Faster isn’t the same as smarter with AI

For International Services Week – Accelerating with AI. Leading with Judgment.

Author: Rachel Chand

There is a version of AI adoption that looks impressive from the outside:

• Faster content
• More touchpoints
• Bigger pipelines
• Decisions made in minutes instead of days.

On the surface, it reads as progress. The harder question is whether the humans involved are getting smarter, or whether they’re just getting faster at offloading.

This matters because the two things are easy to conflate right now. AI can absolutely help organizations move quicker, synthesize more inputs, and surface options that would have taken weeks to develop manually. Those are real gains. But speed of output is different from quality of judgment, and the gap between them is where most AI adoption stories quietly fall apart.

Research has found a significant negative correlation between AI tool usage and critical thinking scores, with the effect most pronounced among younger, more frequent users. A separate MIT Media Lab study on the impact of AI writing assistance found that using AI significantly reduces brain engagement, with measurable decreases in the neural networks associated with focus, memory, and attention. These aren’t fringe findings. They’re showing up consistently enough that “cognitive offloading” has become its own area of study.

Sometimes, more output just means more slop

The practical implication for organizations is straightforward. If you use AI to generate more output without building in the friction that produces better thinking, you are likely producing more AI slop faster. Well-formatted. Plausibly worded. Wrong in ways that are hard to catch because they look right. And that is before you account for the audience problem. The people you most want to reach, the ones with enough experience to act on what you’re saying, are also the ones most likely to clock the tells. The slightly-too-smooth sentence structure. The paragraphs that gesture at insight without committing to one. The listicles that cover everything and say nothing. Sophisticated readers have pattern-matched on AI output long enough now that encountering it in a vendor communication, a thought leadership piece, or an internal briefing triggers the same reaction as a stock photo on a website: technically fine but somehow trust-reducing. You have not just failed to persuade them. You have signaled something about how you operate.

Two ways to use AI that look alike

None of this means the answer is to slow down adoption or treat AI with suspicion. The organizations that will benefit are the ones that get deliberate about where human expertise still must lead, and where AI genuinely removes low-value work.

The distinction matters more than most AI adoption frameworks acknowledge. There is a meaningful difference between using AI to eliminate repetitive tasks so skilled people can focus on harder problems, versus using AI to generate output so you can avoid developing skilled people in the first place. The first accelerates human judgment. The second quietly replaces it.

Build the judgment before you scale the tool

The MIT researchers found something interesting in their study: participants who had written without AI assistance before being given access to it performed better with the tool than those who started with it. The people who had already developed their reasoning muscles were more inclined to ask critical questions, catch errors, and use AI as a genuine aid rather than a crutch. That finding should be uncomfortable for any organization that is onboarding new employees primarily through AI-assisted workflows before those employees have developed domain fluency.

Practical questions worth asking first

These are worth asking before you scale any AI-assisted process. Where in the workflow does an error need to be caught, and is there still a human who is qualified and incentivized to catch it? What expertise does this process depend on, and does AI make it easier to accumulate that expertise, or easier to skip building it? If the AI-generated output were wrong in a subtle way, would anyone notice?

Where good leaders draw the line

The organizations doing this well are led by people who have thought seriously about where human judgment is irreplaceable in their specific context, not just where AI is available. They treat AI as a lever for amplifying the judgment of people who already have it, and they’re protective of the conditions that produce that judgment in the first place. That is harder than deploying a tool. It requires decisions about where and why you want humans in the loop before the process is already running at scale. But it is also the difference between accelerating value and automating mediocrity.