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We weigh the real trade‑offs of building AI in‑house versus buying and configuring proven tools, and map a practical route from pilot to production without blowing the budget. Clear steps on data, governance, ethics, and IP help you create value you can measure.
- Build vs buy decisions tied to strategy and IP
- When generic problems justify off‑the‑shelf tools
- Niche bottlenecks and owning differentiated capability
- Real costs of talent, data architecture and compute
- Governance, scope control and reliability expectations
- Data quality, sourcing and security by design
- Measurable pilots, baselines and explainability
- EBITDA impact, inference costs and ROI discipline
- Ethics beyond bias, oversight and customer impact
- Partner contracts, IP protection and reuse limits
- Scaling blockers across finance, compliance, HR and IT
- Regulations to watch including EU AI Act and GDPR
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