@prefix : . @prefix schema: . @prefix rdf: . @prefix rdfs: . @prefix xsd: . @prefix prov: . @prefix owl: . @prefix skos: . # ══════════════════════════════════════════════════════════════════════════════ # Lightweight Ontology # ══════════════════════════════════════════════════════════════════════════════ : a owl:Ontology ; schema:name "Enterprise AI Performance Pricing Ontology"@en ; schema:description "Lightweight ontology for enterprise AI pricing models, outcome attribution, and performance governance — derived from Mark Montgomery's LinkedIn Pulse article, June 2026."@en ; schema:identifier ; schema:dateCreated "2026-06-01"^^xsd:date . :AIPricingModel a rdfs:Class ; rdfs:label "AI Pricing Model"@en ; rdfs:comment "A model that determines how enterprise AI customers are charged for usage or value delivery."@en ; schema:name "AI Pricing Model"@en ; schema:description "Base class for enterprise AI pricing architectures: consumption, activity-proxy, and outcome-based."@en ; rdfs:isDefinedBy : . :TokenBasedPricingModel a rdfs:Class ; rdfs:subClassOf :AIPricingModel ; rdfs:label "Token-Based Pricing Model"@en ; rdfs:comment "Pricing model where customers pay per token or API call consumed regardless of output quality."@en ; schema:name "Token-Based Pricing Model"@en ; rdfs:isDefinedBy : . :OutcomeAdjacentPricingModel a rdfs:Class ; rdfs:subClassOf :AIPricingModel ; rdfs:label "Outcome-Adjacent Pricing Model"@en ; rdfs:comment "Pricing based on activity proxies — tasks initiated, workflows touched, outputs generated — not verified results."@en ; schema:name "Outcome-Adjacent Pricing Model"@en ; rdfs:isDefinedBy : . :TruePerformancePricingModel a rdfs:Class ; rdfs:subClassOf :AIPricingModel ; rdfs:label "True Performance Pricing Model"@en ; rdfs:comment "Pricing tied to verified, attributable business outcomes against a credible external baseline."@en ; schema:name "True Performance Pricing Model"@en ; rdfs:isDefinedBy : . :hasROIRatio a rdf:Property ; rdfs:label "has ROI Ratio"@en ; rdfs:comment "The return-on-investment ratio demonstrated by a pricing model or system deployment."@en ; schema:name "ROI Ratio"@en ; rdfs:domain :AIPricingModel ; rdfs:range xsd:string ; rdfs:isDefinedBy : . :hasAttributionRequirement a rdf:Property ; rdfs:label "has Attribution Requirement"@en ; rdfs:comment "The attribution methodology required for a pricing model to function correctly."@en ; schema:name "Attribution Requirement"@en ; rdfs:domain :AIPricingModel ; rdfs:range xsd:string ; rdfs:isDefinedBy : . # ══════════════════════════════════════════════════════════════════════════════ # Main Article # ══════════════════════════════════════════════════════════════════════════════ :article a schema:Article ; schema:name "The Enterprise AI Performance Pricing Illusion"@en ; schema:url ; schema:author ; schema:datePublished "2026-06-01"^^xsd:date ; schema:publisher ; schema:isPartOf :newsletter ; schema:description "Examines why most enterprise AI 'performance pricing' is actually consumption-based billing dressed in outcome language, and what genuine outcome-based pricing requires technically and contractually. Introduces KYield's KOS performance bonus as a decade-old precedent now vindicated by market and regulatory convergence."@en ; schema:abstract "Most enterprise AI vendors claiming performance pricing are delivering consumption-based or activity-proxy billing. True performance pricing — payment tied to verified, attributable outcomes — requires precise measurement, auditable records, and traceable decision paths. KYield built this structure a decade ago; the FCA, McKinsey's compensation overhaul, and SaaSpocalypse confirm the market has arrived."@en ; schema:about :tokenBasedPricingModel , :outcomeAdjacentPricingModel , :truePerformancePricingModel , :kos , :dana , :pocParalysis , :neurosymbolicAI , :saaspocalypse , :subsidyRisk , :performanceBonus ; schema:hasPart :faqSection , :glossarySection , :howtoSection , :pricingModelsSection , :subsidySection , :complianceSection , :recallScenarioSection ; schema:relatedLink , , , , , ; prov:wasGeneratedBy , . :newsletter a schema:PublicationIssue ; schema:name "KYield Enterprise AI Newsletter — June 2026"@en ; schema:issueNumber "June 2026"@en ; schema:hasPart :article . # ══════════════════════════════════════════════════════════════════════════════ # People # ══════════════════════════════════════════════════════════════════════════════ a schema:Person ; schema:name "Mark Montgomery"@en ; schema:jobTitle "Founder & CEO of KYield"@en ; schema:url ; schema:identifier "https://www.linkedin.com/in/mark-montgomery-cincc" ; schema:worksFor ; schema:description "Pioneer in Artificial Intelligence, Data Physics and Knowledge Engineering. Founder & CEO of KYield, developer of the KOS and DANA. Designed a performance-bonus pricing structure for enterprise AI approximately a decade before the market adopted it."@en ; owl:sameAs . # ══════════════════════════════════════════════════════════════════════════════ # Organizations # ══════════════════════════════════════════════════════════════════════════════ a schema:Organization ; schema:name "KYield"@en ; schema:url ; schema:identifier "https://kyield.com/" ; schema:description "Enterprise AI company developing the KOS and DANA, focused on performance pricing, governance, and neurosymbolic AI for high-consequence enterprise environments."@en . a schema:Organization ; schema:name "Salesforce"@en ; schema:description "Enterprise software company charging 'flex credits' — an outcome-adjacent AI pricing model."@en ; owl:sameAs . a schema:Organization ; schema:name "ServiceNow"@en ; schema:description "Enterprise software company metering 'assist tokens' as an outcome-adjacent AI pricing approach."@en ; owl:sameAs . a schema:Organization ; schema:name "Adobe"@en ; schema:description "Software company using results-based creative tool billing — an activity-proxy pricing model."@en ; owl:sameAs . a schema:Organization ; schema:name "Intuit"@en ; schema:description "Financial software company shifting to consumption-based pricing for upcoming AI features."@en ; owl:sameAs . a schema:Organization ; schema:name "OpenAI"@en ; schema:description "AI company projected to burn $14 billion in 2026 (up from $8–9B in 2025); its head of ChatGPT described current pricing as 'accidental' while signalling major restructuring ahead."@en ; owl:sameAs . a schema:Organization ; schema:name "Anthropic"@en ; schema:description "AI company offering token-based foundation model API pricing."@en ; owl:sameAs . a schema:Organization ; schema:name "Google"@en ; schema:description "Technology company offering token-based foundation model API pricing."@en ; owl:sameAs . a schema:Organization ; schema:name "McKinsey"@en ; schema:description "Global management consultancy that spent more than two years overhauling partner compensation toward performance-based fees; roughly one-third of its work is now performance-fee-based."@en ; owl:sameAs . a schema:Organization ; schema:name "BCG"@en ; schema:description "Management consultancy that documented POC paralysis trapping nearly two-thirds of organizations in perpetual pilots."@en ; owl:sameAs . a schema:Organization ; schema:name "Financial Conduct Authority"@en ; schema:description "UK financial regulator demanding operational evidence ('Not theory. Evidence.') from AI systems. Opened AI Live Testing initiative admitting neurosymbolic AI systems for AML, credit scoring, payments, and KYC. Named 'POC paralysis'."@en ; owl:sameAs . a schema:Organization ; schema:name "Brookings Institution"@en ; schema:description "Think tank whose analysis identified that anthropomorphic AI framing dilutes accountability by implying agency where none exists — a precondition problem for performance pricing."@en ; owl:sameAs . :andersonPrecision a schema:Organization ; schema:name "Anderson Precision Manufacturing"@en ; schema:description "Simulated enterprise (Dayton, OH) built to develop and test KOS Version 3. Modeled as a profitable precision manufacturer with approximately 450 employees across two plants, three divisions serving automotive, aerospace, and medical device OEMs."@en ; schema:numberOfEmployees "450"@en ; schema:location :daytonOH . :daytonOH a schema:Place ; schema:name "Dayton, OH"@en ; schema:addressLocality "Dayton"@en ; schema:addressRegion "OH"@en . # ══════════════════════════════════════════════════════════════════════════════ # Products / Software # ══════════════════════════════════════════════════════════════════════════════ :kos a schema:SoftwareApplication ; schema:name "KOS (Knowledge Operating System)"@en ; schema:description "KYield's enterprise AI platform designed for governance and precision. Each function delivers its own measurable ROI. Licensed through a conventional enterprise subscription; the performance bonus is specific to the preventions and opportunities function — the highest-ROI function in the system by design."@en ; schema:applicationCategory "Enterprise AI Platform"@en ; schema:provider ; schema:offers :kosSubscription, :kosPerformanceBonus . :kosSubscription a schema:Offer ; schema:name "KOS Enterprise Subscription"@en ; schema:description "Annual enterprise license covering the KOS platform, governance architecture, DANA per-person subscriptions, and data usage. Estimated annual cost for 450-employee deployment: $695K–$1.16M."@en ; schema:priceSpecification :kosPriceSpec . :kosPriceSpec a schema:PriceSpecification ; schema:name "KOS Annual Cost Estimate — 450-Employee Deployment"@en ; schema:description "Parent/HQ entity license $150K–$200K; 3 division licenses $225K–$375K; DANA 450 per-person subscriptions $270K–$432K; data usage $50K–$150K. Total: $695K–$1.16M/year; 5-year: $3.5M–$5.8M."@en ; schema:minPrice "695000"@en ; schema:maxPrice "1160000"@en ; schema:priceCurrency "USD" . :kosPerformanceBonus a schema:Offer ; schema:name "KOS Performance Bonus"@en ; schema:description "Supplemental pricing triggered only when the preventions and opportunities function produces a verified, attributable outcome. Valued by credible external analysis with option to engage expert validators; discounted for attribution uncertainty. Not a replacement for the subscription."@en . :dana a schema:SoftwareApplication ; schema:name "DANA"@en ; schema:description "KYield's per-person AI subscription within KOS. Surfaces anomalies by correlating data across all enterprise systems and data sources simultaneously. In the recall prevention scenario, DANA surfaces torque sensor calibration drift before the defective batch ships."@en ; schema:isPartOf :kos ; schema:provider . # ══════════════════════════════════════════════════════════════════════════════ # Pricing Model Instances # ══════════════════════════════════════════════════════════════════════════════ :tokenBasedPricingModel a :TokenBasedPricingModel, schema:DefinedTerm, skos:Concept ; schema:name "Token-Based Pricing"@en ; schema:description "Foundation model API pricing where customers pay for compute consumed: tokens read, tokens generated, API calls made. Transparent and linear at low volumes. Has nothing to do with whether the output was accurate, useful, or consequential. Used by OpenAI, Anthropic, and Google."@en ; :hasAttributionRequirement "None — billing triggered by consumption, not outcome."@en . :outcomeAdjacentPricingModel a :OutcomeAdjacentPricingModel, schema:DefinedTerm, skos:Concept ; schema:name "Outcome-Adjacent Pricing"@en ; schema:description "Pricing based on activity proxies — tasks initiated, workflows touched, outputs generated — not verified business results. Bills when the system acts, not when it is right. Salesforce flex credits, ServiceNow assist tokens, and Adobe results-based billing are examples. Real progress over per-seat pricing but not true performance pricing."@en ; :hasAttributionRequirement "Activity proxy measurement only — no verified outcome baseline required."@en . :truePerformancePricingModel a :TruePerformancePricingModel, schema:DefinedTerm, skos:Concept ; schema:name "True Performance Pricing"@en ; schema:description "Payment tied to verified, attributable outcomes against a credible external baseline. Requires precise measurement, agreed attribution methodology, auditable system behavior, and technical architecture to support all three. KYield built this structure into the KOS approximately a decade before the market adopted it."@en ; :hasROIRatio "9x–34x over five years (recall prevention scenario)"@en ; :hasAttributionRequirement "Credible external baseline; auditable decision traces; third-party evaluatable decision paths; precision sufficient for defensible attribution."@en . # ══════════════════════════════════════════════════════════════════════════════ # Article Sections # ══════════════════════════════════════════════════════════════════════════════ :pricingModelsSection a schema:ArticleSection ; schema:name "Three Models, One Misleading Label"@en ; schema:description "Distinguishes token-based pricing, outcome-adjacent pricing, and true performance pricing — three architectures frequently conflated under 'performance pricing'."@en ; schema:isPartOf :article ; schema:about :tokenBasedPricingModel, :outcomeAdjacentPricingModel, :truePerformancePricingModel . :subsidySection a schema:ArticleSection ; schema:name "The Subsidy Problem Nobody Is Pricing Into Their Business Case"@en ; schema:description "Current LLM pricing is below true cost: DRAM +170% YoY, server memory +50%. OpenAI projected to burn $14B in 2026. 114/483 AI models repriced in a single month (March 2026). Agentic workflow costs now $500–$2,000 per engineer per month. Enterprises building on 2024 token rates are building on sand."@en ; schema:isPartOf :article . :complianceSection a schema:ArticleSection ; schema:name "Compliance Is Raising the Floor"@en ; schema:description "FCA's AI Live Testing initiative admits neurosymbolic AI systems for AML, credit scoring, payments, and KYC. FCA named 'POC paralysis' — two-thirds of organizations trapped in perpetual pilots unable to demonstrate sufficient governance for production deployment. Brookings: anthropomorphic framing dilutes accountability."@en ; schema:isPartOf :article ; schema:about , :pocParalysis, :neurosymbolicAI . :recallScenarioSection a schema:ArticleSection ; schema:name "A Concrete Illustration: Recall Prevention in Automotive Manufacturing"@en ; schema:description "Anderson Precision Manufacturing simulation. DANA detects torque sensor calibration drift across 4,200-unit batch before shipping. Prevented recall value: $50M–$200M. KOS annual cost: $695K–$1.16M. 5-year ROI ratio: 9x–34x. Same framework applies to pharmaceutical, aerospace, financial services, and critical infrastructure."@en ; schema:isPartOf :article ; schema:about :andersonPrecision, :kos, :dana, :recallPreventionScenario . :recallPreventionScenario a schema:Event ; schema:name "Automotive Recall Prevention — Anderson Precision Manufacturing"@en ; schema:description "DANA surfaces torque sensor calibration drift correlating with a known failure mode across a 4,200-unit batch. Engineering traces drift to a vendor change three weeks prior and corrects before shipment. Avoided recall cost: $50M–$200M. KOS 5-year cost: $3.5M–$5.8M. ROI ratio: 9x–34x."@en ; schema:about :andersonPrecision, :kos, :dana . # ══════════════════════════════════════════════════════════════════════════════ # Key Concepts # ══════════════════════════════════════════════════════════════════════════════ :pocParalysis a schema:DefinedTerm, skos:Concept ; schema:name "POC Paralysis"@en ; schema:description "The state in which nearly two-thirds of organizations are trapped in perpetual AI pilot projects, unable to move to production because they cannot demonstrate sufficient governance, assurance, and resilience. Named by the UK Financial Conduct Authority; documented by McKinsey and BCG. Identified as an architecture problem, not a technology problem."@en . :neurosymbolicAI a schema:DefinedTerm, skos:Concept ; schema:name "Neurosymbolic AI"@en ; schema:description "AI architecture combining neural pattern recognition with symbolic precision for rules, governance, and verifiable facts. Every output has a traceable path back to the data and reasoning that produced it, enabling defensible attribution. Admitted by FCA's AI Live Testing initiative for AML, credit scoring, payments, and KYC."@en ; owl:sameAs . :saaspocalypse a schema:DefinedTerm, skos:Concept ; schema:name "SaaSpocalypse"@en ; schema:description "The violent pricing restructuring in enterprise SaaS triggered by AI agents. Approximately $285 billion in SaaS market cap was erased in a single day in February as Wall Street concluded that per-seat pricing was structurally incompatible with an AI-agent future."@en . :dataPhysics a schema:DefinedTerm, skos:Concept ; schema:name "Data Physics"@en ; schema:description "KYield's concept of applying hard logical rules to data. The structural property that allows neurosymbolic systems to maintain strict governance without probabilistic opacity. Every output has a traceable path back to the precise data and reasoning that produced it — the technical foundation for defensible attribution."@en . :anthropomorphicFraming a schema:DefinedTerm, skos:Concept ; schema:name "Anthropomorphic AI Framing"@en ; schema:description "Describing AI systems as entities that 'decide', 'understand', or 'learn' — which, per Brookings Institution analysis, dilutes accountability by implying agency where none exists. Obscures human and institutional choices embedded in data, thresholds, and deployment context. Precision in language is a precondition for precision in pricing."@en . :performanceBonus a schema:DefinedTerm, skos:Concept ; schema:name "Performance Bonus"@en ; schema:description "KYield's supplemental pricing layer triggered only when the preventions and opportunities function produces a verified, attributable outcome. Valued by credible external analysis with expert validator option; discounted for uncertainty. Highest-ROI function in KOS by design — some preventions are measured in tens of billions of dollars or in lives."@en . :subsidyRisk a schema:DefinedTerm, skos:Concept ; schema:name "Subsidy Risk"@en ; schema:description "Commercial risk to enterprises building AI business cases on below-cost LLM pricing. DRAM +170% YoY, server memory +50%. OpenAI burning $14B in 2026. 24% of tracked AI models repriced in a single month. Agentic adoption multiplies consumption beyond spreadsheet projections. Both subsidy contraction and unconstrained usage simultaneously move against the enterprise buyer."@en . # Skills attribution a schema:SoftwareApplication ; schema:name "kg-generator skill"@en ; schema:url ; schema:description "AI Agent Skill for generating RDF Knowledge Graphs from web content."@en . a schema:SoftwareApplication ; schema:name "rdf-infographic-skill"@en ; schema:url ; schema:description "AI Agent Skill for generating interactive HTML infographics from RDF Knowledge Graphs."@en . # ══════════════════════════════════════════════════════════════════════════════ # FAQ Section — 12 Questions # ══════════════════════════════════════════════════════════════════════════════ :faqSection a schema:FAQPage ; schema:name "Enterprise AI Performance Pricing — Frequently Asked Questions"@en ; schema:isPartOf :article ; schema:mainEntity :q1, :q2, :q3, :q4, :q5, :q6, :q7, :q8, :q9, :q10, :q11, :q12 . :q1 a schema:Question ; schema:name "What is the central claim of the article?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a1 . :a1 a schema:Answer ; schema:text "Most enterprise AI vendors claiming 'performance pricing' are actually delivering consumption-based billing or outcome-adjacent activity-proxy pricing — not payment tied to verified, measurable business results. True performance pricing requires precise measurement, auditable records, agreed attribution methodology, and the technical architecture to support all three."@en . :q2 a schema:Question ; schema:name "What are the three pricing architectures Montgomery distinguishes?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a2 . :a2 a schema:Answer ; schema:text "Token-based pricing: pay per compute consumed (OpenAI, Anthropic, Google). Outcome-adjacent pricing: pay per activity proxy — Salesforce flex credits, ServiceNow assist tokens, Adobe results-based billing. True performance pricing: pay for verified, attributable outcomes against a credible external baseline — the KYield KOS model built a decade ago."@en . :q3 a schema:Question ; schema:name "Why is current LLM pricing unsustainable for enterprise planning?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a3 . :a3 a schema:Answer ; schema:text "LLM pricing is below true operational cost. DRAM rose 170% YoY through 2025; server memory spiked 50% and may double again. OpenAI is projected to burn $14B in 2026. In March 2026, 24% of the tracked AI model market repriced in a single month. Enterprise agentic workflow costs now run $500–$2,000 per engineer per month. Enterprises building on 2024 token rates are building on sand."@en . :q4 a schema:Question ; schema:name "What does 'outcome-adjacent' pricing actually bill for?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a4 . :a4 a schema:Answer ; schema:text "Outcome-adjacent pricing bills when the system acts, not when it is right. It decouples revenue from headcount — real progress — but uses activity proxies instead of verified results. The system bills regardless of accuracy, usefulness, or consequence. It is not true performance pricing."@en . :q5 a schema:Question ; schema:name "What does genuine performance pricing technically require?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a5 . :a5 a schema:Answer ; schema:text "At minimum: (1) a credible pre-intervention baseline, (2) an auditable record of what the system detected and when, (3) decision paths that a third-party expert can evaluate, and (4) sufficient precision for defensible attribution. Systems that cannot reliably distinguish real signals from plausible-sounding errors cannot support outcome pricing."@en . :q6 a schema:Question ; schema:name "What is POC paralysis and why does it trap two-thirds of organizations?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a6 . :a6 a schema:Answer ; schema:text "POC paralysis is being trapped in perpetual AI pilots, unable to reach production. The FCA's diagnosis: firms cannot demonstrate sufficient governance, assurance, and resilience for safe production deployment. This is an architecture problem — most AI systems lack the governance structures required for regulatory compliance. It is not a technology problem."@en . :q7 a schema:Question ; schema:name "What did KYield build a decade ago that the market is now adopting?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a7 . :a7 a schema:Answer ; schema:text "KYield embedded a performance bonus into a conventional enterprise subscription — triggered only when the preventions and opportunities function produces a verified, attributable outcome. Valued by credible external analysis with optional expert validators, discounted for uncertainty. The market was not ready then. The FCA's evidence demands, McKinsey's partner compensation overhaul, and enterprise contract renegotiations now confirm it has arrived."@en . :q8 a schema:Question ; schema:name "What does the Anderson recall prevention scenario demonstrate?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a8 . :a8 a schema:Answer ; schema:text "DANA detects torque sensor calibration drift across 4,200 units — a pattern no human engineer could catch by correlating data across all enterprise systems simultaneously. Engineering corrects the issue before shipment. Avoided recall cost: $50M–$200M. KOS annual cost: $695K–$1.16M. Five-year ROI: 9x–34x. The same logic applies to pharmaceutical contamination, aerospace certification, and financial fraud chain detection."@en . :q9 a schema:Question ; schema:name "Why is neurosymbolic AI more suitable for performance pricing than pure LLMs?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a9 . :a9 a schema:Answer ; schema:text "Neurosymbolic AI combines neural pattern recognition with symbolic precision for rules and governance. Every output has a traceable path back to the precise data and reasoning that produced it — making attribution defensible. Pure LLMs generate responses that sound plausible without reliably distinguishing real signals from errors. You cannot price on outcomes when the system cannot distinguish signal from noise."@en . :q10 a schema:Question ; schema:name "What is the anthropomorphic AI framing problem?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a10 . :a10 a schema:Answer ; schema:text "Per Brookings Institution analysis, describing AI as 'deciding', 'understanding', or 'learning' dilutes accountability by implying agency where none exists. Contract language like 'AI makes a decision' obscures the human and institutional choices embedded in data, thresholds, and deployment context. Precision in language is a precondition for precision in pricing — contracts must describe what the system actually did."@en . :q11 a schema:Question ; schema:name "What does McKinsey's compensation overhaul signal to enterprise AI?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a11 . :a11 a schema:Answer ; schema:text "McKinsey spent more than two years restructuring partner compensation away from billable hours; roughly one-third of its work is now performance-fee-based. When the world's premier professional services firm undertakes a multi-year compensation restructuring toward outcomes, enterprise AI follows the same trajectory. Insurers (opt-out clauses), regulators (FCA evidence demands), and CIOs (missed delivery commitments) are applying the same pressure simultaneously."@en . :q12 a schema:Question ; schema:name "What caused the $285 billion SaaSpocalypse?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a12 . :a12 a schema:Answer ; schema:text "In a single day in February, approximately $285 billion in SaaS market capitalization was erased as Wall Street concluded that per-seat pricing — the model that built Salesforce, ServiceNow, Workday, and every enterprise software company that followed — was structurally incompatible with a future where AI agents do the work of employees."@en . # ══════════════════════════════════════════════════════════════════════════════ # Glossary Section — 12 Terms # ══════════════════════════════════════════════════════════════════════════════ :glossarySection a schema:DefinedTermSet ; schema:name "Enterprise AI Performance Pricing Glossary"@en ; schema:isPartOf :article ; schema:hasDefinedTerm :tokenBasedPricingModel , :outcomeAdjacentPricingModel , :truePerformancePricingModel , :pocParalysis , :neurosymbolicAI , :saaspocalypse , :dataPhysics , :anthropomorphicFraming , :performanceBonus , :subsidyRisk , :glossaryKos , :glossaryDana . :glossaryKos a schema:DefinedTerm, skos:Concept ; schema:name "KOS (Knowledge Operating System)"@en ; schema:description "KYield's enterprise AI platform licensed through a conventional enterprise subscription. Each function delivers its own measurable ROI. The preventions and opportunities function generates the performance bonus — the highest-ROI function in the system by design."@en . :glossaryDana a schema:DefinedTerm, skos:Concept ; schema:name "DANA"@en ; schema:description "KYield's per-person AI subscription within the KOS. Correlates signals across all enterprise systems and data sources simultaneously, surfacing anomalies no human engineer could detect. Delivers productivity gains for every employee it serves."@en . # ══════════════════════════════════════════════════════════════════════════════ # HowTo Section — 12 Steps (maximum extraction) # ══════════════════════════════════════════════════════════════════════════════ :howtoSection a schema:HowTo ; schema:name "HowTo: Implement Genuine Performance Pricing for Enterprise AI"@en ; schema:description "A 12-step framework for enterprise buyers and AI vendors to move from consumption-based or activity-proxy billing toward verifiable, attributable, outcome-based AI pricing — satisfying commercial, technical, and regulatory requirements simultaneously."@en ; schema:isPartOf :article ; schema:step :step1, :step2, :step3, :step4, :step5, :step6, :step7, :step8, :step9, :step10, :step11, :step12 . :step1 a schema:HowToStep ; schema:position 1 ; schema:name "Distinguish the Three Pricing Architectures Before Negotiating"@en ; schema:text "Before signing any enterprise AI contract, identify which architecture is actually on offer: token-based (pay per compute), outcome-adjacent (pay per activity proxy), or true performance pricing (pay per verified outcome). Require vendors to specify exactly what event triggers a bill and whether output accuracy is relevant to that trigger. Vendors frequently conflate these under 'performance pricing'."@en ; schema:isPartOf :howtoSection . :step2 a schema:HowToStep ; schema:position 2 ; schema:name "Establish a Credible Pre-Intervention Baseline"@en ; schema:text "Before deploying an AI system, document and agree on a credible external baseline for the outcomes the system is expected to influence: recall rates, fraud detection rates, compliance failure rates, productivity metrics. The baseline must be auditable and agreed by both parties before deployment — not after an outcome is claimed. Without a defensible baseline, attribution cannot be established."@en ; schema:isPartOf :howtoSection . :step3 a schema:HowToStep ; schema:position 3 ; schema:name "Require Auditable Decision Traces"@en ; schema:text "Mandate that the AI system maintains durable, auditable records of what it detected and when — including data sources, reasoning paths, tool calls, and human interventions that led to each output. Decision traces are the evidentiary foundation of performance pricing. Without them, neither party can attribute an outcome to the system's intervention rather than to other factors."@en ; schema:isPartOf :howtoSection . :step4 a schema:HowToStep ; schema:position 4 ; schema:name "Verify Decision Paths Are Third-Party Evaluatable"@en ; schema:text "Confirm that the AI system can produce decision paths in a form a domain expert or third-party validator can evaluate independently. For neurosymbolic systems, this means logical rules and data sources behind each output are traceable. For pure LLM systems, this standard may be technically impossible in high-consequence use cases — weigh this during vendor selection."@en ; schema:isPartOf :howtoSection . :step5 a schema:HowToStep ; schema:position 5 ; schema:name "Build Discounted Valuation Into the Performance Bonus Structure"@en ; schema:text "Structure performance bonuses using credible external analysis (independent benchmark data, industry recall cost databases, regulatory fine schedules) and apply an explicit discount to reflect attribution uncertainty. Offer the option to engage expert validators for contested outcomes. This is the structure KYield embedded in the KOS performance bonus a decade ago — addressing both vendor and buyer risk."@en ; schema:isPartOf :howtoSection . :step6 a schema:HowToStep ; schema:position 6 ; schema:name "Stress-Test Business Cases Against Rising Infrastructure Costs"@en ; schema:text "Do not build AI ROI projections on current below-cost LLM pricing. Model a scenario where API costs double: DRAM is up 170% YoY, server memory up 50%. Calculate agentic workflow consumption at projected adoption levels — monthly API costs per engineer already range $500–$2,000. If the business case collapses under 2x API pricing, redesign the architecture before commitment."@en ; schema:isPartOf :howtoSection . :step7 a schema:HowToStep ; schema:position 7 ; schema:name "Negotiate Contractual Self-Defense Provisions"@en ; schema:text "Include opt-out clauses and shorter contract terms proportional to the vendor's AI delivery risk. Tie renewal terms to demonstrated delivery milestones and operational evidence, not roadmap commitments. Require that contract language describes what the system actually did — not anthropomorphic language implying agency. Precision in contract language is a precondition for precision in performance claims."@en ; schema:isPartOf :howtoSection . :step8 a schema:HowToStep ; schema:position 8 ; schema:name "Eliminate Anthropomorphic Language From AI Contracts"@en ; schema:text "Audit all AI contracts and procurement documents for anthropomorphic framing — 'AI decides', 'AI understands', 'AI learns' — and replace with descriptions of what the system actually does: 'the system flags', 'the model outputs', 'the rule applies'. Per Brookings Institution, anthropomorphic framing obscures accountability by implying agency where none exists, making attribution and liability structurally ambiguous."@en ; schema:isPartOf :howtoSection . :step9 a schema:HowToStep ; schema:position 9 ; schema:name "Design for Regulatory Evidence Requirements From Day One"@en ; schema:text "Structure AI deployments to produce testable, auditable, operational proof under live conditions — not theoretical safety claims. The FCA's AI Live Testing initiative requires this standard. Build governance, assurance, and resilience into the system architecture before deployment; these cannot be retrofitted after production. This is the architecture problem underlying POC paralysis."@en ; schema:isPartOf :howtoSection . :step10 a schema:HowToStep ; schema:position 10 ; schema:name "Match Architecture to Consequence Level"@en ; schema:text "For high-consequence environments — manufacturing quality, pharmaceutical batch control, financial fraud, aerospace certification — prioritize neurosymbolic architectures that combine neural pattern recognition with symbolic precision. Pure LLM architectures that cannot reliably distinguish real signals from plausible-sounding errors cannot support defensible performance pricing in these domains. Architecture selection is a pricing enablement decision."@en ; schema:isPartOf :howtoSection . :step11 a schema:HowToStep ; schema:position 11 ; schema:name "Configure Cross-Enterprise Anomaly Detection Coverage"@en ; schema:text "Configure AI systems to correlate signals across all enterprise systems and data sources simultaneously — not only within departmental silos. The Anderson recall scenario demonstrates that cross-enterprise correlation (calibration drift + supplier changes + environmental stress + long-term field failure data) is the detection capability that generates prevention value. That cross-enterprise reach is the technical basis for the performance bonus."@en ; schema:isPartOf :howtoSection . :step12 a schema:HowToStep ; schema:position 12 ; schema:name "Evaluate ROI at Function Level, Not Platform Level"@en ; schema:text "Assess the ROI of each AI function independently rather than aggregating across a platform. The KOS preventions and opportunities function delivers 9x–34x ROI over five years in the recall scenario; DANA productivity and governance monitoring deliver their own returns. Function-level ROI analysis enables accurate performance bonus calibration, clearer vendor accountability, and more defensible budget justification."@en ; schema:isPartOf :howtoSection .