TJSTEVENS.NET
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PROFILE 01 / ENTERPRISE ARCHITECTURE + TECHNICAL PRESALES

TJ Stevens

Enterprise Architecture, Technical Presales, and Applied AI.

I help enterprise teams and strategic partners turn ambiguous workflows into credible architectures, evaluation plans, and adoption paths, drawing on 25+ years across solution engineering, enterprise architecture, commercial leadership, integration, governance, and organizational change.

CONNECT ON LINKEDIN VIEW RESUMEEXPLORE PORTFOLIO
  • DISTINGUISHED PARTNER SOLUTION ARCHITECT
  • PRINCIPAL ENTERPRISE ARCHITECT
  • PRINCIPAL SOLUTION ENGINEER
  • 25+ YEARS ENTERPRISE TECHNOLOGY
PROFESSIONAL_OS://LIVE
INPUTEXPERIENCE
PROCESSTRACEABLE SYNTHESIS
OUTPUTBETTER DECISIONS

01 / BOOT SEQUENCE

The jobs were laboratories. The ideas are the throughline.

  1. [OK] OBSERVE WORK
  2. [OK] TRANSLATE CONTEXT
  3. [OK] REDUCE UNCERTAINTY
  4. [OK] ALIGN COMMITMENTS
  5. [OK] DESIGN COHERENCE
  6. [RUN] COMPOUND LEARNING

Before the platforms, my first jobs were in cornfields, grocery aisles, restaurant dining rooms, and overnight closes. I was learning what it takes to keep a promise to a customer.

The first principles arrived early: trust follows demonstrated care, attention is part of the operating model, and the customer experiences the whole system rather than the org chart.

Then came enterprise systems: field support, consulting, program leadership, ecosystem platforms, a large Salesforce rollout, solution engineering, carrying a number, enterprise architecture, and partner strategy.

Each role became another laboratory for recurring ideas: how to make ambiguity visible, translate across boundaries, align a promise with delivery capacity, and help judgment survive scale.

02 / PROFESSIONAL OPERATING SYSTEM

Experience becomes useful when it can change the next decision.

I am building a professional world model that connects events, evidence, ideas, methods, and evolving beliefs. Roles remain the historical record; the idea graph makes the reusable intellectual property visible. AI becomes an interpreter of traceable judgment, not a generator of unsupported biography.

ONTOLOGY
Objects, relationships, context, meaning
EVIDENCE GRAPH
Events, claims, sources, confidence
EVOLUTION LEDGER
Beliefs, contradictions, reasons for change
SYNTHESIS
Analogies, patterns, counterevidence, questions
OUTPUTS
Resumes, talks, methods, workshops, agents
FEEDBACK
Results, correction, revision, learning

03 / IDEA GRAPH

Ideas that accumulated evidence across roles.

Open a concept to inspect its evidence, evolution, and current principle. Roles are evidence, not the organizing identity.

01AMBIGUITY

Make uncertainty visible enough to act.

The work evolved from controlling schedules, to exposing dependencies, to helping leaders and teams make sound decisions before every answer is available.

discovery / dependencies / decisions

EVIDENCEMcGill / Covisint / Salesforce / partner fieldEVOLUTIONschedule control -> dependency visibility -> leadership -> organizational AICURRENT PRINCIPLEAI should reduce organizational uncertainty, not merely automate isolated tasks.

02IDENTITY + AUTHORITY

Identity is infrastructure for trust.

Federated identity began as a technical integration problem. Agentic systems make the larger issue explicit: who may know, decide, act, and remain accountable?

identity / access / accountability

EVIDENCECovisint / enterprise integration / agentic architectureEVOLUTIONfederation -> access -> organizational identity -> agent authorityCURRENT PRINCIPLEAutonomy is only useful when identity, authority, and accountability remain legible.

03TRANSFORMATION

Transformation itself can become software.

Platforms do not transform companies by themselves. The durable system includes governance, operating behavior, adoption, measurement, and the transitions between current and future work.

platform / operating model / adoption

EVIDENCEGE Energy / Salesforce / enterprise architectureEVOLUTIONproject delivery -> platform governance -> operating model -> learning systemCURRENT PRINCIPLEArchitecture is the maintained system of decisions that makes change possible.

04TECHNICAL DISCOVERY

Discovery is architecture before the diagram.

A useful presales motion exposes the workflow, stakes, constraints, evidence, and decision path before selecting the technical story that can earn customer confidence.

context / qualification / technical win

EVIDENCEOnX / Covisint / Salesforce solution engineeringEVOLUTIONrequirements -> translation -> demonstration -> decision architectureCURRENT PRINCIPLEThe best demonstration makes the right future believable and the remaining questions explicit.

05PARTNER LEVERAGE

Capability scales through shared patterns.

Partner ecosystems become more capable when field experience is converted into reusable guidance, stronger technical judgment, and feedback that can travel back to product teams.

partners / patterns / product signal

EVIDENCEstrategic SIs / partner architecture / field engineeringEVOLUTIONdelivery support -> enablement -> joint field work -> ecosystem learningCURRENT PRINCIPLEEnablement works when another team can make a better decision without borrowing your presence.

06ORGANIZATIONAL MEMORY

Experience should compound into judgment.

A model is not institutional memory. Enterprise learning becomes durable when experience is captured, structured, retrieved, tested, governed, and returned to the next decision.

evidence / evolution / synthesis

EVIDENCEservice systems / program leadership / architecture / AIEVOLUTIONindividual memory -> process -> reusable pattern -> evidence graphCURRENT PRINCIPLEThe learning loop is the product; the isolated prediction is only one moment inside it.

04 / CURRENT BUILD

Applied AI grounded in mature enterprise practice.

Current work spans internal implementation design, partner-led customer initiatives, evaluation and delivery readiness, and technical capability at scale.

01DISCOVER
02DESIGN
03EVALUATE
04INTEGRATE
05DEPLOY
06LEARN

INTERNAL IMPLEMENTATIONS

Turn partner workflows into bounded AI systems

Define use cases, source context, prompt templates, semantic retrieval, tools, workflow boundaries, evaluation criteria, and human escalation for internal partner work.

PARTNER-LED DELIVERY

Help implementation teams get the architecture right

Contribute solution strategy, architecture review, technical guidance, and delivery-readiness support to partner-led customer AI initiatives.

EVALUATION + READINESS

Make expected behavior testable

Set success criteria, scenarios, failure cases, groundedness checks, tool-behavior expectations, human review, governance, and readiness decisions.

CAPABILITY AT SCALE

Make technical judgment travel

Build partner capability through office hours, architecture deep dives, case-based problem solving, reusable guidance, and field-to-product feedback.

The point is not to make one agent look clever. It is to help the enterprise notice, remember, decide, act, measure, and improve while evidence, authority, and human responsibility remain visible.

DISCOVERY EVALUATIONS PORTFOLIO

05 / PORTFOLIO CONSTELLATION

Different embodiments. One cognitive operating model.

These systems explore persistent memory, specialized cooperating minds, reflective reasoning, affect, and bounded action. Their maturity labels keep production work, prototypes, and research directions distinct.

SHARED ARCHITECTURECOGNITIVE SYSTEMSMemory / specialized minds / reflection / bounded action
01

DURABLE MEMORY

02

VISIBLE REASONING

03

EMBODIED COGNITION

PROJECT SIGNALWORKING RESEARCH SYSTEM

Memorybank

A local-first, Git-versioned knowledge environment for durable human-AI collaboration, explicit trust boundaries, and model-agnostic memory.

CONTRIBUTION
Creator / systems design / primary implementation
EVIDENCE STATE
Architecture and operating artifacts documented; public walkthrough next
SYSTEM TAGS
LOCAL-FIRST / GIT / AGENT CONTRACTS

MATURITY LABELS DESCRIBE CURRENT EVIDENCE. PRODUCTION CLAIMS ARE SEPARATE FROM PROTOTYPES, CONCEPTS, AND RESEARCH DIRECTIONS.

06 / VERIFIED PATH

History is evidence, not identity.

Titles and dates are anchored to my CV and LinkedIn profile. The memoir supplies the connective tissue, not the chronology.

  1. Salesforce

    Distinguished Partner Solution Architect

    Applied-AI architecture, partner delivery readiness, internal implementation design, and field learning.

  2. Salesforce

    Principal Enterprise Architect

    Enterprise coherence across platform, data, operating model, and change.

  3. Salesforce

    Principal Solution Engineer

    Technical strategy and solution design for complex enterprise accounts.

  4. Salesforce

    Strategic Account Executive

    Executive relationships, commercial accountability, and customer outcomes.

  5. Salesforce

    Principal Solution Engineer

    Enterprise solution engineering in Michigan.

  6. GE Energy

    IM Project Leader & Salesforce Architect

    Enterprise platform rollout, governance, and adoption.

  7. Dagobah Systems

    Independent Consultant

    Independent consulting with a focus on web applications.

  8. Kids Planet

    Co-owner

    An operating system with customers, employees, cash flow, and no abstraction layer.

  9. Covisint

    Solution Engineer to Program Director

    Identity, integration, and programs spanning the automotive ecosystem.

  10. Compuware

    Technical Program Manager

    Enterprise program leadership across technical and delivery teams.

  11. McGill Digital Solutions

    Director, Program Management Office

    Consulting operations, program discipline, and delivery confidence.

  12. OnX

    Solution Consultant

    Technical consulting at the intersection of customers, sellers, and builders.

  13. Fiserv GalaxyPlus

    Field Systems Analyst & Special Projects Manager

    Enterprise reliability, data integrity, and trust in operational systems.

FOUNDATIONUniversity of Windsor

Honours Bachelor of Commerce, 1994 - 1998

07 / OPEN CHANNEL

Let's compare system notes.

I am most interested in the hard middle: the space between an AI demo and a customer decision, then between that decision and an operating capability. That is where discovery, architecture, adoption, delivery, trust, and learning have to become one system.

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