Hastimal Jangid is Director at Coozmoo, AI-powered digital marketing agency built to skyrocket revenue for small & medium-sized businesses.
A founder asked me a few weeks ago whether she should feel guilty about using AI every day. Her team had automated most of their reporting and customer replies, and then she’d read something about data centers draining water supplies. She wasn’t sure which way to lean.
I get versions of that question a lot now. My answer is usually the same: You’re asking the wrong question.
I run cloud engineering and automation, so I sit close to both sides of this. I’ve seen AI save my team days of work. I’ve also opened a cloud bill and wondered why a simple job was burning that much compute. Those two experiences have taught me the same lesson. Productivity and environmental impact aren’t opposites. Both come down to whether you’re using resources on purpose or just using them because they’re there.
The Upside Is Too Big To Ignore
I won’t pretend the productivity gains are hype. For a small or midsize business, AI has changed what a small team can do. A handful of people can now do research, write drafts, analyze customer data and answer support questions at a pace that used to need a whole department. That frees up your best people to do the things only people can do, like building relationships and making judgment calls.
There’s a defensive reason, too. More customers now find businesses by asking an AI assistant instead of scrolling through search results. If you sit this out, your competitors won’t.
I don’t think most owners really have a choice about adopting AI anymore. The choice is how.
Why This Lands On Your Desk
Many owners assume sustainability is for the big cloud companies to worry about. I disagree, and my reasons are mostly practical.
Wasteful computing is expensive computing. When I see a heavy model doing a job a simple script could handle, or processes running around the clock with nobody using them, I see a budget problem and an energy problem at the same time. Fix one and you usually fix the other.
Your customers and partners are also starting to ask. Sustainability questions are showing up in vendor questionnaires, especially from larger companies. If you can’t answer them, you may lose the deal.
And regulation is heading toward more disclosure, not less. It’s easier to build good habits now than to scramble later.
Ask How Much, Not Whether
I’d stop asking whether to use AI and start asking whether each use is worth what it costs. That sounds abstract, but it leads to simple decisions.
Use the smallest tool that does the job. Not every task needs the biggest model on the market. Plenty of routine work can run on something lighter, or on plain old automation.
Track what you consume. A lot of companies can tell me what their AI projects produce, but few can tell me what those projects use. The major cloud providers have reporting tools for this. Turn them on before you scale anything.
Be picky about what you automate. Just because AI can do something doesn’t mean it should. If a process is pointless, automating it only makes the waste faster.
And ask your infrastructure providers hard questions. They differ a lot in how they source power and cool their facilities, and some are far more open about it than others. That should factor into who you hire, right next to price.
Don’t Wait For The Chips To Save Us
I’m optimistic about the technology. Hardware keeps getting more efficient, and the companies building these models have every financial reason to cut their energy bills.
But I wouldn’t bet your strategy on it. When something gets cheaper to run, people tend to run a lot more of it, and the savings disappear. Waiting for the industry to fix this for you isn’t a plan.
My Advice
If I could leave business owners with one thing, it’s that sustainability shouldn’t feel like a tax on your AI plans. It’s a sign you’re doing the work well. The companies that get the most out of AI over the next few years won’t be the ones who adopted it fastest. They’ll be the ones who knew why they were using it and what it cost.
That founder, by the way, didn’t need to stop using AI. She needed to know what her team was running and whether it was worth it. That’s a much easier problem than the one she thought she had.
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