The context
I joined SKAN AI as a Manager in the AI Solutions / Professional Services team. My role is predominantly solution consulting and customer-facing delivery, combined with team leadership and partner enablement. I've worked with major healthcare and financial-services enterprises on taking SKAN from initial engagement and process walkthroughs through POC, production implementation, troubleshooting, final readout and value realization.
Why this is the interesting part
My involvement doesn't stop when a POC succeeds. The real question starts after a customer decides: "Yes, this works. Now how do we actually deploy this?" That's the work I spend most of my time on — understanding customer processes and requirements, solution consulting, POC execution, production implementation, troubleshooting, deployment support, analysing resulting metrics and data, creating customer readouts, surfacing insights and recommendations, and identifying where else value can be found.
Enabling partners, not just customers
I've also worked with major GSIs in India to train partner employees on SKAN, helping them eventually become capable of independently delivering SKAN implementations — extending the work beyond what my own team can directly deliver.
What I learned
This role has given me a very different understanding of enterprise AI implementation: the real work doesn't end with demonstrating that an AI solution works. Value comes from embedding it into enterprise workflows, generating usable insights, and turning those insights into action. The full loop — Problem → Solution → POC → Production → Data → Insights → Recommendations → Value — is the actual unit of work, not any single step in it.
This experience has also helped pave the way toward thinking about how AI solutions can evolve toward more agentic AI implementation — where the system doesn't just surface insights, but starts to act on them.