AI had a substantial impact on my personal and professional life in 2025. This sentiment isn’t unique, as most people would say the same, but I didn’t fully grasp how impactful and ubiquitous it would become. As I look back on what I used this technology for, it spanned from chatbot interactions to significantly speed up the way I retrieved information and decision-making, an academic researcher helping me develop new skills and refine existing ones in ways that match how I learn the best, and unlocking a superpower in application development using coding assistants. As I reflect on 2025 and look ahead to 2026, I wanted to share three standout differences AI made in my life.
Automating My Workflow
Studies have shown that investment in AI in a professional setting likely hasn’t generated as much impact as promised, but the systematic and automated approach I have developed using AI has significantly impacted my work, not only through time savings, but also value generation in the form of higher productivity and quality of delivery.
The clearest example of this was in solving the common challenge of everyday project and task organization and prioritization, while retaining the most important information and actions from meetings.
This journey began with assessing the pain points in my project and task management process. My personal organizational tool (Todoist) was constantly out of sync with my organization’s project management tool (Monday). This led to mismanaged prioritization and missing key information that should have been easily accessible for quick, efficient execution. Using a systematic approach of understanding this problem in detail, using chatbots to help to develop a framework to streamline and solve this problem, and then incorporating MCPs for Todoist to automate and organize the syncing, I significantly cut down on the time spent organizing my workload, made sure the systems were reliably working with each other and increased the speed and value I was able to deliver with my workload.
Following this workflow automation, I tackled another challenge: becoming a more efficient meeting notetaker and better utilizing those notes. I maintained monthly Google Docs with meeting notes and thoughts to reference later, but they never felt complete. In some instances, personal next steps and due dates were missing. When it came time to either retrieve information relevant to a conversation or discussion happening in real-time, there was a struggle for complete memory recall, leading to delays in task delivery.
Solving this challenge required an AI notetaker, Granola, the use of its Recipes function to organize meeting notes into the format of key takeaways, group next steps, and personal action items, and Claude to organize and merge newer meeting summaries into existing notes and summaries in my Google Doc. This approach has given me comprehensive information on all my meetings, allowed me to focus and contribute more to conversations in the meetings themselves, and ensured that I stay on top of everything I said I would deliver.
Here is what my revised workflow looks like:
- Task capture: I start with my Monday.com dashboard, where tasks are organized by missing due dates, due this week, and due in the future. I copy these tasks into Claude to structure them into a Todoist-compatible format.
- Daily sync: If Todoist falls out of sync with Monday, I use Claude's MCP integrations to sync the missing items and assign time blocks for task completion around my meetings.
- Meeting capture: I use Granola during meetings, then apply a custom Recipe at the end of each meeting (or weekly) to structure my notes into key takeaways, team next steps, and personal action items with agreed-upon due dates.
- Weekly consolidation: I copy the past week's meeting notes and use Claude's Google Docs integration to merge them into my monthly Google Doc. Claude organizes everything chronologically and provides a month-to-date view of progress across internal projects and client work. I then manually check off completed action items and create new Monday and Todoist tasks as needed.
This new system has streamlined my project and task management process while also unlocking higher-quality insights and decision-making through better information capture from meetings, which keeps my value contribution high. Beyond workflow automation, AI transformed how I learn and retain knowledge.
Learning That Sticks
I consume a lot of business podcasts, videos, articles, and whitepapers to continue my education, but like many, would struggle with retention and connection of ideas across sources to build a deeper understanding. Google’s NotebookLM solved this challenge.
Unlike traditional chatbots, NotebookLM allows me to take an academic approach to utilizing LLMs, creating thematic notebooks that contain collections of related materials like marketing measurement and business strategy frameworks. Within these notebooks, I can ask questions across all stored materials and receive answers with citations from the saved sources. NotebookLM also allows me to summarize this content with Audio Overviews, PowerPoint summaries, and mind mapping visualizations that synthesize key themes into more digestible formats for better retention. This product has fundamentally changed how I learn, from consuming content in isolation to building interconnected knowledge that I can apply to my everyday life and work.
The Coding Superpower
Claude Code has by far had the most substantial impact on my personal and work life. Using the tool has almost felt like a superpower. Whether it is a quick analysis of structured data, development of internal tools for work, or personal projects, the speed at which I can go from idea to value delivery is incredible. For work, I have built an internal pixel tracker and data compliance check. Tools that automate important, but time-consuming tasks that don’t directly generate analytics insight, freeing my team and me to focus on higher-value work. For my personal life, I have created a LifeOS that handles personal finances, portfolio tracking, and health metric tracking. These projects have allowed me to get comfortable with the tool, create small wins in my life, and open up my imagination to what is possible with this technology. The barrier between “I wish I had a tool for this” and “I have a tool for this” has essentially disappeared.
Looking Ahead to 2026
I found foundational AI use cases across various facets of my life in 2025, but the results didn’t come easily. It took extensive trial and error throughout the year to reach the point where I finally saw the value AI promised for my everyday life. Looking ahead to 2026, I am excited to push my AI use further to stay on top of the technological advancements that are sure to come in the new year, accelerate my education of various topics, and continue my journey streamlining both personal and professional life. My biggest AI lesson from 2025 is that AI’s value does not come from the technology itself, but from systematically identifying friction points in life, spending time fully comprehending the problem, and thoughtfully applying the right tools to solve them. I believe this methodical thinking will be a key value driver in my 2026 journey.