Your engineering velocity is now an AI dependency
Software companies feel AI outages faster than anyone: developer copilots, AI-powered features in your own product, support automation, and internal agents. When your product embeds third-party models, your customers' SLAs silently inherit your AI vendors' availability.
Where AI is embedded
How technology runs on AI today
- Developer copilots and code review
- AI features embedded in your product
- Support deflection and ticket automation
- Internal agents and workflow automation
- Sales engineering and RFP assistance
Sector-specific risk
What makes resilience harder here
Your SLA inherits their outage
If your product's AI features depend on one provider, your customer commitments are only as strong as that provider's worst day. Multi-provider routing is a product reliability feature.
Engineering throughput is recalibrated
Sprint commitments and staffing assume copilot-assisted velocity. A multi-day disruption is a real schedule event that deserves the same continuity thinking as a CI/CD outage.
DIY resilience decays
Tech teams can build failover — the failure mode is maintaining it. Untested fallback paths rot as models and prompts evolve. Quarterly drill discipline is what separates real resilience from a config file.
The regulatory picture
SOC 2 availability criteria, customer contractual SLAs, and enterprise procurement questionnaires increasingly ask how AI dependencies in your product are made resilient.
Assess your technology AI estate
The AIR Assessment maps your AI dependencies against sector-specific failure modes and regulatory expectations — in 3 to 6 weeks.