Robert Brooks

Common theme this open payments submission. More records, which are not locatable on the VPL, NPPL, NPPEs are finding there way into being accepted in CMS. The challenge with that is there is often no rhyme or reason so you have two options. 1) Don't load items that appear by all signs to be not reportable but have enough to make you wonder. 2) Load anything that may hit and submit more "junk" most will not find its way in and leave a lot more clean up. #2 is obviously the answer but what a headache it is to do. Happy reporting yall!

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$118M siphoned through false billing. This wasn’t just a rogue doctor—it was a vendor fraud operation hiding in plain sight. Dr. Jorge Zamora-Quezada spent two decades falsifying diagnoses, administering unnecessary treatments, and billing Medicare, Medicaid, and private insurers. Patients suffered strokes, liver damage—some were never even sick. How did it go undetected? Blind trust in provider billing. What could’ve caught it? AI-powered vendor oversight. At Accord, we flag what traditional audits miss: 1. Billing patterns with the fingerprints of fraud 2. High-margin vendors gaming approvals and thresholds 3. Repeat outliers masked by volume and complexity We’ve uncovered $10M in vendor risk in just two years of procurement data. Dozens of vendors exploiting blind spots—right under internal audit’s nose. We’ve seen patterns like this across multiple organisations. If you're curious what this might look like for you, message me. Happy to share insights.

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The last two weeks marked a *major shift* in the NOW of work. - 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱: Claude Co-Work and the newest Codex agents moved from “assist” to “execute.” These systems can modify entire codebases, run full test suites, draft financial summaries, and reconcile data across tools. This isn’t autocomplete. It’s task ownership. It's the kind of work engineers, analysts, and associates bill for today. It's a lot of the work I do today, maybe you too! - 𝗣𝘂𝗯𝗹𝗶𝗰 𝗺𝗮𝗿𝗸𝗲𝘁𝘀: Public software is ~40% off 12-month highs. Investors are stress-testing companies like Salesforce, ServiceNow, Atlassian, and Adobe whose growth depends on adding seats. If AI trims even 10–20% of knowledge work, seat expansion slows. Services-heavy firms like Accenture feel it if automation compresses billable hours. This isn’t extinction. It’s margin compression. - 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗺𝗮𝗿𝗸𝗲𝘁𝘀: Capital is rotating toward AI-enabled services and outcome pricing. The pitch isn’t “more licenses per account.” It’s “2–3x throughput per employee,” 10–18% cycle-time reductions, and measurable revenue-per-rep or revenue-per-employee lift. Pricing is increasingly tied to EBIT impact — not seat count. That’s a structural shift in value capture and away from seat pricing b/c, well, there are less seats but also I (enterprise buyer) want to pay for value + AI services are basically promising margin improvement. - 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗹𝗲𝗮𝗱𝗲𝗿𝘀: If one AI agent can handle first-pass code review, draft client reports, or generate analysis, the org chart changes. The real question becomes: what jobs must humans uniquely do to build, sell, and support the product. And which layers are leverage? - 𝗔𝗹𝗹 𝗼𝗳 𝘂𝘀: Knowledge work is being repriced. “Leverage per employee” means one person, equipped with AI, can produce what previously required multiple people: more output, faster cycle times, higher revenue per head. But leverage cuts both ways. Expectations rise. Accountability rises. The premium shifts from doing the task to directing, auditing, + owning the outcome. Here's what surprised me most in spending time with super impressive and thoughtful professionals this week that are managing this transition for major enterprises: 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝘁𝗼 𝗮𝗰𝗵𝗶𝗲𝘃𝗲 𝘁𝗵𝗲𝘀𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗶𝘀𝗻'𝘁 𝗽𝗿𝗶𝗺𝗮𝗿𝗶𝗹𝘆 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹. 𝗜𝘁'𝘀 𝗵𝘂𝗺𝗮𝗻. 𝗔𝗻𝗱 𝗶𝘁 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗰𝗼𝘂𝗿𝗮𝗴𝗲𝗼𝘂𝘀𝗹𝘆 𝗮𝗻𝗱 𝘄𝗲𝗹𝗹. Getting to real numbers in the form of more output, faster cycle times, and higher revenue per head requires courageous change management. 𝗧𝗵𝗲 𝘁𝗼𝗼𝗹𝘀 𝗰𝗿𝗼𝘀𝘀𝗲𝗱 𝘁𝗵𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱. 𝗡𝗼𝘄 𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝗿 𝘄𝗼𝗿𝗸: 𝗿𝗲𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗶𝗻𝗴 𝗮𝗿𝗼𝘂𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲. 💭 Would love to know how people are thinking about the human/change management part of this puzzle.

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Why do EMR implementations take 9 months? Because the system requires humans to learn its complexity. Traditional EMR implementations drag on for 6-9 months. Sometimes longer. Agencies delay go-lives, extend training windows, run parallel systems. Staff spend weeks learning where to click, what fields to fill, which workarounds to use. We're doing implementations in 60 days. Not because we cut corners. Because the system is designed differently. When AI handles documentation, coding, compliance, and billing automatically, there's less for staff to learn. When workflows adapt to how clinicians actually work, there's less to configure. When the system does the heavy lifting, training becomes orientation, not education. Implementation timelines reveal something important about a system. If it takes 9 months to learn, the complexity lives in the wrong place. Your technology should learn your agency. Not the other way around.

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The best way to minimize work is to stay on track and course correct quickly. Plus, learn why estimation techniques haven't panned out for our industry. https://lnkd.in/eXE4zx55 #SoftwareDelivery #SoftwareTeam #dotNETdeveloper #ClearMeasureWay

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Sure the dbt news is cool but this is cooler. We just released AGENTIC AUTOMATIONS in Orchestra!!!! 🚀 To say we are excited about this one is an understatement. With Agentic Automations what would have required dozens of different tools is now possible from one. ☑️ Run Agentic Workflows in Orchestra using Open AI, Claude. --> NO NEED for a separate orchestrator ☑️ Use AI to assess "Blast Radius" (impact of any code change to downstream assets). Just run it as a CI/CD check. New Hack UNLOCKED 🤯🤯 ☑️ Use AI to assess code/dbt quality review. Either periodically or as part of CI/CD. New Hack UNLOCKED. No more reviewing juniors' LIMIT 1 or SELECT * -> just automate it with Orchestra 😎 ☑️ ANOMALY DETECTION! One of our most-requested features. Leverage the Orchestra Metadata API to easily identify anomalies and flag these to you --> Improved trust, faster error-catching, lower costs 🎯 ☑️ Enrich data using AI to improve Data Quality ⭐ ☑️ Send customised reports to slack that are nicely formatted just using SQL and a prompt --> massively saves time and reduces friction / cost for people that struggle to log in to BI tools 📊 Agentic Automations also power code development. We are reimagining how agentic workflows get written. You can use Orchestra's AI to generate ENTIRE ORCHESTRATION PIPELINES as it's just .yml/a config file. ☑️ Build Pipelines using AI. This will become more and more important ☑️ Use AI to find, diagnose, and fix errors all in one single flow ☑️ Automatically train your AI on the entire corpus of metadata without needing to stand up a lineage, quality, observability, cataloging tool etc. oh AND an MCP Server. Yeah this is just beginning. Anyone at Coalesce come find me. We can't wait to see what you build. Find out more --> Follow the Orchestra page to stay in the loop. Find out more now --> check-out the link the comments below. #dataengineering #agenticworkflows #aiAgents

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Coding agents are insanely good at getting the first 80% right. But for the last 20%? Better models won't fix it. Here's what I mean: Every time you start a new chat session with an AI coding agent, it behaves like a day-1 employee. It's smart, fast, and eager. But it doesn't know why your codebase looks the way it does. It doesn't understand the trade-offs your team made six months ago. It doesn't know which pattern was intentional and which one is tech debt. So it produces code that's technically correct but contextually wrong. And you spend the next hour fixing what was supposed to save you time. The real unlock for AI autonomy isn't a smarter model. It's product understanding. An onboarded engineer doesn't just read the code. They understand the product, the decisions behind it, and the constraints that shaped it. That's the gap no foundation model can close on its own. That's exactly why we're building Sequa.AI . We're building the product knowledge layer that gives AI agents the context they need to go from "helpful assistant" to truly autonomous engineer. Not just for coding agents, but for any AI agent that needs to deeply understand a product to do its job right. 🔧 If you're feeling the pain of the last mile with AI coding tools or struggle with many different CLAUDE.md files that are out of sync, I'd love to hear your experience. #AI #CodingAgents #DevTools #Startups #SequaAI #ProductKnowledge #AIAgents

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