Verizon’s Tower Deal: A Smarter Path to AI-Ready 5G

Verizon’s new multi-year tower partnership with Array Digital Infrastructure isn’t “just another real estate deal.” It’s a reminder that most 5G strategies fail for a boring reason: the physical layer can’t scale fast enough, cheaply enough, or predictably enough.

Array operates 4,400 towers across 19 U.S. states, and Verizon now has a streamlined path to collocate on many of those sites. Verizon also called out something operators rarely emphasize publicly: pricing stability. That line matters because stable infrastructure costs are what make AI-driven network optimization financially viable at scale.

This post is part of our AI in Telecommunications series, and I’ll take a stance: AI can’t “optimize” capacity you haven’t physically built. Tower access, predictable economics, and faster deployments are what turn AI from a lab demo into a network advantage.

What Verizon actually bought: speed, certainty, and optionality

The core value of Verizon’s agreement with Array is not the towers themselves. The value is getting three things that AI-assisted 5G management depends on.

1) Deployment speed through collocation

Collocation means Verizon can put equipment on existing tower sites instead of going through the full cycle of locating, leasing, permitting, and building from scratch. In practice, this reduces:

• Time-to-air for new radios
• Risk of zoning delays
• Variability in site readiness

When you’re trying to densify 5G coverage—especially where traffic spikes are seasonal or uneven—speed isn’t a nice-to-have. It’s the difference between meeting demand and watching customers churn after a few bad weeks.

2) Cost control via “streamlined pricing”

Verizon says the deal includes a streamlined pricing structure intended to deliver cost efficiency and long-term stability. That’s not marketing fluff; it’s operational math.

AI in telecom (closed-loop automation, predictive maintenance, RAN optimization) often has a payback model that depends on:

• Baseline opex being measurable
• Savings not being swallowed by surprise lease escalators
• Long-term forecasting that finance teams will actually approve

If your tower costs are volatile, your AI business case becomes fragile. Stable pricing is how you keep automation programs from getting cut during budget reviews.

3) Flexibility for “advanced wireless technologies”

Verizon’s VP of engineering, Phillip French, framed the partnership as part of tower management strategy that adds flexibility and accelerates deployment of advanced wireless technologies.

Here’s what that likely means in real terms:

• Faster rollouts of additional bands where capacity is tight
• More room for densification to support enterprise SLAs
• Better physical footprint planning for 5G features like slicing and low-latency services

AI-based optimization works best when the network has degrees of freedom—multiple sites, multiple layers, multiple options. Towers create that optionality.

Why tower partnerships are suddenly central to AI-driven 5G management

AI-first telecom strategies often focus on software: models, data, automation platforms. But the networks that benefit most from AI share a trait: they’re built to be observed and controlled.

Tower access helps in three concrete ways.

Better data quality starts with better topology

AI models need consistent, high-resolution telemetry to make decisions about:

• Congestion
• Interference
• Handover behavior
• Coverage holes

Densification (enabled by collocation) improves topology. More sites can mean:

• Smaller cells and clearer RF boundaries
• More consistent user experience
• Easier root-cause isolation when performance dips

That’s the unglamorous truth: AI accuracy improves when the network is less ambiguous.

Closed-loop automation needs physical headroom

Closed-loop control (where AI detects an issue, decides, and applies a change) breaks down when the network is already maxed out.

If your utilization is pinned and there’s nowhere to shift load, the model can only recommend “build more capacity.” Tower partnerships are what allow the automation roadmap to progress from:

1. Detect and alert
2. Recommend actions
3. Apply actions safely
4. Optimize continuously

You can’t automate your way out of missing infrastructure.

Predictive maintenance is easier when sites are standardized

Operators and tower companies can standardize site access, equipment layouts, and operational processes. That makes predictive maintenance more effective because:

• Failure patterns are easier to compare
• Inventory and spares planning becomes more consistent
• Dispatch decisions can be optimized with fewer exceptions

AI is good at pattern recognition. Standardized environments make the patterns clearer.

The Array factor: a “new” tower company with real scale

Array Digital Infrastructure was formed after UScellular’s $4.4 billion transaction to sell most wireless assets to T-Mobile US in 2025. Array now runs 4,400 towers, and as of September 30, Telephone and Data Systems (the former UScellular parent) owned about 82% of Array.

Two observations matter for telecom and AI strategy teams:

Scale changes negotiation dynamics

A tower footprint of 4,400 sites (across 19 states) is large enough to matter, but not so large that processes are frozen in bureaucracy. Mid-scale tower companies can sometimes move faster on:

• Contract structures
• Collocation workflows
• Portfolio-level modernization

That speed pairs well with an operator trying to accelerate 5G deployment.

New entities often re-write operating playbooks

When infrastructure companies form or restructure, they frequently revisit:

• Pricing models
• Site readiness programs
• Standard terms for amendments and upgrades

That’s exactly where operators can win long-term. If Verizon can lock in predictable terms early, it strengthens their ability to plan multi-year AI-driven network upgrades without renegotiating every quarter.

What this means for 2026 planning: AI-ready 5G is a build-and-operate problem

As we head into 2026, operators face a familiar tension: traffic grows, expectations grow, budgets don’t. The playbook that works is not “spend more.” It’s spend with fewer mistakes—and AI helps reduce mistakes only if you create the right operating conditions.

Where AI delivers the most value after tower expansion

Once you have more collocation options and better density, AI becomes much more practical in these areas:

• RAN optimization: automated parameter tuning, load balancing, mobility optimization
• Energy efficiency: dynamic carrier shutdown, sleep modes, power optimization driven by traffic forecasts
• Predictive maintenance: anomaly detection for power systems, radios, and backhaul degradation
• Customer experience analytics: mapping QoE to specific cells, times, and device cohorts

A simple rule I’ve found useful: AI ROI increases when the network has choices—more sites, more layers, more controllable variables.

The hidden constraint: operations and governance

More sites also means more operational complexity. This is where AI programs often stumble. If you want tower-driven scaling to translate into network performance gains, you need governance that answers:

• Who approves automated changes (and how fast)?
• What are the guardrails for AI actions? (rollback thresholds, blast radius limits)
• Which KPIs matter: latency, throughput, drop rate, or customer-reported experience?

Without those answers, AI stays stuck in “insights dashboards” mode.

Practical checklist: how to turn tower access into AI wins

If you’re a telecom leader (network, IT, or strategy) looking at deals like Verizon–Array and wondering what it implies for your own AI roadmap, use this checklist.

1) Treat tower partnerships as data partnerships

Before you scale collocation, ensure your telemetry pipeline can handle more sites:

• Standardize counters and alarms across regions
• Enforce consistent site and cell naming conventions
• Build a clean inventory-to-performance mapping (site → sector → carrier → KPI)

If your data model is messy, adding towers just adds noise.

2) Build a “time-to-impact” map for AI use cases

Tie use cases to what tower expansion enables:

1. 0–90 days: coverage validation, anomaly detection, congestion hot-spot identification
2. 3–9 months: automated optimization recommendations, predictive maintenance pilots
3. 9–18 months: closed-loop optimization in controlled regions, automated change windows

This keeps your AI program from promising outcomes the network can’t support yet.

3) Demand pricing clarity that matches your automation horizon

AI programs don’t pay back instantly. Your infrastructure agreements should make it possible to plan:

• Multi-year opex and capex
• Upgrade cycles (radio swaps, additional bands)
• Forecast-driven densification

If pricing resets every year, AI becomes harder to justify.

4) Operationalize, don’t “pilot forever”

Most companies get stuck running pilots on 20 sites while the network has 40,000.

Pick one region, commit to operational adoption, and measure:

• Mean time to detect (MTTD)
• Mean time to resolve (MTTR)
• Truck rolls avoided
• Energy per delivered GB

Those are the metrics finance teams respect.

The bigger picture: towers are the quiet enabler of AI in telecom

Verizon’s tower deal with Array Digital Infrastructure is a straightforward infrastructure move—and that’s why it matters. AI-driven 5G management needs scale, stability, and speed, and towers deliver all three.

If you’re building an AI in telecommunications roadmap for 2026, don’t start with models. Start with the constraints: where can you add capacity quickly, what costs are predictable, and how fast can your org safely automate decisions?

The next question is the one that separates serious operators from everyone else: once the network expands, will your AI stack be ready to run it better—or will you still be manually tuning yesterday’s problems?