Broadband and telecom providers today are beginning to focus on breaking down data silos and building foundational data integration. That’s the hard first step — but it’s only the beginning. Once systems are unified and your data is flowing smoothly, the real challenge emerges: how do you actually use AI to drive meaningful outcomes that improve operations, customer experience, and business growth?
That’s exactly the question explored in the recent webinar “After Data Integration, What’s Next for AI?” featuring industry experts Peter Cresse (QueSee AI), Jeff Boozer (ETI Software), and Don Eben (Core Consults). Hosted by the Fiber Broadband Association, this session moves beyond technical readiness toward practical AI adoption strategies for service providers.
Integration Is Just the Beginning
The panel opens with a clear message: having interoperable data isn’t the end goal — it’s the foundation from which all AI value emerges. Once networks, billing systems, OSS/BSS platforms, and customer data are integrated, providers face a new set of decisions around how to apply AI effectively.
Too often, organizations rush into AI experimentation without a strategy, leading to expensive pilots that fail to deliver measurable results. The experts emphasized that not every use case needs a sophisticated AI model, and not every problem should be solved with the latest LLM or generative AI tool. Instead, the focus must be on choosing the right intelligence for the right problem.
Identifying High-Impact AI Use Cases
A core part of the discussion revolved around identifying high-impact AI use cases that deliver real operational value. For broadband providers, some of the most promising areas include:
- Network Operations: Using AI to predict failures, optimize routing, and reduce outages can significantly improve uptime — a major driver of customer satisfaction.
- Customer Experience Enhancements: AI-driven personalization and automated support tools can improve service responsiveness while reducing operational costs.
- Planning and Forecasting: Intelligent analysis of integrated data can reveal trends in subscription behavior, capacity needs, and maintenance planning.
The panel stressed that these use cases should be chosen based on measurable outcomes, not just curiosity. Teams should ask: What problem are we solving? What does success look like? How will we measure impact beyond a prototype? Planning AI adoption this way aligns technical efforts with business goals and avoids “pilot purgatory” — where projects drift without delivering value.
Choosing the Right AI Tools and Models
Another key insight from the webinar is that not all AI technologies are created equal — and the wrong tool can do more harm than good.
Large Language Models (LLMs) are powerful for natural language interpretation, summarization, and conversational automation, but they may be overkill for some structured analytics tasks.
Domain-specific models or rule-based automation systems can be more efficient, interpretable, and less resource-intensive for standard operational workflows.
Providers need a clear strategy for selecting the right intelligence layer based on data quality, context, latency requirements, and expected ROI. This means assessing whether a use case requires deep reasoning, real-time analytics, or simple predictive rules — a decision that should be grounded in business impact, not hype.
Operationalizing AI Without Disruption
AI projects often stall not because of poor models, but because they’re not operationalized into real workflows. The speakers offered practical guidance on embedding AI outputs into existing business processes:
- Start with incremental automation — identify where AI output can augment existing processes rather than replace them outright.
- Ensure that AI insights are accessible and actionable for everyday users, not hidden behind technical interfaces.
- Establish clear feedback loops so AI systems learn from real outcomes and improve over time.
Efficiency gains come when AI becomes part of the rhythm of operations — from the field technician using predictive alerts to executives planning network expansion based on AI-driven forecasting.
Measuring Success Beyond Pilots
One of the most important themes from the webinar was the need to measure success beyond experimentation. Too many AI initiatives end at proof of concept, without scaling into deployment because organizations lack clear KPIs or frameworks to quantify value.
The panel suggested metrics that can anchor AI initiatives to business performance, such as:
- Reduction in operational costs (e.g., fewer manual interventions, lower churn)
- Improvements in service availability and quality
- Time-to-insight for data-driven decisions
- Customer satisfaction and retention rates
Looking Ahead
This webinar reframes the AI conversation for broadband providers. It’s not about chasing technology for technology’s sake — rather, it’s about building pragmatic, measurable, and strategically aligned AI adoption pathways that turn clean, interoperable data into smart decisions and real business value.
© 2026 Enhanced Telecommunications.
