Programs are a powerful way to streamline delivery of multiple related projects, and a program management office (PgMO) is a vital tool in strengthening that approach. The next stage of maturity, building on that foundation, offers utilities the potential to reap significant additional benefits through predictive management.
In this model, program teams use data-driven insight to anticipate risk, shorten timelines and build stakeholder trust. The need is clear for large, multiyear efforts such as broadband fiber deployments that cross regions and jurisdictions. In the U.S., public funding has accelerated this work, including through the Broadband Equity, Access, and Deployment Program, a $42.45 billion grant program under the National Telecommunications and Information Administration to expand high-speed internet access nationwide.
Relying on reactive management alone rarely holds up well at utility scale. Before predictive tools are implemented, common friction points appear: extended early-phase cycle times, uneven quality in design submissions, and issues that only surface during construction, forcing pauses and rework. Permitting and easement reviews can become bottlenecks when objections are received late in the process. Add forestry clearances and outage coordination, and teams can drift into firefighting mode — reordering priorities and filling resource gaps as they go, and subsequently losing sight of the big picture.
Unified Insights
The critical pivot comes from integrating the right data into a single operational view, then acting on the insights that are thereby revealed. This could be seen in action in a recent rural broadband fiber deployment program for a North American utility.
Forecasts from internet service providers (ISPs), permitting milestones, easement approvals, quality checks and construction updates were combined in one environment. Dashboards in Microsoft Power BI translated this data into accessible, shared views for schedule health and throughput, which helped leaders spot patterns early and collaborate on next steps. In parallel, a robust tool that can collect and integrate data quickly provided a backbone for unifying sources without duplicating underlying systems. This enabled execution teams to bring models and operational data together while maintaining a consistent source of truth. For field execution and construction controls, the program team needed to utilize a data-driven tool to collect field data that could be utilized for analytics and insights features connecting day-to-day program-level activity to program-level reporting, including out-of-the-box Power BI reports on each tool’s data.
Once the data was stitched together, the cadence shifted from reporting what happened to predicting what might happen. Shortfalls in ISP design submissions were flagged early enough to adjust regulatory filings. Resource modeling highlighted upcoming crew constraints, giving hiring managers time to backfill. Permitting risks appeared weeks in advance, prompting project manager actions that prevented avoidable delays.
These are typical gains when PgMOs adopt predictive analytics: Risk indicators move from post mortem to near-term action, and all of the teams involved use the same facts to make decisions. Industry research supports this direction, noting that artificial intelligence (AI)-enabled predictive techniques can improve planning, resource allocation and risk identification when applied in conjunction with sound governance.
Earlier Engagement
The impact is practical. Sequencing improves, handoffs firm up and rework drops because quality issues are caught upstream. Just as important, confidence grows among stakeholders — regulators, local agencies and delivery partners — because decisions are backed by transparent, shared data rather than isolated spreadsheets.
As that trust builds, PgMO leaders are pulled into earlier conversations about policy updates and long-range coordination. This aligns with broader industry findings that project professionals create more strategic value when they bring business acumen together with data fluency.
Three lessons stand out from this shift:
- Combining data from different sources is not merely a nice to have; it is the only way to see the program clearly.
- Predictive modeling reduces uncertainty within the team, which raises the quality of commitments to the outside world.
- Technology is only part of the equation. The gains hold when people and processes adapt, as PgMOs set a common cadence, define roles and keep change management in view.
Predictably Powerful
Predictive program management is likely to become the norm on complex utility work. Managers will balance algorithmic insight with professional judgment, using shared dashboards, auditable data trails and well designed playbooks to keep delivery on track. In that sense, predictive management is not about adding bells and whistles. It is about credibility — showing communities and partners how decisions are made, why plans change and how outcomes stay aligned with public goals.
With extensible processes in place, the PgMO is ready for that next step. Augmented by predictive insight, the PgMO becomes a true partner in shaping how critical infrastructure is built.
