AMA: Your top AI questions, answered.

Today, I’m tackling top AI questions!

• What "low-hanging fruit" should I focus on first?
• Which AI tools are best for my business?
• How can I stop experimenting and create more structure?
• How can I ease my team's fear of AI?
• How do I measure ROI on AI investments?
• How do I align AI strategy with my overall business goals?
• How do I keep up without getting overwhelmed?
• How do I teach my team to use AI — not just copy and paste prompts?
• How do I roll out AI without disrupting our current operations?

To see your question answered in our next Q&A edition, submit it here.

1. Where should I even start with AI implementation in my business? What "low-hanging fruit" should I focus on first?

Start with you. Whether you’re a business leader or the unofficial AI champion in your company, it begins with hands-on exploration. You need firsthand understanding of what tools like ChatGPT, Gemini, and others can do — not just in theory, but in the practical context of your work.

From there, the next frontier is culture. AI adoption doesn't work without buy-in. You need to build a culture that’s curious and open, not fearful or defensive. People need to understand that AI isn’t here to replace them — it's here to help them do more meaningful work, faster.

Start to roll out structured training to the internal AI enthusiasts. This doesn’t have to be big or complicated: weekly lunch-and-learns, self-paced courses, or even internal peer demos can go a long way.

In fact, research continually shows that lack of training is one of the biggest barriers to AI adoption. The 2025 State of Marketing AI Report (from SmarterX) found that 62% of marketers cited a lack of education and training as their biggest obstacle to implementing AI.

Even more telling? 68% of respondents said their companies currently offer no AI training at all. And McKinsey found that nearly half of employees want more training on AI.

So to recap: Experiment, build trust, and provide training and support.

2. Which specific AI tools are best for a business like mine, and how can they integrate with my existing tech stack?

The best AI tools for your business are going to be the ones your team uses consistently. Here’s what I’d recommend:

1. Start with your current tech stack. Check what AI features are already baked into tools you use every day. Google Workspace, for instance, now includes Gemini tools that help write emails, build spreadsheets, generate images in Slides, and even assist with research using NotebookLM.
   
   
2. Add specialized tools based on your team’s function. Sales teams might benefit from tools like Fireflies or Otter for call summaries. Content teams may need editing tools like Descript. The key is to pick tools that either automate repetitive work or unlock new capabilities.
   
   
3. Layer in a general-purpose AI model. Tools like ChatGPT or Gemini give you flexibility across departments. They can summarize documents, draft messaging, analyze data, and much more.

We recently published a blog post on this — outlining suggested AI tech stacks tailored for different business types and roles.

Whether you're a content creator, a sales-focused leader, a company brand new to AI, or one with a $0 AI budget, you'll find specific examples that fit your needs. Take a look to get more ideas on tools that might fit your business.

3. How can I stop experimenting with different AI tools and create a more structured process?

You may need to shift your focus from tools to tasks.

This is where many teams get tripped up: they find a tool, get excited about its features, and then try to retrofit it into their workflow. But that often adds more work than it removes.

Flip the approach and start with your work. Identify tasks that are:

• Recurring: You do them regularly
• Structured: They follow clear steps
• Narrow: They have a defined goal

Choose one task that fits this criteria and commit to using AI to complete it every time. Resist the urge to jump to the next shiny tool or exciting use case too soon.

As you get comfortable, expand to other steps in the process. Over time, you’ll build repeatable workflows that chain together AI use cases.

AI adoption is a process — not because using AI is complicated, but because developing any new habit takes time.

That is the part that I think holds people back: They just don't give themselves enough time to get used to these new habits before they start trying to add on.

Instead, nail one use case and then build on it. That’s how you go from one-off experiments to scalable systems.

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4. How can I train my team without stoking fears over AI and job security? Any advice?

You can’t train your way around fear. So start by addressing the big ones head-on:

Fear of exposure. Many employees are already using AI — they’re just afraid to say so. They worry it will devalue their work, make them expendable, or force them into training roles. To speak up, they need to believe using AI makes them more valuable to you, not less.  

Fear of replacement. Listen, if your intent is to reduce headcount or freeze hiring, consider being upfront about this, even if it’s a difficult conversation. False reassurances will likely backfire and cost you your team’s trust.

But I believe there's another way: Using AI to enhance your existing team's value, not replace them.

Because when employees feel empowered, everyone wins: The company becomes more productive (and profitable!), and individuals can focus their time on more strategic, creative, high-value work.

And if your goal is to build a stronger, more capable team with AI? Say that clearly and consistently.

As for AI training, a good program will:

• Explain how AI works (at a high level)
• Help them apply AI to real tasks in their role
• Develop AI-relevant skills, including use case identification, risk mitigation, and prompt optimization  

5. What is the best way to measure ROI for AI initiatives? How do I justify the investment in AI tools?

There are four main outcomes you can track:

1. Efficiency: Doing the same work in less time.
   
   
2. Productivity: Doing more work with the same effort.
   
   
3. Performance: Achieving a (quantitative or qualitative) improvement.
   
   
4. Innovation: Doing new things you couldn’t do before.

Efficiency is typically the lowest-hanging fruit. You can measure it by benchmarking how long a task takes without AI versus how long it takes with AI.

Even modest time savings add up. For example, if an employee saves just one hour per week using ChatGPT, that more than justifies a $20/month subscription.

Productivity metrics focus on volume: Are you publishing more content? Sending more sales emails? Interviewing more candidates? If the output increases without adding more hours, that's a measurable win.

Performance focuses on success metrics: Open rates. Conversion rates. Win rates. Deal sizes. Remember: A conversion rate lift from 1% to 2% might be a mere percentage point, but that's a 100% boost in performance!

Innovation is trickier to quantify but the most transformative. This is where AI opens doors to explore new products, services, capabilities, or markets. In the past, you might have hired a consultant to explore a new business line — Today, you can prototype that strategy using AI. The ROI isn’t just in cost savings, but in opportunity access and strategic agility.

Whatever your goal is, don't overcomplicate it. Track outcomes before and after AI integration, then compare. Often, a single high-leverage use case can justify the cost of licenses across an entire team.

Ultimately, the best ROI comes from intentional use: Match AI capabilities to business goals, equip your team with the right training, and build systems that support sustained behavior change.

6. How should I approach AI in my career? Would going deeper in one discipline or tool be better than trying everything out?

I would go deep on *your* job: How can you use AI to do every aspect of your job faster or better?

Choose one task or skill to improve, then another, then another.

Along the way, you’ll continue to find news ways to level up those tasks, too — a better prompt or a newer tool — but you’ll have a list of immediately useful ways to apply that knowledge.

And ultimately, I don’t think it’ll matter if you can talk about newest tools or the latest model update… It’ll matter what you can *do*.

So I think if professionals focus on results — on how they can be the most AI-assisted marketer/accountant/paralegal/etc they can be — that’s how they can demonstrate real value and stay ahead of the curve.

(Tip: If you're struggling to get started, try this framework.)

7. How do I align AI strategy with my overall business goals?

I believe it was Robert Rose who said "AI is not a strategy." If your business wants to adopt AI, you first need to clarify: to do what?

If you're scaling fast, productivity might be your goal. If you're focused on cost containment, prioritize efficiency. If you're trying to expand into new markets or develop new offerings, innovation should lead.

Zooming out to your long-term roadmap can also clarify trade-offs. For instance, if you emphasize efficiency and free up team capacity, how will you redeploy that time? Toward backburner initiatives or strategic projects?

If you amp up sales productivity, is the team prepared to handle a fuller pipeline? Consider these downstream effects on your business.

While AI adoption is a process, AI isn’t a separate strategy. It should layer over — and accelerate — your existing roadmap.

But it can open up new possibilities for what you're able to achieve as a team, and allow you to set even more aggressive and ambitious goals for your company.

8. How can I stay updated on the latest AI trends and tools without feeling overwhelmed by the constant changes and hype?

This is such an important question because it really is overwhelming right now. The pace of change is relentless — even professionals who work in AI full-time are struggling to keep up with new models, tools, and features being released every week.

You’re not behind.

Are you behind the tech? Yes.

Are you behind everyone else? Definitely not.

The key is to stop trying to "keep up" with everything and start focusing on what's relevant and actionable for you.

Your ability to focus — to filter noise, ignore hype, and stay grounded in your business needs — is a true competitive advantage.

Here are two ways to do that:

1. Boundaries: Be intentional about your sources and your time. Choose 3–5 trusted sources that consistently add value (not just amplify hype). Define what qualifies as "good enough" information — maybe you don't need the deepest technical breakdown. And limit your time spent consuming AI content: block a recurring hour per week, for example, and stick to it. Make that time useful by focusing only on updates or tools aligned with your goals.
   
   
2. Balance: Aim for a healthy ratio of action vs. consumption. Spend 20% of your time learning about AI, and 80% actually using it. It’s easy to get stuck in research mode, bookmarking tools you never try. But the real growth happens when you build with AI, test its limits, and use it in your actual workflow. That’s where skill, understanding, and real value compound over time.

The tech will always evolve faster than you can track. But if you’re actively applying it to solve real problems inside your business, you’re already ahead of most.

9. How do I teach my team to think critically about AI tools, not just copy and paste prompts?

Great question. Start by teaching how AI works. Not in a deeply technical way, but enough for your team to understand what large language models are, how they generate responses, and what their limitations are.

Even this foundational knowledge dramatically improves a person's ability to evaluate outputs, troubleshoot unexpected results, and make better prompting decisions.

This is why structured training is so powerful. Teams that understand the "why" behind AI are more equipped to work with it effectively. It also builds confidence — because they don’t just know how to use the tools, they understand how the tools work.

Lastly, AI should never replace human judgment. Encourage your team to review outputs and challenge assumptions.

In the future, success won’t be about who writes the best prompt — it will be about who manages AI workflows well, gives clear direction, and evaluates results with discernment. So help your team develop the communication and managerial skills needed to work alongside AI systems productively.

10. How do I roll out AI without disrupting our current operations?

In short, containment and quality control.

Start with a pilot. Choose one task, one team, one use case. Train them, monitor impact, and document the process. Then expand slowly. Stack use cases, link workflows, and refine over time.

And always keep a “human in the loop” with clear processes and ownership. AI should be part of your SOPs — not a replacement for them.

That means clearly defining when and how a human reviews, edits, or signs off on AI-generated work. Document where in the process AI is being used and who is responsible for overseeing its output. Assign clear ownership to a specific person or role, so there's always accountability.

By embedding AI into existing processes with defined human checkpoints, you minimize risk, increase reliability, and maintain transparency. This will greatly reduce any disruption to your current operations.

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