A normalized ontology decomposing professional capabilities into composable atomic dimensions — Action, Object, Qualification, Context, Outcome, Audience — for positioning, agent configuration, and knowledge graph construction.
| Namespace | https://brigitteos.dev/ontology/capability# |
| Preferred prefix | acl |
| Version | 1.0.0 |
| Issued | 2026-03-24 |
| Creator | Brigitte |
| License | Proprietary |
| Serializations | JSON-LD · N-Triples · Context |
| Graph statistics | 616 nodes · 600+ edges · 20 capabilities · 527 terms · 60 statements |
| acl:Capability | A discrete professional capability with scored dimensions and zone classification. |
| acl:Cluster | A thematic grouping of capabilities (Architecture, Commercialization, etc.). |
| acl:Zone | Strategic positioning tier: Core signature, Adjacent supporting, Credible expansion. |
| acl:Action | A verb describing what the capability does. |
| acl:Object | The thing the capability acts upon. |
| acl:Qualification | A quality modifier (e.g., governable, scalable, resilient). |
| acl:Context | The engagement or deployment context. |
| acl:Outcome | The measurable result the capability drives. |
| acl:Audience | The stakeholder the capability serves. |
| acl:AtomicStatement | A composed sentence combining one value from each dimension. |
| acl:belongsToCluster | Capability → Cluster |
| acl:inZone | Capability → Zone |
| acl:hasAction | Capability → Action (set) |
| acl:hasObject | Capability → Object (set) |
| acl:hasQualification | Capability → Qualification (set) |
| acl:hasContext | Capability → Context (set) |
| acl:hasOutcome | Capability → Outcome (set) |
| acl:hasAudience | Capability → Audience (set) |
| acl:hasStatement | Capability → AtomicStatement (set) |
| acl:strengthIndex | xsd:decimal — weighted composite of depth, breadth, differentiation |
| acl:depth | xsd:integer — technical depth score (1–10) |
| acl:breadth | xsd:integer — cross-domain applicability (1–10) |
| acl:differentiation | xsd:integer — market uniqueness (1–10) |
| acl:frequency | xsd:integer — term occurrence count across the library |
| acl:capabilityCount | xsd:integer — number of capabilities using this term |
| ID | Capability | Zone | Σ | Actions | Objects | Qualifications | Contexts | Outcomes | Audiences |
|---|---|---|---|---|---|---|---|---|---|
C01 |
Agentic enterprise architecture / AgentOS design Designs modular, governable enterprise agent systems that connect orchestration, memory, control, and deployment. |
Core signature | 10 | architect, orchestrate, modularize, govern, instrument | agent systems, orchestrators, runtime layers, memory layers, control planes | modular, enterprise-grade, governable, scalable, resilient | enterprise transformation, multi-agent platforms, hybrid runtime environments, local-first deployments, system-of-systems design | coordinated execution, greater controllability, repeatable deployment, lower fragility, enterprise-grade performance | cios, ctos, ai leaders, enterprise architects, product and innovation leaders |
C02 |
Meta-architecture / synthesis across strategy, tech, finance, and operations Integrates strategic, technical, financial, operational, and governance perspectives into one coherent system model. |
Core signature | 10 | synthesize, connect, translate, integrate, reconcile | business strategy, technical systems, financial logic, operating models, governance structures | systems-level, cross-functional, economically grounded, executive-legible, internally coherent | enterprise redesign, ai transformation, commercialization planning, operating model design, complex change programs | strategic coherence, cross-functional alignment, better decision-making, lower fragmentation, higher implementation feasibility | executives, transformation leaders, strategy teams, board stakeholders, operating partners |
C03 |
AI operating model design Structures how AI and agents are owned, governed, reviewed, and operated inside real organizations. |
Core signature | 9.4 | define, structure, assign, standardize, monitor | roles, workflows, decision rights, review paths, control layers | scalable, measurable, role-clear, policy-aware, implementation-ready | enterprise ai adoption, ai coe builds, operating model redesign, departmental ai deployment, transformation programs | clear accountability, smoother execution, stronger controls, lower ambiguity, consistent delivery | coos, caios, ai pmo leaders, department heads, transformation teams |
C04 |
Consulting offering architecture / service catalog design Turns capabilities into structured offerings, modules, and service catalogs that are sellable and repeatable. |
Core signature | 9.4 | define, package, tier, map, differentiate | offerings, service modules, capabilities, skills, value propositions | buyer-relevant, repeatable, delivery-aware, differentiated, taxonomy-aligned | consulting portfolio redesign, commercialization programs, service catalog creation, practice strategy, gtm modernization | clearer portfolio, easier selling, stronger delivery consistency, better cross-sell, greater differentiation | offering leaders, practice leaders, sellers, delivery leaders, firm leadership |
C05 |
AI-enabled business development and account planning Uses AI and strategic analysis to identify priorities, buyers, white space, and pursuit paths inside accounts. |
Core signature | 9.1 | analyze, prioritize, target, map, recommend | accounts, buyers, opportunities, relationship networks, strategic issues | commercially actionable, account-specific, issue-led, buyer-aware, evidence-based | account planning, pursuit strategy, relationship expansion, pipeline development, sales enablement | better targeting, stronger account plans, more relevant pursuits, pipeline growth, higher cross-sell potential | account directors, client partners, sellers, practice leaders, growth teams |
C06 |
Local-first AI infrastructure and deployment design Designs durable local and hybrid AI deployments with strong control over compute, storage, and dependencies. |
Adjacent supporting | 9 | design, deploy, package, secure, operationalize | runtimes, containers, storage layers, local compute environments, hybrid architectures | local-first, durable, portable, secure, low-dependency | on-prem deployments, regulated environments, private installations, edge environments, hybrid enterprise builds | deployment control, greater privacy, vendor flexibility, operational durability, lower cloud dependence | enterprise architects, it teams, security-conscious buyers, operators, technical founders |
C07 |
Knowledge graph / ontology / enterprise information architecture Builds semantic structures that make enterprise knowledge reusable, interoperable, and machine-readable. |
Adjacent supporting | 9 | model, classify, define, relate, normalize | entities, relationships, taxonomies, ontologies, metadata | semantically consistent, extensible, reusable, machine-readable, traceable | rag systems, knowledge architecture, data harmonization, agent memory design, capability mapping | better retrieval, stronger reasoning, cleaner structure, interoperability, reusable enterprise intelligence | ai architects, knowledge managers, data leaders, enterprise architects, product teams |
C08 |
Private equity value creation / EBITDA-oriented transformation Frames transformation and AI initiatives in terms of EBITDA, operating leverage, and portfolio value creation. |
Adjacent supporting | 9 | diagnose, quantify, prioritize, model, frame | value levers, cost structures, revenue drivers, operating metrics, portfolio initiatives | ebitda-linked, economically rigorous, investor-legible, action-oriented, operationally grounded | pe diligence, portfolio strategy, transformation planning, cfo conversations, operating partner work | clear value thesis, measurable impact, stronger prioritization, better investment narrative, operating leverage | operating partners, pe funds, cfos, ceos, portfolio company leaders |
C09 |
AI commercialization / product strategy / incubation Shapes AI products and solution concepts into validated, prioritized, monetizable offerings. |
Adjacent supporting | 8.7 | incubate, shape, validate, prioritize, commercialize | ai products, solution concepts, prototypes, commercialization paths, roadmaps | market-aware, monetizable, scalable, differentiated, practical | innovation programs, new venture design, internal productization, solution incubation, advisory product development | stronger product-market fit, clearer roadmap, better monetization, scalable offers, faster validation | product leaders, founders, innovation teams, commercialization leads, investors |
C10 |
Process architecture, SOPs, and current-state analysis Documents and analyzes current operations to reveal pain points, controls, and redesign opportunities. |
Credible expansion | 8.1 | document, map, analyze, decompose, redesign | processes, subprocesses, handoffs, roles, controls | structured, detailed, evidence-based, workflow-aware, improvement-oriented | process improvement, operational diagnostics, documentation programs, service delivery design, transformation initiatives | process clarity, identified pain points, standardization, efficiency improvement, training-ready artifacts | operations leaders, process owners, pmo teams, quality teams, delivery teams |
C11 |
Market intelligence / competitor analysis / strategic research Researches and interprets market and competitor signals to support strategy, positioning, and pursuit planning. |
Adjacent supporting | 8.7 | research, synthesize, compare, interpret, recommend | markets, competitors, trends, strategic signals, industry events | current, evidence-backed, decision-relevant, commercially useful, nuanced | account planning, pursuit prep, executive briefings, offering design, industry strategy | better situational awareness, sharper positioning, stronger recommendations, competitive clarity, more informed decisions | executives, strategy teams, sellers, offering leaders, investors |
C12 |
AI governance, QA, diagnostics, and control frameworks Creates diagnostic, quality, and control layers that make enterprise AI outputs more auditable and trustworthy. |
Adjacent supporting | 8.7 | evaluate, diagnose, classify, score, govern | outputs, agent behaviors, quality criteria, risk types, control layers | structured, auditable, measurable, explainable, governance-ready | enterprise ai deployment, regulated workflows, output review, model oversight, quality management | higher output quality, lower risk, clear remediation, greater trust, better accountability | ai governance teams, risk leaders, legal and compliance teams, executives, qa teams |
C13 |
Training, enablement, and cultural activation for AI adoption Builds the training, scaffolding, and behavior-change mechanisms needed for real AI adoption. |
Adjacent supporting | 8.7 | teach, equip, activate, coach, reinforce | skills, mindsets, behaviors, training materials, templates | practical, behavior-oriented, accessible, repeatable, adoption-focused | ai adoption programs, leadership development, enterprise enablement, change management, consulting team training | stronger adoption, higher ai fluency, improved confidence, behavior change, reduced resistance | leaders, managers, employees, enablement functions, transformation offices |
C14 |
Executive thought leadership and analytical writing Produces high-signal analytical writing that makes complex systems legible and commercially meaningful. |
Adjacent supporting | 9 | write, frame, synthesize, argue, explain | articles, white papers, frameworks, strategic narratives, povs | original, rigorous, readable, high-signal, distinctive | brand building, publication, executive education, market education, business development | stronger credibility, market differentiation, executive understanding, demand creation, clearer narratives | executives, clients, market audiences, conference organizers, investors |
C15 |
AI-native application design / product specs / UX structure Designs AI-native application structures and build specs that connect user flow to agent behavior. |
Adjacent supporting | 8.7 | specify, design, sequence, prototype, translate | app flows, ux logic, build specs, system prompts, configurations | ai-native, implementation-ready, modular, clear, developer-usable | ai app development, internal tool builds, prototype handoffs, solution design, agentic product builds | better build quality, faster execution, stronger usability, clearer developer handoff, reduced ambiguity | developers, product managers, designers, founders, innovation teams |
C16 |
OpenAI / Claude / CustomGPT / agent workflow design Configures platform-specific assistant workflows that improve reliability, reuse, and task completion. |
Adjacent supporting | 8.8 | configure, orchestrate, prompt, constrain, optimize | assistants, prompts, toolchains, knowledge libraries, workflow chains | platform-aware, precise, reusable, quality-controlled, outcome-oriented | assistant development, enterprise copilots, rapid prototyping, multi-agent systems, internal ai tools | better assistant quality, more reliable outputs, improved task completion, reusable patterns, faster prototyping | builders, ai teams, consultants, product owners, end users |
C17 |
Consulting sales methodology and buyer positioning Sharpens buyer-facing narratives, proof points, and pursuit discipline for consulting sales motions. |
Credible expansion | 7.7 | position, tailor, align, differentiate, guide | messages, buyer narratives, talk tracks, proof points, conversation paths | buyer-specific, persuasive, issue-led, context-aware, commercially sharp | business development, proposal shaping, sales conversations, pursuits, sales enablement | better buyer resonance, higher conversion quality, stronger pursuit discipline, clearer messaging, improved win support | sellers, client partners, account teams, bd leaders, practice leaders |
C18 |
Cross-industry solution contextualization Adapts solutions and value stories so they fit specific industries without becoming generic. |
Credible expansion | 8 | adapt, translate, tailor, align, map | offerings, use cases, value stories, messages, solution components | industry-specific, credible, nuance-aware, strategically aligned, non-generic | vertical gtm, account targeting, multi-industry consulting, portfolio design, market expansion | stronger industry fit, better relevance, improved resonance, reduced genericity, better market access | industry leaders, clients, sellers, offering teams, strategy leaders |
C19 |
Risk, misuse, and threat-oriented AI analysis Analyzes misuse patterns and safety threats so AI systems and policies can be designed more defensively. |
Credible expansion | 7.7 | assess, anticipate, analyze, classify, surface | misuse scenarios, threat patterns, behavioral risks, safety gaps, vulnerabilities | safety-aware, scenario-based, ethically grounded, risk-sensitive, analytically rigorous | ai safety research, policy work, enterprise risk discussions, trust and safety strategy, public thought leadership | better preparedness, clearer risk posture, stronger safeguards, safer design, differentiated safety perspective | risk teams, policy audiences, researchers, executives, trust and safety leaders |
C20 |
Venture building around AI advisory and products Packages capabilities into ventures, brands, and monetizable product or advisory platforms. |
Credible expansion | 8.3 | envision, package, launch, brand, commercialize | ventures, brands, services, products, revenue models | entrepreneurial, differentiated, market-oriented, scalable, strategically coherent | startup creation, advisory launch, studio models, adjacent business lines, external commercialization | new revenue streams, clear market identity, scalable ip platform, stronger monetization, ecosystem leverage | founders, buyers, investors, strategic partners, early clients |