AI tools are evolving, which means that using them well is not something you decide once and then continue for the rest of your career. Periodically reviewing both the tools and your use of them is now part of using AI intelligently.

Yet most PIs do not have the time to chase every new tool or development. Instead, they should adopt the discipline of periodically reviewing how these tools fit into their workflows.

Earlier, we discussed developing an intentional relationship with AI. Then we looked at protecting your thinking while using it, which gets to the heart of using AI smartly. Today, the focus is on how to stay current with AI without going down the proverbial rabbit hole.

Start with what is already available through your institution. Many now have a central AI webpage, resource hub, or working group listing institutionally supported tools. Those often come with better security, privacy, or licensing arrangements than something individually adopted -- although you still need to carefully study the details of any tool.

Many tools operate similarly (e.g., ChatGPT and Claude) but each has tips and tricks, for example, for writing prompts you might want to learn. Then, there are tools like NotebookLM that are optimized for source-based retrieval and research which operate differently. Learning about your tool's strengths, weaknesses and best practices will help you get the most from it.

Also, talk to colleagues about what they are using. This is especially important in academia because your work is likely specialized enough so that much YouTube content will not be relevant. Colleagues in related areas may already have found useful workflows and those in very different fields may still give you creative ideas you may not have considered. This is also becoming easier now that people are more open about using AI, and more willing to speak honestly about what works and what does not.

Use trainings, recorded sessions, newsletters and other such sources strategically. The point is not to consume everything available; it is to scan for what may be useful. There is a difference between becoming aware of a tool and deciding to adopt it.

It also helps to be selective about which sources and tools you pay attention to. One approach is to initially subscribe to newsletters broadly, YouTube channels, or other AI-focused sources that catch your eye broadly but then retain only the ones that consistently provide something useful. Some will be more technical, others more focused on practical applications, and not all will be relevant to your work. Keep in mind as well that some self-described AI experts are mostly reporting trends or summarizing news from other sources rather than offering grounded insights. The same is true of tools themselves: some are genuinely useful, while others are little more than a new interface layered on top of one of the major large language models. In those cases, you may find that using the underlying tool directly is just as effective.

Ultimately, staying current is not only about knowing what tools are out there. It is also about reviewing how you are already using them. Make an inventory of the tools you currently use and the tasks for which you use them. Are they actually saving you time? Are there any you should stop using? Are there gaps in your workflow where another tool might be worth trying? A good review rhythm is not just about adding tools. It is also about deciding what to ignore.

That is why it helps to adopt a review cadence. Instead of just planning to “keep up with AI,” make the process concrete. You might review tools once a quarter, before the start of each semester or during a quieter period like summer. The aim is to create a manageable rhythm for checking in with yourself and your workflow.

Along with this it is also important to stay current on rules, expectations, and professional norms. This applies to proposals, conference abstracts, manuscripts, opinion pieces, and public-facing content. In some cases, disclosure may be required. In others, AI use may be prohibited. Even where formal rules are unclear, informal expectations still matter.

For example, there may be no formal rule against sending emails that were heavily shaped by AI, but if those messages feel impersonal or obviously machine-generated, they may still be off-putting to people with whom you are trying to build a relationship. In the same way, openness about AI use can be healthy, but it still needs to be paired with judgment about context, audience, and norms.

Yes, this is another thing for PIs to keep track of and, yes, there is a risk of spending more time chasing tools than benefiting from them. Even so, it is worth investing enough attention to stay informed. You do not need to use every tool, and you certainly do not need to master everything. But it is worth knowing what is available and revisiting your own use periodically so that your choices remain deliberate.

That brings us back to where our series began: The goal of being intentional in how you use AI.

Edited April 1, 2026

With many readers on spring break this month, I wanted to make sure everyone had another chance to see this interview. You will not want to miss this one.

Many faculty wait for the “perfect” DARPA opportunity to appear—when Dr. Rohith Chandrasekar (former DARPA Program Manager, now at Leidos) argues the real advantage comes from engaging early and often. In this interview, Rohith explains how DARPA PMs think about capabilities vs. technologies, what to bring to a first conversation, and why the second meeting is often the one that really matters.

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