The Reverse Camera Effect: What AI Is Quietly Doing to Your Workforce

Confession: I teach others about AI and utilise generative AI, yet I am concerned about its potential impact on our brains if we are not careful. I am also concerned about the potential impact in the workplace of the future if we don’t adopt the right balance and put frameworks in place to protect thinking.

Do you remember learning how to drive? When I was 16, I was excited to get my learner’s licence. I signed up to learn how to drive a standard vehicle, aka stick shift, so that I could get a general license, which allows you to drive anything but trucks in Jamaica. I remember the instructor bringing me to the yard to give me lessons on how to parallel park using the wonderful mirrors. That part of the lesson was stressful, but that was a key skill to learn before you could even dream of passing that driving exam. Well, I passed that exam, and two things happened. I have never driven a standard car after that. I only drive automatic cars. Years later, we were introduced to the wonderful or not so wonderful concept of the reverse camera. Initially, I was hesitant to use the reverse camera as I found it confusing. Eventually, I dabbled until I got it right, and then reality hit me. After years of driving and using my mirrors, I realised that if I’m not using the reverse camera, parallel parking is no longer a breeze.

What does this have to do with AI? Everything. While technology is great, if you don’t use your skills, you will begin to lose them. I unwittingly trained myself to depend on the reverse camera, and retraining myself to use the mirrors only is more difficult than I anticipated, as I hesitate to trust what I know and still find myself glancing at the camera to ensure that everything is just right.

If we think about generative AI, this technology, like the reverse camera, was designed to make things a bit easier for us. We can write a prompt and, in seconds, whip up the ‘perfect’ email, job description, create an image or a video, and this list is by no means exhaustive. Similar to the experience with the reverse camera, we are retraining our brains. The only question is whether we are doing it intentionally. If we create dependency as I did with the reverse camera, will it be easy to retrain ourselves to ensure the skills we worked on all our lives are retained? For example, our critical thinking skills, communication skills and even our ability to just trust ourselves based on our foundational knowledge? The reality is that how we use AI can lead to users bypassing deep thinking and traditional problem-solving as we assign AI the responsibility of reasoning and conducting analysis for us. AI is not just changing how we work. It is changing how we think. Thinking is not a minor skill in the workplace. It is the workplace.

The Brain Risk

There is a growing concern that overreliance on AI is altering cognitive processes, for example, cognitive offloading and cognitive atrophy. Cognitive offloading occurs when individuals use tools to reduce the mental effort needed for a task. For example, you outsource your thinking to AI as it can provide quick solutions. Michael Gerlich explored the impact of AI on cognitive offloading and critical thinking. His study highlighted that younger people (17 – 25 years old) had a higher dependence on AI tools and lower critical thinking scores compared to older people (46 years and older). [1]

Cognitive atrophy, also known as cerebral or brain atrophy, refers to a loss of brain cells [2]. An MIT Media Lab study highlighted the cognitive cost of excessive reliance on large language models (LLMs). ChatGPT, Google Gemini, and Meta’s Llama are examples of LLMs which are used for tasks like writing, summarising, and brainstorming.  The study explored the neural and behavioural consequences of LLM-assisted essay writing. Participants were placed into 3 groups: LLM, Search Engine, and Brain-only (no tools). Electroencephalography (EEG) was used to assess cognitive load during essay writing during 4 sessions. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioural levels. In particular, it was found that:

• Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity. 
• LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. 
• Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users
• Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group
• LLM users also struggled to accurately quote their own work. [3]

While it is too early to draw long-term neurological conclusions, the early research signals that how we use AI will matter.

The Generational Complication

We have new players entering the world of work, Generation Z. Born between 1997 and 2012, Generation Z, colloquially known as Zoomers, bring their unique characteristics, values, expectations and perspectives to the workplace. However, there are mounting concerns about this generation’s attitude to work and the potential implications for employee retention. These workers started work during COVID-19 and into an era with rapid technological changes. They experienced remote onboarding, limited onboarding for job shadowing, or to observe how senior colleagues managed tasks and people. They are experiencing reduced mentorship and apprenticeship-style learning. The matter is further complicated with AI diminishing the time to think through the small but formative details, which will allow them to truly hone their personal skills and build professional judgement.

There is dissonance between the expectations of other generations for Gen Z and Gen Z’s expectations for themselves. However, if mentorship is thin and AI is thick, who is shaping their thinking? If you have been in the workplace for over 7 years, do you remember your start and what contributed to your professional development? The environment is changing, which means we need to rethink how we do things. There are downstream costs for not getting things right. Institutional knowledge begins to weaken, turnover increases, and we do a disservice to the next set of leaders, who will be unprepared for the requirements of leadership.

My Observation as an Educator

I lecture students at the undergraduate and graduate levels. I emphasise that AI is a tool and that at this stage of their journey, their personal understanding of the material is important. Yet there is a stark contrast between the quality of coursework and the quality of the exams. When students sit exams that require live application, depth is often missing. That gap is telling a story. In an effort to address this, I have consistently been innovating the coursework each semester based on data gathered, intuition and observations to encourage experiential learning and negate the overreliance on AI.

In our workplaces, what are we doing to ensure strategic thinking takes place outside of the reliance on AI? Innovation comes from thinking. When every organisation uses the same models trained on the same data, won’t we all just get the same responses? Competitive advantage cannot come from AI output alone. It must come from human judgment.

If employees lose the ability to reason independently, the long-term organisational risks include:

• Weakened leadership pipelines
• Shallow strategic thinking
• Reduced problem-solving ability
• Dependence on external systems for core decision-making

Practical Guardrails

Leaders must move beyond encouraging AI use to governing AI use. A few practical guardrails to consider include:

1. AI is a second brain, not a first brain

It is important to teach people how to use AI properly and in a balanced manner. No one taught us the right way to use the reverse camera. We played around and figured it out on our own. Similarly, many people are figuring out AI on their own. There is an opportunity for structured learning on how to use AI properly to instil the idea that AI is a second brain and not the first. Writing is thinking. Encourage employees to write drafts on their own and leverage AI to polish. This will contribute positively to personal skillsets being developed, rather than overreliance on AI, which can potentially lead to you losing your skills.

2. Introduced Structured Programs for Early-Stage Learning

Early-career professionals need guidance. They need to learn to walk before they run. Now more than ever, the absence of structured programs for early-stage learning is a risk that organisations cannot afford to ignore. AI has further compressed the development window by providing instant answers to questions that, when wrestled with, would have built judgment. This means organisations must be intentional about rebuilding those experiences. This may require revisiting the onboarding experience, reinforcing formal mentoring programs that pair early career employees with experienced colleagues and incorporating job shadowing so newer employees can experience the job in real time. This ensures that when they do run, they know where they are going and why.

3. Design AI Use Policies

As AI becomes embedded in daily workflows, organisations need clear guidelines that go beyond simply permitting or prohibiting use. AI use policies that distinguish between “scaffolding” and “substituting” thinking should be developed. A practical starting point is examining the cognitive risk level on tasks. For high thinking tasks (strategy, analysis, diagnosis), consider requiring a human-first input, meaning the employee must develop their own position or recommendation before AI is consulted. This will help to preserve independent reasoning.

These guardrails will ensure that as organisations move faster with AI, their people do not quietly lose the capacity to think without it.

Conclusion

Foundational knowledge still matters. Honing your skills through practice still matters. Guidance from those who came before you still matters. Generative AI is an important tool. We should remember, it is just that, a tool to help rather than a tool to replace your thinking. Let us be careful about unwittingly rewiring our brains in a quest for ease. The future of work will not belong to those who use AI the most. It will belong to those who can think with or without it.

References

1. Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and Future of Critical Thinking. Societies, 15 (1), 6.
2. Kandola, A. (2024). What to know about brain atrophy (cerebral atrophy). https://www.medicalnewstoday.com/articles/327435
3. Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. https://arxiv.org/pdf/2506.08872

Yolande Hylton is the Managing Director of Hylton Insights, an HR Consultancy Firm with a mandate to guide businesses from the transactional to the transformational HR realm, thereby enhancing individual and organisational performance. For inquiries or to learn more, you can reach out to yolande@hyltoninsights.com or visit www.hyltoninsights.com