How are people actually using AI day to day?

AI is being used in various practical ways across industries, often in ways that are less glamorous than the hype suggests. Based on the most helpful WSO content, here are some real-world applications and insights into how AI is being utilized day-to-day:

How AI is Actually Used:

1. Data Processing and Analysis:

   • Running sentiment analysis on earnings call transcripts.
   • Using neural networks to count objects (e.g., cars in parking lots) or extract text from scanned documents like receipts.
   • Employing machine learning models trained on years of fundamental data to surface interesting investment opportunities.

2. Portfolio Management:

   • Advanced statistical techniques are used to manage risk by hedging out correlations and unwanted factor exposures in portfolios.

3. Task Automation:

   • AI-powered tools can independently perform complex tasks, such as planning events, creating personalized workout routines, or optimizing marketing strategies.

4. Workplace Efficiency:

   • Automating repetitive tasks like data entry or categorization.
   • Refining workflows by assigning subtasks and accessing apps through APIs.

5. Creative and Strategic Support:

   • Generating ideas or drafts for presentations, reports, or marketing materials.
   • Assisting with coding or debugging through AI-powered development tools.

What People Wish AI Could Do:

• Resolve ethical dilemmas or make nuanced decisions in ambiguous situations.
• Provide real-time, accurate data tracking across broader datasets (e.g., employment reports).
• Handle more creative or strategic tasks without human intervention, such as crafting highly personalized strategies or solutions.

What Turned Out to Be a Waste of Time:

• Over-reliance on AI for tasks that require deep contextual understanding or human judgment.
• Expecting AI to "magically" predict outcomes, like stock price movements, without robust data and models.

AI is great at tasks that follow consistent patterns and involve large datasets, but it struggles with ambiguity, creativity, and ethical decision-making. The key is understanding its strengths and limitations to maximize its utility in your day-to-day workflow.

Sources: AI in fundamental investing, Thoughts on AI and the future of the job market?, Will robots replace your consulting or financial career?