What Level of AI do you Use?
Most of us are using AI for personal productivity, but are you using it for wider operational uses?
I had a conversation with some users on LinkedIn this week about the use of AI at work. I added some comments to a post that highlighted the fact that even though we're about two years into this current AI wave, most people are still only using it for some basic use cases, things like:
- Call summaries
- Refining emails
- Basic ideation and research
And they are right. The vast majority of us are only scratching the surface of what we could be doing with the technology, and there might be several reasons for this:
- Lack of knowledge
- Workplace restrictions
- Low interest and/or motivation
AI use can be broken into two threads
Whatever the reason, though, there is a tremendous amount of potential for us to start exploring. Within my network, most of the commentary on AI that I see involves people discussing a new prompt they've found helpful or a new tool they've discovered. These use cases are primarily focused on personal productivity. I have often said that we can break AI down into two threads:
- Personal productivity: Productivity hacks to gain personal efficiencies
- Operational solutions: Corporate solutions that drive better insights and decision-making
On the whole, generative AI is well-suited to personal productivity, whereas more traditional AI, such as Machine Learning, is better suited to operational solutions.
Therefore, it makes sense that we're mainly seeing personal AI use cases discussed. It is far more straightforward for a regular employee to use ChatGPT to help draft a tricky email than it is to build a machine learning model that more accurately predicts customer churn.
Most companies are still stuck with giving basic AI access
This is where companies need to start thinking about their broader AI strategy and employee training. Many are struggling to determine how to enable their staff to utilise public AI solutions like ChatGPT and Midjourney, with concerns about leaking sensitive company data or making a public relations mistake at the forefront. However, at the company level, organisations that allow and encourage their staff to be more involved in data science projects will see significant benefits.
What is your level of engagement like with AI at work? Are you mainly using AI for personal productivity, or are you creating operational systems for your team and departments?
Using generative AI at work still has the ability to cause embarrassment, or worse a large mistake, when we don't check the output.
To prevent that, I've created a Notion document complete:
- Simple checkpoints before using AI.
- A clear process for checking AI output for accuracy and quality.
- Actionable tips on how to add their own valuable insights and polish AI responses.
- Practical examples for common work tasks.
- Essential background info on why AI can make mistakes (like 'hallucinations').
- And much more, all within an easy-to-use, interactive Notion template.
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Thanks for reading, and see you next Friday.
Simon,
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