Generative AI buying decisions made in 2025 will shape enterprise architecture for the next decade. This guide gives procurement and engineering leaders a structured framework for evaluating the options.
The Approach
Model selection is no longer a single-vendor choice. Most mature deployments orchestrate multiple models — frontier APIs for complex reasoning, smaller open-weight models for high-volume tasks, and fine-tuned domain models for regulated workflows.
"Modernization is less about technology and more about managing risk while sustaining the business."
— Priya Subramanian, AI Practice Lead
What Works in Practice
Retrieval architecture is where most projects succeed or fail. Investing in clean, well-chunked, well-permissioned source data pays larger dividends than chasing the latest model release.
Pitfalls to Avoid
Governance frameworks must be built in from day one. Prompt logging, output auditing, PII handling, and human-in-the-loop checkpoints are non-negotiable for enterprise deployment.
Key takeaways
- Decompose monoliths incrementally rather than attempting a big-bang rewrite.
- Use parallel-run strategies to validate behavior before cutover.
- Pair legacy and modern teams to preserve institutional knowledge.
- Treat governance and observability as first-class deliverables.