Insights
Humans in the Loop, Not in the Way
Teams fall in love with shiny demos, skip the boring strategy work, then wonder why nobody trusts them later. The real problem isn't the technology, it's jumping to tools before defining governance.
Starting with vendor selection is how you guarantee yourself a year of rebuilding everything from scratch.
Tools don't fix strategy problems. They make whatever you're already doing happen faster, which sounds great until you realize you're now producing bad decisions at scale. Most organizations I see skip the tedious parts about who reviews what and when, then act shocked when their AI rollout craters after a few months because nobody trusts it.
Let me walk through what actually keeps AI projects from turning into expensive disasters. Where the common failures happen. What questions you should be asking if you want this to work.
Sort out the basics before anyone writes code
Before the first prototype demo ever gets shown in a meeting, make three decisions. Make them early and stick to them.
What breaks things permanently in your business
Not the theoretical stuff about AI safety, but the reality. What actions cause problems you can't just undo with a few clicks?
Billing changes that already hit customer credit cards. Access revisions that lock people out. Public content that's already been seen by thousands of people. That kind of thing.
Two buckets. Reversible and not reversible. The not reversible bucket gets human signoff every single time, with names attached for real accountability.
Where humans own outcomes instead of just taking the blame
Don't rely on human oversight as a safety blanket. Make it specific, and set up three levels:
Propose: AI suggests options, humans pick one. Log everything.
Approve: AI drafts something, humans edit and approve it. Build in rollback from day one.
Execute: AI handles low-risk work automatically within clear boundaries. Full visibility required.
Match your critical workflows to these levels before you build anything. Having this argument after you've already got a prototype means you're already way behind.
How you'll actually know what the system did
Black boxes kill trust faster than anything. Decide now what gets captured for every AI action. The input. Which model ran. Its confidence level. Why it chose what it chose. What the human decided.
This is needed infrastructure not a nice to have feature you add in later. If someone can't trace a decision in thirty seconds you don't have a governance system, you more than likely have organized chaos.
Those three decisions are your foundation. Humans control the irreversible decisions. Automation handles the boring safe work. Everything gets documented somewhere people can actually find it.
When strategy falls apart it looks random
First week everyone's excited. Fifth week there's a new Slackor Teams channel with "AI" in the name and forty people posting ideas. Eighth week you're staring at a bunch of partially built features that nobody uses.
Starting backwards: You sign a contract then go looking for problems it might solve. Your roadmap becomes a feature wishlist. Chat. Summarize. Classify. Generate. Each team invents their own quality process and users get confused by inconsistent experiences. Adoption looks okay initially then drops off hard, which means executives start asking uncomfortable questions about who approved the budget for this.
Egos instead of oversight: You add human review steps everywhere to feel safe. Medium risk items sit in queues. Reviewers rubber stamp 95% because they're busy. The other 5% explodes into arguments about responsibility. Everything takes longer. Quality doesn't improve. Your best people spend hours validating things that are fine instead of fixing things that aren't.
What works: Build proposal systems not automation systems. Show reasoning when stakes are high. Stay quiet when they're low. Put human attention exactly where uncertainty meets real impact. Everything else runs automatically with logging or gets batched for quick reviews.
Keep your exact scoring private. Share the questions but not the thresholds. Once people know the precise cutoff they will game it. Then your data gets weird and you can't figure out why, nor speak to it when asked.
Building a weekly one-pager that executives actually read
Skip the 30-page slide deck. One page. Takes five minutes. Just answers to questions they actually care about.
Top section: Which business outcome are you moving this quarter. Which workflow you're changing and what you're leaving alone. What's irreversible and who approves it with actual names not departments.
What shipped: Concrete workflow changes. How confidence displays. How rollback works. Where humans intervene. True adoption numbers. Accept versus edit rates. How many reversals you've done. No engagement metrics or other vanity numbers.
What didn't ship & why: Risks you hit. Data problems. Confidence issues. Your decision to pause or redesign or escalate. Who owns it. When you'll revisit it.
Next risk: The change you're planning, and what could go wrong. Which checkpoint makes it safe. What data you still need.
Ask these questions every week:
Is anything that should need approval running automatically?
Did any irreversible action happen without a named approver?
Where did humans waste time that better checkpoints could have saved?
What failure taught you something that changes your thresholds?
Three checkpoints that handle most situations
More than three and you're overthinking it.
Proposal checkpoint when things get confusing:
Triggers when model confidence drops or impact rises past your threshold. One screen. AI proposal with reasoning on left. Approve or edit options on right. Target 80 to 90% one tap approvals. Lower than that means your thresholds are off.
Approval checkpoint for permanent changes:
Triggers for anything you can't cleanly undo. Named human approver. Visible rollback option. One screen preflight checklist. Clear ownership and immediate reversibility. When things go wrong, and eventually they will, you'll be grateful.
Observability checkpoint for the rest:
All automated reversible actions logged in one system. Filters for low confidence high impact or unusual patterns. Review by exception. Work keeps moving. Humans catch anomalies without babysitting everything.
Making it stick: Same patterns everywhere so people develop muscle memory. Scale explanations to risk. Teach people what to ignore not just what to check. Treat overrides and edits as training data not criticism. That's how the AI improves assuming you're actually capturing that feedback, of course most aren't.
My Take About strategy
You don't need bigger models. You don't need better tools. You need better decisions about where humans add value and where they add friction.
Most AI initiatives fail because teams pick vendors first then work backwards to problems. They add human review as an afterthought then wonder why trust never happens. They measure activity instead of outcomes. They call random, half-baked experiments progress.
Real strategy means defining what you can't afford to mess up. Putting names on those decisions. Letting everything else move fast with spot checks. If your roadmap looks like a random feature list you're setting yourself up for rework. If your oversight feels performative you're setting up for delays and budget overruns and panicked executives.
Pick the smallest change that moves a real business metric. Ship it with actual checkpoints where humans matter. Prove it works. Do it again.
Everything else is expensive guessing game that makes people feel innovative while burning through money.
More to Discover
Insights
Humans in the Loop, Not in the Way
Teams fall in love with shiny demos, skip the boring strategy work, then wonder why nobody trusts them later. The real problem isn't the technology, it's jumping to tools before defining governance.
Starting with vendor selection is how you guarantee yourself a year of rebuilding everything from scratch.
Tools don't fix strategy problems. They make whatever you're already doing happen faster, which sounds great until you realize you're now producing bad decisions at scale. Most organizations I see skip the tedious parts about who reviews what and when, then act shocked when their AI rollout craters after a few months because nobody trusts it.
Let me walk through what actually keeps AI projects from turning into expensive disasters. Where the common failures happen. What questions you should be asking if you want this to work.
Sort out the basics before anyone writes code
Before the first prototype demo ever gets shown in a meeting, make three decisions. Make them early and stick to them.
What breaks things permanently in your business
Not the theoretical stuff about AI safety, but the reality. What actions cause problems you can't just undo with a few clicks?
Billing changes that already hit customer credit cards. Access revisions that lock people out. Public content that's already been seen by thousands of people. That kind of thing.
Two buckets. Reversible and not reversible. The not reversible bucket gets human signoff every single time, with names attached for real accountability.
Where humans own outcomes instead of just taking the blame
Don't rely on human oversight as a safety blanket. Make it specific, and set up three levels:
Propose: AI suggests options, humans pick one. Log everything.
Approve: AI drafts something, humans edit and approve it. Build in rollback from day one.
Execute: AI handles low-risk work automatically within clear boundaries. Full visibility required.
Match your critical workflows to these levels before you build anything. Having this argument after you've already got a prototype means you're already way behind.
How you'll actually know what the system did
Black boxes kill trust faster than anything. Decide now what gets captured for every AI action. The input. Which model ran. Its confidence level. Why it chose what it chose. What the human decided.
This is needed infrastructure not a nice to have feature you add in later. If someone can't trace a decision in thirty seconds you don't have a governance system, you more than likely have organized chaos.
Those three decisions are your foundation. Humans control the irreversible decisions. Automation handles the boring safe work. Everything gets documented somewhere people can actually find it.
When strategy falls apart it looks random
First week everyone's excited. Fifth week there's a new Slackor Teams channel with "AI" in the name and forty people posting ideas. Eighth week you're staring at a bunch of partially built features that nobody uses.
Starting backwards: You sign a contract then go looking for problems it might solve. Your roadmap becomes a feature wishlist. Chat. Summarize. Classify. Generate. Each team invents their own quality process and users get confused by inconsistent experiences. Adoption looks okay initially then drops off hard, which means executives start asking uncomfortable questions about who approved the budget for this.
Egos instead of oversight: You add human review steps everywhere to feel safe. Medium risk items sit in queues. Reviewers rubber stamp 95% because they're busy. The other 5% explodes into arguments about responsibility. Everything takes longer. Quality doesn't improve. Your best people spend hours validating things that are fine instead of fixing things that aren't.
What works: Build proposal systems not automation systems. Show reasoning when stakes are high. Stay quiet when they're low. Put human attention exactly where uncertainty meets real impact. Everything else runs automatically with logging or gets batched for quick reviews.
Keep your exact scoring private. Share the questions but not the thresholds. Once people know the precise cutoff they will game it. Then your data gets weird and you can't figure out why, nor speak to it when asked.
Building a weekly one-pager that executives actually read
Skip the 30-page slide deck. One page. Takes five minutes. Just answers to questions they actually care about.
Top section: Which business outcome are you moving this quarter. Which workflow you're changing and what you're leaving alone. What's irreversible and who approves it with actual names not departments.
What shipped: Concrete workflow changes. How confidence displays. How rollback works. Where humans intervene. True adoption numbers. Accept versus edit rates. How many reversals you've done. No engagement metrics or other vanity numbers.
What didn't ship & why: Risks you hit. Data problems. Confidence issues. Your decision to pause or redesign or escalate. Who owns it. When you'll revisit it.
Next risk: The change you're planning, and what could go wrong. Which checkpoint makes it safe. What data you still need.
Ask these questions every week:
Is anything that should need approval running automatically?
Did any irreversible action happen without a named approver?
Where did humans waste time that better checkpoints could have saved?
What failure taught you something that changes your thresholds?
Three checkpoints that handle most situations
More than three and you're overthinking it.
Proposal checkpoint when things get confusing:
Triggers when model confidence drops or impact rises past your threshold. One screen. AI proposal with reasoning on left. Approve or edit options on right. Target 80 to 90% one tap approvals. Lower than that means your thresholds are off.
Approval checkpoint for permanent changes:
Triggers for anything you can't cleanly undo. Named human approver. Visible rollback option. One screen preflight checklist. Clear ownership and immediate reversibility. When things go wrong, and eventually they will, you'll be grateful.
Observability checkpoint for the rest:
All automated reversible actions logged in one system. Filters for low confidence high impact or unusual patterns. Review by exception. Work keeps moving. Humans catch anomalies without babysitting everything.
Making it stick: Same patterns everywhere so people develop muscle memory. Scale explanations to risk. Teach people what to ignore not just what to check. Treat overrides and edits as training data not criticism. That's how the AI improves assuming you're actually capturing that feedback, of course most aren't.
My Take About strategy
You don't need bigger models. You don't need better tools. You need better decisions about where humans add value and where they add friction.
Most AI initiatives fail because teams pick vendors first then work backwards to problems. They add human review as an afterthought then wonder why trust never happens. They measure activity instead of outcomes. They call random, half-baked experiments progress.
Real strategy means defining what you can't afford to mess up. Putting names on those decisions. Letting everything else move fast with spot checks. If your roadmap looks like a random feature list you're setting yourself up for rework. If your oversight feels performative you're setting up for delays and budget overruns and panicked executives.
Pick the smallest change that moves a real business metric. Ship it with actual checkpoints where humans matter. Prove it works. Do it again.
Everything else is expensive guessing game that makes people feel innovative while burning through money.
More to Discover
Insights
Humans in the Loop, Not in the Way
Teams fall in love with shiny demos, skip the boring strategy work, then wonder why nobody trusts them later. The real problem isn't the technology, it's jumping to tools before defining governance.
Starting with vendor selection is how you guarantee yourself a year of rebuilding everything from scratch.
Tools don't fix strategy problems. They make whatever you're already doing happen faster, which sounds great until you realize you're now producing bad decisions at scale. Most organizations I see skip the tedious parts about who reviews what and when, then act shocked when their AI rollout craters after a few months because nobody trusts it.
Let me walk through what actually keeps AI projects from turning into expensive disasters. Where the common failures happen. What questions you should be asking if you want this to work.
Sort out the basics before anyone writes code
Before the first prototype demo ever gets shown in a meeting, make three decisions. Make them early and stick to them.
What breaks things permanently in your business
Not the theoretical stuff about AI safety, but the reality. What actions cause problems you can't just undo with a few clicks?
Billing changes that already hit customer credit cards. Access revisions that lock people out. Public content that's already been seen by thousands of people. That kind of thing.
Two buckets. Reversible and not reversible. The not reversible bucket gets human signoff every single time, with names attached for real accountability.
Where humans own outcomes instead of just taking the blame
Don't rely on human oversight as a safety blanket. Make it specific, and set up three levels:
Propose: AI suggests options, humans pick one. Log everything.
Approve: AI drafts something, humans edit and approve it. Build in rollback from day one.
Execute: AI handles low-risk work automatically within clear boundaries. Full visibility required.
Match your critical workflows to these levels before you build anything. Having this argument after you've already got a prototype means you're already way behind.
How you'll actually know what the system did
Black boxes kill trust faster than anything. Decide now what gets captured for every AI action. The input. Which model ran. Its confidence level. Why it chose what it chose. What the human decided.
This is needed infrastructure not a nice to have feature you add in later. If someone can't trace a decision in thirty seconds you don't have a governance system, you more than likely have organized chaos.
Those three decisions are your foundation. Humans control the irreversible decisions. Automation handles the boring safe work. Everything gets documented somewhere people can actually find it.
When strategy falls apart it looks random
First week everyone's excited. Fifth week there's a new Slackor Teams channel with "AI" in the name and forty people posting ideas. Eighth week you're staring at a bunch of partially built features that nobody uses.
Starting backwards: You sign a contract then go looking for problems it might solve. Your roadmap becomes a feature wishlist. Chat. Summarize. Classify. Generate. Each team invents their own quality process and users get confused by inconsistent experiences. Adoption looks okay initially then drops off hard, which means executives start asking uncomfortable questions about who approved the budget for this.
Egos instead of oversight: You add human review steps everywhere to feel safe. Medium risk items sit in queues. Reviewers rubber stamp 95% because they're busy. The other 5% explodes into arguments about responsibility. Everything takes longer. Quality doesn't improve. Your best people spend hours validating things that are fine instead of fixing things that aren't.
What works: Build proposal systems not automation systems. Show reasoning when stakes are high. Stay quiet when they're low. Put human attention exactly where uncertainty meets real impact. Everything else runs automatically with logging or gets batched for quick reviews.
Keep your exact scoring private. Share the questions but not the thresholds. Once people know the precise cutoff they will game it. Then your data gets weird and you can't figure out why, nor speak to it when asked.
Building a weekly one-pager that executives actually read
Skip the 30-page slide deck. One page. Takes five minutes. Just answers to questions they actually care about.
Top section: Which business outcome are you moving this quarter. Which workflow you're changing and what you're leaving alone. What's irreversible and who approves it with actual names not departments.
What shipped: Concrete workflow changes. How confidence displays. How rollback works. Where humans intervene. True adoption numbers. Accept versus edit rates. How many reversals you've done. No engagement metrics or other vanity numbers.
What didn't ship & why: Risks you hit. Data problems. Confidence issues. Your decision to pause or redesign or escalate. Who owns it. When you'll revisit it.
Next risk: The change you're planning, and what could go wrong. Which checkpoint makes it safe. What data you still need.
Ask these questions every week:
Is anything that should need approval running automatically?
Did any irreversible action happen without a named approver?
Where did humans waste time that better checkpoints could have saved?
What failure taught you something that changes your thresholds?
Three checkpoints that handle most situations
More than three and you're overthinking it.
Proposal checkpoint when things get confusing:
Triggers when model confidence drops or impact rises past your threshold. One screen. AI proposal with reasoning on left. Approve or edit options on right. Target 80 to 90% one tap approvals. Lower than that means your thresholds are off.
Approval checkpoint for permanent changes:
Triggers for anything you can't cleanly undo. Named human approver. Visible rollback option. One screen preflight checklist. Clear ownership and immediate reversibility. When things go wrong, and eventually they will, you'll be grateful.
Observability checkpoint for the rest:
All automated reversible actions logged in one system. Filters for low confidence high impact or unusual patterns. Review by exception. Work keeps moving. Humans catch anomalies without babysitting everything.
Making it stick: Same patterns everywhere so people develop muscle memory. Scale explanations to risk. Teach people what to ignore not just what to check. Treat overrides and edits as training data not criticism. That's how the AI improves assuming you're actually capturing that feedback, of course most aren't.
My Take About strategy
You don't need bigger models. You don't need better tools. You need better decisions about where humans add value and where they add friction.
Most AI initiatives fail because teams pick vendors first then work backwards to problems. They add human review as an afterthought then wonder why trust never happens. They measure activity instead of outcomes. They call random, half-baked experiments progress.
Real strategy means defining what you can't afford to mess up. Putting names on those decisions. Letting everything else move fast with spot checks. If your roadmap looks like a random feature list you're setting yourself up for rework. If your oversight feels performative you're setting up for delays and budget overruns and panicked executives.
Pick the smallest change that moves a real business metric. Ship it with actual checkpoints where humans matter. Prove it works. Do it again.
Everything else is expensive guessing game that makes people feel innovative while burning through money.