AI strategy for telecommunications operators

Pillars of an AI strategy in telecommunications

Quick glossary (to align concepts): BSS/OSS (business/operations support systems), CPE (customer premises equipment), truck rolls (technician visits), FCR (first-contact resolution), agentic AI (AI that executes multi-step workflows with supervision), edge (execution close to the user/device) and multimodal (language + signals such as vision/telemetry).

The first pillar is precisely defining the problem that AI must solve. In consumer-oriented operators, the biggest drivers of cost and churn risk tend to be concentrated on three fronts: the contact center, the home environment (WiFi, devices, interference), and field operations. A solid strategy starts with experience outcomes—not with “implementing an LLM”—and translates those outcomes into operational metrics.

The second pillar is operationalizing AI in a heterogeneous stack. The telco reality combines modern cloud with legacy BSS/OSS components and vendor-specific tools. For AI to impact KPIs, it needs integration layers, orchestration, and data pipelines that connect touchpoints: care, network, CPE, apps, inventory, and work orders. Without that foundation, assistants and chatbots provide superficial value, but they don’t move the indicators that matter.

The third pillar is governance, security, and explainability. As AI moves from “assisting” to automating multi-step workflows (agentic AI), the need for traceability grows: which model decided what, with which data, under which rules, and with what human oversight.

A fourth pillar, increasingly visible, is AI at the edge. With smarter CPE and home devices, running diagnostics and inference close to the user makes it possible to detect degradations earlier, act proactively, and reduce noise in the contact center, technician visits, and recurring in-home failures.

Pillars for Scalable Operational AI

of orchestration failures, order cycle time

Pillar	Operational objective (what changes)	KPI(s) that evidence it	Minimum data needed (so it isn’t “blind AI”)
Problem and experience outcomes	Focus AI on resolution (not just automation)	FCR, repeat contact, TTR/MTTR, avoided truck rolls	Contact reasons, resolution outcomes, escalation reasons, time per step
Operationalization in a heterogeneous stack	Turn recommendations into consistent actions across channels/systems	% actions executed without rework, ta	BSS/OSS events, inventory, work orders, network/CPE states, integration APIs/queues
Governance, security, and explainability	Scale without surprises (control, audit, monitoring)	Incidents caused by automation, human override, drift, “safe shutdown” rate	Decision traces, model/prompt versions, policies, action logs, uncertainty signals
Edge (AI near the home)	Detect degradation earlier and reduce contacts/visits	Call volume about WiFi, degradations detected, visits avoided, home stability	CPE/WiFi telemetry, quality metrics, roaming/interference events, local diagnostics

Challenges in implementing AI in telecommunications

The most repeated obstacle is not a lack of tools, but the gap between the lab and operations. Many models work in controlled scenarios, but lose accuracy when faced with everyday complexity: homes with unique configurations, interference, device density, suboptimal router placement, and changing usage habits.

On top of that is the difficulty of scaling: promising pilots stall when trying to integrate with real processes, legacy systems, and multiple vendors. In customer service, recommendations that are “correct in theory” force agents to ask additional questions because the system has no visibility into the environment. In the field, automated flows do not reflect what the technician finds on site.

Another brake is data readiness: silos, inconsistencies, lack of useful telemetry, and absence of resolution data (what was done and what worked). Without that “history of truth,” AI learns incomplete patterns and its ROI erodes.

Problem when scaling	Typical cause in telco	Early signal (before the pilot “dies”)	Practical mitigation
Good performance in demo, poor in production	“Clean” data vs unpredictable homes/operations	Handoffs to humans and clarification questions increase	Include home/field telemetry and resolution data; test with real cases and variability
Recommendations are correct but not actionable	Lack of integration/orchestration with BSS/OSS and vendor tools	The agent “copy/pastes” steps; duplicate tickets; rework	Design “from insight to action”: APIs, queues, runbooks, and idempotency control
ROI erodes over time	Drift + network/CPE/offer changes + incomplete data	Their

ben repeat contact and escalations despite “more AI” Drift monitoring, event-based retraining, and a resolution feedback loop Operational risk from chained automation Agentic AI without clear limits Unwanted actions, loops, or configuration changes without context Guardrails, human approval by threshold, and “safe shutdown” under uncertainty

Importance of governance and security in AI

Governance is not a formality: it is the mechanism that turns AI into a trustworthy asset. In telecommunications, where operational and reputational risk is high, AI must be deployed with clear accountability frameworks: how models are trained, how decisions are validated, what limits they have, and when a human intervenes.

In practice, this implies review boards, audits and decision traces, production monitoring, and “shutdown” criteria or safe degradation when the system detects uncertainty. Explainability matters especially when AI starts executing chained actions—for example, diagnosing, reconfiguring, opening a ticket, and scheduling a visit—without direct intervention.

Security also becomes more complex with distributed models and edge workloads: more execution points require consistent controls, observability, and operational discipline to prevent automation from amplifying errors. The dossier mentions that Gartner anticipates an increase in AI-related security incidents as organizations adopt distributed models; in telco, this usually translates into a greater need for change control, traceability, and continuous monitoring.

Governance from Design to Operations
“Design-to-operations” governance flow (with checkpoints that often fail in telco):
1) Use-case design and limits
– Checkpoint: define what actions the AI can execute (and which it never can) and which KPI validates the value.
2) Validation before production
– Checkpoint: testing with real variability (different homes, different CPE, data noise) and acceptance criteria by error/impact rate.
3) Controlled deployment
– Checkpoint: feature flags, canary/segmentation, and a rollback plan if escalations rise or FCR drops.
4) Production monitoring and auditing
– Checkpoint: decision/action logs, drift metrics, and periodic review of “rare cases” and human overrides.
5) Safe shutdown and degradation
– Checkpoint: uncertainty thresholds (or risk signals) that force “recommend only” or hand off to a human, without executing changes.

Systems and data integration for AI

AI is only as useful as its ability to see the system as

plete. In telco, that means unifying interaction data (calls, chats), network (alarms, performance), home (WiFi telemetry, CPE), field (orders, findings), and business (inventory, billing, eligibility).

Effective integration usually requires:
– Reliable pipelines that normalize and update data in near real time.
– Orchestration layers to execute coherent actions across BSS/OSS and vendor tools.
– Resolution-oriented data models, not just reporting: what symptom was observed, what diagnosis was issued, what action was applied, and what the outcome was.

Without this foundation, AI stays in “conversation” and doesn’t reach “resolution,” which is where the savings are captured: fewer repeat contacts, fewer escalations, and fewer truck rolls.

In practice, the maturity criterion is not “having a model,” but being able to operationalize decisions and actions consistently across channels, systems, and teams (customer care, network, home, and field), with enough traceability to sustain it in production.

Minimum integration for AI resolution
Integration checklist (the minimum for AI to “close the resolution loop”):
– Sources: are interaction + network + CPE/WiFi + field + inventory/orders connected?
– Latency: which decisions require real time (minutes) vs batch (hours/days)?
– Quality: are there completeness/consistency rules and handling of missing values?
– Identity and entity resolution: can you join customer↔line↔CPE↔device↔ticket↔order unambiguously?
– Resolution data model: do you capture symptom→diagnosis→action→outcome (and not just “a ticket was opened”)?
– Orchestration: are there APIs/queues and runbooks to execute actions in an idempotent and auditable way?
– Observability: can you trace what data the AI used, what it recommended/executed, and what happened afterward?

Real-world context and its impact on AI effectiveness

The most costly blind spot is inside the home. Many connectivity problems are not well explained in text: the customer doesn’t know how to describe interference, router placement, channel saturation, or the number of connected devices. That’s why assistants based only on language tend to fail when they must resolve, not just guide.

The industry is moving toward multimodal context: combining LLMs with visual signals and home telemetry. When AI can interpret what the customer sees (for example, installation, wiring, equipment placement) or what the CPE measures (signal quality, congestion, roaming, interference), diagnostic accuracy improves and the recommendation becomes actionable.

That context also enables a leap toward the agentic: AI stops answering questions and starts

to understand the problem space, prioritize hypotheses, run tests, and propose actions more reliably.

More Accurate WiFi Diagnosis
Mini-case (common pattern in home support):
– Before (text only): the customer reports “slow internet.” The assistant suggests rebooting and “moving closer to the router.” The agent ends up asking about location, obstacles, number of devices, and whether the problem is in a specific room. Typical result: more steps, more time, and a higher likelihood of repeat contact.
– After (multimodal + telemetry): the customer shares a quick view of the CPE installation/location and the system incorporates WiFi telemetry (signal, congestion, roaming, interference). The AI can prioritize hypotheses (e.g., poor placement + saturated channel), run guided tests, and recommend a concrete action (repositioning, channel change, 2.4/5 GHz separation, or justified escalation). Typical result: more accurate diagnosis and more straightforward resolution.

Strategies to operationalize AI in telecommunications

A practical roadmap usually follows a disciplined sequence:

1. Define experience outcomes and KPIs: reduce repeat calls, increase first-contact resolution, reduce “truck rolls,” improve home stability. AI should be measured by impact, not by demos.
2. Prioritize high-volume, high-friction journeys: home connectivity and repeat-contact scenarios often offer quick returns; small improvements translate into big savings.
3. Start narrow and scale in phases: contained cases for agents and self-service, then proactive care and, finally, agentic automation. Avoid massive programs that collapse under complexity.
4. Ensure data readiness: accessibility, quality, and consistency; include often-ignored categories such as device telemetry, home signals, and resolution data.
5. Build governance from the start: review, audit, monitoring, and human oversight, especially when AI executes actions.
6. Anchor the strategy in the home: where churn and support costs originate. If AI doesn’t understand the home, it will hardly move the key KPIs.

Evolution by operational phases
Phased playbook (deliverables and exit criteria):
Phase 1 — Narrow pilot (assistance)
– Deliverables: 1–2 journeys (e.g., “Slow WiFi”), baseline KPIs, minimal integration (read), and capture of resolution data.
– Exit: consistent improvement in FCR or reduction in repeat contact in the pilot segment.
Phase 2 — Initial production (actionable recommendation)
– Deliverables: orchestration to execute safe actions (e.g., tesebas/diagnostics), traces, and monitoring.
– Output: high % of recommendations executed without rework and without an increase in escalations.
Phase 3 — Proactivity (before the customer calls)
– Deliverables: degradation signals (CPE/network), prioritization rules, and proactive communication/actions.
– Output: measurable drop in contacts for the objective reason and avoided visits.
Phase 4 — Agentic (chained automation with supervision)
– Deliverables: guardrails, human-approval thresholds, “safe shutdown,” and action auditing.
– Output: stable automation without operational incidents and with sustained KPI improvement.

Emerging AI trends for telecom operators

Three trends stand out for their operational impact:

• Agentic AI: agents capable of executing end-to-end flows (diagnosis, tests, actions, escalation) in customer care and operations. It promises efficiency, but requires strong governance and resolution data.
• AI + edge computing: inference and diagnostics on CPE and home devices to detect degradation before the customer calls. It reduces contact center volume and technician visits.
• Multimodal AI: combining language with vision and telemetry to close the gap between what the customer describes and what is actually happening. It is key to raising the resolution rate and confidence in recommendations.

In parallel, the idea of AI as an operating model change is growing: less focus on “which model we use” and more on visibility, integration, data quality, and execution discipline.

Trend	Most likely operational impact	Prerequisites (if missing, it stalls)	Typical horizon
Agentic AI in CX/ops	Fewer manual steps, lower TTR/MTTR, more consistency	BSS/OSS orchestration, resolution data, guardrails and traceability	6–18 months (by domain/journey)
Edge intelligence in CPE/home	Early detection, fewer WiFi-related contacts, fewer visits	Reliable telemetry, CPE fleet management, security/observability at the edge	6–24 months
Multimodal (vision + telemetry + LLM)	More accurate diagnosis and actionable recommendations	Capture of visual signals/telemetry, consent/operational flow, resolution labeling	3–12 months for support use cases; longer for scale

AI has become a lever to respond to two simultaneous pressures: operating costs and service expectations. In a market where connectivity is perceived as a commodity, competitive advantage shifts toward the reliability of the experience and the ability to solve problems quickly, with less friction and lower cost.

When properly implemented, AI can improve diagnostics, anticipate degradations, guide agents and technicians, and automate repetitive tasks. But its real value shows up when it impacts hard metrics: repeat contacts, resolution times, avoided visits, and home stability.

The main challenge is operational reality: legacy systems, fragmented data, and unpredictable home environments. AI fails when it lacks sufficient context and when it is deployed without deep integration with processes and systems.

There is also the difficulty of scaling: moving from pilot to production requires orchestration, monitoring, governance, and a data strategy that includes signals from the home and the field. Without that, AI remains superficial automation.

A practical strategy is built in reverse of the usual approach: it starts with experience outcomes, continues with data and integration, and only then chooses models and vendors. The sequence matters: first, scoped and measurable use cases, then expansion toward proactivity and agentic automation.

The success criterion is not “having AI,” but solving better: higher diagnostic accuracy, fewer unnecessary steps, fewer escalations, and a more stable home experience.

The convergence of agentic AI, edge intelligence, and multimodality is redefining the landscape. AI moves closer to where problems occur (the home), gains richer signals (vision and telemetry), and automates end-to-end flows. This raises the savings potential, but also the need for control and traceability.

The operators that move fastest will be those that turn resolution data into a strategic asset and design AI as part of the business’s operating system.

AI can be the efficiency engine the sector is looking for, or a graveyard of costly pilots. The difference lies in three decisions: focus on outcomes, capture real context (especially from the home), and govern automation rigorously. In telecommunications, transformation is not won by the flashiest model, but by the operator that gets AI to solve real problems, repeatably and at scale.

This approach aligns with how Suricata Cx understands AI in telco: as a CX operating system that combines automation, human oversight, and operational integrations to take the strategy from pilot to measurable resolution.