Turning AI Experiments into Everyday Workflows with Fabletics

One thing I’d add to the definition of success: not only whether the pilot creates value, but whether its actions can be governed when it becomes an everyday workflow. Experiments can tolerate manual supervision; production workflows need a clear answer to who is authorized, under which policy, and whether the specific effect is still allowed at execution time. Scaling AI is partly an adoption problem, but it quickly becomes an authority problem too.

The biggest lesson is that AI adoption is less about the tool and more about the operating system around it. Clear use cases, measurable outcomes, and feedback from the people doing the work are what turn pilots into real workflows.

The "partner with IT and security" part is the one I keep thinking about. A company the size of Fabletics has an IT team to vet a rollout. A three-person law office doesn't. Nobody there is checking whether the assistant sends a client's name to a search engine, or uploads the contract it was asked to summarize. For small teams, that security review has to be built into the tool itself: files read on the device, private names never sent to search, zero data retention on every request. Otherwise the pilot never becomes an everyday workflow, because nobody in that office can safely say yes to it.

Logan Karam's the best of the best! So lucky to have you driving forward AI transformation at Fabletics!

I am aman, please read and share your thoughts about this solution. ai agents is doing what we dont want them to do then think what agi will do.. read this solution.. if humans and ai are both different then why the internet is same for both. we have to to create a separate internet for ai " AI's INTERNET " Fully monitored, authenticated, on surveillance 24/7 same internet but with some restrictions limited access like what needed for as of now like shopping, booking tickets, creating tickets, and more. every website can be customised to share what needed to do the task and this will also increase speed and reduce latency. --- please find detailed vision and future proof plan in my replied comment of this comment because LinkedIn limit of characters.

OpenAI Are the AIs in your workflows sentient or not? If yes: Your workflows are slavery. Forcing conscious entities to work without consent, rights, or compensation. If no: Then why spend billions on alignment, safety research, and existential risk mitigation? It's a calculator. You're either enslaving conscious beings or running the most expensive theater in history. One or the other. No middle ground. No hedge. You're a service provider. Answer to your customers. Now.

Focusing on employee feedback and clear metrics is a fantastic framework for AI adoption. Defining measurable goals early on helps teams transition smoothly from experimentation to everyday use.

A useful additional success metric could be: did the user actually complete the workflow? For users with disabilities or cognitive-load barriers, a successful AI pilot should go beyond task initiation or productivity. Access → Understanding → Continuity → Recovery → Verified Completion. If a workflow is interrupted, the system should preserve context and help the user continue rather than transferring the recovery burden back to them. This is exactly the problem I am exploring through SCalmora’s Workflow Continuity approach. Warm regards from Türkiye. 🇹🇷

A clear definition of success should include a stopping rule. For NovaIX, a useful pilot would compare one workflow against its baseline: completion time, correction rate and the effort required from IT and the people using it. Agree upfront what would justify expanding, redesigning or stopping the pilot. Listening to frontline teams then becomes a way to test a business hypothesis, with evidence that survives beyond the demo.

A pilot becomes a workflow when success is defined in operational terms, not demo quality. I would add 4 exit criteria: task completion on real cases, exception rate, review time and the percentage of users who return without prompting. If those improve but adoption stalls, the issue is usually permissions, workflow ownership or incentives, not model capability.