Unlocking the Power of AI with Jobs to Be Done (JTBD): When and Where to Leverage Artificial Intelligence
Artificial intelligence (AI) offers limitless potential to transform how businesses operate, innovate, and serve customers. But the key to effectively utilizing AI lies not in chasing shiny technologies or trendy buzzwords, that often result in oversold and underutilized technologies, but in understanding the specific "job" that AI can help your customers (or your organization) accomplish. This is where the Jobs to Be Done (JTBD) framework becomes an invaluable framework for businesses.
What is Jobs to Be Done (JTBD)?
The JTBD framework purpose is to develop a deep, almost first principle approach to understanding why people use products or services. People don’t buy products—they hire them to get a specific job done. For instance, someone buys a drill not because they need a drill but because they need a hole. JTBD helps companies move beyond thinking about the "what" and the "how" and dive into the "why" behind customer decisions (Christensen et al., 2016).
By adopting the JTBD mindset, businesses uncover unmet needs, identify the right solutions to offer, and create innovations that truly solve real-world problems. So, how can this powerful framework be applied to AI?
The Intersection of JTBD and AI: Focus on what matters
AI offers unique capabilities, from automating repetitive tasks to uncovering insights through machine learning algorithms. However, not every problem is a good fit for AI. Just because AI can be applied doesn't mean it should be. To cite Maslow (1966) "it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail." This is where JTBD comes in. By focusing on the specific jobs your customers are trying to get done, you can determine when and where AI is truly the right solution.
1. Identify the Functional Jobs AI Can Improve
Many of the early uses of AI focus on automating repetitive or mundane tasks—jobs that require functional, rule-based operations that machines are particularly good at handling. Start by examining your customers’ functional jobs to be done. For example:
- Customer Service: Customers don’t want to wait in a physical or virtual queue for basic support (and well, probably anything!). The job they’re trying to get done is "resolve an issue quickly." AI-powered chatbots and virtual assistants can help streamline responses to common customer questions and issues and free up your employees for more complex requests (Huang et al., 2019). This is an example of improving both the customer and employee experience, if the AI is implemented well.
- Data Processing: Customers often need to make decisions based on data, but manual data entry and analysis are tedious jobs. By identifying the job of "make data-driven decisions faster and more accurately," businesses can apply AI-driven tools to automate data collection and generate insights that help customers make informed decisions (Brynjolfsson & McAfee, 2017).
2. Address Emotional and Social Jobs with AI
JTBD doesn’t just focus on functional outcomes—customers have emotional and social jobs to get done as well. People use products or services to feel a certain way or to convey a particular image to others. While AI often excels at functional jobs, the emotional and social dimensions shouldn't be overlooked:
- Personalization at Scale: Many customers want to feel understood and valued, especially in digital interactions. AI-powered personalization engines can provide tailored recommendations, content, and experiences that make users feel more connected to your brand (Davenport & Ronanki, 2018). For example, Netflix uses AI to personalize movie suggestions, helping users discover content that aligns with their unique tastes—a job that satisfies both functional and emotional needs.
- Building Trust with Predictive AI: AI can also assist in making predictions that help customers feel more confident about their choices. Whether it's financial services using AI to predict market trends or e-commerce platforms recommending products based on a user's browsing history, AI can take on the job of "reduce uncertainty" and provide customers with a sense of security (Chui et al., 2018).
3. Understand the Struggles of Existing Solutions - The Pain Points
The JTBD framework emphasizes understanding customer pain points—where are existing solutions falling short? This is a great way to discover where AI can fill gaps or optimize inefficient processes.
For example, if customers are frustrated by the slow speed of manual approval processes in insurance claims, the job might be "expedite approval and reduce waiting time." AI can automate claim approvals by analyzing documents and identifying key patterns in minutes instead of days (Agrawal et al., 2018).
- Example in Retail: A retail company might learn that customers are struggling with long lines during checkout. Instead of simply focusing on improving traditional checkout processes, the JTBD approach would reveal the real job: "complete my purchase with minimal delay." AI-driven solutions like Amazon Go’s cashier-less stores can completely rethink the checkout experience using computer vision and sensors to automatically process payments when customers leave the store (Stone, 2019).
4. Recognize When AI Isn't the Solution
One of the most critical aspects of the JTBD framework is that it helps businesses understand when AI isn’t the right solution. AI is powerful but not perfect, and its effectiveness depends on the context. For example, while AI can automate processes and reduce errors, there are jobs where human touch and intuition are irreplaceable, such as complex negotiations or tasks requiring empathy.
By identifying the emotional or social aspects of a customer’s job, companies can avoid over-automating experiences that benefit from human involvement. For instance, in healthcare, AI might support diagnostic processes, but patients still value the reassurance and compassion that a human doctor provides during consultation—a job that machines simply can’t fulfill (Topol, 2019).
5. Use JTBD to Innovate with AI
The JTBD framework also encourages businesses to think beyond incremental improvements and consider where AI can enable radical innovation. By understanding the jobs customers are trying to get done, companies can apply AI to create new business models or services that solve problems in ways that were previously impossible.
- Transportation Example: Uber disrupted the transportation industry by recognizing the job people wanted to get done: "get me from point A to point B with ease and affordability." By integrating AI to predict ride demand, optimize routes, and match riders with drivers in real time, Uber solved this job in a way that taxis could not (Christensen et al., 2016).
Final Thoughts: AI as a Strategic Tool for Getting Jobs Done
When viewed through the lens of JTBD, AI becomes more than just a technology—it's a strategic tool that can transform how businesses help customers achieve their goals. But the success of AI depends on a deep understanding of the underlying jobs your customers are hiring your product or service to do.
By applying the JTBD framework to your AI strategy, you can ensure that you're leveraging AI in a way that truly adds value to both your business and your customers. You’ll not only identify when and where AI is the right solution but also avoid the common pitfalls of applying AI for AI’s sake. The key to unlocking AI's full potential lies in focusing not on the technology itself but on the job it can help you—and your customers—get done.
Are you ready to explore how AI can help your customers get their jobs done? Start by asking, "What job are they really hiring us to do?" Then, see where AI can take your business to the next level.
References
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your business. McKinsey Quarterly. Retrieved from https://www.mckinsey.com/
Christensen, C. M., Hall, T., Dillon, K., & Duncan, D. S. (2016). Competing against luck: The story of innovation and customer choice. HarperBusiness.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
Huang, M.-H., Rust, R. T., & Maksimovic, V. (2019). Artificial intelligence in service. Journal of Service Research, 22(1), 3–7. https://doi.org/10.1177/1094670517752459
Stone, B. (2019). The everything store: Jeff Bezos and the age of Amazon. Little, Brown and Company.
Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.