Before You Greenlight AI: 5 Questions That Separate Vision from Hype

Everyone’s talking about AI.
But behind the buzz, a lot of projects flop.

Not because the tech is broken, but because teams chase the trend without clarity, alignment, or a plan for what comes after the launch.

If you want your AI initiative to actually work, here are five questions worth asking before you greenlight anything.

They won’t guarantee success. But skipping them? That’s how things go sideways.

1. What ARE WE Really Trying to achieve AND How Will We Measure IT?

AI projects often start with vague ambitions like:

“AI will make us more efficient and effective.”

Sounds reasonable, but it’s too fuzzy to drive real decisions.

The problem? Vague goals often hide competing priorities. And when things get messy (which they will), your team won’t know what matters most.

Instead:

  • Break goals into clear, specific outcomes

  • Define each one (no hidden assumptions)

  • Prioritize what truly moves the needle

Then ask: How will we know if it worked? Define your success metric(s). Quantitative metrics are ideal, but a well-defined qualitative indicator is far better than a number that doesn’t reflect real success. If your team can’t measure success in a meaningful way, you’re not ready to start.

2. How Will THIS Affect OuR People?

AI introduces change. And where there’s change, there’s uncertainty.

If you don’t clearly communicate the “why” behind your initiative, people will fill the silence with fear, and that fear kills momentum.

Be honest about what success looks like:

  • Are you trying to reduce headcount?

  • Avoid hiring as you grow?

  • Free people from tedious work?

  • Help your team work faster or smarter?

Then answer the questions your employees are actually thinking:

  • What does this mean for my job?

  • What skills will I need to stay relevant?

  • Am I still valued here?

When people understand how AI helps them succeed (not just survive) you turn resistance into buy-in.

3. What is Our Appetite for Risk?

AI isn’t always predictable, especially when it’s customer-facing.

If your expectation is zero mistakes, AI might not be the right fit (yet).

Before launch, align on:

  • What types of customer questions we expect

  • What responses are ✅ Acceptable, ⚠️ Borderline ❌ Completely Off-Limits

  • What happens when the AI gets it wrong? What will be our process to monitor and intervene when necessary?

Can we limit the scope? Add guardrails? Phase the rollout?

Risk isn’t the enemy. But ignoring it is.

4. How will this impact the customer experience?

AI might make your business more efficient, but your customers don’t care about efficiency.
They care about how it feels to interact with you.

If your brand is built on personal service, relationships, or trust, introducing AI without intention can backfire.

Ask:

  • Will this make things faster and better for customers?

  • Are we automating the right things?

  • Where does the human touch still matter most?

You can use AI to streamline the experience, just don’t strip out the parts that make your brand human.

5. What support will This need after launch

A common mistake: assuming that once AI goes live, the work is done.

In reality, launch is just the beginning.

Plan for:

  • Knowledge base ownership
    → AI only knows what it’s fed. Who’s keeping your content current and usable?

  • Ongoing monitoring
    → Are you regularly reviewing responses and improving accuracy?

  • Human handoffs
    → When AI fumbles (and it will), who steps in and how quickly?

If no one owns these responsibilities, your AI will quietly drift from useful to frustrating.

Final Thought

AI can drive real business value. But only when it’s approached with clarity, intention, and a plan for what happens after the hype.

If your team can answer these five questions clearly and confidently, you’re probably ready to move forward.

If not it might be smarter to pause, realign, and build the foundation first.

Thinking About AI?

Let’s start the conversation before the budget’s gone and the problems start showing up.
No jargon. No hype. Just a clear look at what’s possible and what to watch out for.