LATERAL AI | PO THINKING | AIYING | #003
PO: Ideas Need Employment
From AI-Generated Possibilities to Productive Opportunities
What if the next challenge of Generative AI is not creating more ideas, but creating productive opportunities for them?
By Ravindra Dastikop | AIYING
1. The Provocation: Ideas Need Employment
Generative AI has dramatically increased our capacity to generate ideas.
Ask an AI system for ten business opportunities, and it can produce a list in seconds.
Ask for alternative software architectures, new product features, marketing strategies, or educational approaches, and it can rapidly generate candidate solutions.
But what happens to those ideas?
Many remain in chat histories, presentations, brainstorming documents, and innovation repositories.
They are generated, admired, discussed, and forgotten.
Perhaps the problem is not a shortage of ideas.
Perhaps it is a shortage of productive opportunities for ideas.
PO: Ideas Need Employment.
An idea cannot literally apply for a job.
It cannot earn a salary, sign an employment contract, or report to a manager.
That is precisely why this statement is useful as a lateral thinking provocation.
Rather than asking whether it is true, we ask:
Where can this impossible statement take our thinking?
2. The Connection to the Possibility Dividend
This provocation follows two earlier propositions in the AIYING Lateral AI series.
PO #001: GenAI Is Data-Borne
The first proposition explored how data can be viewed not merely as evidence for decisions, but as a foundation for generating possibilities through AI systems.
PO #002: GenAI Generates a Possibility Dividend
The second proposition drew an analogy with the demographic dividend.
A favorable population age structure can create economic potential, but that potential requires productive employment, education, institutions, and opportunity.
Similarly, GenAI expands the supply of candidate ideas and alternatives, but abundance alone does not guarantee innovation or economic value.
PO #003: Ideas Need Employment
The third proposition asks how organizations might provide productive opportunities for AI-generated possibilities.
DATA-BORNE
Possibilities emerge
↓
POSSIBILITY DIVIDEND
Potential value expands
↓
IDEAS NEED EMPLOYMENT
Possibilities need productive opportunities
The progression moves from the origin of possibilities to their economic potential, and finally to the organizational mechanisms needed to turn potential into accomplishment.
3. The Hidden Assumption
Most organizations operate with a familiar innovation model.
People generate ideas.
Ideas are collected.
Committees or managers evaluate them.
A small number are approved.
The remainder are rejected, archived, or forgotten.
This model contains an important assumption:
An idea is an output to be evaluated, not a resource that needs a productive role.
What if we challenge that assumption?
What if an idea is treated as a potential contributor to an organizational problem?
What if every idea needs a task, a context, a sponsor, and an opportunity to demonstrate value?
The emphasis shifts from collecting ideas to finding productive uses for them.
4. Authentic Lateral Thinking: From Provocation to Movement
Edward de Bono's lateral thinking distinguishes provocation from ordinary judgment.
A provocation is deliberately introduced to disrupt established patterns of thinking.
It is not intended to be accepted as a literal truth.
The important activity is movement: exploring where the provocation can lead.
Let us apply that discipline.
The Provocation
PO: Ideas Need Employment.
Movement 1: Ideas Need Job Descriptions
A job description specifies the contribution expected from an employee.
What if every idea also required a job description?
Instead of asking whether an idea sounds interesting, we would ask:
- What problem is this idea expected to solve?
- Who would benefit?
- What measurable improvement should it produce?
- What resources would it require?
Emerging possibility: An Idea Job Description.
Movement 2: Ideas Need Employers
Employment requires someone willing to provide an opportunity.
What if every promising idea needed an organizational sponsor?
An idea without an owner might remain a suggestion.
An idea with a responsible sponsor could become an experiment.
Emerging possibility: An Idea Sponsorship System.
Movement 3: Ideas Need Apprenticeships
People often begin their careers by learning through supervised work.
What if ideas also began with small, supervised assignments?
Instead of demanding a full business case immediately, an organization could give an idea a limited trial.
Emerging possibility: An Idea Apprenticeship Programme.
Movement 4: Ideas Need Performance Reviews
Employees are evaluated on their contribution.
What if ideas were assessed on demonstrated outcomes rather than initial enthusiasm?
An idea might be evaluated for:
- Customer value
- Time saved
- Cost reduction
- Quality improvement
- Learning generated
- Risks and unintended consequences
Emerging possibility: An Idea Performance Review.
Movement 5: Ideas Can Change Careers
People sometimes move into new roles when their skills are better suited elsewhere.
What if an idea rejected in one department could be employed in another?
A customer support idea might become a training innovation.
A software testing concept might inspire an operational audit method.
Emerging possibility: An Idea Redeployment Marketplace.
The Discovery
The provocation has moved us from the familiar idea repository toward a different organizational concept:
What if organizations created employment systems for ideas?
5. The Idea Employment System
An Idea Employment System is a proposed innovation management approach in which promising ideas are matched with meaningful problems, responsible sponsors, small experiments, and measurable outcomes.
The expression is a metaphor and a design concept, not an established industry standard.
Its purpose is to shift attention from the number of ideas collected to the contribution ideas can make.
Five Components
| Employment Metaphor | Innovation Mechanism |
|---|---|
| Job description | Problem, expected contribution, success criteria |
| Employer | Business sponsor or accountable owner |
| Apprenticeship | Small-scale experiment or prototype |
| Performance review | Evidence-based assessment of outcomes |
| Career mobility | Adaptation or redeployment to another problem |
The system does not imply that every idea deserves implementation.
Some ideas will be unsuitable, unsafe, duplicative, or economically unjustified.
The purpose is to create disciplined pathways for promising possibilities, not indiscriminate experimentation.
6. A New Organizational Role: The Idea Employer
Organizations already have managers responsible for people, budgets, processes, products, and projects.
What if they also assigned explicit responsibility for giving promising ideas productive opportunities?
An Idea Employer would not necessarily be a new job title.
It could be a responsibility assigned to a product manager, innovation leader, engineering manager, educator, or team lead.
The role would involve:
- Identifying valuable problems needing solutions
- Matching candidate ideas to those problems
- Assigning appropriate human ownership
- Providing limited resources for experimentation
- Evaluating outcomes
- Scaling, adapting, or discontinuing ideas
This leads to another provocative question:
What if every manager were responsible not only for the productivity of people, but also for the productive utilization of possibilities?
Such a responsibility could make innovation more closely connected to everyday operations.
7. From Idea Repository to Idea Marketplace
Many organizations maintain suggestion boxes, innovation portals, knowledge repositories, or digital idea boards.
These systems are useful for capturing suggestions.
But storing an idea does not guarantee that anyone will use it.
The employment metaphor suggests a different design.
Instead of an Idea Repository, imagine an Idea Opportunity Marketplace.
Business units publish problems that need solutions.
Employees and AI-assisted teams propose alternatives.
Promising ideas are matched with sponsors and experiments.
Results are documented and made available for reuse.
Illustrative Workflow
Business Problem
↓
Idea Generation
↓
Idea–Problem Matching
↓
Human Sponsorship
↓
Small Experiment
↓
Evidence-Based Review
↓
Implement, Adapt, or Retire
This marketplace would need clear ownership, appropriate governance, transparent evaluation criteria, and safeguards against overwhelming teams with low-quality suggestions.
The objective is not to employ every idea.
It is to ensure that valuable possibilities have a reasonable opportunity to be discovered and tested.
8. The Connection to Human Work
The provocation also changes how we think about human work in the age of AI.
GenAI can assist with generating candidate ideas at scale.
But ideas still need people to understand the context in which they might be useful.
They need judgment, sponsorship, experimentation, organizational coordination, and accountability.
This creates a potentially important form of human work:
Turning AI-generated possibilities into productive opportunities.
Human contribution is not limited to producing ideas personally.
It also includes:
- Discovering meaningful problems
- Challenging assumptions
- Recognizing valuable alternatives
- Connecting ideas with real needs
- Designing responsible experiments
- Evaluating evidence
- Implementing successful solutions
This suggests a shift from viewing AI only as a tool for individual productivity toward viewing it as part of a wider system for human-led innovation.
9. Five Practical Applications
Application 1: AI Engineering
An engineering team uses GenAI to generate twenty suggestions for improving a software service.
Instead of placing all suggestions into a backlog, the team creates job descriptions for the most promising ideas.
One idea is assigned to improve response latency.
Another is assigned to reduce maintenance complexity.
A third is assigned to improve automated testing.
Each receives a responsible engineer, a measurable objective, and a limited evaluation period.
The team evaluates actual results rather than the attractiveness of the generated suggestions.
PO: Every software idea needs a production problem to serve.
Application 2: Education
A teacher uses AI to generate several alternative ways of explaining a difficult concept.
Rather than selecting the most impressive explanation, the teacher matches each approach with a specific learning difficulty.
One idea supports visual learners.
Another supports students who need additional practice.
A third supports collaborative learning.
Small classroom experiments help determine which approaches improve understanding.
PO: Teaching ideas should apply for classroom jobs.
Application 3: Customer Experience
A company uses GenAI to explore improvements based on appropriately protected customer feedback.
Each promising idea is matched with a specific customer frustration.
A service team sponsors a small pilot and measures the effect on customer outcomes.
PO: Every customer problem is a vacancy for an idea.
Application 4: Manufacturing
A production team uses AI to generate possible improvements in maintenance, quality, or workflow design.
Rather than implementing suggestions immediately, engineers assign each idea to a clearly defined operational problem.
Ideas are tested using appropriate safety and engineering controls.
PO: A factory should employ ideas alongside machines.
Application 5: Entrepreneurship
An entrepreneur uses GenAI to explore multiple business concepts.
Instead of writing complete business plans for all of them, each concept is assigned a customer problem to investigate.
The entrepreneur tests whether real customers experience that problem and whether the proposed solution offers value.
PO: A business idea needs a customer before it needs a business plan.
10. A Practical Framework: Employing an Idea
The following six-stage framework adapts the established AIYING Lateral AI process to the Idea Employment concept.
FOCUS — Identify the Vacancy
What meaningful problem needs a solution?
Describe the expected outcome before generating ideas.
PROVOKE — Challenge the Job Description
What assumptions are restricting the kinds of solutions being considered?
Introduce a deliberate PO statement.
MOVE — Discover Unconventional Candidates
Explore the consequences of the provocation without immediately judging it.
GENERATE — Create a Diverse Candidate Pool
Use GenAI to develop alternative approaches.
Look for meaningful differences rather than superficial variations.
JUDGE — Select for Fit
Evaluate candidate ideas against the problem, evidence, feasibility, cost, and risk.
ACCOMPLISH — Give the Idea a Trial Assignment
Assign an owner, run a small experiment, measure results, and decide whether to implement, adapt, or discontinue the idea.
FOCUS → PROVOKE → MOVE → GENERATE → JUDGE → ACCOMPLISH
11. A Reusable GenAI Prompt: The Idea Employment Officer
The following prompt can help teams explore the metaphor systematically.
Prompt: Act as an Idea Employment Officer
You are an innovation facilitator combining Edward de Bono's lateral thinking principles with Generative AI.
Treat ideas metaphorically as candidates seeking productive employment.
Our organization faces this problem: [Describe the problem.]
Our current approach: [Describe the existing process.]
Desired outcome: [Describe measurable improvement.]
Follow these steps:
- Identify five assumptions that may restrict our thinking.
- Generate three deliberate PO provocations.
- Demonstrate movement from each provocation.
- Generate ten distinct ideas that could address the problem.
- Create an Idea Job Description for each promising idea, including its intended contribution and success criteria.
- Identify the type of human sponsor or team required.
- Recommend a small apprenticeship experiment for the three strongest ideas.
- Define measurable performance criteria.
- Identify risks, dependencies, and required evidence.
- Suggest how an unsuccessful idea might be adapted or redeployed to another problem.
Distinguish evidence from assumptions. Do not invent supporting facts. Do not recommend implementing untested high-risk ideas.
12. A Student Innovation Challenge
Challenge: Give One AI-Generated Idea a Job
Select a practical problem from your college, workplace, or community.
Examples:
- Long waiting times in an administrative office
- Difficulty finding useful learning resources
- Food waste in a cafeteria
- Confusing software documentation
- Low participation in student activities
Use GenAI to generate ten alternative solutions.
Select one promising idea.
Then create its employment profile.
Idea Employment Card
Idea Name: __________________
Problem to Solve: __________________
Expected Contribution: __________________
Human Sponsor: __________________
Trial Assignment: __________________
Success Measure: __________________
Risks and Constraints: __________________
Review Decision: Implement / Adapt / Retire
Run a small, appropriate experiment.
Record what happened.
The objective is not to demonstrate that AI can generate ideas.
It is to demonstrate that an idea can be connected with a real problem and tested for useful contribution.
13. How Would We Measure Idea Employment?
Organizations commonly measure the number of ideas submitted or generated.
The employment metaphor suggests additional measures.
| Measure | What It Reveals |
|---|---|
| Problem–idea match rate | Share of reviewed ideas connected to defined problems |
| Experiment initiation rate | Share of eligible ideas receiving a trial |
| Time to first experiment | How quickly promising ideas reach practical testing |
| Validated adoption rate | Share of tested ideas adopted after evaluation |
| Net value realized | Benefits after relevant costs and risks |
| Redeployment rate | Ideas adapted successfully to other contexts |
These are proposed operational measures, not universally established innovation benchmarks.
A high experiment initiation rate is not automatically desirable if experiments are poorly chosen.
The primary objective remains meaningful value creation.
14. The Risks of Employing Ideas
The metaphor should not be taken too literally.
Ideas do not possess rights, needs, or independent interests.
People remain responsible for deciding which ideas deserve attention.
An Idea Employment System could also introduce new problems if poorly designed.
Idea Overload
A flood of AI-generated suggestions can overwhelm teams.
Artificial Activity
Organizations might run unnecessary experiments merely to demonstrate innovation.
Weak Ownership
Ideas without accountable human sponsors may never reach implementation.
False Novelty
AI-generated variations may be mistaken for genuinely different solutions.
Governance Risks
Experiments involving sensitive information, safety-critical processes, or consequential decisions require appropriate controls.
Opportunity Costs
Time and resources devoted to weak ideas may divert attention from more valuable work.
A disciplined system must therefore be able to reject, pause, or retire ideas without treating every rejection as a failure.
15. Five Questions for Executive Leadership
-
Are we collecting ideas or creating opportunities for them?
What happens to promising ideas after they are generated?
-
Do ideas have clearly defined problems to solve?
Can we explain the expected contribution of each selected idea?
-
Who sponsors experimentation?
Are managers accountable for moving ideas from suggestion to evidence?
-
Can ideas move across organizational boundaries?
Could an idea rejected by one department solve a problem elsewhere?
-
Do we measure accomplishment rather than idea volume?
What additional value has emerged from implemented ideas?
These questions move AI leadership beyond tool adoption toward the organizational design of innovation.
16. A Small Experiment for Every Reader
Try the following exercise with your team.
Select one genuine, low-risk business problem.
Ask GenAI to generate twenty distinct candidate ideas.
Do not immediately choose the most impressive suggestion.
Instead:
- Identify the specific problem each idea could serve.
- Group ideas by their potential contribution.
- Select three with plausible value.
- Assign each a human sponsor.
- Design a small experiment for each.
- Evaluate the evidence and decide what to do next.
Then ask:
Did treating ideas as candidates for productive work change our innovation process?
The answer may reveal whether your organization needs more ideas or better mechanisms for employing them.
17. The Deeper Insight: Every Problem Is an Employer
Once we accept the provocation temporarily, another possibility emerges.
If ideas need employment, perhaps problems are the employers.
A problem creates demand for a useful contribution.
An idea offers a possible response.
The organization becomes the environment in which ideas and problems can be matched, tested, and developed.
PO: Every Problem Is an Employer.
This secondary provocation opens further possibilities.
What if organizations maintained a marketplace of unsolved problems rather than only a repository of ideas?
What if employees could apply their creativity to problems outside their formal departments?
What if AI agents helped discover potential matches between problems and candidate solutions?
What if an organization's innovation capability were measured partly by how effectively it connected problems with promising possibilities?
The original provocation has now generated a new direction for organizational design.
18. Conclusion: The Future Needs Employers of Ideas
Generative AI makes it increasingly easy to produce candidate ideas.
But generating possibilities and realizing their value are different activities.
The possibility dividend depends on productive opportunities for ideas.
Those opportunities require human judgment, meaningful problems, organizational sponsorship, experimentation, and responsible implementation.
The provocation PO: Ideas Need Employment encourages us to rethink innovation systems.
Instead of treating ideas merely as outputs to collect and rank, we can explore how to give promising ideas clearly defined roles and opportunities to demonstrate value.
The result may be a shift:
From Idea Generation → To Idea Employment → To Accomplishment
The Final PO
PO: Ideas Need Employment.
GenAI generates possibilities.
Lateral thinking discovers unconventional roles.
Human judgment matches ideas with problems.
Experimentation gives them work.
Accomplishment creates value.
The challenge is no longer simply to generate ideas. It is to create productive opportunities for them.
19. Your Turn: A PO Thinking Invitation
Imagine that your organization has appointed you as its first Idea Employment Officer.
Your responsibility is not to generate more ideas.
It is to ensure that promising ideas find meaningful work.
What would you create first?
- An Idea Job Board?
- An Idea Apprenticeship Programme?
- An Idea Recruitment System?
- An Idea Performance Review?
- An Idea Redeployment Marketplace?
Choose one.
Develop it into a small practical experiment.
That is the movement from provocation to possibility, and from possibility to accomplishment.
Further Reading and Conceptual Foundations
The following references provide background for lateral thinking, innovation management, and responsible AI. The Idea Employment System is a proposed metaphor and conceptual framework developed in this article.
- Edward de Bono, Serious Creativity (1992) — provocation and movement in lateral thinking.
- ISO 56002:2019 — Innovation management system guidance. Explore
- NIST AI Risk Management Framework — managing risks associated with AI systems. Explore
- United Nations Population Fund — demographic dividend. Explore
