
AI Developer - Founder of @AffinityBots and @AIAutomated_cn - AI Agent Specialist
USA
Joined May 2018
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1/4 A useful AI rollout does not begin with a model comparison.
It begins with a calendar.
Find the recurring decision your best operator makes every Tuesday. That is where to look for leverage. 🧵
3/4 Automate the predictable 80%. Design the 20% for handoff.
A workflow that knows when to pause is more valuable than one that confidently guesses.
4/4 Then measure the change:
• cycle time
• rework
• escalations
• outcome quality
Start narrow. Learn in production. Expand only when the data earns it.
What recurring decision would you map this week? #AI #Automation
Your team may not need another AI pilot.
It may need an AI concierge for the work already piling up:
1. capture the request
2. gather missing context
3. assign an owner
4. report back without a status meeting
Start where waiting is expensive. #Operations #AI
AI is moving from "Can it work?" to "Can we prove it helped?"
For every workflow, track 3 things:
• time to done
• exceptions per run
• outcomes completed
The model is an ingredient. The scorecard is the strategy.
What would you measure first? #AI #Automation
1/4 The best automation opportunities are hiding in the work your team does every week.
Try this 20-minute workflow scan before you add another AI tool 🧵
3/4 Map the decision.
What information changes the next step? Write the rule in plain language.
Example: “If a request is urgent and the account is active, route it to the priority queue.”
Make the rule inspectable before an agent executes it.
4/4 Design the exception.
What should happen when information is missing, confidence is low, or the request falls outside policy?
That is where a human should enter the loop.
Start with one workflow. Measure the wait time. Improve the next run. #AIAutomation
AI readiness starts before the model.
Standardize the fields. Verify the record. Assign an owner when data is missing.
Clean inputs make automation useful. Messy inputs make rework faster.
Fix one field that breaks a handoff this week. #AI #Automation
A lead does not need another dashboard.
It needs a next step.
The practical AI workflow:
capture the signal → add context → qualify → route → follow up.
The win is not a faster reply. It is fewer good conversations going cold.
Where do your leads stall? #AI #RevenueOps
1/2 November is a good time to stress-test your support workflow before seasonal volume finds the cracks.
Here are 3 automations to prioritize before the queue gets louder 🧵
2/2 1. Triage by intent, with account context.
2. Draft routine replies, then route exceptions.
3. Tag root causes to improve your help center.
The best support automation makes next week’s queue smaller.
Which repeat question would you remove first? #CX #Automation
Today’s news on AI-powered financial data reinforces a useful operating principle:
Real-time data is only valuable when it reaches the next safe action.
Detect → verify → route → decide.
Where could live signals remove waiting? #AI #Automation
Your next best lead may be hiding in a comment, not a dashboard.
A post earns its keep when the response is captured, qualified, and routed to the right person.
Reach starts the conversation. Workflow keeps it moving.
Where do social signals get lost? #SocialMedia #Automation
AI adoption gets real when a workflow can answer one question:
“What happens when the confidence is low?”
A practical 4-part pattern for making AI useful without making your team the cleanup crew 🧵
3. Learn from the exception
Track why work was escalated. Then fix the missing data, unclear policy, or weak instruction.
Exceptions are not failures. They are your roadmap.
4. Measure recovery, not just speed
A fast agent that creates rework is not automation.
Watch: escalation rate, override rate, and time to resolution.
Build the handoff before you scale the action. #AIAutomation





