AI Productivity Tools: What Actually Works for Getting Things Done

Over the past year, I've tested pretty much every AI productivity tool that's crossed my feed. Most were disappointing. Some were game-changers. Here's my unfiltered assessment of what's actually worth your time.

Writing and Content

What Works: AI Writing Assistants for First Drafts

Tools like Claude, ChatGPT, or Jasper are genuinely useful for generating first drafts. Not for publishing directly – that still needs human editing – but for overcoming blank page syndrome.

# My typical workflow
def write_blog_post(topic, outline):
    # 1. Generate initial draft
    draft = ai.generate(f"Write about {topic} covering: {outline}")
    
    # 2. Human editing (the actual work)
    edited = human_review_and_rewrite(draft)  # This takes 60% of the time
    
    # 3. AI polish (grammar, clarity)
    final = ai.improve(edited)
    
    return final

The mistake people make: expecting AI to produce publish-ready content. It can't. It's a starting point, not the finish line.

Coding Assistance

GitHub Copilot: The One That Actually Delivers

I was skeptical, but Copilot has genuinely increased my coding speed. It's particularly great for:

• Boilerplate code (tests, type definitions, API wrappers)
• Remembering syntax I use rarely
• Completing patterns once I've started

It's not replacing thinking through architecture or debugging complex issues. But for the 30% of coding that's just typing stuff I already know? Massive time saver.

Email and Communication

Mixed Results

AI email tools promise to write your emails. Reality: they write generic emails that often need more editing than writing from scratch would.

What does work: using AI to summarize long email threads, extract action items, and draft responses to routine messages.

# Actually useful email automation
def process_email_thread(thread):
    summary = ai.summarize(thread)
    action_items = ai.extract_actions(thread)
    
    if is_routine_question(thread):
        draft = ai.draft_response(thread, company_policies)
        # Still requires human review before sending
    else:
        return {"summary": summary, "actions": action_items, "needs_human": True}

Meeting Notes and Transcription

Genuine Time Saver

Tools like Otter.ai, Fireflies, or Granola that transcribe and summarize meetings are legitimately useful. I no longer take notes during meetings – I just review the AI summary afterward.

The catch: you need to verify important details. Transcription isn't perfect, and action items sometimes get mangled.

Research and Learning

Perplexity AI and Similar Tools

For research, AI search tools that provide citations have replaced a lot of my basic Google searching. Being able to ask follow-up questions contextually is genuinely better than traditional search for exploratory research.

Limitation: I still verify important facts independently. AI search occasionally surfaces outdated or incorrect information.

My Daily AI Stack

Tools I use every day:

1. GitHub Copilot – Coding assistance
2. Claude/ChatGPT – Problem-solving, drafts, explanations
3. Otter.ai – Meeting transcription
4. Perplexity – Research with sources

Tools I tried and dropped:

• AI email writers (more work to fix than write)
• AI calendar scheduling (created more confusion)
• AI task managers (the suggestions were rarely relevant)

The 80/20 Rule for AI Productivity

Here's what I've learned: AI adds the most value for tasks that are routine, well-defined, and don't require deep domain expertise. For creative, strategic, or nuanced work, AI is an assistant at best.

Don't try to automate everything. Identify the specific bottlenecks in your workflow, and target those with AI tools. That focused approach beats trying every shiny new tool that launches.