By this point, every integrator has been pitched at least three AI "copilots" in the past six months. Almost none of them can answer a simple follow-up question: How many hours does your team actually spend producing a complete proposal today (labor, materials, and margin included) and what would a 40% reduction in that number be worth?
Before you buy another tool, run the benchmark. It's the only way to tell whether AI is shortening your sales cycle or just adding another tab to the workflow.
88% Close Rate vs. 69%... Which Do You Want?
The recent D-Tools 2025 Year in Review Report using data derived from nearly 100,00 signed contracts and more than 250,000 proposals in D-Tools Cloud in 2025 showed the sales cycle is brutally sensitive to proposal speed. Integrators who deliver a proposal within 48 hours close at 88% win rate. The slowest quartile of dealers are the firms that are taking more than 60 days to generate proposals, close at 69.6%. That’s an 18%+ gap in the close rate compared to those who can move quickly… a significant difference even for those who are math challenged.
Speed isn't the only variable, as the complexity of the project compounds also has a major effect on proposal generation. Small projects that have fewer than 10 line items (equipment + labor) require on average just a single proposal (specifically 1.08 quote versions per opportunity). That’s the good news. It means integrators should be able to crank out proposals for smaller jobs in the first attempt that are ready to be signed immediately.
However, the news is not as good as projects become more sophisticated. The number of required quote revisions ramps up quickly as a project becomes more complicated. Specifically, complex installations of 50 or more line items average nearly 3 quote versions (2.98 to be exact) before the customer will sign on the dotted line. Extreme-complexity projects (500+ line items) average between 8 and 9 versions. That’s 7.9x more proposal iterations than a simple installation.
D-Tools data also shows that larger projects are the most profitable ones. Digging into the details, according to the D-Tools Special Report on Project Margins, projects that that are under $10,000 have a gross profit margin between 30% and 39%. Larger projects over $10,000 have a minimum 45% gross profit margin for integrators.
Translation: Larger projects with the highest margin potential are the ones eating the most of your team's time. That is a time suck that AI can liberate.
Where the Hours Actually Go
For the creation of a mid-complexity residential proposal, let’s compare a typical manual workflow compared without AI versus a workflow using the AI intelligence like what is embedded in D-Tools Cloud
Step 1: Designing the Project
Manual Workflow: Site notes are taken for an equipment list and hand-written or keyed in on paper or a tablet, followed by looking up every SKU from an individual vendor’s website.
AI-Assisted Workflow: Natural language-based search of D-Tools Integrated Product Library auto-resolves compatibility between products, finds accessories, and locates current pricing.
Step 2: Assembling Bill of Materials
Manual Workflow: Spreadsheet is pasted from three suppliers with manual reconciliation
AI-Assisted Workflow: BOM built from templates and prior similar jobs
Step 3. Estimating labor
Manual Workflow: Tribal knowledge applied per category
AI-Assisted Workflow: Labor phases pre-mapped to product categories
Step 4: Setting margin and pricing
Manual Workflow: Calculator on a second monitor; rules tracked in someone's head
AI-Assisted Workflow: Margin engine applies firm-wide rules automatically
Step 5: Proposal document creation
Manual Workflow: Word doc reformatted from a 2019 template
AI-Assisted Workflow: Template engine generates a branded, client-ready proposal
Step 6: Making Revisions
Manual Workflow: Each round = repeat steps 1–5
AI-Assisted Workflow: Change one line; the rest cascades |
Steps 1, 2, and 6 are where most teams bleed time. They are also the steps AI compresses most cleanly, because they're pattern-matching against structured data the catalog already contains.
What Getting Back 6 to 8 Hours Looks Like
A five-person residential/commercial integration company will produce on average about 80 proposals a year. If each proposal currently takes 14 hours of combined sales and engineering time and AI-assisted quoting cuts that by 6 to 8 hours (a 43% to 57% reduction), the firm reclaims roughly 480 to 640 hours annually. That’s the equivalent of one-quarter to one-third of a full-time estimator’s salary without hiring one.
The revenue model is where it gets interesting. At an average contract value of $13,739 (D-Tools 2025 average), converting just 20% more of the leads currently lost to slow turnaround adds 14 to 16 contracts a year. That's $192,000 to $220,000 in incremental installation revenue without spending another dollar on lead generation.
The trap, though, is treating AI as the win itself. It isn't. The win is operational discipline: a single source of truth for products and pricing, templates that capture your firm's actual scope language, and a margin engine that doesn't depend on the senior estimator being in the office. AI just accelerates a system that already exists. If your current quoting stack is a folder of spreadsheets and a shared inbox, an AI layer will produce wrong answers faster.
