JP Galido · Business AI Architect · San Diego
Most enterprise AI doesn’t fail in the demo. It fails later — when people ask how it works, who approved it, what data it touched, and whether anyone can trust the output. That’s the gap I work in.
San Diego · Replies personally
The work
Applied AI for enterprise work — revenue, service businesses, SAP transformation, delivery governance, decision support. Every build starts with one question: what’s the safest, simplest mechanism that can do this job reliably?
Why miniapps…
Agents are powerful. But an exposed agent is not a product. A product needs boundaries — an interface, a workflow, permissions, approvals, exception handling, and a clear record of what happened.
Foundation models
the electricity
Agents
the motor
Miniapps
the machine
That is why I build miniapps. A miniapp gives AI a safe place to work: it defines the task, limits the blast radius, captures the evidence, and shows the human where judgment is still required.
The AI handles variation. The miniapp provides structure. The business keeps control. This is the pattern I trust for enterprise AI — not bigger demos, but smaller, governed systems that do one important job well.
Agents aren’t the product. They’re a component inside it — and the miniapp is the product the enterprise actually runs.
Who I am
More than two decades inside SAP programs, enterprise transformation, compliance-sensitive environments, steering committees, delivery teams, and business operations. That experience shaped how I think about AI.
Organizations don’t need more impressive demos. They need systems that can survive the questions that come after the demo:
I use AI where it’s justified, deterministic logic where it’s safer, and human approval where consequences matter. The standard is simple: Deployable. Governed. Honest.
Today, an Enterprise Solution Architect and SAP CX Capability Director. On my own time I run RevTXM, a revenue-management SaaS in production, and I’m the founder of TXM Institute, where I publish the method free. The prototypes that earned a no taught me as much — a prototype that proves something shouldn’t be built is a cheap success, not an expensive failure.
It runs in production, not in a slide deck. If it can’t survive a real deploy, it isn’t finished.
Approval gates, permission limits, audit trails. Autonomy is granted in inches and logged in full.
No claim ships unless it’s true. That rule governs my products, my resume, and this page.
The framework behind the work
TXM treats transformation as an execution system — decisions, constraints, roles, incentives, process, technology, adoption, governance, and the human resistance that appears when change becomes real.
A way to move from intent to execution with evidence, ownership, governance, and less theater.
The structured knowledge base behind the approach: business architecture, governance, adoption, delivery discipline, and enterprise systems.
The role that connects business intent, process, data, application logic, governance, risk, and AI capability.
The governed way to turn AI-enabled work into something usable, bounded, auditable, and real.
Framework · Authored by JP Galido
TXMBOK is the structured body of knowledge behind my approach to transformation execution. It brings business architecture, enterprise systems, governance, adoption, decision quality, and delivery discipline into one execution-centered framework.
View TXMBOK ↗
Fiction · Authored by JP Galido
A novel about the person who has to make AI useful, governed, trusted, and real inside an organization — when perfect systems create perfect chaos.
View the book ↗Why this matters now
AI lowers the cost of creating outputs.
AI increases the volume of prototypes.
AI exposes weak execution systems.
AI makes judgment the bottleneck.
The goal is not to make AI sound impressive. The goal is to make enterprise work more navigable, more honest, and more executable.
Design & brand
The universe here isn’t decoration — it’s the argument, rendered. Enterprise AI is a vast dark field where most things fail. You don’t cross it by guessing; you cross it by fixed stars. So the brand is built from the same idea as the work: a few things that don’t move, and careful motion around them.
It’s a real design system: tokenized colors, system fonts, a monospace voice for labels, and
motion that always respects prefers-reduced-motion.
Every color is checked for WCAG AA contrast — the ratios live in the stylesheet, because honesty
extends to the pixels too.
This galaxy language recurs wherever I design — a visual second-brain rendered as a galaxy, product surfaces built on the same dark, navigable field. It’s my signature because it’s my thesis: vast and dark on the outside, fixed and navigable once you know the stars. Drama from restraint, never from neon — the same discipline I hold in the products.
Public diary
I keep a public diary because trust does not come from pretending the evidence chain is perfect. It comes from building it in public.
June 12, 2026
I built audit trails for my agents and none for myself.
Unassembled is not untrue. But unassembled is not good enough.
Apply your own standards to yourself first, not last.
Structure is not overhead. It’s the kindness you owe the next person who has to understand your work — including the future you.
Read the entry →I consolidated the whole portfolio into four GitHub organizations and wrote a single portfolio map — one document that lets any fresh collaborator, human or AI, orient in one read. It felt like administrative work until I watched it function: a new session with no memory of anything I’d built found the right repository, the right doctrine, and the right next step, unaided.
Discipline is not a personality trait; it’s an architecture. If the gate isn’t in the path, assume it gets skipped.
Read the entry →Uncomfortable discovery: two recent designs were built without loading my own canonical rules. I wrote those rules. I enforce them on everything else. And I still drifted the moment no gate stood in my path. I spent today retrofitting both into the portfolio governance, and it cost more than building them right would have.
When a structure gets too large to hold in your head, change the medium before you change the structure.
Read the entry →I scaffolded Cortex today — a visual second brain that renders my portfolio doctrine as a galaxy. Products as systems, principles as stars, dependencies as orbits. I built it because text files were failing me: I could no longer see how the products related, only read about it. The first render showed me what the documents never did — which parts of the portfolio are dense, and which are drifting alone at the edge.
The runbook cost twenty extra minutes. The next deploy will cost an evening less.
Read the entry →First production deploy of Rapid Impact Analyzer to SAP BTP Cloud Foundry, migrated off Render. The deploy fought back the whole way: certificate chains that wouldn’t verify, a buildpack stuck in a rebuild loop, database permissions that failed only in production. I fixed each one and wrote each fix down as I went, and by the end the notes had become a reusable runbook — the same one I now publish, in full, in the library below.
The self-audit is the current entry above ↑ — the rest of the trail is shipping, structure, control, and runbook notes.
These are quick notes. The longer write-ups — lessons turned into something you can use — live in Field Notes ↗
Public library
Methods you can use without asking permission.
The library is free because the method is more useful when more people use it. Practical tools for deciding when AI is justified, how to label outputs, how to design approval gates, how to write with fact discipline, and how to keep interfaces honest.
Use the cheapest mechanism that is reliably correct.
Apply todayPick one “AI feature” you own and demote it if a rule, template, or calculator can do it reliably.
Before reaching for AI, walk the ladder: rule → router → template → calculator → validator → AI → AI agent → human. Each step up costs more and runs slower than the one below it, and from validator through agent each step is harder to predict and to audit. The human sits at the top not because people are unpredictable, but because someone must own the consequences.
Use the cheapest mechanism that is reliably correct; escalate only when the rung below demonstrably can’t do the job — and “demonstrably” means a failing test set, not a hunch. In my own products, most “AI features” turned out to be rules, templates, or calculators wearing a costume — and they got faster, cheaper, and more auditable the day I demoted them.
Every AI-touched output must say how it should be trusted.
Apply todayLabel every number on one dashboard. The values you can’t label are the ones to worry about.
Every AI-touched output in my products carries exactly one of four labels: Verified (traced to a source system), Calculated (deterministic math you can recompute), AI-Suggested (a model produced it; no human has accepted it), or Approved (a named human accepted it). There is never a fifth label and never an unlabeled value. Users always know what they can trust; auditors always know what to check. The renderer refuses any figure without a provenance — it’s a type, not a guideline.
A button that lies is a bug.
Apply todayClick every control on your main screen and verify it does exactly what the label promises.
A button that lies is a bug — same severity as a crash. If a control says “Export,” it exports. If a feature is a preview, the screen says so. If the data is sample data, it’s labeled “sample — not your result.” No disabled buttons posing as features, no progress bars animating over nothing, no “AI-powered” badge on a rule engine. Users forgive missing features. They don’t forgive being deceived — and in regulated industries, neither do auditors.
Build in CI, deploy an image, and keep the error log.
Apply todayBuild in CI, deploy an image, and document the first failure.
The pipeline that finally worked for shipping a Node.js app to SAP BTP Cloud Foundry: GitHub Actions builds a multi-stage Docker image on every push to main, publishes it to GitHub Container Registry, and cf push pulls the image — no local Docker, ever.
rejectUnauthorized:false — that passes the demo and fails the audit.CREATE EXTENSION; run provisioning separately.Stuck on a step? Email me — I answer these personally.
No score without evidence.
Focus on the two lowest constraints. Ignore the rest.
Apply todayScore last quarter’s pipeline with evidence beside every number. Let the two lowest scores define the next 90 days.
Ninety minutes of evidence-scored review is enough to find the two constraints that matter. Score your revenue engine across six dimensions — pipeline quality, conversion, pricing, offer clarity, sales motion, retention — on a 1–5 scale, with one non-negotiable rule: no score without evidence. A 4 you can’t substantiate is a 2. Then let the two lowest scores define your next ninety days, and deliberately ignore the rest — spreading effort across all six is how diagnostics turn into wallpaper.
Want the scoring worksheet? Email me and I’ll send it complete and free.
Tag every serious claim.
Apply todayTag every serious claim in your next status report and leave the tags in.
Every claim in a serious document gets one of four tags: OBSERVED (you saw it or measured it), INFERRED (it follows from things you observed), ASSUMPTION (you’re treating it as true in order to proceed), UNKNOWN (you don’t know, and you’re saying so out loud). The tags do their best work before anyone else reads the document: the moment you must choose one, you discover how much of what you “know” is actually assumption. I use this on architecture decisions, status reports, and on this website.
Do it yourself · Free
This free tool helps you decide where a task belongs on the cost ladder. It doesn’t use AI, it doesn’t send your data anywhere, and it runs entirely in your browser. Pick one task you’re thinking of putting AI on — it helps you decide whether it belongs as a rule, router, template, calculator, validator, AI assist, agent, or human decision. It’s the first question I ask on almost every engagement.
The last word
— between the AI demo everyone liked and the deployment nobody wants to sign off on, write to me. I read everything personally. Sometimes the answer is a deep dive, sometimes a build, sometimes a role conversation — and sometimes it’s simply “you don’t need me for this.” That answer is part of the standard too.
Write to me