AI Can Help You Work Faster. It Can Also Help You Be Wrong Faster
I was going to post this to LinkedIn but I forgot I deactivated my account last night & there is a 24-hour cooling-off period, so this will be the missing article.
AI is the best intern you’ve ever had.
It works instantly, writes quickly, answers questions, anddrafts emails. It can even summarise documents for you, and it never complains.
But it also occasionally produces something completely wrong with the confidence of a keynote speaker. This combination is powerful and slightly dangerous.
The call is coming from inside the house
Whether there is a policy or not, AI is already being used.
People are:
- drafting emails
- rewriting documents
- generating code
- summarising meetings
- asking for explanations
- exploring ideas
Quietly and efficiently, oh and Without waiting for permission. This isn’t a future problem, its already here.
And the question is not:
“Should we allow AI?”
The question is:
“How do we use it without creating new problems?”
AI feels different
AI tools feel different because they behave like they understand things.
They produce language that feels:
- confident
- structured
- coherent
- plausible
Which makes it very easy to assume they are correct, but it doesn’t understand your business.
It does not know:
- your customers
- your products
- your engineering standards
- your internal processes
- your regulatory obligations
It predicts outputs based on patterns. Very useful, and confidentally incorrect.
Where AI can add value
If used well, AI can:
1. Speed up drafting
Emails, documents, proposals, summaries.
You still review them, but you start faster.
2. Improve clarity
Explaining technical concepts, simplifying language, restructuring ideas.
3. Support problem-solving
Generating options, approaches, or starting points.
4. Reduce repetitive effort
Formatting, summarising, rewording.
Where the risk is
The risks are not dramatic. They are subtle.
1. Confidently incorrect outputs
AI can generate something that looks correct but contains:
- factual errors
- incorrect assumptions
- missing context
This is particularly risky in:
- engineering
- finance
- compliance
- customer communication
2. Data exposure
Users may paste:
- customer data
- internal documents
- pricing information
- engineering designs
into tools that are not approved or controlled.
3. Loss of accountability
If AI generates content, who owns it?
The “copy and paste” problem
One of the most common patterns is:
- ask AI a question
- receive a detailed answer
- copy and paste it into work
And without verification, small errors can:
- enter documents
- affect decisions
- reach customers
- become embedded in processes
Why this needs a policy
Without guidance, AI use becomes:
- inconsistent
- invisible
- difficult to govern
A simple, clear policy helps:
- define acceptable use
- protect sensitive data
- set expectations for verification
- provide confidence to users
The cultural shift
AI changes how work is done.
It accelerates:
- thinking
- drafting
- communication
But it also changes:
- responsibility
- ownership
- verification