This post was originally published on this site.
Rick Kelly is the Chief Strategy Officer at Fuel Cycle. His passion is helping brands unlock the power of customer intelligence.
As AI capabilities expand, there is a constant push to implement AI tools into everything that we do.
Implementation, however, shouldn’t be the primary focus. AI tools must be integrated with a purpose. One way to ensure that AI tools have a purpose is to use the jobs-to-be-done (JTBD) framework.
Overview Of JTBD
The JTBD framework was popularized by the late Clayton Christensen and emphasizes hiring products or services to accomplish specific tasks. Christensen described it as, “When we buy a product, we essentially ‘hire’ something to get a job done. If it does the job well, when we are confronted with the same job, we hire that same product again.”
In essence, whether our stakeholders are internal team members or clients, they invest in our services to achieve particular objectives. Christensen illustrated this concept with a few examples:
• Uber: Revolutionized urban transportation by enabling users to book rides via smartphone apps, addressing pain points such as slow response times and non-mobile-friendly options.
• Care.com: Provides a platform for finding various types of care services, helping users address personal challenges related to childcare and other caregiving needs.
• American Girl Dolls: Marketed as tools for self-expression and bonding, resulting in high sales and revenue. Consumers “hire” the product to validate and enhance a pre-teen’s sense of self-worth.
Einstein In A Tiger Cage—Thought Exercise
As I’ve written about previously on my company’s blog, a recent thought experiment by Kevin Kelly, founder of Wired magazine, can help apply the lessons from JTBD to AI: “Intelligence itself is overrated. … To get things done is not necessarily the most important, but take Einstein and a tiger and put them in the cage. Who wins? It’s not the smartest guy.”
Kelly further explains: “It’s not the company with the smartest people that will dominate. It’s not the smartest person in the room that necessarily knows what to do. Intelligence is necessary but not sufficient to get things done in the world.”
Building off my blog post mentioned above, the LLMs available to the public are astonishing, with their ability to rapidly process and predict information far beyond human capabilities—they are superintelligent. However, similar to humans and human-designed institutions, their effectiveness hinges on how well they address stakeholders’ needs.
Advice To Discover JTBD Using Market Research Methods
So, how do you discover the job to be done for your stakeholders? Identifying the true JTBD requires leveraging various market research methods to gain a deep understanding of your stakeholders’ needs:
User Interviews And Surveys
First off, conducting one-on-one interviews allows you to delve deeply into individual pain points, goals and desired outcomes, revealing the specific tasks users struggle with and their underlying needs.
For a broader perspective, surveys gather quantitative data on tasks, challenges and desired results. For instance, asking questions like, “What are the top three tasks you need to accomplish when using our product?” helps pinpoint critical areas that need attention.
Unstructured Feedback
To utilize unstructured feedback, start by analyzing customer reviews and comments across diverse platforms (review sites, social media, etc.) to identify recurring themes and unmet needs. Repeated mentions of workflow difficulties, for instance, can highlight critical JTBD.
User Communities
Communities allow for constant connection and engagement with stakeholders, helping you stay attuned to their evolving needs and challenges.
Additionally, you can segment your community into focused subgroups dedicated to specific topics or products, creating targeted insight communities that provide continuous and relevant feedback based on your needs.
Observational Research
When it comes to observational research, begin with passive observation—monitor how users naturally interact with your product and discuss their issues, revealing hidden JTBD through frequent mentions of workarounds and frustrations.
Complement this with ethnographic research by immersing yourself in the community’s culture to gain a deep understanding of real-life scenarios where JTBD emerges. For instance, grasping the nuances of users’ daily routines offers critical context that highlights the underlying needs and challenges they face.
Together, these approaches provide a comprehensive view of the JTBD, ensuring your solutions are both relevant and impactful.
Tying It Together: Implementing AI For JTBD
To achieve a successful AI implementation driven by the JTBD framework, you’ll need to integrate insights from the various research methods discussed above into a cohesive strategy:
1. Synthesize insights by combining qualitative and quantitative data collected from the various methods. This holistic approach provides a comprehensive view of JTBD, allowing you to understand both the explicit needs and the underlying challenges faced by stakeholders.
2. Prioritize needs by evaluating the importance and urgency of the identified JTBD. This involves analyzing the frequency and impact of different needs as highlighted by your research. For example, if your research reveals that users consistently struggle with a specific aspect of customer support, prioritize this JTBD based on its prevalence and significance.
3. Develop AI strategies tailored to address the prioritized JTBD. Use these insights to inform your AI implementation approach, ensuring that the solutions you develop are precisely aligned with the most critical needs identified. Using the same example, if feedback indicates that users face significant challenges with response times in customer support, focus on developing and/or integrating AI tools that enhance automation and efficiency in this area.
By aligning your AI initiatives with the true JTBD, you ensure that your technology meets stakeholder needs, and more importantly, drives meaningful improvements in their experience.
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