Fractional AI Adoption Strategist & Discovery Architect
Your AI initiative won't fail
because of the technology.
It will fail — like 80–95% of enterprise AI projects do — because nobody mapped your processes, interviewed your people, or documented what you actually have before the vendor showed up. That foundational work is exactly what I do, for healthcare organizations who can't afford to get it wrong.
of enterprise AI projects
fail to deliver measurable value
most AI initiatives in 2025
abandoned enterprise initiative
on people & process — not tech
failure rate specifically
Sources: RAND Corp 2024 · MIT NANDA 2025 · BCG 2025
S&P Global 2025 · Pertama Partners 2026
The Problem
Why Most Healthcare AI Projects Fail Before They Start
MIT, RAND, McKinsey, and BCG all point to the same root causes. The failure isn't the AI. It's everything that should have been done before the AI.
No Process Mapping Before Deployment
Organizations deploy AI into workflows they've never formally documented. The result is automating the wrong things — or automating chaos. You can't improve what you haven't mapped.
Poor Problem Definition
Healthcare leaders know they need AI. But Gartner's research shows 85% of AI models fail due to poor data quality and unclear problem framing — before a single model is trained.
Skipping Stakeholder Discovery
Clinical staff, IT teams, and administrators all have different pain points, data sources, and workflows. Without structured interviews and requirements gathering, no AI implementation can reconcile them.
Successful AI resource allocation follows a specific pattern: 10% algorithms, 20% technology and data infrastructure, and 70% people and processes. Organizations that invert this ratio — investing primarily in technology while neglecting process design and change management — consistently fail.
SOURCE: MIT / INDUSTRY BEST PRACTICE 2025 · REPORTED IN TALYX ENTERPRISE AI ANALYSISHealthcare Focus
Where AI Can Actually Help Healthcare Organizations
The use cases below are where Discovery, process mapping, and governance documentation deliver the most direct ROI — and where the absence of that foundational work causes the most expensive failures.
AI-Assisted Prior Authorization
The highest-volume administrative burden in healthcare. Discovery maps current auth workflows, identifies payer-specific rules, and documents the integration specs EHR-connected AI tools need to function correctly.
Ambient Documentation & AI Scribes
53% of healthcare AI implementations in clinical documentation are considered successful — the highest success rate in the sector. Success depends on workflow mapping and staff adoption documentation.
AI Scheduling & No-Show Reduction
No-show rates cost hospitals millions annually. Discovery maps scheduling workflows, EHR integration points, and patient communication pathways — the foundation AI scheduling tools require to deliver the projected 20–40% no-show reduction.
Predictive Risk Stratification
CMS penalty exposure for readmissions is measurable and urgent. AI risk stratification requires clean data pipelines and governance frameworks — exactly what Discovery and blueprint documentation provides.
EHR/EMR AI Readiness
Healthcare data is fragmented across EHRs, labs, and imaging systems. AI-ready data requires documented integration specs, data dictionaries, and governance frameworks before any model touches it.
AI Governance & Oversight Frameworks
FDA, CMS, HIPAA, SOC 2, and NIST all create compliance obligations for clinical AI. I build the policy documentation, audit frameworks, and oversight protocols that make your AI initiatives defensible to regulators.
Services
Measure Twice, Cut Once.
That's AI Strategy Done Right.
I deliver the blueprint your implementation team needs to actually succeed — because the 70% of AI success that depends on people and process requires the kind of rigorous, professional systems and processes Discovery and comprehensive Requirements Gathering work that most organizations skip entirely.
AI Readiness Discovery & As-Is Mapping
A comprehensive examination of your organization's current state — every department, every workflow, every data system, and every pain point — documented and illustrated before a single AI vendor is engaged.
- Department-by-department IT & process discovery
- Business use case identification and documentation
- Data flow mapping and system dependency analysis
- SME & stakeholder interview summaries
- UML diagrams, process flows, and runbooks
- AI opportunity inventory (where AI can actually help)
Requirements Gathering & Gap Analysis
Structured elicitation of your target "To-Be" state — where you want to go, what risks need to be considered and mitigated, what the gaps are between there and here, and what the realistic path looks like at every level of the organization.
- Business Requirements Document (BRD)
- Functional Requirements Document (FRD)
- As-Is vs. Target State gap analysis
- Requirements Traceability Matrix
- HIPAA, NIST, SOC 2, and ISO compliance framework alignment
- Risk identification and mitigation documentation
AI Blueprint & Migration Planning
The complete architectural blueprint your cloud developers and AI vendors need to actually build what you need — without the guesswork that derails 80% of projects.
- Target state architecture diagrams
- Data flow and integration specifications
- AI use case prioritization matrix
- SOPs for AI-augmented workflows
- Change management and staff enablement plan
- Governance and oversight framework
AI Governance & Compliance Documentation
Healthcare AI carries regulatory exposure that other industries don't. I build the governance frameworks, audit trails, DevSecOps, and policy documentation that keep your AI initiatives compliant and defensible.
- HIPAA-aware AI integration documentation
- NIST, SOC 2 & ISO 27001/42001 compliance alignment
- Clinical AI oversight policy frameworks
- Data governance and data quality standards
- Vendor evaluation and selection criteria
- AI audit trail and accountability documentation
How We Work Together
A Low-Risk Way to Start
Every engagement begins with a structured exploratory assessment — so both of us can confirm the fit before committing to a longer engagement. No long-term lock-in until you've seen what I deliver.
Exploratory Assessment
A structured preliminary discovery: stakeholder interviews, current systems review, pain point identification at a high level, and a written summary of where AI could realistically help your organization. Both of us evaluate fit before going further.
Fractional Retainer
Ongoing fractional engagement with agreed deliverables — deep dive full Discovery, requirements gathering, pain point details, bottlenecks, blueprinting, governance documentation, and stakeholder alignment, on your schedule and budget. Typically no more than 2–5 clients at any one time.
Blueprint Delivery
A complete, professional documentation package your internal teams or cloud developers can use to execute. You own everything I produce. No vendor dependency, no lock-in, no black box.
Portfolio & Proof
Work That Speaks for Itself
Over 30 years of enterprise engagements across Fortune 500 clients — producing exactly the kind of documentation, architecture, and discovery work that healthcare AI adoption requires.
Con Edison IT Systems Discovery
Comprehensive departmental IT discovery across all of Con Edison Customer Operations — systems, business processes, UML diagrams, runbooks, data dictionaries, and data dependencies. The exact model for healthcare AI readiness discovery.
Inventory of Automated Processes ("Robo-Reps")
Catalogued an organization's entire ecosystem of agentic automations — APIs, inputs, outputs, departments, and frequencies. In today's language, this is an AI asset inventory — the essential first step before any AI adoption.
Medical Data Modeling
Prior DOH, Medicare and Medicaid work, including UML entity relationship diagram for a healthcare organization in star schema — demonstrating direct familiarity with HIPAA, HL7, ADA Section 508, and healthcare data architecture and how clinical data supports AI-ready reporting structures.
EAGLES Business & Functional Requirements
Full BRD and FRD for a major system replacement at Con Edison — showing structured requirements gathering from SMEs, use case modeling, and sign-off framework across multiple stakeholders.
Inactive Gas Monitoring Process Flow
End-to-end Visio process flow for a multi-step regulatory workflow with automated reporting, each step cross-referenced to detailed documentation — directly transferable to clinical workflow mapping and AI-assisted process automation planning.
Statistical Dashboards & Automated Reports
Azure cloud-based C# dashboard with GIS incident mapping; automated VBA statistical reporting with month-over-month trending. Demonstrates ability to design monitoring frameworks for AI initiative performance.
JetBlue Data Warehouse and Reservation System Documentation
Complete documentation of a major DW/BI system including ETL mapping, OLAP cube entity relationships, data dictionaries, bus matrices, and SSIS packages — the data architecture skills essential to healthcare AI data readiness.
Thomson Reuters RESTful API Guide
Developer integration guide for a Java/JSON/SSL financial API platform — demonstrating the API documentation skills critical for healthcare EHR/EMR integration specifications and HL7 FHIR interface documentation.
UML Project Viability Analysis
Used UML use case project blueprint modeling with development time estimations to demonstrate that a client's proposed project was impossible in their timeline and budget — preventing a costly failure before it started. A client saved is a reputation earned.
What Clients Have Said
"Ray's contributions to our organization have been nothing short of outstanding. He has gone above and beyond by creating automations and data processing tools that not only streamline the workflows, but also ensure the safe and encrypted transmission of critical data."Eugene Finas, Section Manager — Con Edison
"Ray's documentation has been of the highest quality, and rapidly produced. He is a versatile team-player of many talents, and would be an asset to any organization."Orlando Hernandez, Department Manager, Customer Operations — Con Edison
"The quality of his documentation was over-the-top, and Ray's skill at working efficiently with our developers was the icing on the cake."Tyrone Paige, Manager of IT Development — JetBlue Airways (now Azure Solution Architect, Microsoft)
"Ray's broad skillset and flexibility proved invaluable as project timelines changed. He is a true utility player — in our very dynamic project environment, it was a welcome change to have the same resource be able to wear so many hats."Brandon Palatt, Sr. Manager PMO/IS — Coach Inc. (now independent IT Consultant)
Credentials
The Certifications That Matter
Formal AI certifications from the platforms your cloud team will deploy on — plus the institutional education and 30-year track record that gives them meaning.
AWS Certified AI Practitioner (AIF-C01)
Issued June 12, 2026 · Valid through June 12, 2029
Validation: 20c6c45324a545dca4788a1b548742ac
NVIDIA Certified Associate: Generative AI & LLMs (NCA-GENL)
Issued April 20, 2026 · Valid through April 20, 2028
Signed by Greg Estes, VP — NVIDIA
Machine Learning: Implementation in Business
MIT Sloan School of Management · January 2020
Certificate #1520168829
Unsolicited Letters of Recommendation from 11 organizations spanning 30 years —
including three independent letters from Con Edison alone, spanning different departments and
different years of an 11-year engagement. Con Edison, JetBlue Airways, Coach Inc., Thomson Reuters,
IBM, RiverRock Systems, Yokogawa, VAC, Image2Web, GTESS, Mirus, and ValueWise.
The consistent theme across every letter: self-initiative, highest-quality deliverables,
and expanding beyond the original brief.
#9,397,830
#10,554,399
FRE™ Encryption
About Ray
Raymond R. Feliciano
I've spent 30 years walking into complex organizations, reverse-engineering what they have, interviewing the people who run it, and producing documentation so clear and thorough that my clients keep me far longer than they originally planned. Con Edison engaged me for 6 weeks. I stayed 11 years.
My career has spanned the roles of Enterprise & Solutions Architect, Senior Technical Writer, Business & Systems Analyst, Programmer, and QA Supervisor — across Fortune 500 clients including Con Edison, GE Vernova, JetBlue Airways, United Airlines, Coach Inc., Sony Music, Thomson Reuters, Bank of America, Deutsche Bank on Wall Street, and IBM.
I don't pretend to be an AI vendor. I don't sell a platform or a widget. What I offer is the rigorous, professional Discovery work that makes every other piece of an AI adoption project possible to execute correctly — the 70% of success that MIT identifies as people and process, delivered with the same standard that generated 14 letters of recommendation across 30 years.
I am also the inventor of Felician Randomized Encryption (FRE)™, a non-deterministic encryption method with two U.S. patents, particularly relevant to HIPAA-compliant data security planning.
Most recently, I served as Enterprise Architect for the NYS Office of Information Technology Services (ITS), teaching them DevSecOps methodology, and co-leading their AI adoption initiative for the NY Department of Transportation — identifying AI inventory, use cases, and agentic AI integration under NIST compliance.
U.S. Patents #9,397,830 and #10,554,399
Let's Talk Before Your Board Asks Why the AI Pilot Failed
The exploratory assessment is low-risk by design — a few weeks, a clear deliverable, and a decision point for both of us. If it's a fit, we build the blueprint together. If it's not, you walk away with a clearer picture of your AI readiness than you had before.
[email protected] · (518) 275-2114 · Delmar, NY (Albany County) · Remote Engagements Welcome