Lately, something has been changing. It’s a sign of growing AI maturity and advancement of the hype towards more realism. I’ve been seeing many early adopters moving from “I use AI for everything” to “I use AI for a few things.” Witnessing this shift is equivalent to how you should approach AI in your business on a larger scale, too. You don’t need to use AI for everything, butfor a few things that move the needle for you. Time to take a closer look at how to actually approach this…
Between Abundance and Decision Fatigue
Earlier this year, I’ve seen top AI influencers share how easy it has become to generate 100 variations of a website or draft a dozen different versions of a sales email. It’s great that we’ve apparently solved generating information. But now comes the hard part: filtering, prioritizing, and selecting what’s actually useful and what hits the mark. That’s a task that humans have traditionally been pretty bad at. We’re easily overwhelmed by choice and experience decision fatigue. So, just “generating more options” is likely not very helpful.
Business leaders are facing the same dilemma when vendors open their tray of AI tools and start pitching them. There’s usually not just one AI feature in a product anymore, but several. Depending on the level of granularity, you get down into the weeds of things pretty quickly. That level of choice makes it incredibly and unnecessarily difficult to identify those AI features that deliver measurable business value and address a problem that the business actually has. Usually, a mix of decision fatigue and pilot purgatory sets in with little progress and measurable benefits.
It might sound contradictory, but no, you don’t have to be a “AI-first” business at any cost, but you should be looking for pockets of value where AI makes a difference in your work. That makes sense, right?
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If you follow the tech news, you might have experienced this situation yourself: One headline about new AI tools and platforms chases another. There’s AI for lead generation, AI for marketing automation, AI for accounting, AI for call transcription, AI for productivity, AI for coding, AI for … — you get the idea. It’s overwhelming. And if you sign up for each one, it gets quickly get costly, too.
Yes, it’s great to use AI for all of these tasks. But do you really need to? What seems like productivity gains on the surface can quickly create new problems for integrating different tools to exchange data or to automate workflows. Are you solving a problem you actually have? Or is your “AI-first” mantra just creating new problems to solve?
Personally, I’ve refrained from locking myself into year-long subscriptions despite the typical 15-20% discount that most vendors offer (compared to month-by-month payments). This approach provides more flexibility to try out a tool, switch, or cancel it when it doesn’t offer the value you were looking for, or when the task is done.
If you are considering moving from “AI or everything” to “AI for impact,” here’s how to approach it:
Take an inventory of the tools you use regularly.
Ask yourself on which tasks you actually spend a significant amount of your time.
Determine which of these tasks you could safely automate with AI or delegate to AI agents without sacrificing quality and accuracy.
Consider where you could gain new insights faster or augment existing processes (e.g., by using Deep Research capabilities of AI tools).
Try out a tool via a free trial or a monthly subscription.
After 2-3 months, evaluate how often you have uses it and whether it has delivered any measurable benefits.
If not, cancel the subscription. Otherwise, keep the monthly subscription for a quarter or two before switching to an annual model.
Trying out various tools is an important step on a company’s and team’s AI-readiness journey. When leaders encourage the use of AI and empower their teams to do so with intent, team members can improve or re-think workflows and increase their business impact.
Summary
The conversation has recently been shifting from using AI for each and every task in a business towards being more selective and deliberate in the approach. This not only prevents decision fatigue when AI tools generate dozens of options, but it also contains organizational spending and limits the risks of data leakage. Leaders can take a simple approach to evaluate the potential and the actual benefits of using AI in their domain and ultimately save cost without creating more problems than they intend to solve in the first place.
What do you think? Does using less AI actually make you more productive?
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