Rethinking the Build-vs-Buy Debate
For utility teams navigating the vegetation intelligence space, deciding to build in-house or buy new tech isn’t always an easy choice.
Vegetation remains one of the largest operational expenses in utility management. In some areas, it accounts for up to 75 percent of overhead maintenance budgets. Yet despite that investment, nearly a quarter of distribution outages in the United States are still caused by vegetation. It’s a reminder of how complex, resource-intensive, and reactive these programs can be.
Vegetation intelligence tools are beginning to shift that reality. With the ability to convert satellite and aerial imagery into usable data, they’re helping teams prioritize risk across entire networks and make better use of their time, labor, and budgets. Instead of relying on cyclical schedules or historic patterns, utilities can now evaluate specific spans, respond faster to storm events, and defend operational decisions with data.
But implementing these solutions raises a familiar question. Do you build it in-house or buy from a technology provider? This piece offers a framework to help clarify that decision: what to build, what to buy, and how to make the best choice for your team.
The case for building
For some utilities, building and maintaining a vegetation intelligence solution in-house makes strategic sense. Internal workflows may be highly specific, regulatory requirements complex, or a territory so distinct that commercial platforms fall short. In these cases, off-the-shelf tools might not offer the level of customization required to support operations well.
Building often allows for deeper integration across internal systems, especially when workflows span multiple platforms like OMS, GIS, and asset management systems. If your team has a strong bench of AI specialists, data scientists, machine learning engineers, and UX designers ready to deploy, an internal build can give you full control over architecture, functionality, and evolution.
But each integration introduces potential delays. Building from the ground up also means taking on the responsibility of maintenance, scalability, and continuous improvement. Without a clear roadmap and the internal capacity to support it long-term, even the best-built tools can struggle to deliver results or keep pace with shifting operational demands.
Building isn’t inherently better or worse. It is a choice that works best when your needs differ significantly from industry peers and your team is equipped to design, iterate, and sustain the system for years to come.
The case for buying
Rather than starting from scratch, some utilities have opted to buy systems already designed to deliver field-ready insights at scale. These platforms are built by teams with deep experience in vegetation intelligence, remote sensing, and machine learning. Many have been tested across a range of environments and circuit types, with proven methodologies for identifying and prioritizing risk.
The clearest advantage of buying is speed. Instead of investing years into custom development and internal integration, teams can get up and running quickly with systems already built to scale. Not only does this accelerate implementation, but it also gives utilities access to platforms that are regularly improved as technology evolves in the space. These updates often draw on insights from a wide range of geographies, user feedback, and rigorous testing. Hence, allowing utilities to benefit from predictive technology that might take significantly more time or resources to build internally.
Buying these solutions gives teams immediate access to powerful datasets they wouldn’t otherwise have if implementing the systems independently. By working with software trained on vast, diverse datasets collected across millions of spans and varied utility environments, companies gain highly accurate and useful data from day one. This allows utilities to benefit from years of refined machine learning performance without having to invest the time and resources required to build such extensive data foundations from scratch.
For example, an Ontario-based utility faced frequent tree-related outages, increasingly exacerbated by extreme weather. Rather than building a solution in-house, the team implemented Overstory’s platform to get up and running quickly. The platform immediately gave them full visibility across the network, allowing them to shift from reactive to proactive vegetation management. Within one year, they reduced tree-related outages by 48 percent and improved crew safety by significantly cutting unnecessary truck rolls.
Buying isn’t about offloading responsibility or going for the technology that costs the most. It’s about accelerating progress, freeing up internal teams to focus critical expertise on business priorities, and choosing tools that support your goals at the speed and scale that matters.
Build or buy: what to consider
There is no single answer to whether utilities should build or buy. The right choice depends on the scope of your network, the resources available to your team, and how quickly you need the solution to perform. The matrix below offers a practical way to assess your starting point and frame internal conversations around priorities, constraints, and capabilities.
| Criteria | When to Build | When to Buy |
|---|---|---|
| Timeline | You have time to develop, test, and scale a custom solution. | You need results quickly to meet operational goals. |
| Internal Resourcing | Your team has strong data science, GIS, and engineering support. | Internal capacity is limited or focused on other initiatives. |
| Scope of Use | The solution applies to a narrow use case or localized network. | The platform needs to work across a broad service territory. |
| Budget Structure | Your budget allows for long-term investment and maintenance. | You prefer predictable costs and a clear path to return on investment. |
| Adaptability | Requirements are stable and not likely to shift significantly. | Use cases may evolve and flexibility is important. |
| Risk Profile | You are comfortable with extended development and iteration. | You prefer tested solutions with demonstrated success in the field. |
| Integration Needs | You require deep customization across internal systems. | You are comfortable prioritizing solutions that integrate with platforms already in use. |
| Advanced Machine Learning Infrastructure | Your internal teams are equipped to develop and maintain models tailored to a specific regional context that requires relatively limited scope and complexity. | You want to leverage models trained on large-scale datasets, utilizing multiple utilities, that can deliver a high level of accuracy across varied environments without the time and cost required to build that capacity in-house. |
Buying well means choosing the right partner
The most effective utility teams today are making a conscious decision to buy well. It means selecting a partner who understands the operational realities on the ground, can adapt as needs evolve over time, and who can prove their expertise. A strong partner understands buying isn’t a one-time transaction, but the beginning of a long-term collaboration.
In the southeastern United States, a large investor-owned utility wanted to improve their storm readiness. Their team used Overstory to identify the 188 spans most at risk from ice-loading just days before a winter event. Crews completed work on 83 percent of those spans in under 48 hours. What would have taken 240 labor hours to inspect manually was narrowed down to the highest-risk areas, making response faster and more targeted
Similarly, for a Midwestern transmission co-op, Overstory helped eliminate guesswork from their inspection program. Instead of sending crews to review thousands of spans manually, they focused only on the 0.8 percent that showed immediate grow-in risk. They also fine-tuned their side-trimming strategy based on the data. The result was a 64 percent increase in vegetation cleared, all within the same budget. The program ultimately delivered a 13-to-1 return on investment.
In each of these examples, an effective partnership helped the teams move faster without losing control. Internal expertise remained at the center, but having the right partner helped them scale that expertise, unlock value, and adapt to changing priorities.
Learn more about how to effectively evaluate potential partners in our Guide to Evaluating Vegetation Intelligence Technology.
You’ve made the call—now what?
Once you’ve made a decision —to build, or partner with a vendor—the next steps matter just as much as the decision itself. This is where good intentions either gain traction or stall out. Here are some ways to keep things moving:
Get aligned on what success looks like
Before any rollout, take time to define what success means for your team. Fewer outages? Better insight ahead of a storm? Clarity here shapes everything that comes next.
Make sure the right people are involved from the start: not just leadership teams, but operations, IT and field managers. The individuals who use the system every day will see things others may miss.
2. Start with a smart pilot
You don’t need to prove the technology works but you need to prove it works for you. Choose a pilot area that reflects the challenges of your network. Let it test more than just accuracy: see how well the tool fits into daily routines, whether it saves time, and how it helps crews do their jobs better. A pilot should surface friction points early, when it’s easier to address them. Learn more about how to execute a successful pilot program.
3. Make it easy to use
The best tool in the world won’t help if it’s hard to use. Make sure insights land where individuals already work whenever possible, whether that’s GIS, internal dashboards or other software. Prioritize training, and keep the feedback loop open. If something’s confusing or clunky, you want to hear about it early. This isn’t a set-it-and-forget-it rollout but an ongoing conversation.
4. Track what matters, not just what’s easy to measure
Don’t wait for end-of-year results to see if it’s working. Look for quick wins that show traction:
Are inspection times going down?
Are high-risk spans being prioritized more effectively?
Are crews getting better direction before or after a storm?
Make sure your wins tie back to the goals you set early on and adjust if you need to.
5. Keep checking in
Technology in this space is evolving quickly. What wasn’t available a year ago might be common practice now. That means your strategy to build, buy, or partner isn’t a static decision.
Keep checking in with your internal teams and external partners. What’s changed and what’s improved in the vegetation intelligence space? Is the solution still solving the right problems? The best systems aren’t just reliable but more importantly, they stay relevant.
Paving the way forward
There’s no single answer to the build vs. buy question. Many utility teams are finding that the right partner helps them move faster, stay nimble, and complement their internal expertise. Partnering doesn’t mean giving up control. Instead, it expands your team’s capacity with support that evolves alongside shifting risks, regulations, and tools. In the vegetation space where change is constant, that flexibility makes all the difference.
If your team is looking to explore next steps, our Guide to Evaluating Vegetation Intelligence Technology walks through what to look for in a solution and a partner built for long-term success.
