Friday, November 13, 2026

PO: Every Problem Is an Employer — Let Problems Recruit AI-Generated Possibilities

LATERAL AI | PO THINKING | AIYING | #004

PO: Every Problem Is an Employer

From Generating More Ideas to Recruiting the Right Possibilities

What if problems could recruit ideas instead of waiting for people to discover solutions?

By Ravindra Dastikop | AIYING

1. The Provocation: Every Problem Is an Employer

Organizations are surrounded by problems.

Some are visible: rising costs, dissatisfied customers, delayed projects, and declining productivity.

Others remain hidden in routine work, outdated assumptions, and processes that nobody has questioned for years.

Meanwhile, Generative AI makes it increasingly easy to generate candidate ideas.

Yet having more ideas does not automatically mean solving more problems.

What if we reverse the familiar relationship?

Instead of people continually generating ideas and then searching for applications, imagine that problems themselves could recruit the ideas they need.

PO: Every Problem Is an Employer.

A problem cannot literally employ anyone.

It cannot advertise a vacancy, conduct interviews, or offer a salary.

That deliberate impossibility is the starting point of the provocation.

Rather than rejecting the statement as absurd, we use it to move toward new possibilities.

What would an organization look like if its problems actively recruited solutions?

2. The Fourth Step in the Lateral AI Series

This proposition follows three earlier provocations.

PO #001: GenAI Is Data-Borne

Data becomes a foundation from which AI systems can generate new possibilities.

PO #002: GenAI Generates a Possibility Dividend

The growing supply of candidate ideas creates potential value, but realizing that value requires productive opportunities.

PO #003: Ideas Need Employment

Ideas need meaningful problems, human sponsors, experiments, and opportunities to contribute.

PO #004: Every Problem Is an Employer

Now the perspective changes.

Instead of asking how to find work for ideas, we ask how problems can attract, select, and test ideas.

DATA-BORNE
Where possibilities originate

↓

POSSIBILITY DIVIDEND
Why possibilities matter

↓

IDEAS NEED EMPLOYMENT
How possibilities find productive work

↓

EVERY PROBLEM IS AN EMPLOYER
How problems recruit possibilities

Together, these provocations suggest a broader innovation architecture connecting data, possibilities, problems, experiments, and accomplishment.

3. The Hidden Assumption: Innovation Begins with Ideas

Many innovation programmes begin by inviting people to submit ideas.

Organizations run brainstorming sessions, hackathons, innovation competitions, and suggestion campaigns.

The resulting ideas are collected, evaluated, and sometimes implemented.

These activities can be valuable.

But they often assume that the most important starting point is idea generation.

Innovation begins when people generate ideas.

What if we challenge that assumption?

What if innovation begins when an organization makes its unsolved problems visible, specific, and available for exploration?

Instead of asking employees to contribute more ideas, it could invite them to help solve carefully described problems.

Instead of maintaining only an idea repository, it could maintain a living inventory of problems seeking solutions.

The shift is from a supply-led model of innovation toward a problem-led model.

4. Authentic Lateral Thinking: Provocation and Movement

Edward de Bono's lateral thinking uses deliberate provocation to escape established patterns of thinking.

A PO statement is not a claim that must be proved true.

Its purpose is to create movement toward possibilities that conventional reasoning might overlook.

Let us apply that approach.

PO: Every Problem Is an Employer.

Movement 1: Problems Advertise Vacancies

Employers advertise positions when they need work performed.

What if organizations advertised unsolved problems in the same way?

A problem vacancy might describe:

The problem and its context
The outcome required
The constraints that must be respected
The people affected
The evidence needed to demonstrate improvement

Emerging possibility: A Problem Vacancy Board.

Movement 2: Problems Write Job Descriptions

A job description explains what a successful candidate must accomplish.

What if every important problem had an equivalent description?

For example, instead of stating:

We need innovative customer service ideas.

The organization could state:

We need to reduce repeated customer contacts without compromising service quality, privacy, or customer satisfaction.

The second statement creates a more useful opportunity for candidate ideas.

Emerging possibility: A Problem Job Description.

Movement 3: Problems Interview Ideas

Employers interview candidates to evaluate their suitability.

What if problems interviewed ideas?

A candidate idea would be asked:

Which part of this problem can you address?
What assumptions must hold for you to work?
What resources do you require?
What risks might you introduce?
How could your contribution be tested?

Emerging possibility: An Idea–Problem Fit Interview.

Movement 4: Problems Offer Probation

Employment sometimes begins with a probationary period.

What if ideas were offered limited trials before full adoption?

An idea could receive a small experiment, prototype, simulation, or controlled pilot.

Its suitability would be judged using evidence.

Emerging possibility: A Solution Probation Programme.

Movement 5: Problems Recruit Across Boundaries

Employers may recruit people from different industries and disciplines.

What if problems could recruit ideas from unrelated fields?

A hospital scheduling problem might learn from airline operations.

A software reliability problem might learn from manufacturing quality control.

A classroom engagement problem might learn from game design.

These are analogies to explore, not ready-made solutions.

Emerging possibility: A Cross-Domain Idea Recruitment System.

The Discovery

The provocation has moved us from a conventional brainstorming exercise toward a different organizational mechanism.

What if organizations created recruitment platforms where problems attract ideas?

5. The Problem Recruitment Platform

A Problem Recruitment Platform is a proposed system that connects well-defined problems with candidate ideas, relevant expertise, responsible sponsors, and structured experiments.

The name is a deliberate extension of the employment metaphor.

It is not presented here as an established industry standard or a proven product category.

The platform would organize innovation around the demand for solutions.

How It Could Work

Publish the Problem
A team describes an unsolved problem and the outcome it needs.
Define the Vacancy
The team specifies constraints, available evidence, resources, and success criteria.
Recruit Candidate Ideas
Employees, experts, and GenAI-assisted workflows propose alternative approaches.
Interview the Candidates
Human reviewers assess relevance, feasibility, novelty, risk, and expected value.
Offer a Trial Assignment
Promising ideas receive small, controlled experiments.
Hire, Adapt, or Reject
The team adopts validated ideas, modifies promising candidates, or discontinues unsuitable approaches.

PROBLEM VACANCY

↓

CANDIDATE IDEAS

↓

IDEA–PROBLEM MATCHING

↓

HUMAN EVALUATION

↓

CONTROLLED EXPERIMENT

↓

VALIDATED ACCOMPLISHMENT

The important change is not merely using AI to produce more suggestions.

It is creating a disciplined mechanism through which real problems can discover and test useful possibilities.

6. The Problem Vacancy: A New Unit of Innovation

An organization might describe a vacancy for an employee using a job description.

A Problem Recruitment Platform could describe an innovation opportunity using a Problem Vacancy Card.

Problem Vacancy Card

Problem ID: PR-001

Problem Title: Reduce Repeated Customer Contacts

Problem Owner: Customer Service Manager

Current Situation: Customers frequently contact support more than once for the same issue.

Desired Outcome: Reduce avoidable repeat contacts while maintaining service quality.

Constraints: Privacy, accessibility, accuracy, cost, and customer satisfaction.

Evidence Available: Appropriately anonymized service records and customer feedback.

Candidate Ideas Wanted: Workflow improvements, communication changes, self-service tools, and process redesign.

Trial Opportunity: Limited pilot involving a suitable customer segment.

Success Measures: Repeat contact rate, resolution quality, satisfaction, and cost.

The card does not prescribe a solution.

It creates a meaningful opportunity for different kinds of ideas to compete and collaborate.

That distinction matters.

A problem described too narrowly may recruit only familiar solutions.

A problem described too vaguely may attract suggestions that cannot be tested.

The quality of the problem description influences the quality and diversity of the possibilities explored.

7. The AI Engineering Example: Recruiting Ideas to Reduce Inference Cost

Consider an AI engineering team operating an application that uses a large language model.

The team faces increasing inference costs.

A conventional approach might be to ask:

How can we reduce LLM inference costs?

GenAI could generate a list of suggestions.

But the Problem Recruitment approach begins with a vacancy.

Problem Vacancy: AI Inference Cost Reduction

Recruit ideas that can reduce cost per successful request while maintaining acceptable response quality, latency, privacy, reliability, and user experience.

Candidate Ideas

Cache suitable repeated responses
Route simpler requests to smaller models
Reduce unnecessary prompt context
Batch compatible requests where latency permits
Eliminate redundant model calls
Use deterministic software for tasks that do not require an LLM

Each candidate has a different profile.

Caching may be unsuitable for highly personalized or rapidly changing information.

Model routing requires quality evaluation across request types.

Prompt reduction may remove useful context.

Batching can affect latency.

Replacing model calls with deterministic logic may work well for some tasks but not others.

The Interview

The team evaluates each candidate against:

Cost per successful task
Task quality
Response latency
Operational complexity
Security and privacy
Reliability under realistic workloads

The Probation

The strongest candidates receive controlled experiments using representative workloads and agreed evaluation criteria.

The team measures whether savings are real and whether unacceptable regressions occur.

The Accomplishment

Only validated improvements are adopted.

The outcome is not a longer list of optimization ideas.

It is an evidence-based engineering improvement.

PO: The inference cost problem has recruited its own solutions.

8. The Surprising Possibility: Problems as AI Agents

The metaphor suggests a more experimental possibility.

What if a problem were represented by a software agent?

Such an agent would not literally possess intentions or independent organizational authority.

It would be a software workflow acting on a problem definition supplied and governed by people.

A Problem Agent might:

Maintain a structured description of an unsolved problem
Identify missing information and assumptions
Search approved knowledge sources for relevant approaches
Generate candidate ideas with an AI model
Suggest cross-domain analogies
Compare ideas against predefined constraints
Prepare experiment proposals for human review
Track evidence from approved experiments

The agent could support problem owners, but should not independently authorize consequential deployments or bypass required oversight.

The architecture might include a problem registry, a retrieval system, a candidate generation workflow, an evaluation component, and a human approval process.

PROBLEM AGENT

Under human-defined goals and controls

↓

SEARCH + GENERATE + COMPARE

↓

HUMAN REVIEW

↓

APPROVED EXPERIMENT

↓

MEASURED OUTCOME

This is a design possibility to explore, not evidence that autonomous problem-solving agents are universally effective.

The real value would depend on data quality, evaluation methods, system reliability, costs, and human accountability.

9. The Role of Lateral Thinking in Recruitment

A recruitment platform could easily become a system that matches problems only with familiar solutions.

That would improve organization but might not produce much creative movement.

Lateral thinking introduces a different capability.

It challenges the assumptions defining the vacancy itself.

Example: A Hospital Waiting-Time Problem

A conventional problem statement might be:

How can we reduce waiting time?

A typical solution search may focus on staffing, scheduling, and queue management.

Now introduce a provocation:

PO: The Patient Never Waits.

This is not a claim that waiting can literally be eliminated.

We use it to explore movement.

What if the patient's experience began before arrival?

What if certain administrative steps were completed in advance?

What if waiting became predictable rather than uncertain?

What if some visits could be handled through appropriate remote services?

What if the organization measured unnecessary waiting separately from clinically necessary time?

These possibilities would require clinical, operational, accessibility, and safety review.

The key point is that the provocation changes which kinds of ideas the problem can recruit.

The recruitment platform supplies structure.

Lateral thinking expands the range of candidates.

10. Five Applications Beyond AI Engineering

Application 1: Education

A college publishes a problem vacancy:

Improve students' ability to apply AI engineering concepts in practical projects.

Candidate ideas may include peer learning, guided labs, case studies, short project challenges, or industry mentoring.

Faculty and students test promising approaches and assess actual learning outcomes.

Application 2: Manufacturing

A production team publishes a vacancy for ideas that can reduce avoidable equipment downtime.

AI-assisted exploration may suggest maintenance scheduling improvements, better monitoring, or workflow changes.

Engineers evaluate technical feasibility and safety before controlled trials.

Application 3: Customer Experience

A company publishes customer problems instead of broad calls for innovative ideas.

Teams propose approaches targeted at specific customer frustrations.

The strongest candidates are tested against customer outcomes.

Application 4: Public Services

A public service organization describes a problem such as reducing avoidable delays in processing applications.

Candidate ideas might involve clearer instructions, better workflow design, or appropriate digital assistance.

Any implementation would need to respect accessibility, fairness, privacy, and applicable public-sector requirements.

Application 5: Entrepreneurship

An entrepreneur begins with a portfolio of validated customer problems rather than a portfolio of product ideas.

AI helps generate alternative approaches to each problem.

Customer research and small experiments determine which possibilities merit further investment.

The emphasis moves from enthusiasm for a concept toward evidence of a problem worth solving.

11. The Lateral AI Framework: FOCUS to ACCOMPLISH

The established AIYING six-stage framework can be adapted to Problem Recruitment.

FOCUS — Publish the Vacancy

Identify a meaningful problem, its context, and the outcome required.

PROVOKE — Challenge the Vacancy

Identify assumptions that restrict how the problem is understood.

Introduce a deliberate PO statement.

MOVE — Discover Unexpected Qualifications

Explore what the provocation suggests about alternative approaches.

Look for new principles, differences, and possible applications.

GENERATE — Recruit Candidate Ideas

Use GenAI and human expertise to produce genuinely different approaches.

JUDGE — Interview for Fit

Evaluate candidates against evidence, constraints, feasibility, risk, and expected value.

ACCOMPLISH — Offer a Trial and Measure Results

Assign a responsible owner, run an appropriate experiment, and decide whether to adopt, adapt, or reject the candidate.

FOCUS → PROVOKE → MOVE → GENERATE → JUDGE → ACCOMPLISH

The framework ensures that the provocation is not reduced to an unusual slogan.

It becomes a method for discovering and testing new possibilities.

12. Reusable GenAI Prompt: The Problem Recruitment Officer

Use the following prompt to explore a real organizational challenge.

Prompt: Act as a Problem Recruitment Officer

You are an innovation facilitator combining Edward de Bono's lateral thinking principles with Generative AI.

Treat the following problem metaphorically as an employer recruiting ideas.

Problem: [Describe the problem]

Context: [Describe the organization or situation]

Desired outcome: [Define the improvement needed]

Constraints: [List cost, safety, time, quality, privacy, or other constraints]

Follow this process:

Create a clear Problem Vacancy Card describing the problem, expected outcome, constraints, owner, and success measures.
Identify five assumptions that may be limiting how the problem is understood.
Generate three deliberate PO provocations that challenge those assumptions.
Demonstrate movement from each provocation. Do not merely explain why it is unusual.
Recruit ten genuinely different candidate ideas, including at least three cross-domain approaches.
Conduct an Idea–Problem Fit Interview for each promising candidate.
Select three candidates for small, low-risk experiments.
Define the evidence required to judge each experiment.
Identify the human sponsor, risks, dependencies, and implementation requirements.
Recommend whether each candidate should be adopted, adapted, tested further, or rejected, based on available evidence.

Distinguish facts from assumptions. Do not fabricate evidence or claim that proposed experiments have already succeeded. Seek human approval before consequential action.

13. Student Project: Build a Problem Recruitment Platform

This provocation can become a practical AI engineering project for students and early-career developers.

Project Title

Problem Recruitment Platform — An AI-Assisted Innovation Matching System

Project Objective

Build a prototype that allows users to publish problems, generate candidate ideas, evaluate matches, and prepare small experiments.

Suggested Minimum Features

Problem Registry: Store problem descriptions, context, constraints, and desired outcomes.
Problem Clarifier: Use GenAI to suggest questions that make a problem more precise.
PO Generator: Generate deliberate provocations for exploring hidden assumptions.
Movement Explorer: Help users derive alternative directions from provocations.
Candidate Recruiter: Generate or retrieve possible approaches.
Fit Evaluator: Compare candidates against defined criteria.
Experiment Planner: Create small, reviewable test plans.
Outcome Tracker: Record human-reviewed results and decisions.

Possible Technical Architecture

USER INTERFACE

↓

PROBLEM REGISTRY

↓

LLM-ASSISTED CLARIFICATION AND IDEATION

↓

OPTIONAL RETRIEVAL / SEMANTIC SEARCH

↓

CANDIDATE EVALUATION WORKFLOW

↓

HUMAN REVIEW

↓

EXPERIMENT AND OUTCOME TRACKER

A student prototype could use Python, a lightweight web interface, a relational database, and an LLM API.

Embeddings and vector search could be added when there is a meaningful collection of existing ideas or problem descriptions to retrieve.

A multi-agent architecture is optional, not a prerequisite.

Evaluation Criteria

Clarity of problem descriptions
Relevance of candidate ideas
Diversity of generated alternatives
Quality of the proposed experiments
Traceability from problem to recommendation
Human oversight and error handling
Usefulness compared with a simple brainstorming baseline

The project succeeds if it helps users move from vague problems to clearer, testable solution candidates.

It does not need to demonstrate autonomous innovation.

14. Measuring the Effectiveness of Problem Recruitment

An organization should not measure success simply by the number of problems posted or ideas generated.

More useful measures may include:

Measure	Purpose
Problem clarity	Whether vacancies specify meaningful outcomes and constraints
Candidate relevance	Whether proposed ideas address the actual problem
Idea diversity	Whether candidates represent substantively different approaches
Time to experiment	How quickly promising ideas receive appropriate testing
Validated adoption	Whether experiments lead to evidence-supported improvements
Net value realized	Benefits after accounting for relevant costs and risks

These are proposed evaluation dimensions rather than established benchmarks.

The most important question is whether the approach produces better decisions and outcomes than the organization's existing innovation process.

15. Risks and Limitations

Poorly Defined Problems

A vague or misleading problem description may attract irrelevant solutions.

Premature Solution Bias

A vacancy may unintentionally prescribe a favored solution before alternatives are explored.

AI Hallucinations

GenAI may generate plausible but incorrect claims about feasibility, evidence, or expected benefits.

False Diversity

A long list of candidate ideas may contain only superficial variations of the same approach.

Evaluation Bias

Human and automated reviewers may favor familiar ideas and overlook unconventional candidates.

Implementation Risk

Promising ideas may fail when deployed in real environments.

Privacy, Security, and Accountability

Problem descriptions may contain sensitive business, personal, or operational information.

Appropriate access controls, data handling, review, and accountability are essential.

The recruitment metaphor should never imply that an AI system has independent authority to make consequential organizational decisions.

16. Five Questions for Executive Leadership

Do we know which problems most urgently need solutions?
Is there a visible, prioritized inventory of meaningful unsolved problems?
Are our innovation programmes driven by problems or by idea volume?
Do we reward the generation of suggestions or the resolution of valuable challenges?
Can problems recruit ideas across organizational boundaries?
Are teams able to discover relevant approaches outside their own departments?
Can GenAI help us discover unexpected matches?
Are we using AI only to brainstorm, or also to connect problems with diverse candidate solutions?
Do we provide opportunities for responsible experimentation?
Can promising ideas move quickly into small, measurable, human-supervised tests?

17. The New Human Work: Becoming a Problem Recruiter

The growing capability of GenAI changes the economics of generating candidate ideas.

But valuable innovation still requires people who can recognize meaningful problems.

It requires people who can frame challenges without prematurely narrowing the solution space.

It requires people who can recognize unconventional possibilities, evaluate evidence, manage risks, and coordinate implementation.

The Problem Recruitment metaphor suggests a useful description of emerging human work:

The human role is not only to generate ideas, but to create the conditions in which the right ideas can solve the right problems.

This does not eliminate the importance of human creativity.

It expands the range of activities through which creativity creates value.

Problem discovery, problem framing, lateral exploration, judgment, and accomplishment become central capabilities.

18. The Next Provocation Emerges

The recruitment metaphor leads to another unexpected question.

If problems are employers, what happens when a problem cannot find a suitable idea?

An employer might change the job description.

It might develop candidates.

It might redesign the work.

Or it might discover that the position itself is unnecessary.

This leads to a new provocation:

PO: Problems Need Career Counselling.

What if some problems are difficult to solve because they are framed incorrectly?

What if the most useful intervention is not a better solution, but a better understanding of the problem?

That possibility opens the next stage of lateral exploration.

19. Conclusion: Let Problems Recruit Possibilities

Generative AI has expanded our ability to generate candidate solutions.

But idea abundance does not automatically create innovation.

Organizations still need mechanisms that connect possibilities with meaningful problems.

The provocation PO: Every Problem Is an Employer invites us to reverse the conventional innovation process.

Instead of starting only with idea generation, we can make unsolved problems visible, define their needs, recruit diverse approaches, and evaluate candidates through responsible experimentation.

The proposed Problem Recruitment Platform offers one way to explore that shift.

PO: Every Problem Is an Employer.

Problems create demand.
GenAI expands the candidate pool.
Lateral thinking discovers unconventional matches.
Human judgment selects and tests.
Accomplishment creates value.

Stop asking only for more ideas. Start helping problems recruit possibilities.

20. Your Turn: Publish a Problem Vacancy

Choose one meaningful problem from your organization, classroom, business, or community.

Write a Problem Vacancy Card.

Describe the outcome required without prescribing a solution.

Use GenAI to recruit ten diverse candidate ideas.

Apply one deliberate PO provocation to challenge the original problem framing.

Select one promising candidate for a small, appropriate experiment.

Record what you discover.

The goal is not to prove that AI can generate answers.

It is to discover whether a problem-led approach produces more useful possibilities and better outcomes.

What problem in your organization would you advertise first?

Friday, November 13, 2026