Ray Malone

Team members and stakeholders often arrive at distinct explanations for why something is happening, what it means, or what may happen next. Collaboration and decision-making can be derailed by collaborators invested exclusively in their own conclusions. Analysis of Competing Hypotheses (ACH) is a powerful analytical and communication tool. It allows a team to evaluate all the viable hypotheses relative to the evidence that validates or invalidates them—how likely each hypothesis is to be "true." Kirby Plessas has created a valuable tool to introduce ACH and facilitate its application to investigative questions. This is a fantastic example of how adopting Generative AI helps analysts enhance their creativity and use these tools to structure and guide their analysis. #analysis #strategy #genai #competitiveintelligence

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Are you concerned about what to tell your kids about AI's impact on their career? If so this has some great tips!

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Is AI really as easy as everyone says? 🤖 At Community Week Online 2026, Suzi Nelson, Customer Intelligence & Activation Strategist at TEKsystems, will share lessons from building AI-assisted workflows for community operations. In “The Myth of Easy AI: Lessons from Building Agentic Workflows in Community Ops,” Suzi will explore what it actually takes to make AI useful in practice, from defining decision criteria and restructuring intake processes to reducing ambiguity and building the human oversight needed to make these systems trustworthy. ✨ Explore the agenda and save your spot: https://lnkd.in/d5BuzQu8

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If you’re in/near Atlanta, don’t miss the Agentic AI meetup by AICamp this Wednesday, Sep 18 at Improving (Alpharetta, GA). If you’re building with GenAI/LLMs or exploring agent patterns, expect deep-dive talks, hands-on code labs, and plenty of hallway learning. 💻 My colleague Michael Hall, Principal Developer Evangelist, will present “Device to Datacenter: How Mobile & Cloud AI Coexist on Arm.” He’ll cover when to run inference on-device vs. in the cloud (or hybrid), and how to design for portability and performance across the stack. https://okt.to/UbRIA4

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I’ve been told I have some Better Off Dead–level opinions. You might be better off dashboard-less. Better off debt-less. Definitely not better off pretending AI can resurrect a shaky foundation. Sometimes data strategy feels like: "And: new tool" "Followed by BI dashboard" "And to drink: Ta-da! AI" It’s confidently delivered. But are we actually serving value? I’ll be hanging out with the Super Data Brothers (Ryan and Eric) on March 5th talking about what it really takes to deliver outcomes. If none of this makes sense, just join the podcast... and maybe watch the 80s classic Better Off Dead. No paperboys were harmed in the making of these opinions. And no one owes anyone two dollars.

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Since Opus 4.6 launched on Feb 5, the team here at Ways2Well have written: 1. New business intelligence system - Replaced Power BI 2. New referral system - Replaced Village Referrals 3. New Survey/NPS system - Replaced Survey Monkey 4. New SMS Prescription Refill System 5. New system to use AI to capture ideas and refine them to AI buildable specs 6. Pre Encounter Chart Preparation Automation And we did all of that while in the middle of launching a massive overhaul of the Ways2Well customer experience app and backend. We did all of this while decreasing the size of the dev team. ***Our primary coding language is now English*** Feb 5 will be looked back on by archeologists as significant as the Trinity test, Magna Carta, Internet, Bitcoin, etc. The punch line = I feel like a CTO like me can finally deliver on all the hopes and dreams of a technology aggressive company like Ways2Well and ReviveRx. I wrote a new AI Agile Manifesto that is still a work in progress. Would love your comments: An AI-First Engineering Manifesto (for building fast, sane software) We build around business capabilities, not repositories. Ownership follows outcomes. Engineers own features end to end — frontend, backend, data. No handoffs. No silos. AI isn’t a side tool. It’s how we build. We default to an opinionated stack. Consistency beats novelty. Deviation is intentional and documented. Product and business teams are AI-literate. Specs are inputs to AI systems. Clear problems. Clear acceptance criteria. Clear prompts. Engineers are not translators of vague intent. We optimize for developer velocity. Choose the simplest approach that moves us faster. Security, reliability, and compliance are non-negotiable. Complexity must earn its place. Sustainable velocity beats hero output. Success means: – Shipping faster – Fewer regressions – Lower cognitive load Target: 2-5× velocity. Zero burnout. Tool freedom. Outcome accountability. Use what helps you ship. We measure results, not preferences. Prompt → Prototype → Iterate. Early builds are disposable. Learning is not. Monolith or microservices? Both are fine. We decompose only when it makes us faster, more reliable, and better for customers. Everything else is noise.

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🔍 The new Search experience in Copilot adds AI context on top of classic search, without getting in the way. The two big improvements for me are seeing _why_ certain results are relevant (and across sources like SharePoint, Emails, conversations, etc) but the big one, is the ability to get an "AI Overview" summary about the file BEFORE you open it and start leafing through. It's a thoughtful upgrade that improves search and I'm very glad it's "in addition" to search and not "in replacement of" 😅 #Microsoft365 #Copilot Microsoft 365 Microsoft Copilot

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Maintaining software is like maintaining fences. The fence on the left is 10 years old, the fence on the right is less than one year. Both are made of cedar, but the one on the left is regularly power-washed and resealed every 9-15 months. Don't neglect your software maintenance. Although the debt may be hidden, it is nevertheless there, waiting to reveal itself at a most inopportune time. Fold maintenance into your sprints so it doesn't have to be a chore.

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