Completely, 100% written by a human.
As people and organizations sprint to use AI – whether they’re doing it well or not – there’s one point that’s become crystal clear.
Technology is no longer reserved for the technologists. It’s time we all got a little more technical.
This is true regardless of which side of the AI debate you fall. So if you’re someone that’s eager to jump in, a strong technical understanding will help you cut through the noise, be more effective in leveraging AI’s capabilities, and know its limitations.
And on the flip side, if you’re someone who is reticent to even touch an AI chatbot – having a base will clarify the technical underpinnings driving your concerns. But it will also carve space for you to start to be able to mitigate those risks – assuming you’re not in a position to ignore AI completely, that is.
Now when I say “get more technical”, I don’t mean to suggest that everyone become a machine learning engineer overnight. That’s not a good use of your time or mine (unless, of course, you are in fact a current or aspiring ML engineer).
However, part of the responsibility in using or championing any disruptive technology is having the baseline understanding of how it works, and knowing where your own knowledge is limited.
So start getting more technical today, by doing the following:
1) Pursue opportunities to build foundational AI literacy
This is all about knowing the basics: like how generative AI works, prompting best practice, and general understanding of large language models (LLMs).
You don’t need to know all the nuts & bolts. But when you or your staff chat with an AI or build a workflow, the goal is that everyone can at least conceptualize what’s happening behind-the-scenes
2) Touch up on data & cybersecurity literacy
If you’re currently using AI tools but have never come across terms like sensitive data, PII (personally identifiable information) or prompt injection attacks, then this is your moment.
Be intentional. Make it a point to position yourself – and your team – to use AI tools in a more informed, safe manner. Some familiarity with these concepts is better than none at all
3) Get clear on what you don’t know
That isn’t to say go out and learn all those things. This is just about your own self awareness, and making sure you at least know where your gaps are. That way, you don’t misapply confidence in the wrong areas of your AI journey.
An extreme example of this is someone who’s neither a coder nor security pro, using AI to vibecode an entire platform without expert input. That’s just flat out risky. But a lesser example could be building agents at your organization, without understanding how integrations work or what to look out for.
p.s. How does one know what they don’t know? You’ve got a few options – which include talking to more techies, expanding your AI nighttime reading, and even (dare I say it) using AI to help identify what those gaps might be
4 ) Stay informed on what’s happening with AI broadly
AI is in a uniquely chaotic place right now, as we all try to catch up to how fast the technology – and the major firms driving it – are moving.
Therefore, choosing to be in the loop at a higher level can help inform your day-to-day use of AI as new info drops. And it can help you do this in realtime, particularly as you stay informed on what models are out, how the big players are moving, and how governments are ultimately responding/legislating.
p.s. The Rundown is one of 2-3 AI newsletters I currently review. But I really don’t recommend going overboard on AI content. Too much noise simply isn’t productive (and thanks to AI, there’s a lot more of it to sift through)
5 ) Learn to pin down your business requirements faster
You may recall your tech person, project manager or consultant trying to nail down your team’s needs and use cases before running off to build solutions. This requirements gathering step is par for the course with any project.
But the ability to do this need not only live with those roles. Identifying what’s important to accomplish with AI, what isn’t, and how you’ll know if its successful is an invaluable discernment skill that we’d all benefit from calling on regularly.
To Wrap This Up
One final thought on #5. Recall how I said that requirements gathering is a key step in tackling any project. But these days, AI is making it easy to want to skip this – in the name of speed and its perceived inherent efficiency, due to all its capabilities.
But I would encourage your team not to fall into this trap. Tech is tech at the end of the day, even if AI is nothing like what we’ve seen before. The same disruptive technology that can drive efficiency will just as easily accelerate inefficiency & dysfunction. And it will also make it harder to dial that back.
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