Generative AI in Enterprise: A Financial and Operational Feasibility Study

Executive Viability Abstract

This feasibility study evaluates the integration of Generative AI (GenAI) within enterprise environments, focusing on a North American market deployment. The study concludes that the project is financially viable with an anticipated IRR of 24.5% in the base case, provided that initial high Capex in hardware and specialized engineering is offset by operational efficiencies in legal, customer service, and R&D departments. Technical feasibility is high, leveraging RAG architectures, while regulatory compliance remains the primary operational risk.

## Executive Feasibility Thesis Resolva Insights has determined that GenAI enterprise integration is currently at a critical inflection point. Unlike general-purpose AI, enterprise-specific GenAI focuses on 'Small Language Models' (SLMs) and 'Retrieval-Augmented Generation' (RAG) to ensure data sovereignty and accuracy. The thesis posits that a $15M initial investment in bespoke AI infrastructure will yield a 3.2x return over five years by reducing manual document processing time by 70% and accelerating R&D cycles by 30%. The project assumes a North American TAM (Total Addressable Market) for Enterprise AI Software of $55B, with a focused SAM (Serviceable Addressable Market) of $2.4B in high-compliance sectors (Finance/Healthcare). ## Technical Feasibility & Operational Specifications The proposed architecture utilizes a hybrid-cloud approach to balance latency and security. - **Core Engine:** Deployment of Llama-3-70B or equivalent open-weights models fine-tuned on proprietary corporate datasets. - **Vector Infrastructure:** Integration of Pinecone or Milvus for semantic search capabilities across 50TB of unstructured internal data. - **Operational Capacity:** The system is designed to handle 500,000 queries per day at a peak concurrency of 5,000 users. - **Utilization:** Expected capacity utilization starts at 20% in Q1 (Pilot) and scales to 85% by Year 3 as internal departments onboard workflows. ## Detailed Capital Expenditure (Capex) The initial investment is front-loaded to secure compute resources and build the foundational data layer. | Item | Unit Cost | Quantity | Total | Reasoning | | :--- | :--- | :--- | :--- | :--- | | NVIDIA H100 GPU Nodes | $320,000 | 20 | $6,400,000 | On-premise secure cluster for proprietary model training. | | InfiniBand Networking | $450,000 | 1 | $450,000 | High-speed interconnects required for distributed model training. | | Vector DB Licenses | $120,000 | 2 | $240,000 | Enterprise tier for high-availability production environments. | | Custom Middleware Dev | $250/hr | 8,000 hrs | $2,000,000 | Integration of GenAI into existing ERP and CRM systems. | | Data Sanitization Suite | $350,000 | 1 | $350,000 | Automated PII masking tools to ensure data privacy. | | **Total Capex** | | | **$9,440,000** | | ## Realistic Operating Expenditure (Opex) Operational costs are dominated by specialized human capital and cloud token overflow management. - **MLOps Engineers:** 4 FTEs at $225,000/yr ($900,000 total). Required for model drift monitoring and pipeline maintenance. - **Cloud Inference Overflow:** $0.03 per 1k tokens (Est. $1.2M/yr). Covers peak loads handled via Azure/AWS OpenAI services. - **Fine-tuning Compute (Spot):** $45,000/month ($540,000/yr). Ongoing costs for updating the model with monthly enterprise data refreshes. - **Cybersecurity & Red-Teaming:** $300,000/yr. Periodic adversarial testing to prevent prompt injection and data leakage. - **Maintenance & Electricity:** $120,000/yr. Cooling and power for the on-premise GPU cluster. ## Financial Model & Sensitivity Range **Key Assumptions:** - Cost of Capital (WACC): 11.5% - Corporate Tax Rate (USA): 21% - Depreciation: 3-year MACRS for hardware. **Sensitivity Analysis (IRR):** - **Base Case (Target Efficiency):** 24.5% IRR. Assumes $18M annual OpEx savings across the enterprise. - **Optimistic Case (High Adoption):** 36.2% IRR. Assumes 95% utilization and rapid displacement of legacy third-party SaaS licenses. - **Pessimistic Case (Regulatory Friction):** 8.4% IRR. Occurs if strict local privacy laws require re-architecting data pipelines, increasing OpEx by 40%. ## Regulatory & Environmental Compliance In the North American context, the project must adhere to the 'NIST AI Risk Management Framework'. - **Regulatory Context:** Compliance with the CCPA/CPRA (California) is mandatory, requiring rigorous 'Right to Forget' protocols within the latent space of trained models—a significant technical hurdle. - **Environmental:** The GPU cluster's carbon footprint is estimated at 180 tonnes of CO2e annually. To achieve bankability for ESG-conscious lenders, we have budgeted for $25,000 in carbon offsets and the utilization of a data center with a Power Usage Effectiveness (PUE) rating of <1.2. ## Strategic Takeaways 1. **Hardware Sovereignty:** Investing in on-premise Capex for core models protects IP, while Opex is used for non-sensitive burst capacity. 2. **Phased Value Realization:** Feasibility is contingent on a 'Low-Hanging Fruit' strategy, starting with internal knowledge management before moving to client-facing autonomous agents. 3. **Margin Expansion:** The project is expected to expand net corporate margins by 450 basis points over 60 months through the radical reduction of administrative overhead.