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Two cases this week, both about companies that couldn't see their own numbers until AI cleaned up the plumbing underneath. A PE-backed precision optics and photonics manufacturer that grew by acquisition was running about 15 separate SAP instances and could only pull an enterprise-wide view once a quarter. Ideas2IT unified them into one governed platform, freeing 200-plus analyst hours a quarter and moving reporting from quarterly to weekly. And a PE-backed distributor was burning three to four days of finance time every month hand-assembling its board pack from three disconnected systems. Pluto AI got that down to four hours, with real-time alerts when the numbers drift from plan.

New this past week: cases from BlueLabel, Ideas2IT, and Dewey Labs cleared for publication, with dozens more coming into the queue behind them. BlueLabel alone added three, an insurer cutting claims adjudication from 45 minutes to 12, a telecom saving $11K a month on a single dispatch task, and a manufacturer cutting expert-lookup time 75%. Ideas2IT brought the manufacturer consolidation featured above plus a jewelry-CAD tool that cut modeling time 95%, and Dewey Labs published a solo OB-GYN practice now answering 4,000 patient questions a month.

If you want to stand out in a noisy market, the best way to do it is through the proof of your work, and adding yours takes nothing on your end: we pull the case from your own site, reformat it to the AI Use Case Library's standard, send it back for your approval, publish it, and fill in any gaps after. Reply and point me to your site, and we'll take it from there.

Key takeaways from this week's cases:

One thing I'm seeing more of: readers clicking straight through to talk to the expert behind a case. That's the point. If one of the two below fits what you're working on, hit the link and I'll make the intro.

Expert: Murali Vivekanandan, President at Ideas2IT

Industry: Manufacturing & Industrial, PE-owned

Build type: AI-Accelerated Custom Software + Data Synthesis & Reporting + Process Automation (RPA + AI)

Result: 200+ analyst hours freed per quarter · ~80% of data-accuracy problems eliminated · Quarterly to weekly reporting

Implementation time: 6–12 months

Cost: Not disclosed

Full case: Read it (and connect with Murali) →

This manufacturing company grew the way a lot of industrial companies do, by acquisition, and every business it bought arrived with its own SAP instance. Roughly 15 of them ran independently, alongside a pile of Microsoft systems and hundreds of PL/SQL stored procedures, each producing its own numbers in isolation. The result was a company that couldn't see itself. Any enterprise-wide view had to be assembled by hand, so consolidated reporting happened once a quarter and was months old by the time leadership read it. Procurement and Sales had stopped waiting on central analytics and were funding their own BI tools. The board wanted to move on AI and had the budget for it, but with no single source of truth, every initiative stalled before it started.

Ideas2IT took over the reporting operation from the California team in six weeks, then rebuilt the whole estate on one governed platform, SAP Datasphere with BW, unifying the 15 SAP instances and the surrounding Microsoft systems behind a single semantic layer the entire company could read from. More than 50 legacy Business Objects reports collapsed into Power BI with role-based access and row-level security, and one-off datasets gave way to reusable, certified data products, including a purchasing-spend cube that gave procurement cross-enterprise visibility for the first time.

Here's where the AI actually shows up. Ideas2IT's agentic tool, MigratiX, profiled every source system, mapped schema and business logic with confidence scoring, generated the transformation scripts from an internal knowledge base, and ran dry runs against real data before any cutover. That took roughly 80% of the manual effort out of a migration usually done by hand. Every dataset came out governed, documented, and certified, the step that separates a reporting platform from one an AI model can actually trust.

The numbers: 200-plus analyst hours freed per quarter, about 80% of data-accuracy problems eliminated, and reporting cadence up from quarterly to weekly. The bigger result is the foundation. Procurement spend optimization is now scoped as the first AI use case on top of the new platform, the AI work the board wanted all along and couldn't start.

Best fit for: PE-backed manufacturers and multi-site companies that grew through acquisition and now run a sprawl of disconnected systems with no enterprise view, especially where consolidated reporting still takes weeks, business units have started buying their own BI tools, and there's appetite for AI but no data foundation under it yet.

→ Want to talk to Murali? Reply to this email and I'll make the intro. Or request one directly on the case study page.

Expert: Yayati Tanwar, Founder at Pluto AI

Industry: Manufacturing & Industrial, PE-owned

Build type: Data Synthesis & Reporting

Result: Board pack: 4 days → 4 hours · Real-time visibility between meetings · Live in 6 weeks

Implementation time: 4–8 weeks

Cost: $25K – $100K

Model: Custom / proprietary

Full case: Read it (and connect with Yayati) →

A PE-backed distributor was spending three to four days every month producing its board pack by hand, pulling data out of three separate systems, the ERP, the CRM, and an operations platform, and reconciling them into something the board could read. Between reporting cycles the sponsor had no visibility into how the business was actually performing, so leadership was making decisions on numbers that were weeks old. When the sponsor asked for a mid-month update, that meant another two days of manual extraction. The trigger was a board meeting where actuals came in materially off what the monthly pack had shown, and nobody had seen it coming.

Pluto AI wired the three systems into a single reporting layer that runs on the company's existing infrastructure. It continuously ingests data from the ERP, CRM, and ops platform, normalizes it against one schema, and auto-populates the board pack the sponsor and directors already use. KPI alerts fire when revenue, margin, fulfillment, or headcount costs drift from plan, routed to the right people before anyone has to ask. The month-end pack that took four days now takes four hours, and the sponsor gets a live view in between.

The hardest part was data quality. Fields meant to match across the ERP and CRM didn't, because different teams had entered data their own way for years, and the new system exposed inconsistencies nobody knew were there. Pluto's takeaway, worth borrowing: lead with a dedicated data-quality audit, and bring the sponsor into defining the metrics early, not just the CFO. They locked the thresholds that mattered, revenue, margin, fulfillment rate, and headcount, with the operating partner in the room. The whole engagement ran fixed-scope over six weeks, between $25K and $100K, with one full month-end cycle run in parallel against the old manual process before handoff.

Best fit for: PE-backed distribution, logistics, or light-manufacturing companies in the 200–1,000 employee range where the finance function is still largely manual and the sponsor is frustrated by reporting lag between board meetings, particularly where ERP, CRM, and ops data all live in separate systems nobody has connected yet. Less useful if you already run a modern data warehouse or a dedicated FP&A team with BI tooling.

→ Want to talk to Yayati? Reply to this email and I'll make the intro. Or request one directly on the case study page.

The most-read cases in the AI Use Case Library this week. Real problems, real results, each one a click from the person who solved it.

💬 Why we build Pluris as an open market. A note landed in my inbox this morning — an expert in the network got a big lead off one of our podcast episodes — and it captured exactly the bet we're making: an economy around this network, where the best applied AI experts get found on the strength of their work. → Read it on LinkedIn

Working on something where one of these patterns might fit? Reply to this email and I'll either make the intro to the right expert directly, or open an Expert Review and come back with 4-5 expert perspectives and pricing in a few days. Both are free, and I read every reply.

— Stu

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