As I mentioned in my June blog post, AI is here to stay. It will be something we take for granted in several years as the tools we use evolve to seamlessly accommodate it. Until then, we should still avoid getting caught up in current AI hype, and no – there won’t be future job titles of AI prompt engineer once it becomes as ubiquitous as Googling.

Since AI will overhaul tech-related industries long before others, it’s important that tech professionals leverage it as much as possible over the next few years to remain competitive. In other words, AI will not replace humans, but humans who leverage AI will definitely replace those who don’t, especially in tech-related industries. Luckily, most tech professionals are well versed in adapting to change and often embrace a growth mindset that allows them to do so easily. In this blog post, I’ll outline six tips on how to embrace and leverage AI today in your tech job.

Tip 1: Reframe AI as a tool, not a threat

There have been no shortage of news articles and media coverage about the dangers of AI, or how it could threaten job security. This is expected, of course, since every new technology that has the potential to change how we work is met with fear and criticism until it becomes commonplace. And according to the following rules of new technology from Douglas Adams, this often depends on your age:

1. Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works.
2. Anything that’s invented between when you’re fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it.
3. Anything invented after you’re thirty-five is against the natural order of things.

However, to ensure that you are always on the lookout for ways to improve your job using AI at the present time, you need to treat AI as an opportunity to enhance your productivity. In other words, think of AI as a partner rather than a competitor. If you don’t do this, your mind will subconsciously skip over most of the tasks that AI could help with.

Tip 2: Start by using AI for brainstorming and content generation

One of the best ways to dive into AI is to use it to do things that save us tremendous effort or time. Brainstorming is one example of a task that can be painful at times – especially if you’re not in the right mindset or haven’t had enough caffeine. But if you ask an AI to help you brainstorm topics, you’ll likely get some good ideas pretty quickly, or a more specific topic you can ask AI to help brainstorm around. Similarly, AI can be used to save tremendous amounts of time we’d otherwise spend creating menial content, such as verbiage for documentation or proposals, or lengthy summaries for a report or presentation. AI can generate this content for us based on the rough information we provide so that the only time we need to spend is for reviewing the results for accuracy and making necessary modifications to content or flow.

To get the best results from an AI, don’t ask it exactly what you want – instead, start a dialog by telling it what general problem you have and ask it if it can help with that problem. It will say Yes and ask you to provide more detailed information to allow it to generate results, which you can provide. This stepwise process provides the AI with the context and situational awareness it needs early on to provide relevant and accurate results.

Say, for example, I’m trying to come up with a blog post idea for this month. Of course, I didn’t need help for this, but here is an example of how a typical conversation with ChatGPT could transpire and result in 12 relevant blog post titles and descriptions (only the first title is shown below):

Next, you could keep the dialog open (which preserves the context and situational awareness) and ask ChatGPT to generate a description for a blog post title you decided on as shown below:

Note that this is merely an example of how to use AI to generate content – it can generate much lengthier content, especially if you give it more direction during your dialog, or if the information is of wide scope. For example, general Cybersecurity awareness training materials suitable for a web portal or employee training could be generated with a single request:

Tip 3: Use AI for automation and scripting

Automation and scripting have fast become the norm for tech professionals in recent years, and AI is only going to accelerate this trend. You can leverage AI to generate programs and scripts that provide a specific functionality, or code that can be used to automate configuration using a tool such as Ansible. For example, a stepwise dialog that could be used to generate an Ansible playbook for managing Ubuntu hosts could resemble the following: Note that AI reiterated the requirements and broke them down into actionable tasks for Ansible, as well as provided copyable code with clear comments (several pages, actually). Additionally, the AI provided a usage summary following the code:

The same process would work for a Python script that reports which systems on the network have port 80 and 443 open (for web services). Note that AI also broke down the requirements and included the Python libraries needed to run the script (which are imported at the beginning): Following the script code section, the AI added key points, instructions for execution, and some ideas for customizing the script – which is necessary, since we didn’t tell the AI which IPv4 network we wanted to scan:

Tip 4: Use AI to learn new technologies and skills

In our tech programs at the college, AI has been an incredible asset for students. They can ask questions, clarify doubts, get help with assignments, generate sample quiz questions or flash cards for practice, get textbook sections or articles simplified in a way that is easy to understand, or even translate content into their native language. And they can do this at any time, making learning more accessible.

The same is true for all tech professionals, as we are constantly learning new technologies and practices as our industry evolves. We can leverage AI to:

• Learn a new topic (e.g., how Ansible works),
• Summarize a lengthy or difficult text passage (e.g., part of an SLA) into a form that is easier to understand,
• Find, explain, and correct errors in code that we have created, or
• Explain (trace) a difficult code example to learn how it works, as shown below:

Tip 5: Decide what types of content you are willing to generate using AI

One of the most important things you should be mindful of is whether a task should be leveraged using AI. In general, try to leverage AI to save time where it makes the most sense.

Using AI to generate an Ansible playbook, Python script, or full Cybersecurity awareness training as illustrated earlier will save you tremendous time while producing results that are accurate and require little modification. Similarly, leveraging AI to learn something, such as how a complex shell script works, is also an excellent use of AI.

However, do you want to use AI to generate important professional or personal content? Probably not. Know where to draw the line. And while this line will be different for everyone, you should avoid using AI when creating content that you must internalize and use in important situations. Personally, I only use AI to generate content that I consider menial – I do not use AI to generate important professional or personal content, including important documents within my organization and my blog posts. Nothing on this website is AI-generated.

Tip 6: Be mindful of what you share with AI

All of the example screenshots in this blog post were from chatgpt.com in the Microsoft Edge web browser. That means that the AI model that you are interacting with is running on some server in the cloud, and not your local computer. And when you provide an AI model with the information it needs to produce output, that information can be used by that server (and the organization that owns it) in any way they wish if you read the fine print.

Now, the information I provided within the examples in this blog post are relatively harmless. To generate some blog post ideas, I shared a list of previous blog post titles from my website. And I had to provide it with the code of a shell script that I needed it to explain. Neither of these contained any personal or corporate data that should be kept confidential, and the shell script I provided did not contain any copyrighted code.

Before providing any content to an AI that is run in the cloud, remember that the data you share may be used by the AI to produce future content. So avoid sharing any personal or corporate data with an online AI that you wouldn’t otherwise share publicly.

It’s also important to know that AI apps, such as the Copilot app built into Windows 11, also function like chatgpt.com – they provide a nice interface, but still take your requests to a cloud server.

That being said, hardware advancements in PCs are making it increasingly possible to run AI models locally. Devices are becoming more powerful, and AI accelerators (called Neural Processing Units, or NPUs) are being embedded directly into many systems today, including Copilot+ PCs. Running an AI model locally would allow you to share data with the model without that data being used elsewhere. And while this is certainly possible today with tools like Ollama, we’re still a few years away from it being a common and easy experience for most people. When we do get to a time where running local AI models is commonplace, there will still be advantages to running AI models in the cloud because cloud servers have access to far more hardware and information.

Conclusion

The ability to see new technologies and paradigms as opportunities to grow is essential in the tech industry, and part of a healthy growth mindset. And a sustainable career in tech is built on a combination of technical expertise, adaptability, and a willingness to evolve with emerging technologies like AI.