5 Questions We Hear In (almost) Every AI Awareness Workshop

In almost every AI awareness workshop we run, the same questions come up. 

Different industries, different seniority levels and different departments, yet the concerns are remarkably consistent.

The workshops we facilitate are designed for teams, departments and leadership groups who want a grounded understanding of what artificial intelligence actually is, what it is not and where it actually creates value within their own business context. We focus on practical realities, commercial implications, operational impact, risk, accountability and adoption.

1. Is the output from AI actually correct?
2. Why do different AI tools give different answers?
3. “Can we trust AI with internal documents?”
4. “Will AI replace decision-making in my role?”
5. “Can AI be trusted with numbers, pricing or analysis?”

Once the conversation moves beyond headlines and all the noise you hear in the news, social media and in the corridors at work, the discussion becomes much more serious.

People are asking if AI is reliable, governed, appropriate for their environment and capable of strengthening judgement rather than complicating it. They want to understand how it fits within financially and reputationally sensitive work.

Over time, these 5 questions appear again and again, because people are thinking responsibly. What follows are those 5 most common questions and what they reveal about how organisations are approaching AI today.

If you would like to understand how these discussions are structured inside your own organisation, you can find more detail on the workshops here: https://www.koshima.ai/awareness-workshops

Most common question #1.

“Is the output from AI actually correct?”

This is almost always the first serious question in the room. Not because people doubt the technology, but because they understand consequences.

Most professionals have spent their careers working with systems that behave predictably.

You input figures into a finance platform and you expect the same result every time. You run a compliance check and you expect consistency. That reliability creates confidence.

When they open ChatGPT or Copilot and see a well‑written summary, a clean analysis or a confident answer, the instinct is not excitement. It is caution. If this looks right, how do I know it is right? And more importantly, who carries the responsibility if it is wrong?

That is the real substance of the question.

AI produces language that sounds authoritative. It structures arguments clearly, it can present numbers in a persuasive way, but sounding convincing is not the same as being correct and experienced professionals know the difference.

The issue is not whether AI makes mistakes, humans do as well (as you will see from our recent posts regarding Microsoft Copilot email sensitivity issues). The issue is where review sits in the workflow.

If a tool is used to draft a board paper, prepare a client proposal or summarise a contract, does it go out as it is? Or is it checked, challenged and validated before it leaves the organisation?

Used properly, AI accelerates preparation. It reduces the time spent drafting, structuring and formatting. What it does not remove is accountability. Someone still attaches their name to the document. Someone still answers questions about it. Someone still carries the commercial and reputational consequences of what is written.

So when someone asks whether the output is actually correct, they are not resisting AI, they are clarifying where professional responsibility now sits.

That is not necessarily hesitation but discipline.

Most common question #2

“Why do different AI tools give different answers to the same question?”

This question usually appears once people have experimented with more than one tool.

They try the same prompt in ChatGPT, Copilot or Gemini and the responses are not identical. One is more detailed, one is more cautious, one seems more creative. The natural reaction is confusion. If the question is the same, why is the answer different?

What sits behind this question is not technical curiosity. It is a search for reliability.

In most business environments, consistency builds trust. If two analysts review the same dataset, you expect broadly aligned conclusions. When AI tools diverge, it raises a more fundamental concern. Is one of them wrong? Or are they simply approaching the problem differently?

The important understanding here is that these tools are not search engines retrieving a fixed record. They are language models generating responses based on patterns, training data and design choices. That means variation is built into how they operate.

Once people grasp that, the focus moves away from trying to find the single “best” tool and toward something more practical. What context was provided? How clearly was the task framed? What assumptions were embedded in the prompt?

The difference in answers often reveals more about the input than the tool itself. Seen this way, variation is not necessarily a flaw. It can be useful. Comparing outputs can expose blind spots, alternative perspectives or missing considerations that a single response might not surface.

The deeper question is not which tool is correct. It is whether the person using it understands how to guide it properly!

Most common question #3

“Can we trust AI with internal documents?”

This is a great question and it is rarely casual. It usually comes with a pause before it is asked!

People are thinking about confidential contracts, pricing models, employee data and board-level material. They want efficiency, but they do not want exposure and they have probably uploaded more than they feel comfortable about.

The concern is not abstract regulation. It is practical risk. If I paste internal information into this tool, where does it go? Who can access it? Is it stored? Is it used for training? Does it sit inside our 'safe' environment or outside it?

This is where clarity matters.

Not all AI tools operate in the same way. Consumer versions behave differently from enterprise versions, some environments are isolated within an organisation’s data boundary, others are not. Without that distinction, the question becomes loaded and unanswerable. With it, the conversation becomes concrete.

• Which tool are we referring to?
• How is it configured?
• What are the data handling policies?
• What contractual protections are in place?

Trust in this context is not blind confidence. It is understanding the architecture and the governance around the tool being used. When that is explained properly, the discussion moves from fear to informed decision-making.

Most common question #4

“Will AI replace decision-making in my role?”

This question used to be "will AI replace me" but times have moved on. This question mostly comes from managers, not executives and it almost never comes from fear but from professional awareness.

Managers are responsible for judgement. They review information, weigh competing factors and decide what level of risk the organisation is willing to carry. When they open Gemini, Claude, Copilot or ChatGPT and see it summarising reports, modelling scenarios or even suggesting a recommendation, the reaction is not anxiety, it is reflection. If the tool is producing the analysis and pointing toward a direction, where exactly does my judgement add value in that chain?

That is not usually insecurity but clarity about accountability.

In practice, AI changes the input to a decision, not the ownership of it. It helps structure information faster, compare options at scale and identify patterns that might otherwise be missed, but it does not sit in the room when the outcome is challenged or absorb the consequences of a wrong call.

Someone still signs off, still answers for it and still carries the reputational and commercial risk. This is not a move from human decision to machine decision, it is a move from spending time preparing information to spending time interrogating what has been produced.

The manager’s role doesn't disappear, it should actually be much more focused. It becomes about challenging assumptions in the output, testing scenarios properly, identifying what might be missing and ultimately deciding how much risk the organisation is willing to accept.

If anything, the responsibility increases, because now you are not only judging the situation itself, you are also questioning the strength of the AI’s reasoning before making the call.

Most common question #5

“Can AI be trusted with numbers, pricing or analysis?”

 This question often comes from commercial and finance teams and it usually follows quickly after the correctness discussion.

People recognise that AI is strong at structuring information, summarising data and highlighting patterns. At the same time, they understand that small numerical errors can have material consequences. A misplaced decimal point in pricing, a misread figure in a forecast or an incorrect assumption in a cost model can ripple through an organisation.

The concern is not if AI can work with numbers, it clearly can. The concern is how it should be used in analytical workflows.

Used properly, AI can accelerate the early stages of analysis. It can draft comparison tables, outline scenarios, explain trends in plain language and prepare the groundwork for deeper review. That can save significant time.

What it should not do is become the final authority without verification.

In financially sensitive environments, there is always a control layer. Figures are checked, assumptions reviewed, calculations are validated against source data. AI fits into that process as an accelerator, not a replacement for professional scrutiny.

When people ask whether it can be trusted with numbers, they are usually asking where that control layer sits once AI is introduced.

Answer that clearly, and confidence increases for the right reasons.

So What Does This All Mean in Practice?

Another great question!

These 5 questions are indicators of professional standards. They show that leaders and teams are looking for clarity, control and commercial relevance. That is exactly how we structure AI Awareness Workshops at Koshima.

We work with leadership teams, commercial functions, technical departments and operational units to build a serious understanding of what AI is, what it is not and where it delivers measurable value within their specific business context. The focus is always practical. Real workflows. Real risk considerations. Real accountability.

An awareness session is often the starting point. Once leadership has clarity, it is typically followed by task-tailored training for the teams within that function or department. That training is built around the actual work people do every day, not generic tool demonstrations.

Our approach at Koshima.ai is remarkable straightforward, disciplined and we think simply follows common sense:

• We anchor discussions in business objectives rather than features.
• We connect AI use to defined tasks and responsibilities.
• We clarify governance, data boundaries and accountability before scaling usage.
• We measure impact based on time saved and decision quality improved.

The value is practical and immediate:

• Stronger judgement supported by better prepared information.
• Reduced time spent drafting and structuring routine work.
• Clearer understanding of where human oversight remains essential.
• Faster, more confident adoption across teams.

If you would like to explore how this would work inside your organisation, you can find more information here:

• AI Awareness Workshops: https://www.koshima.ai/awareness-workshops
• Task-Tailored AI Training: https://www.koshima.ai/training