A synthesis of the ideas your leaders generated — clustered into strategic themes, prioritized by impact and effort, and sequenced into a practical roadmap leadership can act on. Anchored to the revenue, cross-brand, and procurement priorities the leadership team and the Validor sponsors named going in.
The MBP leadership team sees AI as the lever to move from five brands operating in parallel to one platform operating in concert — and to take the friction out of the moments that today cost the most revenue. Across 72 ideas, three patterns stood out: collapsing the quote-to-order cycle (the team's top two voted ideas, plus a third on pricing strategy), activating cross-brand and cross-channel sales motion (M&A targeting, dealer lead-gen, new-market identification, customer scoring), and unifying the back office across acquisitions (procurement & supplier discovery, SKU rationalization, capacity & labor scheduling — the foundation the finance and supply-chain leadership are already laying).
The strongest opportunities aren't technology projects in isolation — they're places where AI unlocks something the operating leaders have been wanting to do for years but couldn't justify five times over. The CPQ use case the team took through the Canvas is the clearest example: a single template that, once proven at one BU, scales to all five. This report organizes the opportunities so the board can decide where to invest first.
When we clustered the workshop output, the same big ideas kept showing up under different functional banners and different brands. These are the strategic threads worth pulling at the platform level.
Quoting & take-off, RFQ-to-quote, CPQ automation, custom door pricing, visual stair quotes, profitability-driven pricing strategy. The room's clearest unified message: every brand bleeds revenue at the quote step, and AI is the tool to stop it.
M&A target analysis, dealer lead generation, customer identification & scoring, distribution-partner inventory recommendation, new-market conversion to pre-fab, identification of segments/verticals. The "stickiness between business units" the CEO named — operationalized.
Find suppliers & generate RFP (#7 voted), SKU rationalization (#8), auto-schedule deliveries & labor (#9), production scheduling, capacity planning, enterprise spend / demand forecasting. MBP's ~$95M annual spend, now addressable as a single platform play.
Design Automation (#4 voted), stairs configurator, quick visual stair quoting with in-context preview, take-offs for contractors, plan + spec to quote, preview products on/in home. The bridge between engineering capacity and sales speed — and a moat the five brands can build together.
Product Knowledge Tribal (#5 voted), capturing long-term employee knowledge, cross-training, onboarding experience, IMR coaching, train-the-rep, succession planning, citizen innovation. The bench is aging across all five BUs — this theme is how the institutional knowledge survives the next decade.
Live Chat close-to-human for sales & tech support (#6 voted), installation support, DIY videos, customized literature, social posts across 5+ brands, customer experience innovation, ease of doing business. The five brands' fragmented buyer experience, addressed as one.
"Ask MBP" — pull information from multiple sources via one tool, integrate One Stream into Copilot, financial close & analysis, Q&A parts selection, HR Bot for employee FAQs. The Copilot-compatible BI layer that respects the data-firewall decision and unifies the five ERP environments.
After generating 72 ideas across the five pillars, participants voted on the ones they most wanted MBP Group to pursue first. These nine rose to the top — and the pattern leaves no ambiguity about where the operating leaders' energy is concentrated.
Quoting & Take-off Process — the gateway every order has to pass through, automated end-to-end.
CPQ Automation — Configure, Price, Quote as a single agentic workflow across all five brands. This is the use case the team took through the AI Canvas.
Develop pricing strategies that increase profitability — move from cost-plus to AI-informed dynamic pricing.
Design Automation — drafters & engineering spend on standardizable design tasks, recovered.
Product Knowledge Tribal — capture the long-tenured product knowledge before retirement-driven attrition takes it.
Live Chat — close-to-human conversational support for sales and tech, working across the five brands' product catalogs.
Find suppliers & generate RFP — automated sourcing & RFP draft, plugged directly into the platform's enterprise procurement build.
SKU Rationalization — find the long tail of low-velocity SKUs across all five brands and rationalize working capital.
Auto-schedule deliveries & labor allocation — the scheduling layer that smooths capacity across plants.
Five of the nine top-voted ideas live in Operational Excellence, and the two highest-voted are both about the quote. The room agrees — across the five BU presidents and the corporate team — that the single biggest unit of value AI can deliver at MBP Group sits in the quote-to-order corridor: every brand quotes; every brand loses orders to slow or wrong quotes; every brand can share the same template once it works.
Two votes (#3 Pricing strategy, #6 Live Chat) reach beyond pure ops — toward margin expansion and customer experience. Both are differentiators that compound once the quote engine is running. And Product Knowledge Tribal (#5) is the room's quiet acknowledgment that the bench is aging and the platform can't afford to lose the institutional memory un-captured.
Each dot is a strategic theme, placed by the value it can unlock at the platform level and the effort required to get there. Hover for detail. Quick Wins are where to start; Strategic Bets are where to plan.
If MBP leadership only acts on a handful of opportunities in the next year, these are the ones we'd build a business case around first. Each is anchored in a real idea the team named — seven of them appear inside the top-voted nine, plus an enterprise foundation that quietly powers most of the others.
A single AI-driven quote agent that ingests plan + spec input, applies BOM & design rules, computes pricing against material cost, stock, and internal profitability expectations, and returns a quote — touch-free where possible, sales-rep-assisted where needed. Built once at one BU, then templated to the other four. This is the use case the team took through the AI Canvas.
→ View AI Canvas Deep DiveThe companion to CPQ — automated take-off from drawings, plans, and customer specs, with AI extracting quantities, materials, and configurations. The "first mile" of the quote that today consumes a disproportionate amount of estimator time. Natural sibling pilot that runs alongside the CPQ canvas pick.
Move from cost-plus and tribal pricing rules to AI-informed dynamic pricing that considers material cost movement, competitive position, customer segment, and order velocity. Doesn't automate pricing decisions — equips the commercial team with the leading indicators to make them. Pairs naturally with CPQ.
Drafters & engineering across the five BUs spend significant time on standardizable design — pocket-door framing variants, stair configurations, column & pergola spec sheets. AI-assisted design generation that takes a customer requirement and produces a manufacturable spec frees the drafter for the truly custom 20%.
An AI sourcing agent that scans MBP's $95M annual spend across the five BUs' fragmented ERP environments, identifies overlapping vendors and unconsolidated categories, surfaces alternative suppliers, and drafts the RFPs. Two-to-three percent savings translates to ~$2M/year — and creates the foundation the platform's enterprise supply-chain function needs.
Across five brands sit thousands of SKUs that no longer earn their working-capital cost. An AI-powered analysis of velocity, margin, customer overlap, and engineering complexity produces a "keep / consolidate / sunset" recommendation per SKU — the kind of cross-BU cleanup the platform has wanted to do since acquisition but couldn't justify five times manually.
An AI that scores existing customers across all five brands' books and identifies the right next product to pitch — a Marwin attic-stair customer who's a strong fit for HB&G columns, a Summit dealer who hasn't yet bought SSI spiral stairs. Operationalizes the platform-level ambition for "stickiness between business units." Highest-velocity lever on top-line growth in the near term.
A natural-language agent that sits inside the existing Microsoft Copilot stack, connected to financial close (One Stream), order & inventory data per ERP, and the consolidated spend pipeline. Answers board-level questions in plain English — and forms the data backbone every other use case ultimately rides on. Foundational, but staffable inside Copilot policy.
After generating the use-case list, the team chose to take CPQ Automation through the AI Use-Case Canvas — a structured exercise that pressure-tests an idea across thirteen dimensions before resources get committed. Here's what surfaced, in the team's own words.
Built to balance momentum with foundation-laying. Now gives the team early wins and confidence — and lands the CPQ POC. Next builds the platform layer (cross-brand revenue, pricing engine, "Ask MBP" agent). Later is where the multi-year transformation lives.
All 72 ideas, preserved in the team's own language. Filter by pillar to see what came up where.