Organizations are making a mistake that undermines their AI investments: automating inefficient processes without first redesigning them.
This approach creates a dangerous illusion of progress while merely accelerating existing problems.
The “Faster But Still Broken” Problem
I’ve seen it countless times in my consulting work: a company excitedly rolls out an AI solution only to discover they’ve made their problems worse, not better. They’ve fallen into what I call the “faster but still broken” trap.
Consider these real-world examples:
- A manufacturer implements AI to automate quality control inspections, but its underlying quality standards are outdated and inconsistent, resulting in faster approval of substandard products.
- A customer service department uses AI to route support tickets automatically, but its issue categorization system is flawed, leading to faster misrouting of customer problems.
- A marketing team implements AI for content personalization while using an outdated customer segmentation model, resulting in faster delivery of irrelevant content.
In each case, the organization invested significant resources in AI technology only to accelerate inefficient operations rather than solve fundamental problems.
The goal shouldn’t be to automate what you’re currently doing—it should be to reimagine what’s possible.
This reimagining requires a fundamentally different approach to understanding workflows. Instead of documenting current processes with all their inefficiencies, product teams need a methodology that focuses on what customers are trying to accomplish.
Want to learn more about applying Outcome-Driven Innovation to your AI initiatives?
Schedule a complimentary consultation with innovation pioneer Tony Ulwick and AI implementation expert Laks Srinivasan.
Process Maps vs. Job Maps: The Critical Distinction
To avoid the “faster but still broken” trap, organizations need to understand the fundamental difference between process maps and job maps:
Process Maps document what customers or employees are currently doing in solution space. They include:
- Solution-specific steps tied to current tools
- Organizational constraints
- Workarounds and iterations
- Often, multiple overlapping jobs
Job Maps detail what customers or employees are trying to do in problem space. They are:
- Solution-agnostic
- Universal across all customers, regardless of current solutions
- Focused on eliminating unnecessary steps and iterations
- Centered on what people are trying to accomplish, not what they’re currently doing
The Job Mapping Process
The Outcome-Driven Innovation (ODI) process provides a systematic framework for designing optimal processes before applying AI. This begins with creating a Job Map that represents the ideal sequence of steps for completing a job-to-be-done:
- Define – Determine what needs to be accomplished
- Locate – Gather necessary inputs
- Prepare – Set up and organize
- Confirm – Verify everything is ready
- Execute – Perform the core activities
- Monitor – Check progress and results
- Modify – Make necessary adjustments
- Conclude – Finish and assess
By mapping the job this way, organizations can identify the optimal sequence for efficient execution, regardless of current solutions or technologies.
How AI Enhances Optimized Processes
Once an optimal process is designed, AI can dramatically enhance it by:
- Anticipating needs – Using contextual data to predict requirements before they arise
- Eliminating manual inputs – Automatically gathering necessary information
- Providing real-time verification – Constantly checking for potential issues
- Enabling proactive adjustments – Suggesting modifications before problems occur
Lets take the job of listening to music as an example:
The job hasn’t changed (listen to music), but technology has enabled increasingly better execution—from CDs to MP3s to streaming services. AI further enhances this by predicting preferences, generating playlists, and automatically adapting to listening patterns.
The Roadmap to AI Implementation Success
Organizations seeking to leverage AI effectively should follow this sequence:
- Map the job-to-be-done independent of current solutions
- Identify the optimal sequence of steps for job execution
- Pinpoint inefficiencies and iterations in current processes
- Redesign processes to eliminate these inefficiencies
- Determine where AI can enhance the optimized process
- Implement AI solutions that accelerate value creation
Conclusion
Organizations that simply automate current practices will move faster in the wrong direction, while those that first optimize their processes will unlock AI’s transformative potential.
The Job Map methodology provides a critical foundation for effective AI implementation. By focusing first on the optimal sequence for job execution, organizations can ensure their AI investments accelerate value creation rather than inefficiency.
Want to learn more about applying Outcome-Driven Innovation to your AI initiatives?
Schedule a complimentary consultation with innovation pioneer Tony Ulwick and AI implementation expert Laks Srinivasan.
