<\/span><\/h3>\nSelecting an AI agency<\/strong> should be treated as a strategic decision, not just a vendor comparison. Therefore, the evaluation should cover both technical depth and business fit.<\/p>\n<\/span>Core evaluation criteria<\/span><\/h4>\n\n- Experience and track record<\/strong>: years operating, scale and complexity handled, and production wins, not just pilots.<\/li>\n
- Portfolio relevance<\/strong>: sector-aligned case studies, reusable accelerators, and relevant operating context.<\/li>\n
- Client feedback and references<\/strong>: measurable outcomes, communication quality, and delivery reliability.<\/li>\n
- Technical expertise and currency<\/strong>: LLMs, vector databases, orchestration, MLOps, plus depth in data engineering, security, cloud, and integration.<\/li>\n
- Industry specialization and compliance<\/strong>: familiarity with GDPR\/CCPA, HIPAA, SOX, PCI, and sector-specific standards.<\/li>\n<\/ul>\n
<\/span>Practical assessment tips<\/span><\/h4>\n\n- Ask for detailed case studies with metrics on value realized, time-to-value, adoption, and lessons learned.<\/li>\n
- Demand clarity on security and governance, including data residency, encryption, audits, explainability, and model risk management.<\/li>\n
- Probe the integration approach across ERP, CRM, data platforms, identity systems, and observability.<\/li>\n
- Evaluate communication and delivery discipline, including steering cadence, artifact quality, backlog management, and change control.<\/li>\n<\/ul>\n
<\/span>Interview the people who will do the work<\/span><\/h4>\n\n- Meet the project lead, solution architect, data science lead, and change manager.<\/li>\n
- Review methodology documents, coding standards, and QA practices.<\/li>\n
- Align roles, escalation paths, and decision rights before kickoff.<\/li>\n<\/ul>\n
<\/span>Red flags to avoid<\/span><\/h4>\n