Integrators are feeling squeezed on all sides: Labor is tight, customers want faster responses, and projects are becoming more complex. If you listen to the marketing, AI implementation will answer all these problems.
There’s no doubt that AI streamlines workflows and augments your team, yet it has to be approached with a grounded view and careful planning, not a belief that it will magically make work better.
The technology is being layered onto field‑service and ticketing systems, popping up in pricing tools, and showing up inside ERPs and CRMs as smart assistants. It can summarize meetings, route tickets, scan contracts, and even suggest prices. But just because AI is bundled into a certain platform or tool doesn’t mean it serves a purpose inside your specific business.
Before you commit to any AI initiative, and to get real value from AI, you need a clear view of what you want to fix with it … and how you’ll know whether it worked.
Avoid Random Acts of AI
When conversations turn to AI in commercial integrator, we often hear declarations like: “We should be using AI for that!” It’s tempting to want to use these new features and capabilities, even if you don’t know exactly what they’ll do for your company.
But when an AI tool is launched without a defined problem to solve, it won’t help you get work done. In fact, it may hinder operations if pilot projects stall due to lack of results, very few users see real value (they’re probably experiencing frustration instead), and your organization decides that AI is a waste. Despite good intentions, teams are left wrestling with issues because AI implementation wasn’t tied back to a clear workflow or success metric from the start.
Problem‑first thinking turns that around. Instead of getting excited about an AI-driven platform that makes promises about automation and insights, start with the work you need to get done better and faster. Where are you losing time, margin, or customer relationships—and can AI help? This forces you to map AI to a specific workflow and outcome: faster quote turnaround, fewer truck rolls per day, or less installation rework.
Map AI to Your Business Pain Points
To focus AI efforts where they’ll make a difference, start by listing your top organization-wide pain points. We often hear about problems with:
- Field services: dispatching, first‑time fix rates, callbacks, documentation
- Operations: job costing, scheduling, change orders, project handoffs
- Logistics: inventory visibility, staging, material shortages
- Sales and design: quoting, proposals, SOW quality, handoff to delivery
- Back office: AP/AR, time entry, payroll, revenue recognition
For each pain point you identify, write a simple problem statement and attach a measurable goal to it. For example: “Quotes take five days; we want 80% of our quotes out within 24 hours to improve win rates.”
Once you’ve done this, you better understand where decision support or automation would move the needle on performance.
Put Guardrails Around AI Efforts
Once you’ve framed the problem(s) you want to solve, it’s time to decide where you want to build or buy AI tools to support those priorities.
Some large integrators have a strong internal champion or team who understands the business and can own the AI roadmap. Others will need to bring in an external long‑term partner. No matter who’s taking the reins, you need to think through how AI will be governed inside your organization.
Data Handling
Every AI initiative will involve your data, so you need clarity on how that data will be managed before anything goes live.
- How will operational and customer data be handled, stored, shared, and protected as it flows through AI systems?
- Are we comfortable with how vendor tools train on or reuse our data?
Ownership
AI won’t run itself; someone has to be responsible for how it’s used and maintained.
- Who inside your company will be accountable for AI decisions, monitoring, and ongoing tuning?
- Will AI tools be owned by IT, operations, or a cross‑functional group?
Literacy
Leaders need enough understanding of AI to make sound decisions.
- Do our executives have enough foundational understanding of AI to challenge assumptions and make informed trade‑offs around cost, risk, and scope?
- Are our leaders prepared to ask questions about data use, risk, and expected outcomes before approving new AI projects?
Build vs. Buy for AI
After problems and guardrails are defined, you can decide how to implement AI. Most integrators today face two choices:
Highly productized solutions
These are built‑in AI capabilities inside the tools you already use: meeting assistants in collaboration platforms, email or ticket summarization in service tools, basic forecasting in ERP, etc.
They’re easy to turn on, require minimal setup, and can deliver quick wins, especially in well‑defined use cases like taking meeting notes or routing tasks. But they’re also limited in their scope; they solve the narrow problem the vendor designed them for, not your full workflow.
If you only need accurate meeting summaries, a productized feature might be enough.
Development platforms
These are low‑code/no‑code automation tools and orchestration platforms that connect multiple systems, agents, and datasets. They support sophisticated workflows like reading purchase‑order PDFs, updating your ERP, etc., but they demand internal expertise and a strong champion who understands the process and the technology.
With these platforms, nearly anyone can build a simple AI workflow, but designing and integrating it so it reliably improves margin, efficiency, or customer experience is a different story.
If you want to re‑engineer dispatch or project handoffs, you may need to take this approach.
Right now, there aren’t many “middle‑ground” options to consider. These would be semi‑custom solutions tuned to integrator workflows but still easy to deploy and manage.
Make AI Work for Your Business
When you keep a problem-first mindset, AI becomes a tool to improve how you serve customers and improve your business, not an iffy experiment that may or may not pay off.
The technology can help you do more with the team you have and deliver more consistent experiences across every project … as long as you your organization’s challenges and goals drive AI implementation.
This article was developed with insights from members of NSCA’s AI and Cyber Committee, who continue to examine how AI and automation can be responsibly integrated into the commercial integration industry.
