You mention so many critical aspects regarding real-world implementations of AI (but I’d say more broadly analytics). Data Strategy is key and many organizations miss that - what is especially important often there is no common language between business and tech - something which ontology and knowledge graphs promise to deliver (see Gartner report - KG is next to GenAI as critical „emerging” techs). Feedback from users is another critical factor - many years of implementation taught me that you need to have a process for situation when something went bad - it doesn’t have to be AI, it can be any kind of automation - a manual process of correcting the results (usually with additional human supervision)
Great insights! I completely agree—closing the gap between tech, business (and humanities) is essential. The best solutions happen when we bring those perspectives together, especially to align technical capabilities with real-world needs.
You're absolutely right about data—it’s the foundation for every business today. Without a solid strategy, even small companies risk falling behind. It’s the first step toward staying relevant in a data-driven world.
Continuous user feedback is crucial, but as you said, it’s meaningless without a process to act on it. Timely and thoughtful responses are what build trust and drive real improvement.
And yes, human verification and oversight are non-negotiable. Without it, automation risks losing direction and missing the bigger picture. It’s about more than fixing errors—it’s about ensuring alignment with broader goals.
What’s worked for you in bridging these gaps and turning feedback into actionable results? Would love to hear!
You mention so many critical aspects regarding real-world implementations of AI (but I’d say more broadly analytics). Data Strategy is key and many organizations miss that - what is especially important often there is no common language between business and tech - something which ontology and knowledge graphs promise to deliver (see Gartner report - KG is next to GenAI as critical „emerging” techs). Feedback from users is another critical factor - many years of implementation taught me that you need to have a process for situation when something went bad - it doesn’t have to be AI, it can be any kind of automation - a manual process of correcting the results (usually with additional human supervision)
Great insights! I completely agree—closing the gap between tech, business (and humanities) is essential. The best solutions happen when we bring those perspectives together, especially to align technical capabilities with real-world needs.
You're absolutely right about data—it’s the foundation for every business today. Without a solid strategy, even small companies risk falling behind. It’s the first step toward staying relevant in a data-driven world.
Continuous user feedback is crucial, but as you said, it’s meaningless without a process to act on it. Timely and thoughtful responses are what build trust and drive real improvement.
And yes, human verification and oversight are non-negotiable. Without it, automation risks losing direction and missing the bigger picture. It’s about more than fixing errors—it’s about ensuring alignment with broader goals.
What’s worked for you in bridging these gaps and turning feedback into actionable results? Would love to hear!