flows (automation, escalation to a human, and traceability), rather than into isolated dashboards.

Network–Customer Correlation in Operations
How to operationalize network–customer correlation in a service operation (practical steps)
1) Map critical journeys (activations, outages, billing, quality of service) and define which network signals provide real context for each one.
2) Unify minimal identifiers (customer/service/area/time) so that an interaction can “see” the relevant network status.
3) Prioritize by impact: simple rules first (high-value segment + degradation in area + inbound contact) before complex automations.
4) Automate with escalation: resolve what is repeatable; when there is ambiguity, escalate to a human with the diagnosis and evidence already attached.
5) Close the loop: measure whether recontacts, resolution times, and complaints go down; adjust rules and playbooks based on results.

Checkpoint: if the automation cannot explain “why” it took an action and “what changed,” it is usually better to keep it as a recommendation until traceability is improved.

This text is based on publicly available announcements and statements at the time of writing regarding MWC 2026. Capabilities, integrations, and levels of automation may vary depending on the operator and the configuration adopted. In AI initiatives for operations and CX, results depend on data quality, traceability, and the definition of use cases, so changes may occur as new updates are released.