How Leaders Can Turn AI Speed Into Strategic Advantage
Most leaders I talk to are no longer asking whether their teams should use AI. That ship has sailed, picked up speed, and is somewhere in international waters with half the sales team on board.
The better question is whether all that AI use is moving the business in the right direction.
AI has made it easier than ever to be good. A person can draft a sales email, summarize a meeting, or polish a LinkedIn post in minutes. It’s lovely, useful, and occasionally suspiciously over-punctuated. (You know the posts I mean.)
Good is becoming the baseline rather than the differentiator.
When everyone has access to the same tools and same very polite writing assistant, the risk, in my experience, isn’t that your business becomes obsolete overnight. The risk is that it becomes ordinary.
Good is the new baseline. What you build above it is the advantage.
The Floor Is Rising
AI is raising the floor for everyone. That’s genuinely exciting, especially for people staring at a blank page or squeezing five hours of admin into a 47-minute gap between meetings optimistically labeled “focused time.”
But standing on a rising floor isn’t a strategy. It just means you’re keeping up, which falls into that “good” camp again (sigh).
That’s the real work of AI leadership, giving teams clear standards and knowing where human judgment still belongs.
The leaders creating real advantage move beyond asking, “Can AI do this faster?” They ask better questions.
- What human judgment still matters here?
- How do we make this sound, feel, and perform more like us?
- What can we now do that we couldn’t do before?
That last question is where the real opportunity lives.
Your Team Doesn’t Need You to Know Every Button
This may come as a relief to every leader wondering if they need to become the office prompt wizard by next Tuesday. You don’t!
You can lead before you’ve mastered every tool, and even if you never do. Keep learning alongside your team, but remember that what they need most is a clear picture of what good looks like and permission to experiment along the way.
Your job is to set principles, model the behavior, and decide where the line is. Without that direction, AI creates very confident chaos. (It’s also not cheap, so bear that in mind.)
One of my favorite AI principles is simple: AI handles the mechanical so we can do the meaningful.
If your team uses AI to remove repetitive work, brilliant. If they use it to outsource judgment, relationships, accountability, or what makes your business distinctive, it’s time to stage an intervention.
Your team doesn’t need an AI expert at the top. They need a leader who can define what good looks like.
Build Above the Floor
The strongest AI teams aren’t simply doing the same work faster. They’re rethinking the work.
A floor-level team says things like, “AI, help me draft an outreach email.”
A Second Story team asks, “What do our best salespeople do differently, and how could AI help us do more of it?”
I’ve spent decades in sales, and we now use AI to run our 6 Factor Formula on every account. The process used to be manual, rushed, and based more on feelings than facts. Now AI applies the analysis consistently, whether we’re researching a prospect or understanding an existing client, while my experience gives the work its lens. The result is focused research tailored to our needs and market instead of another pile of generic data.
AI will happily give you the commodity answer, which is useful as a starting point and a problem when you mistake it for your strategy.
The advantage comes when you bring your judgment, customer insight, methodology, and taste into the work. That’s how AI becomes more than a faster keyboard.
AI can apply the process. Your experience is what makes it distinctive.
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Your Homework
Pick one workflow your team already uses AI for, ideally the real one that saves time instead of the shiny one in the innovation deck.
Then ask:
- What part is mechanical, and what part is meaningful?
- What do our best people do differently that we should build into the process?
- What would make the output unmistakably ours?
Leadership starts right there, with those answers.
AI can bring speed, leverage, and frankly a ridiculous amount of first-draft energy. Only leaders can decide where that speed should go and which moments should stay human. That takes thought, but it also means the most important part is still yours.
Frequently Asked Questions
What does it mean to give AI direction as a leader?
Giving AI direction means deciding where AI speed should be applied, what standards the output must meet, and which parts of the work stay human. It’s a strategy and judgment role, not a technical one.
In practice, this looks like setting clear principles for AI use, modeling good AI habits yourself, and drawing the line on tasks where human judgment, relationships, or accountability can’t be delegated.
Do leaders need to be AI experts to lead AI adoption?
No. Leaders don’t need to know more about AI than everyone on their team. Their job is to provide direction, set principles, and decide where AI fits, not to master every tool.
Someone on your team will always know a feature or shortcut you don’t, and that’s fine. What only the leader can do is define what good looks like and connect AI use to business strategy.
Why does AI make companies ordinary instead of obsolete?
When every competitor uses the same AI tools and models, everyone’s output starts to look and sound the same. The bigger risk isn’t being replaced by AI, it’s becoming indistinguishable from everyone else using it.
Ordinariness is quieter than obsolescence, which makes it more dangerous. Deals slow down, differentiation fades, and by the time the market stops noticing you, the damage is already done.
What’s the difference between using AI for speed and using AI for advantage?
Speed means doing the same work faster. Advantage means rethinking the work itself, building your team’s judgment, insight, and methodology into the process so the output can’t be replicated by competitors with the same tools.
A team using AI for speed asks, “Can AI do this faster?” A team building advantage asks, “What can we now do that we couldn’t do before, and how do we make it unmistakably ours?”
Which parts of work should stay human?
Judgment, relationships, accountability, and anything that makes your business distinctive should stay human. AI can handle mechanical, repetitive work, but the meaningful work belongs to people.
A useful test is asking whether the task requires trust, ethical judgment, or genuine relationship-building. If it does, AI can support the work but shouldn’t own it.
How can leaders tell if their team is using AI well?
Look at what the AI use is producing. If it’s removing repetitive work and freeing people for higher-value thinking, it’s working. If it’s replacing judgment, flattening your voice, or producing generic output, it needs direction.
A quick audit helps: pick one real AI workflow and ask what part is mechanical, what part is meaningful, and whether the output could have come from any competitor. Generic output is the warning sign.
What does “AI handles the mechanical so we can do the meaningful” mean?
It’s a working principle for dividing labor between AI and humans. AI takes on repetitive, mechanical tasks like drafting, summarizing, and formatting, which frees people to focus on judgment, creativity, relationships, and strategy.
The principle also acts as a guardrail. When AI starts handling the meaningful work, like decisions, client relationships, or accountability, the division has gone too far and needs to be pulled back.
How can AI improve a process without making it generic?
The key is letting AI apply the process consistently while a human supplies the judgment and context. AI handles the repeatable analysis; your experience decides what matters and how to read the results.
For example, running a structured account analysis like the 6 Factor Formula through AI makes the research consistent and fast, but a salesperson’s real-world lens is what turns that consistency into insight tailored to the market rather than another pile of generic data.
What’s the first step for a leader who wants to improve AI use on their team?
Start with one workflow your team already uses AI for and audit it. Ask what part is mechanical and what part is meaningful, what your best people do differently that should be built into the process, and what would make the output unmistakably yours.
This works better than launching a big AI initiative because it improves something real. Once you’ve done it for one workflow, the same questions apply everywhere else AI shows up in your business.
