Why AI Transformation Requires Patience, Not Hype
Adam Henderson explains why enterprise AI transformation demands patience, due diligence, discipline, and a proven implementation process.
Don't Buy AI Until You Do This
Adam Henderson explains why enterprise AI buyers must test vendor support, escalation ownership, and recovery plans before production deployment.
Overview
About this session
A polished enterprise AI demo can make every platform look production-ready. In this short conversation, Adam Henderson explains why leaders should evaluate something less visible but equally important: the customer support and escalation model behind the technology.
For leading-edge AI deployments, a generic help desk may not be enough. When a serious failure happens days into implementation, enterprise teams need access to someone who understands the system, can protect completed work, and can resolve the issue without forcing the organization to rebuild. The practical takeaway for CX, operations, procurement, and technology leaders is to test vendor accountability before signing—not after production is at risk.
Key takeaways
What comes out of it
A successful AI demo does not prove production readiness. Buyers also need evidence that the vendor can support the system when real workflows fail.
Leading-edge AI requires more than a generic help desk. Critical incidents need knowledgeable people who can understand the implementation and act quickly.
Protect completed implementation work. Strong escalation and recovery processes reduce the risk of losing days of configuration, prompts, integrations, and testing.
Vendor accountability belongs in the buying criteria. Response ownership, recovery procedures, and production support should be evaluated alongside model performance and features.
TranscriptRead the full session
Adam Henderson: The last point that I would make on this vetting process is the end customer service. And that's why I wanted to differentiate between the commodity based ones. If I'm going with an ADP or a Paylocity, customer service is okay. A help desk is okay, right? But on these leading edge technologies where I might be days into a project and have a major failure, I need to contact somebody because I don't want to lose all of the work in the background or have to recreate it. And that right now on the AI side has been a challenge.
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