Method 1: Follow important blogs #

Blogs such as the OpenAI blog, Anthropic, Google Developer blogs can help surface really large updates that can improve your quality of life, and help you understand new technologies. These blogs are often beginner friendly and are not very complex (though they might link to more technical papers, if relevant for your use case).

Follow these accounts on Youtube and Twitter as well - oftentimes, they might post summary/quick bites that can help you digest the information, or walk through an implementation. This is easily digestible during your lunch break!

Curating your Twitter feed to follow people in OpenAI research, for example, can also be a great way to see what people are talking about. It’s difficult to separate the noise from the truth, but curating your feed to be only comprised of high-impact people in AI can help you see the most impactful tools that are out there.

Method 2: Try new tools #

There are a TON of new tools that have come up recently. I recommend spending an hour (at least) per week trying a new tool. Whether that’s Cursor, Lovable, or Canva AI, these tools might help you move faster, but you can only unlock their potential if you know what they can help you with!

This might seem overwhelming at first - set up systems within your team to have a lunch and learn, or an accountability session so that engineers and scientists are taking the time to explore new tools that come out and share their knowledge with the team!

Method 3: Join AI communities #

People are really excited to share what they are learning, whether that is offline, or in-person meetups. Find one close to you, or explore subreddits (r/MachineLearning) to learn from collective knowledge.

Set aside 20 minutes twice a week to scan discussion threads. Look especially for “Weekly Digest” or “What We Learned” posts that summarize key developments. Contributing your own findings helps build connections with others working on similar challenges.

Method 4: Establish an AI review cadence #

Hold monthly “AI Capability Reviews” (~1 hour long) where team members rotate presenting:

• One AI tool with potential impact on your workflow
• A brief demo showing it applied to a current project
• A decision framework: adopt now, monitor progress, or dismiss

This cadence creates accountability and transforms vague “keeping up” into concrete action. The key is consistency—these sessions should be treated as immovable appointments, not optional when workload permits.

Method 5: Implement small proof-of-concepts #

Reading about AI advancements is informative, but implementing them creates true understanding. Each month, choose one technology that could benefit your workflow and build a minimal proof-of-concept. For example:

• If you’re a developer, try using GitHub Copilot or Claude for code completion for a day
• If you handle data, test a new visualization library with AI-assisted chart generation
• If you write documentation or grants, experiment with an AI writing or summarization tool

Keep these experiments small—aim for something you can complete in 2-3 hours. Document what worked, what didn’t, and where you see potential for integration into your regular workflow. Share these findings with your team.

Conclusion #

The AI landscape is evolving weekly, but with these five methods, you can stay informed without becoming overwhelmed. The goal isn’t to adopt every new technology, but to recognize opportunities where AI can meaningfully enhance your work. Start small, be consistent, and focus on practical applications rather than chasing every trending headline. Remember that most breakthrough AI tools started as simple experiments. Your team’s competitive advantage may come not just from using these tools, but from being early adopters who shape how they’re applied in your industry.

Unsure of where to start? #

Book a call with me to talk more about how you can use AI to speed up and transform your workflow, saving you and your team hours of time!