Insights
AI Won’t Save You From Your Bad Strategy
Companies are spending millions on AI that doesn't work because they're solving the wrong problems. Here's how to avoid the common disasters and build systems people actually use.
Let's get something out of the way: "We're adding AI" isn't a strategy.
It's what executives say when they know the company needs to stay current but they have no idea what to do. If your product is broken or dated, your teams are siloed, and your goals change every time someone reads a new article, no algorithm is going to save you. AI will just make your dysfunction faster and more expensive.
This is a reality check for leaders who want AI actually to drive business growth, not just generate press releases. The approach is straightforward: pick real problems, build systems people trust, keep humans in charge of what matters, and measure outcomes that actually count.
Start With Strategy, Not Hopes & Dreams
A strategy isn't a pile of AI tools. It's a choice about what you'll accomplish and what you'll ignore.
Define two or three concrete outcomes for the next six months. Not "improve customer experience with AI" but "cut onboarding drop-off by 15% using smart form guidance" or "reduce support ticket volume by 25% with proactive help on the three most common issues."
Then face your real constraints. Do you have clean data or a mess of spreadsheets? Can your team actually build and maintain this stuff? What's your actual tolerance for risk when things go sideways?
Pick the smallest scope that still matters to the business. Too small and nobody cares. Too big and you'll spend a year building something nobody uses. If you can't explain your strategy in one slide without buzzwords, you don't have a strategy. You have a mood board.
Pick Problems, Not Features
Teams that start with "let's add a chatbot" end up with a chat box that talks to itself. Smart teams begin with a real problem that's bleeding time or money.
Find your workflow chokepoints. Where do tasks stall? Where do people get frustrated and quit? Where does work pile up waiting for someone to make sense of it?
Maybe it's that confusing checkout flow where half your customers bail out. Maybe it's research reports that take three weeks to write and five minutes to ignore. Maybe it's the same support questions flooding in because your settings page makes no sense.
Now design AI interventions with clear success metrics: "Smart suggestions reduce checkout abandonment by 20%" or "Automated insight clustering cuts research time from three weeks to four days while tripling stakeholder engagement."
You're solving problems now, not collecting new features.
Build Trust In Your Infrastructure
If people don't trust your system, they won't use it. If your team doesn't trust it, they'll work around it.
Trust isn't a security badge on your homepage. It's dozens of small interactions that make your system predictable and controllable.
Show your work when it matters. If AI is making a recommendation that costs money or affects people, explain the main reason. Don't write a dissertation for every suggestion, but provide people with enough context to feel confident about their choice.
Always provide exits. Let people override, undo, or opt out easily. Treat these as normal user flows, not edge cases for demanding customers.
When possible, link to sources. If your AI suggests a policy change, link to the actual policy. If it recommends an approach, show the data it's using. Your system should earn trust through transparency, not demand it through marketing.
Keep human judgment in the loop on purpose. Decide upfront: What does AI draft for human approval? What does it recommend for human decision? What can it execute automatically?
Set confidence thresholds that make sense. High confidence plus low stakes? Automate it. Lower confidence or higher stakes? Require human review. When AI is uncertain or the impact is significant, require someone to be accountable to sign off.
Measure What Actually Moves
If your success metrics are model accuracy scores, you're measuring the wrong thing. The question isn't whether your AI is technically impressive. It's whether your business improved.
Track adoption and usage depth. How many people use AI features regularly? Do they complete more tasks or abandon them? How much time do they actually save per task?
Measure real efficiency gains. Are research cycles actually faster? Is support resolution quicker? Don't just count "hours saved" and call it success. Show that those hours went somewhere useful.
Watch for quality and trust signals. How often do people override AI suggestions? When confidence is high, how frequently are the results wrong? Are escalation rates going up or down?
Most importantly, tie it to business outcomes. Did conversion improve? Did churn decrease? Did customer satisfaction scores move? Did support costs actually drop?
Set baselines before you ship anything. Without a starting point, your "improvement" is just wishful thinking with charts.
Ship Smart, Not Fast
The fastest way to kill an AI project is to promise a platform and deliver a demo. Instead, ship in phases that build on each other.
Start with foundations: nail down your use cases, set up proper data access, and build the trust and measurement systems. Get the boring infrastructure right before you get fancy.
Then integrate into real workflows. Replace your demo with actual usage in the product. Train your team on when to trust AI recommendations and when to push back.
Finally, scale what works. Automate the low-risk loops, keep humans involved in the high-stakes decisions, and connect your measurements to the dashboards executives actually read.
Skip the Common Disasters
Learn from others' mistakes. Don't add chat interfaces to products that nobody wanted to chat with in the first place. Don't treat any single AI model like a religion. Mix and match based on the needs of each job.
Don't slap "secure" labels and icons on your interface without real privacy boundaries. Don't report technical metrics to executives who care about revenue, and don't automate tasks that require judgment, context, or carry significant risk.
When things go wrong, don't hunt for success stories to make yourself feel better. Find the failures, understand them, and fix them publicly.
The Reality Check
Here's what nobody wants to admit: AI doesn't rescue you from having to make the hard choices. It just makes the consequences of bad choices arrive faster and cost more.
Your users will tell you when your system is guessing. Your team will let you know when your governance is merely for show. Your metrics will tell you when you're not actually moving the business.
So be honest about what you're trying, why it matters, where it can fail, how you'll measure success, and when you'll quit if it's not working.
The companies that win with AI won't be the ones with the flashiest demos or the biggest budgets. They'll be the ones that pick real problems, build systems people actually trust, keep humans in charge of what matters, and measure results like adults.
Everything else are just expensive ways to avoid doing the actual work of running a business.
More to Discover
Insights
AI Won’t Save You From Your Bad Strategy
Companies are spending millions on AI that doesn't work because they're solving the wrong problems. Here's how to avoid the common disasters and build systems people actually use.
Let's get something out of the way: "We're adding AI" isn't a strategy.
It's what executives say when they know the company needs to stay current but they have no idea what to do. If your product is broken or dated, your teams are siloed, and your goals change every time someone reads a new article, no algorithm is going to save you. AI will just make your dysfunction faster and more expensive.
This is a reality check for leaders who want AI actually to drive business growth, not just generate press releases. The approach is straightforward: pick real problems, build systems people trust, keep humans in charge of what matters, and measure outcomes that actually count.
Start With Strategy, Not Hopes & Dreams
A strategy isn't a pile of AI tools. It's a choice about what you'll accomplish and what you'll ignore.
Define two or three concrete outcomes for the next six months. Not "improve customer experience with AI" but "cut onboarding drop-off by 15% using smart form guidance" or "reduce support ticket volume by 25% with proactive help on the three most common issues."
Then face your real constraints. Do you have clean data or a mess of spreadsheets? Can your team actually build and maintain this stuff? What's your actual tolerance for risk when things go sideways?
Pick the smallest scope that still matters to the business. Too small and nobody cares. Too big and you'll spend a year building something nobody uses. If you can't explain your strategy in one slide without buzzwords, you don't have a strategy. You have a mood board.
Pick Problems, Not Features
Teams that start with "let's add a chatbot" end up with a chat box that talks to itself. Smart teams begin with a real problem that's bleeding time or money.
Find your workflow chokepoints. Where do tasks stall? Where do people get frustrated and quit? Where does work pile up waiting for someone to make sense of it?
Maybe it's that confusing checkout flow where half your customers bail out. Maybe it's research reports that take three weeks to write and five minutes to ignore. Maybe it's the same support questions flooding in because your settings page makes no sense.
Now design AI interventions with clear success metrics: "Smart suggestions reduce checkout abandonment by 20%" or "Automated insight clustering cuts research time from three weeks to four days while tripling stakeholder engagement."
You're solving problems now, not collecting new features.
Build Trust In Your Infrastructure
If people don't trust your system, they won't use it. If your team doesn't trust it, they'll work around it.
Trust isn't a security badge on your homepage. It's dozens of small interactions that make your system predictable and controllable.
Show your work when it matters. If AI is making a recommendation that costs money or affects people, explain the main reason. Don't write a dissertation for every suggestion, but provide people with enough context to feel confident about their choice.
Always provide exits. Let people override, undo, or opt out easily. Treat these as normal user flows, not edge cases for demanding customers.
When possible, link to sources. If your AI suggests a policy change, link to the actual policy. If it recommends an approach, show the data it's using. Your system should earn trust through transparency, not demand it through marketing.
Keep human judgment in the loop on purpose. Decide upfront: What does AI draft for human approval? What does it recommend for human decision? What can it execute automatically?
Set confidence thresholds that make sense. High confidence plus low stakes? Automate it. Lower confidence or higher stakes? Require human review. When AI is uncertain or the impact is significant, require someone to be accountable to sign off.
Measure What Actually Moves
If your success metrics are model accuracy scores, you're measuring the wrong thing. The question isn't whether your AI is technically impressive. It's whether your business improved.
Track adoption and usage depth. How many people use AI features regularly? Do they complete more tasks or abandon them? How much time do they actually save per task?
Measure real efficiency gains. Are research cycles actually faster? Is support resolution quicker? Don't just count "hours saved" and call it success. Show that those hours went somewhere useful.
Watch for quality and trust signals. How often do people override AI suggestions? When confidence is high, how frequently are the results wrong? Are escalation rates going up or down?
Most importantly, tie it to business outcomes. Did conversion improve? Did churn decrease? Did customer satisfaction scores move? Did support costs actually drop?
Set baselines before you ship anything. Without a starting point, your "improvement" is just wishful thinking with charts.
Ship Smart, Not Fast
The fastest way to kill an AI project is to promise a platform and deliver a demo. Instead, ship in phases that build on each other.
Start with foundations: nail down your use cases, set up proper data access, and build the trust and measurement systems. Get the boring infrastructure right before you get fancy.
Then integrate into real workflows. Replace your demo with actual usage in the product. Train your team on when to trust AI recommendations and when to push back.
Finally, scale what works. Automate the low-risk loops, keep humans involved in the high-stakes decisions, and connect your measurements to the dashboards executives actually read.
Skip the Common Disasters
Learn from others' mistakes. Don't add chat interfaces to products that nobody wanted to chat with in the first place. Don't treat any single AI model like a religion. Mix and match based on the needs of each job.
Don't slap "secure" labels and icons on your interface without real privacy boundaries. Don't report technical metrics to executives who care about revenue, and don't automate tasks that require judgment, context, or carry significant risk.
When things go wrong, don't hunt for success stories to make yourself feel better. Find the failures, understand them, and fix them publicly.
The Reality Check
Here's what nobody wants to admit: AI doesn't rescue you from having to make the hard choices. It just makes the consequences of bad choices arrive faster and cost more.
Your users will tell you when your system is guessing. Your team will let you know when your governance is merely for show. Your metrics will tell you when you're not actually moving the business.
So be honest about what you're trying, why it matters, where it can fail, how you'll measure success, and when you'll quit if it's not working.
The companies that win with AI won't be the ones with the flashiest demos or the biggest budgets. They'll be the ones that pick real problems, build systems people actually trust, keep humans in charge of what matters, and measure results like adults.
Everything else are just expensive ways to avoid doing the actual work of running a business.
More to Discover
Insights
AI Won’t Save You From Your Bad Strategy
Companies are spending millions on AI that doesn't work because they're solving the wrong problems. Here's how to avoid the common disasters and build systems people actually use.
Let's get something out of the way: "We're adding AI" isn't a strategy.
It's what executives say when they know the company needs to stay current but they have no idea what to do. If your product is broken or dated, your teams are siloed, and your goals change every time someone reads a new article, no algorithm is going to save you. AI will just make your dysfunction faster and more expensive.
This is a reality check for leaders who want AI actually to drive business growth, not just generate press releases. The approach is straightforward: pick real problems, build systems people trust, keep humans in charge of what matters, and measure outcomes that actually count.
Start With Strategy, Not Hopes & Dreams
A strategy isn't a pile of AI tools. It's a choice about what you'll accomplish and what you'll ignore.
Define two or three concrete outcomes for the next six months. Not "improve customer experience with AI" but "cut onboarding drop-off by 15% using smart form guidance" or "reduce support ticket volume by 25% with proactive help on the three most common issues."
Then face your real constraints. Do you have clean data or a mess of spreadsheets? Can your team actually build and maintain this stuff? What's your actual tolerance for risk when things go sideways?
Pick the smallest scope that still matters to the business. Too small and nobody cares. Too big and you'll spend a year building something nobody uses. If you can't explain your strategy in one slide without buzzwords, you don't have a strategy. You have a mood board.
Pick Problems, Not Features
Teams that start with "let's add a chatbot" end up with a chat box that talks to itself. Smart teams begin with a real problem that's bleeding time or money.
Find your workflow chokepoints. Where do tasks stall? Where do people get frustrated and quit? Where does work pile up waiting for someone to make sense of it?
Maybe it's that confusing checkout flow where half your customers bail out. Maybe it's research reports that take three weeks to write and five minutes to ignore. Maybe it's the same support questions flooding in because your settings page makes no sense.
Now design AI interventions with clear success metrics: "Smart suggestions reduce checkout abandonment by 20%" or "Automated insight clustering cuts research time from three weeks to four days while tripling stakeholder engagement."
You're solving problems now, not collecting new features.
Build Trust In Your Infrastructure
If people don't trust your system, they won't use it. If your team doesn't trust it, they'll work around it.
Trust isn't a security badge on your homepage. It's dozens of small interactions that make your system predictable and controllable.
Show your work when it matters. If AI is making a recommendation that costs money or affects people, explain the main reason. Don't write a dissertation for every suggestion, but provide people with enough context to feel confident about their choice.
Always provide exits. Let people override, undo, or opt out easily. Treat these as normal user flows, not edge cases for demanding customers.
When possible, link to sources. If your AI suggests a policy change, link to the actual policy. If it recommends an approach, show the data it's using. Your system should earn trust through transparency, not demand it through marketing.
Keep human judgment in the loop on purpose. Decide upfront: What does AI draft for human approval? What does it recommend for human decision? What can it execute automatically?
Set confidence thresholds that make sense. High confidence plus low stakes? Automate it. Lower confidence or higher stakes? Require human review. When AI is uncertain or the impact is significant, require someone to be accountable to sign off.
Measure What Actually Moves
If your success metrics are model accuracy scores, you're measuring the wrong thing. The question isn't whether your AI is technically impressive. It's whether your business improved.
Track adoption and usage depth. How many people use AI features regularly? Do they complete more tasks or abandon them? How much time do they actually save per task?
Measure real efficiency gains. Are research cycles actually faster? Is support resolution quicker? Don't just count "hours saved" and call it success. Show that those hours went somewhere useful.
Watch for quality and trust signals. How often do people override AI suggestions? When confidence is high, how frequently are the results wrong? Are escalation rates going up or down?
Most importantly, tie it to business outcomes. Did conversion improve? Did churn decrease? Did customer satisfaction scores move? Did support costs actually drop?
Set baselines before you ship anything. Without a starting point, your "improvement" is just wishful thinking with charts.
Ship Smart, Not Fast
The fastest way to kill an AI project is to promise a platform and deliver a demo. Instead, ship in phases that build on each other.
Start with foundations: nail down your use cases, set up proper data access, and build the trust and measurement systems. Get the boring infrastructure right before you get fancy.
Then integrate into real workflows. Replace your demo with actual usage in the product. Train your team on when to trust AI recommendations and when to push back.
Finally, scale what works. Automate the low-risk loops, keep humans involved in the high-stakes decisions, and connect your measurements to the dashboards executives actually read.
Skip the Common Disasters
Learn from others' mistakes. Don't add chat interfaces to products that nobody wanted to chat with in the first place. Don't treat any single AI model like a religion. Mix and match based on the needs of each job.
Don't slap "secure" labels and icons on your interface without real privacy boundaries. Don't report technical metrics to executives who care about revenue, and don't automate tasks that require judgment, context, or carry significant risk.
When things go wrong, don't hunt for success stories to make yourself feel better. Find the failures, understand them, and fix them publicly.
The Reality Check
Here's what nobody wants to admit: AI doesn't rescue you from having to make the hard choices. It just makes the consequences of bad choices arrive faster and cost more.
Your users will tell you when your system is guessing. Your team will let you know when your governance is merely for show. Your metrics will tell you when you're not actually moving the business.
So be honest about what you're trying, why it matters, where it can fail, how you'll measure success, and when you'll quit if it's not working.
The companies that win with AI won't be the ones with the flashiest demos or the biggest budgets. They'll be the ones that pick real problems, build systems people actually trust, keep humans in charge of what matters, and measure results like adults.
Everything else are just expensive ways to avoid doing the actual work of running a business.