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A doctor whom I met, was upset about dealing with a lot of people coming to clinic equipped with wikipedia and LLM generated knowledge. Years of education and practice puts a doctor in the zone of unconscious competence, but for an expert beginner with no formal education or practice it is just a fact in some context without reflecting what is in hand. The doctor’s intuition will be right and often arrived without conscious thought, asking them to explain in detail may prove counter effective, in some case make the doctor doubt their judgement and end up treating poorly.

Let us take a few other examples from other domain. If you have come across the Monty Hall problem, where a host in a game shows 3 doors to a contestant, behind 2 doors are goats and the remaining 1 door with a huge reward. Once a door has been chosen, the host will open a door which has a goat behind it and give the contestant an option to switch the door if they think they would have made the wrong choice. If we do not think deeply, we think the odds are always 1/3rd irrespective of switching or not. In reality the odds are 2/3 if you switch and that has even stumped degree holders in math.

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Another counter intuitive one is bank teller queue management. I read at this blog, which mentions that an average five hour waiting time to service a customer can be reduced to 3 minutes to just by adding one teller extra. That is 90x productivity jump by doubling cost yet a lot of decision makers won’t believe the expert who analyses the situation and recommends them the solution because it does not make sense for a non expert. The example is dramatic but can happen in real situations as well.

When I observe a lot of people with expertise, their unconscious competence helps them navigate with ease without even thinking about it. The moment you question their judgement, their instincts take a back seat and suddenly their competency goes down. I was at a restaurant, I requested a cook for fried eggs. The cook asked if I wanted both sides to be cooked, and I said “yes, but don’t break the yolk”. The cook left out a nervous laughter and when turning the eggs to the other side, the yolk broke. I disrupted the cook’s flow just by doubting their ability.

This happens frequently at work. Decision makers who are expert beginners often want to get into the details, what this does is, it interrupts the flow mode of expertise. Explaining the solutions and decision making process which would otherwise be unconscious nature, requires a good deal of effort and often leads to sub optimal solutions. If you have an expertise on some area and are tasked with solutions, then keep in mind that you have to explain your decisions to people who know details at a surface level. People are naturally curious and LLMs feed their curiosity a great deal.

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For this reason, whenever I come up with solutions, I make it a multi step approach. This is I learnt from many different sources which help you harvest your unconscious competence.

Step 1 – Read the problem statement, re-read and think very hard to solve the problem. Too often no solution emerges, but all of a sudden when you are at a break, a solution emerges and when it happens immediately write it down. Beware, this solution is ephemeral and its details vanishes within a few minutes. Keep noting down the solutions that pop up at odd times like driving, cleaning etc.

Step 2 – Find reference material from internet and previous assignments to back your solutions. If it is a novel solution, dive deeper to explain but do not change the solution because you can’t find explanations.

Step 3 – KYEB (Know Your Expert Beginners) and be equipped with ELI5 answers to anticipated questions to impart confidence of the solution.

Before presenting your solution, establish your credentials which helps setting the right expectation using info from the KYEB research. This helps to present your views as an expert without getting into a loop of explanations and doubts. For the doctor’s case I discussed at the beginning, I recently see a few doctors have a dossier to quickly explain their decisions and cut short the questioning from the patients. We also will be expert beginners at many topics, the best we can help there is to let the experts do their job.

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When I joined Thoughtworks, one of the first few things that I enjoyed was how the teams learnt and solved problems together. Until then, I was used to tasks getting assigned to me and taking it to completion. Asking for help was seen as a weakness, knowledge sharing was considered to be detrimental to career as you will be replaced easily. I was wrong, learning and sharing together which I came to know as ensemble learning made my work life very rewarding.

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Simple things that ensured that we learnt as a group were

  1. Information radiators on the wall, omnipresent in the entire office. We even used to complain that we do not have enough walls.
    • Gotchas
    • Skill/Knoweledge matrix of who knows what and how much
    • Pair rotation matrix, to ensure that no silos form
    • Story wall and Release plan, to know what are the upcoming tasks in the near term
  2. Learning sessions
    • Collective code review and refactor sessions.
    • Deliberate KT based on Skill/Knowledge matrix
  3. Huddles
    • No questions asked, cry for help when stuck. This meant that if someone is stuck for more than 30 minutes they have to raise an alarm and entire team stops their work and jumps in to unblock.

Seems like a simple list, but it had a profound impact and kept the working life stress free and productive. In a remote first environment, the radiators can be managed with pinned messages on group chat. Learning sessions and huddles should happen as it can in physical environments.

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Many countries have experienced “technology leapfrogging,” where populations moved directly from having no phones to widespread mobile phone usage—skipping the era of landlines entirely. For end consumers, this was a clear leap. However, for service providers, the shift was less revolutionary. While providers avoided the costly task of wiring every household, the core work of enabling large-scale communication didn’t disappear; in fact, networks had to be more robust and scalable to handle the surge in data and voice traffic. Significant effort went into strengthening foundational technologies so that the infrastructure could support this growth.

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Lately, I’ve been part of conversations, organisations urging to “leapfrog” with AI technology, mirroring the mobile phone revolution. While the enthusiasm is understandable, many underestimate the critical value of foundational IT systems. For mid to large organisations, adopting AI isn’t like the mobile leapfrogging where consumers moved straight to a modern tech. Skipping essential architectural elements—like solid API design, security frameworks, and enterprise integration—is akin to skipping the main course and jumping straight to dessert.

Building a scalable, secure, and maintainable AI-enabled system still requires strong foundations. Effective AI integration demands robust data pipelines, secure access controls, and clear interoperability standards. Ignoring these will lead to challenges in scalability, security vulnerabilities, and fragmented systems.

AI adoption is transformative but must be layered on a strong technological foundation. Just as mobile networks demanded fortified infrastructure behind the scenes, AI initiatives need reliable architecture to truly deliver on their promise without risking systemic issues.