Generative AI – Saving Cost, Time, or Both?

How AI is Optimizing Telecom Operations While Avoiding Cost Traps

Generative AI is transforming the telecom industry by automating customer support, optimizing networks, and predicting failures. However, misusing or overloading AI models can lead to excessive costs rather than savings. While AI has the potential to save both time and money, if not implemented strategically, it can drain resources.

This article explores how telecom companies can harness AI’s benefits while minimizing risks—ensuring it is a true cost and time saver.

Cost Considerations: AI Can Save or Waste Millions

Overloading Generative AI Models Can Be Expensive

Risk: Many telecom companies blindly load millions of data points into AI models to process every query. However, Generative AI is not free—it costs per token processed.

Solution: Instead of using AI for everything, companies should focus on optimized, rule-based AI models for repetitive tasks while using Generative AI selectively for complex decision-making.

Example: A telecom provider spent $10M in a year on unnecessary AI queries, whereas an optimized AI solution saved 60% of costs by limiting redundant processing.

The Hidden Cost of AI Hallucinations

Risk: Generative AI sometimes generates incorrect, misleading, or biased responses, known as AI hallucinations. In telecom, a wrong calculation in billing or a misguided suspension of service can erode customer trust and invite legal troubles.

Solution: Human-in-the-loop oversight ensures critical decisions are reviewed before action. AI must be trained on accurate, telecom-specific data to reduce hallucination risks.

Impact of AI Errors in Customer Service:

• 45% of customers switch providers after an incorrect charge.
• $1B in losses globally due to telecom billing disputes.

Generative AI Must Be Used Strategically, Not as a Buzzword

Risk: Many companies feel pressured to integrate Generative AI just for the sake of it. AI discussions have become a management meeting ritual, where teams force-fit unnecessary AI projects—wasting time and money.

Solution: A governance body must approve AI projects based on ROI and real business impact before implementation.

Example:
A leading telecom company cut 30% of its AI budget by focusing only on high-impact AI projects, rather than every trend-driven idea.

Saving Time: AI’s Speed and Efficiency

AI-Powered Customer Support – Instant Responses

Traditional customer support involves long wait times, call transfers, and manual troubleshooting. AI chatbots resolve 80% of queries instantly.

Time Saved: No hold times, instant resolutions.
Example: An AI agent reduces customer support call duration from 7 minutes to 1.5 minutes.

Graph: Impact of AI on Call Resolution Time

Network Troubleshooting & Predictive Maintenance

Generative AI predicts failures before they occur, suggesting fixes automatically.

Time Saved: 50% reduction in network downtime.
Example: AI detects tower signal degradation and reroutes traffic autonomously, preventing outages.

Network Downtime Before & After AI Implementation

AI-Driven Network Planning – Months of Work in Days

Network deployment traditionally took months. AI now models, optimizes, and suggests rollout plans within days.

Time Saved: 6-month planning cycle reduced to 6 weeks.
Example: AI-powered 5G rollout planning reduced deployment time by 40%.

The Verdict: Maximizing AI’s Benefits While Avoiding Pitfalls

When AI is implemented wisely:

Time Saved – Faster network rollouts, customer support, and issue resolution.
Cost Saved – Reduced operational costs, optimized resources, and fraud prevention.

When AI is misused:

Cost Wasted – Overuse of AI models leads to huge unnecessary expenses.
Time Lost – AI-generated errors require manual intervention and damage control.

Final Thought:

Generative AI can save both cost and time, but only if strategically planned, carefully implemented, and continuously monitored. Telecom leaders must balance automation with human oversight, ensuring AI enhances operations rather than creating new inefficiencies.

📊 Governance Recommendation:

• Use AI for high-impact areas.
• Optimize models to reduce token costs.
• Keep humans involved in critical decision-making.
• Monitor and measure AI’s ROI continuously.