🔥 Gen AI Won’t Save Telecom Overnight — But It Will Save Its Future

• Gen AI is not magic — its real power emerges only with clean data, unified systems, and governance.

• Telcos are underusing gen AI by limiting it to chatbots, while ignoring massive value in automation, design generation, and scenario modeling.

• Gen AI offers faster, clearer ROI than 5G, thanks to software-driven improvements vs. infrastructure-heavy upgrades.

• The technology won’t fix fragmented OSS/BSS or poor data quality — it amplifies strengths, not flaws.

• Scaling gen AI requires serious architecture: cloud, hybrid, or edge computing with automated model management.

• Who: Soumyadeep Roy Chowdhury, Senior Industry Analyst at Frost & Sullivan

• What: His insights on how generative AI is reshaping telecom operations

• When: Recent conversation with RCR Wireless News

• Where: Telecom industry globally

• Why: Because telcos risk missing the transformative potential of gen AI if they underestimate or misapply it

Telecom doesn’t need magic.

It needs clean data, disciplined systems… and a lot fewer assumptions about what gen AI can “magically” fix.

Because gen AI isn’t a wand — but it is a force multiplier. ⚡

Let’s set the stage.

For years, telcos have hoped for a miracle technology — something that would unravel OSS/BSS chaos, streamline operations, and eliminate manual firefighting.

But here's the truth many don’t want to hear:

Gen AI is powerful, but it doesn’t come with a cape. 🦸‍♂️

It won’t instantly repair fragmented systems or make sense of data that’s messy enough to scare away even the bravest engineers.

Yet… when the environment is ready?

Gen AI becomes transformational — detecting problems faster than humans, predicting issues before they land, and even fueling adjacent technologies like AR and advanced fraud detection.

Chowdhury puts it perfectly: this convergence will shift telecom from reactive drudgery to proactive, predictive, autonomous operations.

That’s not magic.

That’s engineering evolution.

If telcos treat gen AI as “just another chatbot,” they’re missing 80% of its value.

Chowdhury warns of two extremes that are equally harmful:

❌ The Underuse Problem:

Limiting gen AI to chatbot deployments

→ Leaving enormous value on the table

❌ The Overhype Problem:

Expecting gen AI to magically fix:

• Fragmented OSS/BSS

• Bad data

• Isolated, legacy systems

Reality check:

Gen AI amplifies your foundation — it doesn’t replace it.

Here’s where it shines:

• 📄 Auto-generation of documentation & RFPs

• 🧩 Technical design creation

• 🧾 Ticket summarization & root-cause narratives

• 🔎 Instant knowledge retrieval

• 📊 Scenario simulation: churn, traffic, rollout planning, tariff modeling

These capabilities don’t just cut workload — they compress cycle times across engineering, operations, sales, and procurement.

The difference is dramatic enough to feel magical…

but only because the underlying systems are sound.

5G required enormous investment with long, murky payback.

Gen AI?

Not so much.

Its ROI is faster because it’s built primarily on:

• Software

• Data

• Process integration

Not towers and concrete.

Gen AI directly impacts revenue and outcomes across:

• Customer experience

• Marketing

• Fraud prevention

• Operational efficiency

That means one thing:

Lower entry barriers, higher immediate value.

Something telecom desperately needs.

No matter how advanced gen AI becomes, it won’t thrive in a chaotic environment.

A few hard truths:

• Dirty data breaks AI

• Fragmented systems confuse AI

• Missing governance derails AI

• Lack of APIs strangles AI

Chowdhury is blunt:

A fully autonomous gen AI-powered network won’t happen overnight.

And unsupervised gen AI?

Say hello to hallucinations. 🌀

(And probably a very long meeting with the compliance team.)

To get beyond experiments and pilots, telcos must invest in data unification, governance, and intelligent integration.

Once the data foundation is established, scaling gen AI requires serious infrastructure thinking:

• ☁️ Cloud, hybrid, or edge architectures

• ⚙️ Scalable compute

• 🔀 Distributed processing

• 🤖 Automated model management

• 📡 Real-time monitoring and updating of AI systems

These are the building blocks of a future where networks become self-healing, self-optimizing, and truly intelligent — not just automated.

Gen AI won’t replace telecom operations.

But it will replace the telcos that fail to use it wisely.

Your actionable playbook from this edition 👇

✔️ 1. Audit Your Data Health

• Identify data silos

• Clean and unify operational data

• Establish governance frameworks

✔️ 2. Build API-First Infrastructure

• Prioritize interoperability

• Modernize legacy systems

• Enable system-to-system intelligence

✔️ 3. Think Beyond Chatbots

Explore gen AI for:

• Documentation automation

• Ticket summarization

• Engineering design generation

• Knowledge retrieval

• Scenario modeling

✔️ 4. Set Realistic AI Expectations

• Treat gen AI as an enabler, not a cure-all

• Focus on augmenting teams, not replacing them

• Establish human-in-the-loop oversight

✔️ 5. Architect for Scale

Invest in:

• Cloud/hybrid/edge environments

• Scalable compute

• Distributed processing

• Automated model lifecycle tools

✔️ 6. Start Small — But Design for Big

Pilot fast, learn fast, scale intentionally.

1. Nokia AVA AI Operations

A cloud-native AI platform built specifically for telecom networks.

Helps with:

• Predictive network maintenance

• Automated anomaly detection

• Energy optimization

  Ideal for operators who want AI-driven, self-healing network capabilities.

2. Amdocs amAI

A generative AI framework tailored for OSS/BSS modernization.

Helps with:

• Customer service automation beyond chatbots

• Intelligent documentation + ticket summarization

• Knowledge retrieval for engineering teams

  Excellent for reducing operational cycle times.

3. Ericsson Operations Engine + Gen AI Extensions

Ericsson’s AI-driven operational suite with gen AI capabilities layered on top.

Helps with:

• Network planning and optimization

• Automated design generation

• Incident prediction and resolution at scale

  Useful for large telcos modernizing network operations.

4. Google Cloud Telecom AI

A set of AI/ML services optimized for telecom environments.

Helps with:

• Fraud detection

• Subscriber churn modeling

• Traffic and demand forecasting

  Perfect for telcos building scenario simulations (“what-if” models).

5. ServiceNow Telecom Service Management + Gen AI

A gen-AI-enabled workflow system purpose-built for telecom operations.

Helps with:

• Automating workflows across OSS/BSS

• Ticket summarization + root-cause narratives

• End-to-end service orchestration

  Great for improving operational efficiency without ripping out legacy systems.

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Gen. AI Explorer