Every Telecom Board Talks About AI-But None Have a Working Strategy

AI as the New Boardroom Buzzword

In telecom boardrooms from London to Lagos, AI has become a near-constant topic. Directors nod sagely at the mention of ‘machine learning’, approve digital transformation budgets, and debate ethical concerns over generative models. But dig below the surface, and the reality emerges:

Most telecom boards have no working AI strategy.

This gap is not just operational—it’s existential. AI will reshape margins, workforce models, fraud detection, customer experience, and even core network design. Without a real AI strategy, boards risk being left behind—by regulators, customers, or competitors.

This article explores why AI strategies often stall at board level, the illusions of action that mask inaction, and how boards can take control.

1. The Illusion of Progress: When 'AI' Is Just a Line Item

Most boards believe they’re making progress on AI simply because:- It’s in the annual strategy deck- There’s a pilot with a vendor or consultancy- A data team gave a jargon-heavy presentation last quarter

But these are signals of interest, not strategy. A working AI strategy requires:

- Clear organisational priorities where AI can create defensible value

- Budget alignment with measurable outcomes

- Talent strategies to attract and retain AI-capable personnel

- Governance models to manage AI risk and ethics

Without these, boards are performing theatre - not executing change.

2. The Three Types of AI Blind Spot

1. Overhype – Some boards assume AI is a panacea, capable of automating away operational inefficiencies. They approve large-scale initiatives without clear ROI models.

2. Underestimation – Others assume AI is a niche IT topic. These boards treat it as ‘just another tool’, failing to see how AI will affect core strategic functions: pricing, fraud, infrastructure management.

3. Delegation Fallacy – Many boards delegate AI entirely to tech teams or vendors, avoiding strategic oversight altogether. This leaves them vulnerable to vendor capture and compliance risks.

In all three cases, AI becomes a strategic liability - not a source of transformation.

3. What a Working AI Strategy Actually Looks Like

A functional AI strategy for a telecom operator should contain:

- Use Case Prioritisation: AI for fraud detection? Dynamic pricing? Network optimisation? Start with impact.

- Data Readiness Assessment: Are data silos integrated? Is data governance robust enough for ML training?

- Talent Roadmap: Can we build internal AI capability or should we partner? What training do mid-level teams need?

- ROI Modelling: What’s the business case per use case? How are results measured?

- Ethics and Oversight: Who approves model deployment? What bias checks are in place?

- Vendor Policy: Clear criteria for build vs buy, and independence from proprietary ‘black box’ tools.

None of this should be left to middle management alone. It’s a board-level issue.

4. Why Boards Struggle to Govern AI

There are structural reasons why boards find AI hard to engage with:

- Abstract Language: Technical teams present AI with vague or academic language that directors don’t challenge.

- Fear of Looking Ignorant: Non-technical board members avoid asking questions they worry are 'too basic'.

- No Shared Framework: There’s no standardised way to talk about AI across finance, strategy, and operations.

This leads to avoidance. But telecom boards that sidestep AI are missing the next wave of margin control, customer targeting, and competitive positioning.

5. AI in the Telco Value Chain: Missed Opportunities

Across the telecom value chain, AI is already proving transformative:

- Customer Service: NLP-driven agents resolve more queries with lower churn.

- Fraud Detection: ML models spot unusual call routing patterns, SIM box fraud, or identity spoofing.

- Infrastructure Maintenance: Predictive analytics reduce downtime, improving SLA performance.

- Churn Prediction: Algorithms flag at-risk customers with increasing precision.

- Revenue Assurance: Intelligent audits catch unbilled usage, revenue leakage, and margin erosion.

Boards unaware of these possibilities are approving OPEX-heavy strategies while low-cost AI tools sit idle.

6. What Boards Must Do - Now

1. Demand a Board-Level AI Framework – Require quarterly updates framed in business impact, not just data science terms.

2. Clarify Strategic Objectives – Define what success looks like for AI across customer, network, and finance domains.

3. Build a Governance Model – Establish ethical, legal, and performance review boards for AI deployment.

4. Run Executive Simulations – Use real-world scenarios to explore AI decisions and risk trade-offs.

5. Upskill the Board – Arrange short, focused AI literacy sessions with independent advisors.

Boards that act now can own their AI narrative. Those that wait will inherit one written by vendors, regulators—or competitors.

Conclusion: AI Strategy Is Governance Strategy

AI is not a ‘tech’ topic - it’s a governance one. For telecoms firms, AI will touch pricing, resilience, data rights, and future revenue models.

Boards that treat it as a marginal issue risk becoming strategically irrelevant.

A working AI strategy starts with clarity - not code. It’s about focus, use-case realism, capability building, and control.

Bridge Connect offers AI strategy audits, board briefings, and risk frameworks tailored to telcos, regulators, and critical infrastructure players. Our approach is independent, pragmatic, and aligned to future commercial performance - not hype cycles.

Is your board actually in control of its AI strategy?

Contact Bridge Connect Ltd for an independent AI Strategy Review or Board Readiness Session.