🔑 AI Ops Architect for Scale-Ups, Exits & Turnarounds | 75+ AI Deployments that actually shipped
Nobody can agree on what “success” means when it comes to AI projects. “We’ll know it’s working when people like it.” “The goal is… adoption?” “Success = it’s running.” Nope. That’s not a metric. That’s vibes. 👉 Pilot Purgatory: Company spends 6 months building an “AI assistant” for ops. No baseline. No outcomes. A year later? Nobody knows if it saved time, money, or just burned brain cells. That’s why the S in VITAS = Success Metrics. Ask: 💬 What does success look like in numbers? 💬 What baseline are we comparing against? 💬 How will we know if this compounds over time? If you’re in the seat when this happens, you’re not just inheriting the project— you’re inheriting the mess. The only way out? Define the rules up front. Think of it like a dot painting. Each metric = a dot. If you can’t see the picture forming, you’re not transforming—you’re just finger painting with AI. 💬 What’s the worst “success metric” you’ve ever heard? Mine: “We’ll know it worked when the AI feels smart.” 😂 That’s not a metric. That’s a sci-fi plotline. (Skynet in Terminator became “self-aware” and tried to wipe out humanity.) If your KPI sounds like a doomsday movie, you’re not running an ops strategy — you’re writing a screenplay. 👉 Your turn: What’s the most absurd “success metric” you’ve ever heard tossed around? Drop it in the comments so we can build a hall of shame. Hashtags #AIops #AgenticWorkflows #DigitalTransformation #ThinkingInDots #Leadership
📍 If you had to pick just one metric to prove an AI project was worth it, what would you choose?
These metrics need to be set up before the build even begins and need to tie back to the problem that the build is solving.
Without clear baselines and measurable outcomes AI projects can drift. read that Andrew Ng is suggesting small language models will help prevent this. Tracey have you heard anything about this?
I love how you're highlighting the importance of defining clear success metrics in ai projects, it's crazy how often companies just kind of wing it and end up with no idea if their investment actually paid off.
🔑 AI Ops Architect for Scale-Ups, Exits & Turnarounds | 75+ AI Deployments that actually shipped
1d📍 Why do you think teams dodge the baseline question — lack of data, or fear of being exposed?