What Telecommunications Leaders Still Get Wrong About AI

By Joel Comm | A Trusted Voice in a Noisy Tech World

Your telecommunications company doesn’t have an AI problem. It has a confidence problem.

After 45 years watching technology waves crash over industries, I’ve noticed something telecommunications leaders keep doing. They’re making the same mistakes banking executives made in the early 2000s, retail leaders made during e-commerce adoption, and media companies made when streaming emerged.

The pattern is always the same. Smart people overthinking the wrong questions.

I’ve spent decades around organizations like Microsoft, IBM, Cisco, Alibaba, T-Mobile, and Twitter as they wrestled with each new technology wave. Every time, the same three misconceptions surface.

Let me save you some time and money by addressing them directly.

Misconception 1: AI is Coming for Telecommunications Jobs

Picture the typical first conversation between a telecom CEO and their team about AI. It usually starts with some version of: “We need to figure out which positions AI will eliminate so we can plan the layoffs.”

Wrong question.

The telecommunications companies actually getting traction with AI aren’t shrinking their workforce. They’re eliminating busy work, not bodies.

Think about what AI-powered diagnostic tools do for field technicians. They cut troubleshooting time dramatically. Customer service representatives with AI assistants surface relevant information instantly, so they can focus on solving problems instead of searching through databases.

Network engineers use AI for predictive maintenance, catching potential failures before customers notice. The result?

Higher customer satisfaction and fewer emergency calls at 2 AM.

AI doesn’t replace telecommunications workers. It makes them more valuable.

Think about what your teams spend their time on today. How much of it is searching for information, generating routine reports, or dealing with repetitive technical issues?

That’s not where human intelligence creates value. Human intelligence creates value when it’s applied to complex problem-solving, relationship building, and strategic thinking.

I’ve seen this pattern across every industry over the past four decades. The companies that thrive during technological disruption are the ones that understand this distinction.

They don’t ask “What jobs will AI eliminate?” They ask “What human capabilities will AI amplify?”

Misconception 2: You Need to Understand AI Before You Can Use It

I remember when the internet was first becoming mainstream in the mid-1990s. I was building one of the first 18,000 websites, and executives would constantly ask, “But how does TCP/IP actually work?”

My answer was always the same: “Do you understand how electricity works before you turn on the lights?”

The same misconception is happening with AI in telecommunications today.

Think about the CTO who spends 45 minutes explaining neural networks to his board, then can’t answer a simple question: “What problem are you trying to solve?”

That’s the trap. Technical fluency without operational clarity.

You don’t need to understand how AI works to use it effectively. You need to understand what problems it can solve.

Take network optimization. Your network engineers don’t need to understand the mathematics behind machine learning algorithms. They need to understand that AI can analyze traffic patterns, predict congestion, and automatically adjust routing in real-time.

Customer service managers don’t need to know how natural language processing works. They need to know that AI can analyze customer sentiment, route complex issues to specialists, and provide real-time coaching suggestions to representatives.

The most successful AI implementations in telecommunications start with a simple framework:

1.

Identify the pain point. What’s costing you time, money, or customer satisfaction?

2.

Find the AI tool. What specific AI application addresses that pain point?

3.

Start small. Pick one team, one process, one measurable outcome.

4.

Measure and scale. Track results and expand what works.

This is the practical path through what I call the Disruption Confidence Cycle. Instead of getting stuck in doubt trying to understand every technical detail, you move toward clarity and competence.

Misconception 3: Your Industry is Different

Every telecommunications executive eventually says some version of this: “Telecommunications is different. We have regulatory requirements, legacy infrastructure, and customer expectations that other industries don’t understand.”

I’ve heard this exact statement from banking executives, healthcare leaders, manufacturing CEOs, and government officials.

Every industry thinks it’s special. None of them actually are.

Here’s what I learned from selling my company to Yahoo in 1998: The fundamental principles of technology adoption are universal. The implementation details vary, but the patterns remain consistent.

Yes, telecommunications has unique challenges. So does every industry.

Banking has compliance requirements that make telecom regulations look simple. Healthcare has life-or-death decisions that make network uptime seem manageable. Manufacturing has physical constraints that telecommunications networks don’t face.

But here’s the pattern I’ve watched play out: The companies that succeed with AI focus on similarities, not differences.

They all have customers who want faster service. They all have employees who spend too much time on repetitive tasks. They all have data that could drive better decisions if properly analyzed.

They all need to reduce costs while improving quality.

These are not telecommunications problems. These are business problems that telecommunications companies happen to face.

The telecommunications leaders who move fastest learn from other industries instead of reinventing every process. They adapt proven AI applications from manufacturing, retail, and financial services instead of building everything from scratch.

The Disruption Confidence Cycle in Telecommunications

Every technology wave I’ve watched — from the ATM rollout in the 1970s and 80s, to online banking in the late 1990s, to mobile in 2008, to cloud computing, to social media — follows the same five-stage pattern. AI in telecommunications is no exception.

1. Disruption. A new technology arrives and forces real change. For telecom, AI is already rewriting what’s possible in network operations, customer service, and fraud detection. The status quo — rule-based systems, manual troubleshooting, reactive maintenance — is no longer reliable.

2. Doubt. Leaders question whether the change is real, whether their team can handle it, and whether to act now or wait. This is where most telecommunications companies are stuck right now. Committees. Studies. Whiteboard sessions about “AI strategy.” Paralysis dressed up as prudence.

3. Clarity. The fog lifts. Leaders see which use cases matter (predictive maintenance, sentiment analysis, fraud detection, network optimization) and which to ignore (AI for AI’s sake). Decisions become possible. The right questions surface: What problem are we solving?

What does success look like in 90 days?

4. Confidence. Teams begin to operate with AI as a regular tool. The field tech uses AI-assisted diagnostics without thinking about it. The customer service manager looks at sentiment dashboards the way they used to look at call volume. Skepticism gets replaced by competence.

5. Momentum. Early wins compound. The diagnostic tool that cut truck rolls funds the predictive maintenance program. The fraud detection model frees up budget for network optimization. AI becomes a multiplier on the strengths your organization already has.

Most telecommunications leaders I encounter are stuck between Disruption and Doubt. The companies that will lead this industry in five years are the ones moving deliberately toward Clarity right now.

What Actually Works in Telecommunications

Here’s what consistently works across telecommunications companies that have moved past doubt.

Start with customer-facing processes. Every telecommunications company struggles with customer service efficiency. AI-powered chatbots, sentiment analysis, and predictive routing aren’t experimental anymore. They’re proven solutions with clear ROI.

Focus on predictive maintenance. Your network infrastructure generates massive amounts of data every day. AI can identify patterns that predict equipment failures, optimize preventive maintenance schedules, and reduce unplanned downtime. The cost savings on emergency maintenance alone often pay for the entire implementation.

Automate network optimization. Traffic patterns are constantly changing. AI can analyze these patterns in real-time and automatically adjust routing, bandwidth allocation, and load balancing without human intervention.

Enhance fraud detection. Telecommunications companies lose billions to fraud every year. AI can identify suspicious usage patterns, detect SIM swapping attempts, and flag unusual account activities faster and more accurately than traditional rule-based systems.

The key is starting with processes where you can measure clear outcomes. Don’t begin with abstract goals like “improving innovation.” Begin with specific targets like “reducing customer hold times by 30%” or “decreasing network downtime by 20%.”

Successful AI adoption follows a predictable pattern:

Phase 1: Pick one measurable problem.

Phase 2: Find an AI tool that addresses that specific problem.

Phase 3: Test it with a small team for 90 days.

Phase 4: Measure results and decide whether to scale or pivot.

This approach works because it focuses on outcomes, not technology. It gives you concrete data to make decisions. And it builds confidence throughout your organization as people see AI solving real problems.

Beyond the Misconceptions

The biggest mistake I see telecommunications leaders make isn’t technical. It’s cultural.

They treat AI adoption as a technology project instead of a business transformation.

Technology projects have endpoints. You implement the system, train the users, and declare victory. Business transformations are ongoing processes that change how your organization thinks and operates.

When I work with conference organizers planning telecommunications events, I always recommend focusing on the human side of AI adoption. The technical implementation is often the easy part.

The challenging part is helping your teams understand that AI makes their work more interesting, not more replaceable. It’s showing them that AI tools can eliminate the boring parts of their jobs so they can focus on the creative, strategic, and relationship-building aspects that humans do best.

The telecommunications companies that transform their culture involve employees in the AI adoption process. Instead of having AI imposed on them, employees become part of identifying problems and testing solutions.

When a field technician suggests using AI to automatically generate preliminary diagnostic reports, they’re not just adopting technology. They’re taking ownership of innovation.

The Competitive Reality

Here’s the truth that many telecommunications executives don’t want to face: Your competitors are already using AI.

Some of them are using it well. Others are making expensive mistakes. But none of them are waiting for perfect understanding or ideal conditions.

The companies that will lead the telecommunications industry five years from now aren’t the ones with the most sophisticated AI strategy. They’re the ones that started experimenting today.

This is the same dynamic that played out in the early days of e-commerce. Traditional retailers spent years studying online shopping behaviors while Amazon was busy shipping products. The retailers had better analysis. Amazon had better results.

The same thing is happening with AI in telecommunications right now.

While some companies are forming committees to study AI implications, others are already using AI to improve customer service, optimize networks, and reduce operational costs.

The gap is widening every quarter.

Your Next Move This Week

Stop asking whether your company should adopt AI. Ask this question instead: “What’s the most frustrating part of your job that AI could potentially solve?”

This week, walk through your organization and ask that question to customer service representatives, network engineers, field technicians, and operations managers.

You’ll be surprised by the answers. More importantly, you’ll identify specific, measurable problems where AI can make an immediate impact.

Don’t start with grand visions of AI transformation. Start with the daily frustrations that cost your company time and money.

If you’re planning a telecommunications conference or leadership event and want your audience to leave with clarity instead of more confusion about AI, I’m available to speak.

Bring me in to walk your industry through the Disruption Confidence Cycle and give your leaders a practical path from doubt to momentum.

The telecommunications industry has successfully navigated every major technology shift over the past four decades. The companies that thrived didn’t do it by waiting for perfect understanding or ideal conditions.

They did it by identifying clear problems, testing practical solutions, and scaling what worked.

AI is no different.

What’s the first problem your team wants to solve?