Four Pillars of AI Success for Service Providers

Artificial Intelligence (AI) is everywhere, but operators aren't diving in blindly. Many of our service provider clients see AI's potential yet remain cautious, shaped by past technology waves that overpromised and underdelivered. So, it's no surprise we hear questions like:

"Show me the use cases."
"Where's the incremental value?"
"How can AI solve our actual problems?"
"What's the ROI?"
"How do we implement this securely?"

These aren't just technical questions. They're strategic, and they signal a growing recognition that AI could reshape how operators run their businesses and deliver value to customers. But uncertainty persists around where to begin, how to scale and how to measure success.

To move from curiosity to clarity, operators must reframe their approach, not as connectivity providers, but as technology enablers. With the right use cases, governance and execution discipline, AI can turn decades of operational data into a platform for innovation and growth.

Check out WWT's Data Maturity Model and see how to make the most of your data assets. 

Why AI, why now?

The operator business model is under pressure. Margins are tightening. Expectations are rising. Traditional revenue streams are eroding as customer demands shift faster than infrastructure can evolve, and tech giants and cloud providers are moving aggressively into telecom territory.

AI offers a strategic inflection point. Done right, it can help operators:

• Optimize and modernize network operations
• Improve service reliability and customer experience
• Unlock new, data-driven revenue streams
• Create defensible advantages in an increasingly competitive landscape

Read A Guide for CEOs to Accelerate AI Excitement and Adoption to learn about WWT's strategic guidance for the C-suite. 

For decades, service providers thrived in a regulated, stable environment where innovation has been slow and difficult. Today, that landscape has fundamentally changed. AI presents a chance to transform by repositioning infrastructure as a platform for innovation.

Operators can move from selling connectivity to selling intelligence, offering capabilities like:

• AI-powered enterprise consulting
• Intelligent network optimization
• Predictive maintenance services
• Data-driven platforms for analytics and automation
• Edge intelligence for vertical-specific services (e.g., smart cities, retail, manufacturing)

In some cases, operators are beginning to monetize their internal AI capabilities as external offerings. For example, a Tier 1 operator is exploring how its AI-enabled NOC tools, originally developed to improve internal operations, could be offered to other operators as a managed service or productized solution.

Four strategic pillars for AI success for service providers

Based on our work with operators and enterprises globally, here is our blueprint for moving forward.

1. Start with high-value use cases

Avoid starting with tools or technology. Begin by asking: What business problems are we solving? Prioritize use cases that are measurable and tied to performance:

• Network optimization: AI can analyze traffic patterns in real time to manage congestion, reduce energy usage and improve quality of service (QoS). Operators are showing up to 30 percent efficiency gains by utilizing AI and ML solutions in their networks.
• Predictive maintenance: Identifying failures before they happen saves time, money and customer frustration.
• Customer experience: From intelligent support to proactive recommendations, AI can personalize interactions at scale. We are seeing encouraging results from clients using AI to help predict "churn" and implementing systems to help keep customers satisfied via network tweaks and sales and marketing enhancements.
• Automated documentation and compliance: Reducing manual workloads and error rates with AI-driven workflow tools. Networks are very difficult to maintain and support; there are numerous documents, regulations, security protocols and software updates that overwhelm support staff. AI helps engineers and planners get the right data faster to make more informed, timelier decisions.

One promising area of experimentation is the use of LLMs to assist network engineers with day-to-day tasks, like reading router logs, troubleshooting configuration issues or writing change plans. Since one of AI's biggest benefits is its ability to adapt to ongoing changes in network environments, AI systems can automatically detect and respond to updates, such as software changes or configuration shifts, that would traditionally break integrations or require manual fixes. 

These tools can reduce toil, lower cognitive load and accelerate issue resolution.

Learn how the utility company Southern California Edison is using generative AI to transform network operations. 

2. Treat data as a strategic asset

AI depends on data. But operators often face data fragmentation, governance gaps or legacy system challenges. Effective AI requires:

• A clear understanding of what data you have and where it lives
• Strong data governance and security frameworks
• Architectures that enable secure, real-time access to actionable data
• Integration between network, customer and operational data sources

The goal is to turn raw data into reliable, governed and accessible fuel for your AI use cases.

Some operators are creating common data "languages" (metadata standards) and shared access points (APIs or data platforms) so that different teams, like engineering, operations and planning, can easily use and share the same data.

For example, a network log entry might be labeled and formatted the same way across all systems, making it easier for an AI tool to analyze problems. A central data layer might also allow a planning team to pull real-time network performance data without needing direct access to every monitoring tool. 

AI's ability to stay current with evolving network conditions is a critical enabler of data-driven operations. AI can correlate changes in APIs, KPIs and operational capabilities, like software updates or configuration shifts, so they are reflected across systems in real time. This reduces the risk of system failure due to outdated linkages and supports continuous optimization. These capabilities are especially impactful when paired with shared access points like APIs or centralized data platforms, enabling teams to work from consistent, up-to-date information.

These efforts are often part of data modernization programs or cloud migrations, where the goal is to make data cleaner, more consistent and easier to use for automation and analytics.

3. Build the right organizational muscle

Successful AI adoption requires the right people and processes as much as the right platforms. 

• Stand up an AI Center of Excellence (CoE) to guide strategy, coordinate pilots and scale learning across the organization.
• Develop cross-functional teams that combine domain knowledge, data science and engineering.
• Create repeatable, use-case-driven workflows to speed experimentation and reduce risk.
• Invest in upskilling and change management to build buy-in and long-term capability.

Governance and security must be embedded from the start. Operators work in sensitive, highly regulated environments, and trust and compliance can't be afterthoughts.

Leaders and executives must work together to build internal conviction, more than executive sponsorship, to instill belief among frontline teams that AI will help, not threaten, their roles. An effective way to do this is to include the teams that will ultimately use or be impacted very early in the discussion and development processes. 

See how WWT's own AI Center of Excellence is helping drive efficient AI use across our organization. 

4. Pilot, prove and scale

Move deliberately, but don't get stuck in "wait and see" mode. The most effective operators:

• Start small with a clear goal. Targeted pilots tied to specific business KPIs, like reduced truck rolls or improved QoS, can quickly demonstrate measurable impact.
• Build internal momentum. Use early wins to build executive confidence, secure investment and energize cross-functional teams.

WWT has done this internally through our AI-powered RFP Assistant (RFP-A) tool, which streamlines the response process for complex customer proposals. Since its rollout, the assistant has delivered measurable results, including an 80 percent reduction in proposal managers' time-to-first-draft, cutting manual effort from over 45 hours to just 8 hours by automating key steps such as RFP ingestion, outline generation and initial response drafting using Salesforce data and historical proposals.

These figures are tracked through internal usage analytics and sales attribution models tied to RFP-A-supported pursuits. The data was compiled through a combination of Salesforce reporting, proposal team usage logs and attribution modeling that links RFP-A engagement to closed-won deals. 

Scale what works

Continuously refine and expand successful use cases, while retiring those that don't deliver value.

• Let use cases drive infrastructure. Cloud, edge and hybrid decisions should follow operational requirements, not precede them.
• Embed security from day one. Especially in regulated environments, secure AI deployment requires robust data governance and privacy frameworks from the outset.

Piloting AI isn't just about testing tools. It's about proving that AI can solve real problems, drive efficiency at scale and create competitive advantage.

AI pilot efforts are increasingly being connected to network modernization programs, where 5G rollouts or Operations Support Systems (OSS) and Business Support Systems (BSS) upgrades create a natural entry point for new data architectures and automation layers.

Ready to move from curiosity to clarity?

Learn how WWT helps operators define and execute AI strategies rooted in real-world impact, from use case discovery through secure, scalable deployment.