Pro Pluribus
Business systems + AI for founder-led companies
Bring me the questions you’re already asking: Where do we start? What should we buy? What should we build? Do we need to hire for this? I’m Murray Arenson, a longtime operating executive and systems builder; AI expanded what I can build. I turn those questions into a ranked plan and working solutions.
Free and confidential. You’ll get a written evaluation before you’re asked to commit to anything.
A WSI Certified AI Consultancy
Specialist suppliers, purpose-built tools, and delivery capacity behind every engagement. I bring in what the work needs and stay hands-on throughout.
Companies come to AI from different places. All three of these are the right first move.
That’s the right instinct, and the wrong place to buy tools. Start with a conversation; a free evaluation follows, showing where AI can create real value in your business, and where it won’t.
Start the conversation →Output nobody quite trusts, licenses nobody quite uses. That’s a system-design problem, not a model problem, and it’s fixable. I run the checks and guardrails in my own production AI system every day.
Get the pilot unstuck →I define the role, deliver the first wins, and write the job description your eventual hire actually needs. If it turns out you don’t need the hire, that’s cheaper to learn in month two than year two.
Talk before you post the req →The gaps worth pricing usually look ordinary. If any of these ring true, there’s value on the table:
More of these are AI-addressable than you’d guess, and a few are cheaper to fix without it. Knowing which is which is the job.
Wherever you recognize yourself above, the work moves along the same path, in pieces small enough to judge as they land. You decide how far it goes, and part of my job is telling you which problems aren’t worth solving.
See the business and the market clearly, before anything changes. Reporting that arrives in time to matter, operating visibility in one place, customer feedback synthesized. The lowest-risk place to start: it sharpens decisions without changing how the work is done.
Take the repetitive work off your team. Reconciliation and close, data moved by hand between systems, campaign and follow-up work, the report that eats half of someone’s Monday.
Change what the business does, or how it runs. End-to-end processes reworked, AI built into the systems you operate on, new offerings where the numbers support them. Earned one step at a time, not launched as a single project.
Running alongside all three: training that makes your team genuinely fluent with AI in their own workflows, from short working sessions to full programs.
Each of the three levels applies across four parts of the business: product and service, go-to-market, operations, and finance. That map is how an engagement decides what to do first, and what to leave alone. See the full framework →
I’ve been building business systems since long before anyone called them AI.
A founder-led craft whiskey producer with data sprawled across systems, workflows held together by effort, and SaaS that fit 80% of the need.
Eleven years as COO/CFO, building the operating systems along the way: CRM, inventory, shipping, cash, daily reporting. Each at the smallest tool that solved the problem.
A company operable beyond the founder’s bandwidth, with several paid subscriptions replaced by purpose-built tools.
Government meetings are public but practically opaque: hours of video, dense agendas, votes nobody tracks.
Built an AI system that turns the raw record into published analysis and scoring, with the checks and testing that make its output trustworthy enough to sell.
100+ meetings analyzed across two governments. A multi-year scored dataset. Paying subscribers. Run by one operator.
Also on the record: CFO of a Denver entrepreneurship ecosystem · President of the Denver chapter of Keiretsu Forum, the world’s largest angel investor network · mentor and advisor to early-stage companies, from organic wine (Wander + Ivy) to enterprise analytics (PieAX).
AI should start with the business, not the technology: where work gets stuck, where hours vanish without creating value, where better information would change a decision, where it might even change what you sell. Most companies start with the impressive demo instead. The practical wins usually come first: automate the work between the judgments, and be deliberate about which judgments you hand over.
I’ve built systems both ways: around a person running the process, and around AI running it. Knowing the difference is most of the job.
Field notes from building, not commentary from the sidelines.
Operational understanding is finally becoming buildable. Seven systems from eleven years inside one company, and why the value was never the technology.
Six lessons from building and operating a production AI system: the judgment boundary, silent failure, and why reliable AI is an operations problem.
No pressure and no pitch deck, a practical conversation about where you stand and what’s worth doing. Everything after it is earned, one step at a time, and you’ll get a written evaluation before you’re asked to commit to anything.
Whatever you’re working on: a stalled pilot, a role you’re about to post, a process nobody can explain, a subscription you resent paying for. Straight answers, no homework.
Book a conversation →