Artificial Intelligence
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.
Tools don't fix strategy problems. They make whatever you're already doing happen faster, which sounds great until you realize you're producing bad decisions at scale.
Most organizations skip the tedious parts about who reviews what and when. Then they act shocked when their AI rollout craters after a few months because nobody trusts it. Starting with vendor selection is how you guarantee yourself a year of rebuilding everything from scratch.
Here's what actually keeps AI projects from turning into expensive disasters.
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
Not the theoretical stuff about AI safety. The reality. What actions cause problems you can't 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.
Two buckets that you should focus on are reversible and not reversible. The not reversible bucket gets human signoff without fail, with names attached for real accountability.
Where humans own outcomes
Don't rely on human oversight as a safety blanket, instead make it specific. 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 behind.
How you'll know what the system did
Black boxes kill trust faster than anything. Decide now what gets captured for every AI action. The input and which model ran, along with its confidence level. Why it chose what it chose. What the human decided.
This is infrastructure, not a feature you add later. If someone on your team can't trace a decision in thirty seconds, you have gotten yourself into organized chaos.
Those three decisions are your foundation. Humans control the irreversible, and automation handles the boring safe work. Everything gets documented somewhere people can actually find it.
When strategy falls apart
First week everyone's excited. Fifth week there's a new Slack 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. Users get confused by inconsistent experiences. Adoption looks okay initially then drops off hard. Executives start asking uncomfortable questions about who approved the budget.
Oversight as ego protection: 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.
A weekly one-pager executives will actually read
Skip the 30-page slide deck. One page. 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 vanity metrics.
What didn't ship and 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: Triggers when model confidence drops or impact rises past your threshold. One screen. AI proposal with reasoning on the left. Approve or edit options on the right. Target 80 to 90% one-tap approvals. Lower than that means your thresholds are off.
Approval checkpoint: 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, you'll be grateful this exists.
Observability checkpoint: 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. That's how the system improves, assuming you're actually capturing that feedback. Most organizations aren't.
My Take About strategy
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. Human review gets added as an afterthought and trust never develops. Activity gets measured instead of outcomes. Random, half-baked experiments get called 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, and then do it again.
More to Discover
Artificial Intelligence
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.
Tools don't fix strategy problems. They make whatever you're already doing happen faster, which sounds great until you realize you're producing bad decisions at scale.
Most organizations skip the tedious parts about who reviews what and when. Then they act shocked when their AI rollout craters after a few months because nobody trusts it. Starting with vendor selection is how you guarantee yourself a year of rebuilding everything from scratch.
Here's what actually keeps AI projects from turning into expensive disasters.
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
Not the theoretical stuff about AI safety. The reality. What actions cause problems you can't 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.
Two buckets that you should focus on are reversible and not reversible. The not reversible bucket gets human signoff without fail, with names attached for real accountability.
Where humans own outcomes
Don't rely on human oversight as a safety blanket, instead make it specific. 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 behind.
How you'll know what the system did
Black boxes kill trust faster than anything. Decide now what gets captured for every AI action. The input and which model ran, along with its confidence level. Why it chose what it chose. What the human decided.
This is infrastructure, not a feature you add later. If someone on your team can't trace a decision in thirty seconds, you have gotten yourself into organized chaos.
Those three decisions are your foundation. Humans control the irreversible, and automation handles the boring safe work. Everything gets documented somewhere people can actually find it.
When strategy falls apart
First week everyone's excited. Fifth week there's a new Slack 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. Users get confused by inconsistent experiences. Adoption looks okay initially then drops off hard. Executives start asking uncomfortable questions about who approved the budget.
Oversight as ego protection: 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.
A weekly one-pager executives will actually read
Skip the 30-page slide deck. One page. 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 vanity metrics.
What didn't ship and 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: Triggers when model confidence drops or impact rises past your threshold. One screen. AI proposal with reasoning on the left. Approve or edit options on the right. Target 80 to 90% one-tap approvals. Lower than that means your thresholds are off.
Approval checkpoint: 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, you'll be grateful this exists.
Observability checkpoint: 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. That's how the system improves, assuming you're actually capturing that feedback. Most organizations aren't.
My Take About strategy
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. Human review gets added as an afterthought and trust never develops. Activity gets measured instead of outcomes. Random, half-baked experiments get called 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, and then do it again.
More to Discover
Artificial Intelligence
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.
Tools don't fix strategy problems. They make whatever you're already doing happen faster, which sounds great until you realize you're producing bad decisions at scale.
Most organizations skip the tedious parts about who reviews what and when. Then they act shocked when their AI rollout craters after a few months because nobody trusts it. Starting with vendor selection is how you guarantee yourself a year of rebuilding everything from scratch.
Here's what actually keeps AI projects from turning into expensive disasters.
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
Not the theoretical stuff about AI safety. The reality. What actions cause problems you can't 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.
Two buckets that you should focus on are reversible and not reversible. The not reversible bucket gets human signoff without fail, with names attached for real accountability.
Where humans own outcomes
Don't rely on human oversight as a safety blanket, instead make it specific. 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 behind.
How you'll know what the system did
Black boxes kill trust faster than anything. Decide now what gets captured for every AI action. The input and which model ran, along with its confidence level. Why it chose what it chose. What the human decided.
This is infrastructure, not a feature you add later. If someone on your team can't trace a decision in thirty seconds, you have gotten yourself into organized chaos.
Those three decisions are your foundation. Humans control the irreversible, and automation handles the boring safe work. Everything gets documented somewhere people can actually find it.
When strategy falls apart
First week everyone's excited. Fifth week there's a new Slack 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. Users get confused by inconsistent experiences. Adoption looks okay initially then drops off hard. Executives start asking uncomfortable questions about who approved the budget.
Oversight as ego protection: 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.
A weekly one-pager executives will actually read
Skip the 30-page slide deck. One page. 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 vanity metrics.
What didn't ship and 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: Triggers when model confidence drops or impact rises past your threshold. One screen. AI proposal with reasoning on the left. Approve or edit options on the right. Target 80 to 90% one-tap approvals. Lower than that means your thresholds are off.
Approval checkpoint: 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, you'll be grateful this exists.
Observability checkpoint: 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. That's how the system improves, assuming you're actually capturing that feedback. Most organizations aren't.
My Take About strategy
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. Human review gets added as an afterthought and trust never develops. Activity gets measured instead of outcomes. Random, half-baked experiments get called 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, and then do it again.