I don’t know about you, but it sometimes feels like the “to do list” is never ending. As I prepare to deliver a keynote in Paris next week, there are so many jobs to be done - from preparing the talk, packing my bags, ironing my shirt(!) and planning my journey.

It’s the same when people are buying products or services, there are so many things to consider and so many competing priorities, they aren’t really looking to spend money, they are seeking tools that help them make progress in their lives and work.

In a world where AI can automate, predict, and personalize, the real differentiator lies in deeply understanding why people make the decisions they do. That’s where Jobs to Be Done (JTBD) comes in.

JTBD helps teams uncover the underlying motivations behind customer choices: the "jobs" they are trying to accomplish. Instead of guessing which features to build or how to market, JTBD gives you clarity on what really drives usage, satisfaction, and switching behavior. It shifts focus from "what customers say they want" to what they need to get done.

A step-by-step guide for people with too many jobs to do

Step 1: Research planning & data acquisition

Goal:

Identify and gather data from customers who have recently experienced the "job" you're exploring.

Approach:

• Identify customer segments and target decision-makers, users, and influencers.
• Conduct in-depth interviews ("Switch Interviews") that explore the context, triggers, and decision-making process.
• Capture functional, emotional, and social drivers.
  
  

How AI can help:

• Transcription and smart note-taking : Use tools like Otter.ai or Fathom for fast, accurate transcripts.
• Data scraping: AI can analyze reviews, chat logs, or support tickets to surface themes.The new Deep Research connector from OpenAI lets you connect directly to your HubSpot CRM and gain deep insights with just a few keystrokes.

Where AI falls short:

• AI can't build trust or rapport: interviewing is still a human craft.
• It struggles to interpret subtle tone, sarcasm, or cultural nuance.

Step 2: Data analysis & coding

Goal:

Structure the raw data into meaningful categories and patterns.

Approach:

• Read transcripts thoroughly.
• Apply JTBD tags (Situation, Motivation, Desired Outcome, Pain Point, etc.).
• Use affinity mapping or thematic coding to group insights.

How AI can help:

• Auto-coding: Tools like Dovetail, Insight7, and NVivo can auto-tag data.
• Theme clustering: Natural Language Processing (NLP) can identify frequently mentioned terms or concepts.
• Sentiment analysis to gauge emotional weight.

Where AI falls short:

• Lacks contextual judgment, may overgeneralize or misinterpret meaning.
• Needs human oversight to ensure consistency and avoid hallucination.

Step 3: Data synthesis & JTBD mapping

Goal:

Distill findings into clear Job Statements and customer needs.

Approach:

• Create Job Statements: "When ___, I want to ___, so I can ___".
• Identify functional, emotional, and social needs for each job.
• Prioritize unmet needs (importance vs. satisfaction).

How AI can help:

• Drafting: AI can suggest job stories or maps from tagged data.
• Clustering: Can group similar needs or pain points.
• Visualization: Some platforms generate job maps or dashboards.

Where AI falls short:

• AI lacks strategic judgment - which job matters most?
• Can't infer business implications or opportunity sizing.

Step 4: Reporting and activation

Goal:

Communicate findings and guide product, marketing, or innovation strategies.

Approach:

• Summarize core jobs, needs, and pain points.
• Include customer quotes and visuals.
• Develop "How might we..." opportunity statements.

How AI can help:

• Summarization: Use AI to create concise summaries and insight decks.
• Quote extraction: Automatically pull relevant quotes for reports.
• Suggest "how might we" statements or solution prompts.

Where AI falls short:

• AI doesn't know your business priorities.
• Final storytelling and decision-making should stay human-led.

Get the jobs done, smarter

JTBD isn't just a theory, it's a blueprint for action. In an era of AI overload and feature creep, the teams that win are the ones who focus on solving real problems. Use this guide to start where it matters: the customer's job.

Blend curiosity with technology. Let AI speed up the grunt work, but keep your ears tuned to human insight. When you understand the job behind the choice, you don't just build better products, you build momentum.

So, pick a job, gather the data, and get to work. Your next breakthrough starts there.