I build the systems that turn AI into delivered outcomes.
Enterprise AI does not stall on model selection. It stalls on how the work gets discovered, priced, governed, and delivered. I build the machinery for all four, then run it live on Fortune 500 programs.
- NowPrincipal, phData
- OwnsMDM capability
- FocusAI-accelerated delivery
- BasedTexas, remote
Design once. Deploy to any platform.
The core decision behind the delivery systems I build: keep the design vendor-neutral, and put the platform difference in a translator. The client's model outlives their platform choice, and the same design deploys to eight targets.
Five systems, one loop.
A delivery model that changes what a small team can ship, two skill suites that do the specialist work, a value estimator that proves the case to finance, and a reviewer pointed at the contract itself.
AI delivery model
Context repo, data repo, an end-to-end AI Jira workflow, and live status wired into a single loop that runs inside the client's own network. Live today on a Fortune 500 program, where one consultant carried a design scope normally staffed as a team.
Read →- Data repo raw and unprocessed
- Context repo the load-bearing layer
- PM layer end-to-end AI in Jira
- Live status feeds back to context
MDM Skill Suite
A complete AI delivery system for master data, covering a 50-step lifecycle and deploying one canonical design to eight platforms.
- Skills267
- Platforms8
- Gates332
- Version8.7.4
DG Skill Suite
A governance practice packaged as six governed phases over a substrate of signed decision logs and audit packs.
- Skills38
- Gov. tools45
- Vertical packs13
- Version2.8.0
AI Value Estimator
A business value assessment built to survive a CFO. Four-pillar TEI, three-point ranges, and every input traced to its source.
- FrameworkForrester TEI
- Simulation4,000 runs
- Discovery Qs32
- Version1.0
Contracting
AI pointed at the document that decides whether delivery makes money. Two lenses, three passes, a redlined output and a verdict.
- Models5
- Practices6
- OutputTracked redline
- Version2.6
Discover the value. Align the solution. Deliver it.
Most enterprise AI does not fail on the technology. It fails on the decisions made before anyone writes code: which problems are worth solving, what proof is required, and who owns the outcome. That sequence is the work.
Discover the value
Start with pain, not with AI. Get the people who actually run the business in a room and go wide on what is slow, error-prone, or keeping them up at night, then converge on themes. Leading with the technology produces a hammer looking for a nail, and the ideas come out narrower than what the room already knows.
from scattered ambition → a shared thesisAlign the solution
Rank candidates on business value against speed to value, using the dimensions that actually decide it: technical risk, delivery risk, total cost of ownership, and whether the organization is ready for the outcome it is asking for. Then build a value case finance can audit, with ranges rather than one confident number.
from a list of ideas → a funded first moveDeliver, then industrialize
Ship one real outcome in weeks with a lean paired team, one business-first and one engineering-first, sitting with the people whose workflow is changing. Prove the thesis early, then harden the win into a platform so the second use case costs less than the first and the tenth is routine.
from a hero project → how the enterprise runsWhere the range came from.
Enterprise transformation, platform depth, and the commercial side of consulting. Each stop added something the next one needed.