Investment in AI is surging, and companies are scrambling to integrate it into their products and services. Yet, despite the hype and the massive investment, a staggering number of AI projects fail to deliver on their promise.
That’s because many organizations jump on the AI bandwagon without a clear understanding of what problems they’re actually solving for their users, leading to costly experiments that ultimately fall flat. They build impressive AI-powered features, only to find that no one is using them.
The core problem is simple: technology-first, not user-first. The key to flipping this mindsight is through a Jobs to be Done (JTBD) framework. This framework identifies the meaningful “jobs” that AI can be hired to do, shifting the focus from technological capabilities to real user needs.
By understanding the underlying motivations and struggles of your customers, you can leverage AI to create truly valuable solutions, increasing your chances of success and maximizing your ROI.
5 Steps to Building a JTBD Framework for AI Investments
A JTBD framework aligns technology with real-world needs by helping companies identify processes ripe for automation. Uncovering employee tasks, inefficiencies, and pain points ensures that AI investments address genuine needs rather than chasing fleeting trends.
To achieve this, however, a B2B research effort is crucial to gaining a deep understanding of the challenges users face and the jobs they are trying to accomplish.
The following five steps will help you build a JTBD framework that uncovers the right AI opportunities, prioritizes them effectively, and ensures your AI investments drive meaningful impact.
Step 1: Define JTBD Research Goals to Uncover AI Needs
Building a JTBD framework starts with research – specifically, a deep exploration of user behaviors, pain points, and decision-making processes. Without structured research, AI development risks being guided by assumptions rather than real-world needs. The first step is to define clear research goals that will shape interviews, focus groups, and qualitative data collection.
Before conducting any research, clarify what you want to learn:
- What tasks are users trying to complete?
- What pain points or inefficiencies exist in their current processes?
- What triggers them to seek a new solution?
- What workarounds do they use today?
- What emotional or social factors influence their decisions?
These questions will guide discussions and help identify critical areas where AI could provide real value. For instance, a company developing AI-powered customer service tools would benefit greatly by understanding the specific frustrations customers face when seeking support, rather than simply building a chatbot with general capabilities. This ensures the AI solves real problems, like reducing wait times or providing accurate, personalized solutions.
Step 2: Conduct IDIs and Focus Groups to Gather Insights
JTBD research is qualitative at its core, and IDIs and focus groups serve as essential tools to uncover how users experience their work, where they struggle, and what they need from AI-driven solutions. These are not simple customer satisfaction surveys—they are in-depth conversations designed to uncover the true “jobs” your customers are trying to get done.
To get meaningful insights, focus on past experiences and ask open-ended questions such as:
- “Tell me about a time when you were trying to [achieve a specific outcome related to your focus area].”
- “What were the biggest challenges you faced in trying to [achieve that outcome]?”
- “What made that experience frustrating or difficult?”
- “If you could wave a magic wand and make that process easier, what would it look like?”
Additional key areas to explore include:
- When did customers first realize a job was being underserved?
- What business goals or metrics does this job address?
- What existing tools did customers use before searching for a new solution?
- What made them realize those tools were insufficient?
- Which stakeholders were involved in finding a solution?
- How do customers evaluate the quality of AI solutions in meeting their needs?
Consider a company developing AI for project management. By conducting IDIs, they might discover that teams struggle with accurately predicting project timelines. This insight would lead to AI features that focus on predictive analytics, rather than generic task automation, ensuring the tool truly addresses a critical need.
Step 3: Identify and Categorize Jobs-to-be-Done
Once qualitative data is collected, the next step is analyzing responses to define clear jobs-to-be-done. These jobs typically fall into three categories:
- Functional Jobs – The core tasks users need to complete.
- Example: “I need to manage inventory efficiently so I don’t run out of stock.”
- Emotional Jobs – The feelings users want to experience or avoid.
- Example: “I want to feel confident that I have accurate data before making decisions.”
- Social Jobs – How users want to be perceived by others.
- Example: “I want my team to see me as proactive and strategic, not reactive.”
These categories align closely with the B2B Elements of Value pyramid, which highlights how B2B customers prioritize different factors when evaluating solutions. At the base of the pyramid are functional needs – such as price, capabilities, and features – which directly relate to functional jobs. Higher up the pyramid are emotional and social needs, which become key differentiators for businesses that go beyond simply meeting functional requirements.
By understanding these types of jobs and their place in the B2B Elements of Value pyramid, businesses can ensure their AI solutions address not only functional needs but also the emotional and social factors that drive decision-making. This approach helps companies differentiate themselves, enhance customer experiences, and develop AI solutions that deliver meaningful value beyond just automation.
For example, an AI tool used by sales teams might not just automate data entry (functional) but also provide real-time insights that make the sales rep feel more confident (emotional) and be perceived as highly knowledgeable by their clients (social).
Step 4: Prioritize AI Opportunities Based on Business Impact and Feasibility
Once potential AI opportunities have been identified, the next step is prioritization. Not every job requires AI intervention, and automating the wrong tasks can lead to poor adoption, wasted investment, or unnecessary complexity. The best way to prioritize is by evaluating two key factors:
- Business Impact – How critical is this job to user success and organizational goals? AI should address jobs that improve efficiency, accuracy, or customer experience.
- Example: AI-powered demand forecasting in retail can prevent costly overstocking and stockouts, making it a high-impact opportunity.
- AI Feasibility – How easily can AI be implemented to improve this job? Some tasks, like automating structured data processing, are well-suited for AI. Others, like AI-driven customer sentiment analysis, may require more complex models and ongoing refinement.
Focus on these high-priority jobs for AI implementation:
- High-friction jobs – Tasks that cause major frustration due to inefficiencies.
- High-frequency jobs – Recurring tasks that take up significant time.
- High-value jobs – Jobs where AI could provide significant ROI.
Look at how AI tool roundups have evolved since 2023. Many tools from a year ago have vanished because they focused on automation where it wasn’t needed. The ones that lasted—AI-powered contract review, predictive analytics in supply chains, fraud detection in finance—tackled high-friction, high-frequency, high-value jobs.
For each AI opportunity, ask:
- Does AI meaningfully improve the process?
- Would AI remove friction or create new barriers?
- Is AI solving a problem that users truly care about?
Companies can use a two-tiered approach:
- High-impact, high-feasibility jobs – AI can deliver immediate value with minimal risk.
- Medium-feasibility, high-impact jobs – May require further testing or phased implementation.
Jobs with low impact or low feasibility should be deprioritized or reconsidered for alternative solutions. This ensures AI investments focus on solving the right problems – those that are critical to business success and realistically automatable – leading to better adoption and ROI.
Imagine a company deciding whether to invest in an AI-powered email filtering system vs. an AI-driven marketing campaign generator. Filtering emails is high-feasibility and high-impact (reduces time wasted), while the marketing campaign generator, though potentially high-impact, might be low-feasibility (requires complex creative input). The company would prioritize the email filter.
JTBD Framework for AI Investments: Start with the Job, Not the Tech
“If you don’t understand the job your product is being hired for, you have no way of knowing if AI is the right tool for the job.” – Bob Moesta
Many AI projects fail because they prioritize technology over user needs, resulting in impressive solutions that ultimately go unused. The JTBD framework flips this approach, ensuring AI development starts with the user and their real-world challenges. By focusing on jobs, pain points, and desired outcomes, JTBD prevents companies from falling into the trap of building AI for AI’s sake.
Now, take a moment to reflect on your AI initiatives. Are they driven by technology trends, or are they solving real user pain points?
If you’re unsure, let’s talk. With over 17 years of expertise in B2B tech research, Cascade Insights can help you apply the JTBD framework to uncover AI opportunities that truly matter. Reach out today, and let’s ensure your AI investments deliver real impact.
For more than 17 years, Cascade Insights has conducted powerful B2B market research for tech companies. Learn more about our jobs-to-be-done research.
