AI Adoption Through Jobs-to-be-Done Framework

When organisations adopt AI, the natural instinct is often to chase the latest tool or algorithm. But a technology-first approach can lead to solutions that look impressive but fail to solve real problems. A more effective strategy is to start with the Jobs-to-be-Done (JTBD) mindset, which focuses on what people are actually trying to accomplish, and the obstacles that stand in their way, rather than the features of the technology itself.

Understanding JTBD for AI

JTBD in AI asks: Where are employees or customers struggling? Which tasks consume time, create stress, or result in mistakes? Identifying these critical points uncovers opportunities where AI can generate real value, rather than automating work for the sake of it.

Why JTBD Matters?

AI adoption succeeds when it addresses meaningful needs. By centering on the underlying “jobs” people are trying to complete, organisations can identify solutions that:

• Streamline repetitive or manual tasks – freeing employees for higher-value work.

• Reduce errors and risk – applying AI to tasks prone to mistakes ensures accuracy.

• Accelerate slow processes – removing bottlenecks that limit efficiency.

• Boost confidence and satisfaction – tools that genuinely help users achieve goals increase trust and adoption.

Applying JTBD to AI Adoption

Map Work and Outcomes

Look beyond tasks to understand what people are trying to achieve. Ask: What does success look like? Where do processes break down? Understanding outcomes helps identify where AI can deliver the greatest impact.

Observe and Gather Insights

Shadow users, conduct interviews, and collect feedback to uncover pain points. Hidden inefficiencies, repetitive steps, and emotional frustrations often reveal the highest-value AI opportunities.

Prioritise Opportunities

Not every job should be automated.

Focus on jobs that are:

• High-friction: slow, stressful, or error-prone tasks

• High-frequency: repetitive work that consumes significant time

• High-value: improvements that drive measurable business impact

Illustration Adapted, Credit: Samuel Hulick

Validate and Scale Carefully

All automated jobs require testing. Start with pilots, measure results, and refine solutions before scaling across teams or processes. This ensures feasibility aligns with potential impact.

A Practical Example

A customer service team spends hours manually categorising support tickets. By analysing the underlying job, ensuring requests are correctly prioritised and routed, AI can automatically classify tickets and suggest responses. This approach saves time, reduces errors, speeds up resolution, and frees staff to handle complex issues requiring human judgment.

Final Thought

AI adoption works best when guided by the work people actually need to accomplish. The JTBD mindset shifts focus from technology for its own sake to solutions that solve real challenges, enhance efficiency, and build trust. Start with the job, and AI becomes more than a tool—it becomes a valued partner in driving results.