---
id: "framework-ai-deployment-process"
type: "framework"
source_timestamps: ["§ Understand Market Trends", "§ Map Business Processes", "§ Incubate-Pilot-Scale", "\\\"§ Buy", "Build", "or Partner for Speed\\\"", "§ Measure and Quantify the Impact", "§ Adapt to Your Context"]
tags: ["strategy", "implementation"]
related: ["claim-business-problem-first", "contrarian-problem-over-tech", "action-incubate-via-crowdsourcing", "action-baseline-measurement", "question-build-vs-buy-split", "concept-product-context-ai-adaptation"]
steps: ["\\\"Understand Market Trends: Identify business problems or growth opportunities (e.g.", "shift to cloud", "new SME market) before considering AI.\\\"", "Map Business Processes: Chart the customer journey to identify high cost-to-serve areas where scalable tech investments make sense.", "\\\"Incubate-Pilot-Scale: Crowdsource ideas internally", "pilot to quantify value", "and scale via a unified platform integrated with existing infrastructure.\\\"", "\\\"Buy", "Build", "or Partner: Overcome internal engineering bias to utilize a hybrid of internal and third-party tools for speed.\\\"", "\\\"Measure and Quantify: Establish baselines for specific tasks (e.g.", "reaching 1", "000 prospects) and measure time saved and conversion rates.\\\"", "\\\"Adapt to Context: Tailor the approach based on customer tech-readiness", "product complexity", "and organizational culture.\\\""]
sources: ["commercial"]
sourceVaultSlug: "hbr-seg-commercial"
originDay: 5
articleStem: "hbr-foci-64-ai-broaden-customer-base"
sourceUrl: "https://hbr.org/2025/03/how-one-company-used-ai-to-broaden-its-customer-base"
sourceTitle: "How One Company Used AI to Broaden Its Customer Base"
---
# Strategic AI Deployment Process

A generalized framework derived from [[org-sap|SAP]]'s success for deploying AI in an enterprise setting, emphasizing **business fundamentals over technology hype**.

1. **Understand Market Trends** — Identify business problems or growth opportunities (e.g., the shift to cloud, the untapped SME market) *before* considering AI. This is the operationalization of [[claim-business-problem-first]] and [[contrarian-problem-over-tech]].
2. **Map Business Processes** — Chart the customer journey to find high cost-to-serve areas where scalable tech investments make sense (see [[framework-sap-customer-journey]] and [[action-map-customer-journey]]).
3. **Incubate-Pilot-Scale** — Crowdsource ideas internally (see [[action-incubate-via-crowdsourcing]]), pilot to quantify value, then scale via a **unified platform integrated with existing infrastructure**.
4. **Buy, Build, or Partner for Speed** — Overcome internal engineering bias and use a **hybrid** of internal and third-party tools to move fast (open detail in [[question-build-vs-buy-split]]).
5. **Measure and Quantify** — Establish baselines for specific tasks (e.g., reaching 1,000 prospects) and measure **time saved and conversion rates** (see [[action-baseline-measurement]]).
6. **Adapt to Context** — Tailor the approach to customer tech-readiness, product complexity, and organizational culture (see [[concept-product-context-ai-adaptation]]).

> **Enrichment check:** The framework is consistent with mainstream enterprise AI deployment methodologies (SAP Discovery Center's stepwise use-case → reference-architecture → best-practices guidance; SAPinsider's insistence on data harmonization as a prerequisite; McKinsey-style use-case → pilot → build/buy → measure → adapt sequences). It is a reasonable abstraction of SAP's practice even if SAP does not formally brand it this way. One enrichment caveat: the framework under-weights **data foundation / harmonization** as an explicit prerequisite step.