There’s a big flurry of interest in Job-Task Analysis (an old discipline) to break down jobs into tasks, categorize and inventory these tasks, and then “Voila,” you can build AI agents to automate these tasks.
It sounds like a great idea. So data providers like Lightcast, Draup, SAP (TechWolf), Reejig, and Workday (using Stanford data) are busy building these huge “Job-Task” data sets so you can now look at your company or department as a “basket of tasks” and apply AI to build just the right agents to streamline work. (And also make decisions like centralizing some tasks or building better learning to cross-train people for such tasks.)
As you’ll hear me describe, this kind of work is extremely valuable for regulated, structured, safety-related, and operational jobs. If we know that every airline pilot has to master the task of “lowering landing gear at the right speed and time” it makes perfect sense for the aircraft to do this (or tell the pilot) automatically.
Similarly if every sales person has to update Salesforce in a 2 minute “task” we should build an agent to do it accurately. So there are task automation opportunities in every business area, every function, and every job.
The challenge is that these “tasks” don’t always stand alone: they’re related to broader job roles and business processes. So taking an inventory of tasks is great (we know all the tasks an HR Business Partner does, for example), but we have to think strategically about which of these are suited for AI, which are suited for regular IT automation, which could be outsourced, and which are very “context sensitive” and may need to be treated differently.
In our interviews with clients we find a hierarchy to this work. First, what is the business goal for this job or work? Second, what are the broader product, service, or customer outcomes we’re trying to achieve? Third, what are the steps or tasks and are they “rigid or mandatory” or are they “judgemental and optional?” Fourth, how do these individual tasks relate or chain together? And Fifth, what human or technical or judgement skills are needed?
In other words, just listing the tasks and knowing the “size” of each is not enough. We really do need to re-engineer these things in the context of outcomes, customers, important workflows, and the subtlties of “doing the task well.” And when this is done well we can use it for recruiting, training, performance management, and AI agent development.
What we want to prevent is a project to do task analysis for its own sake, because this kind of work has been around for decades and it’s often used primarily for safety, compliance, operations, and highly structured work. I think that AI, with all its learning capabilities, may make this domain far bigger than ever before. So this domain is a very fast-growing part of HR 2030 and our ongoing work to “agentify” our companies.
We are doing case studies in this area now – please contact us if you’d like help or share your work intelligence project.
PS: Our new book Superpowered explains this process with many examples.
Additional Information
How To Do Job-Task Analysis
Workday Introduces Work Intelligence to Help Customers Design Work for AI
Lightcast Work Intelligence
Draup Task and Work Intelligence Guideboooks
TechWolf (SAP) Work Intelligence
Reejig Operating System for Work
...
Chapters - (00:00:00) - Introduction to Task Intelligence
- (00:01:33) - How Task Data Is Built from Jobs
- (00:04:32) - Where Task Analysis Works Well
- (00:06:32) - The Risk: Institutionalizing Old Ways of Working
- (00:10:07) - Lessons from the Skills Taxonomy Era
- (00:12:17) - Finding Value: Cross-Functional Job Consolidation
- (00:14:41) - Process Mining as a Related Approach
- (00:15:20) - Using AI and Digital Twin for Process Mining via Email
- (00:18:05) - Data Retention and Practical Challenges
- (00:19:16) - Final Thoughts: AI Must Serve Business Goals
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