Economics
A fact-based model for validating cost, governance, and value before rollout.
TCO model inputs
The economics of enterprise AI should be calculated from the customer's real workflow, not from a universal savings claim. A Proof of Value should collect these inputs before any rollout decision.
| Input | Why it matters | PoV measurement |
|---|---|---|
| Users and workflows | AI value depends on repeated, measurable work rather than seat count alone. | Selected team, baseline task volume, before/after timing. |
| Data-flow scope | Security and compliance exposure depends on where prompts, documents, embeddings, outputs, and logs are processed. | Data-flow map and approved/blocked external paths. |
| Governance controls | IBM 2025 research shows AI governance and shadow AI are measurable risk gaps. | Audit events, roles, approvals, monitoring, retention policy. |
| Infrastructure | Local AI shifts cost from provider usage to customer-controlled compute and operations. | Node usage, latency, maintenance effort, availability. |
| Compliance timeline | EU AI Act obligations are already partially active and current planning still points to 2 August 2026 for Annex III and Article 50 unless amended. | Risk classification and control mapping for the pilot use case. |
ROI evidence to collect
| KPI | Measurement | Evidence for business case |
|---|---|---|
| Time saved | Minutes per task before and after the PoV. | Validated productivity delta for the business case. |
| Quality | Error rate, review effort, and citation quality. | Qualitative and quantitative quality assessment. |
| Governance | Percentage of tasks with reviewable audit records. | Evidence of compliance readiness for regulated workflows. |
| Data control | Confirmed location of data, models, and logs. | Security audit sign-off on data residency and flow. |
| Adoption | Weekly active users and repeated workflows. | Proof of user acceptance and workflow integration. |
Pricing hypothesis
Pricing should be treated as a validation hypothesis until several target customers confirm willingness to pay.
| Offer | Purpose | Status |
|---|---|---|
| Discovery workshop | Map data flows, use case, governance scope, and success metric. | Validate with first buyers. |
| Paid Proof of Value | Run one bounded workflow in the customer environment. | Primary early-stage offer. |
| Annual local deployment | License, support, governance updates, and deployment documentation. | Price after PoV evidence. |
| Integration support | Setup, workflow design, documentation, and security review. | Scope-dependent. |
Implementation scope
| Workstream | Deliverable | Evidence created |
|---|---|---|
| Infrastructure | Local deployment, node connection, runtime policy. | Deployment record and operational constraints. |
| Data flow | Document, prompt, embedding, output, and log map. | Security and governance review material. |
| Workflow | One bounded AI workflow with before/after metric. | ROI and adoption evidence. |
| Governance | Audit, oversight, retention, and access-control configuration. | EU AI Act control mapping draft. |
Proof of Value
For early customers, High-X should be sold through a structured pilot before any full license commitment.
| Pilot Aspect | Configuration | Strategic Evidence |
|---|---|---|
| Duration | Defined with the customer; usually 60-90 days. | Long enough to compare repeated work and timing. |
| Users | Selected pilot team with a real workflow. | Ownership and internal adoption proof. |
| Pricing | To be validated with the specific customer. | Evidence-based numbers for the business case. |
| Scope | Local deployment, workflow setup, security review. | Data-flow and governance proof in your environment. |
| Outcome | Measurable productivity, quality, and control. | Defensible data for a later commercial proposal. |
Risk register
Public benchmarks and regulatory references for the business case.
| Risk category | Public benchmark | High-X validation need |
|---|---|---|
| AI Act penalties | Up to EUR 35M or 7% worldwide annual turnover for prohibited practices; up to EUR 15M or 3% for several operator obligations. | External legal review of actual use case, risk class, and deployment controls. |
| Data breach cost | IBM 2025: USD 4.44M global average; IBM Germany: EUR 3.87M in Germany. | Data-flow proof and connector policy for pilot workflows. |
| Shadow AI | IBM 2025: 63% lacked AI governance policies; high shadow-AI usage associated with USD 670k higher breach cost. | Employee workflow discovery and approved-tool adoption measurement. |
| AI market growth | Gartner forecasts AI spending above USD 2T in 2026, driven partly by infrastructure. | Customer-specific total cost model including local operations. |
| EU AI Act timing | 2 August 2026 remains the current planning date unless Omnibus amendments are formally adopted. | Controls mapped to the pilot use case before broader rollout. |
Commercial readiness
The next step is not a spreadsheet with invented payback periods. It is collecting customer evidence that can survive procurement, security, and finance review.
| Milestone | Evidence | Decision |
|---|---|---|
| 10 discovery calls | Repeated pain, budget owner, procurement path. | Confirm ICP. |
| 3 PoV candidates | Data access, workflow owner, measurable KPI. | Prioritize pilots. |
| 1 paid pilot | Usage, quality, governance, security feedback. | Validate pricing. |
| External review | Security and EU AI Act control mapping. | Remove compliance overclaim risk. |
| Reference case | Approved quote or anonymized case metric. | Write investor narrative. |
Source anchors: Eurostat 2025, McKinsey State of AI 2025, IBM Cost of a Data Breach 2025, EU AI Act Service Desk, Gartner AI spending forecasts.