StewAI Blog · AI Workflow Design · 9 min read
The Model Was Never the Hard Part: Five Convictions Behind StewAI
Salesforce, MIT NANDA, Klarna, CMU, and Gartner all point to the same conclusion: enterprise AI value has moved from the model to the system around it. Five convictions for why StewAI exists.
Frequently asked questions
Why does StewAI focus on workflows instead of just using a smarter AI model?
Benchmarks from Salesforce and CMU show that models can perform bounded tasks, but reliability falls when work becomes multi-step, contextual, and policy-sensitive. StewAI focuses on the system around the model: decomposition, context, contracts, execution, and iteration.
What does the MIT NANDA GenAI Divide imply for enterprise AI?
MIT NANDA attributes the enterprise AI gap to learning, context, and workflow integration rather than model quality alone. The organizations that succeed use process-specific, learning-capable systems instead of static tools that require constant prompting.
What is the practical lesson from Klarna's AI customer service case?
Klarna showed that AI can handle large volumes of support work, but also that cost-first automation can lower service quality. The lesson is to redesign the process around AI and humans together, not simply replace the old process operator.
Read the full article on StewAI