One of the most common questions organizations ask about artificial intelligence is:
“What should we use AI for?”
It is an understandable question.
It is also usually too broad.
Artificial intelligence can be applied to thousands of tasks across nearly every organizational function. Beginning with all of those possibilities can quickly become overwhelming.
A better question is:
“What problems are worth solving?”
That changes the conversation.
Instead of searching for places to insert artificial intelligence, the organization begins examining where meaningful improvement is needed.
Where are employees losing time?
Where are customers experiencing frustration?
Where are decisions being made with incomplete information?
Where do errors repeatedly occur?
Where are employees overwhelmed by data?
Where are opportunities being missed?
Where does important information arrive too late?
Where is organizational knowledge difficult to access?
Where are employees performing work that adds little value?
These are not technology questions.
They are organizational questions.
And they are where some of the best AI opportunities begin.
Do Not Begin With “We Need an AI Project”
Organizations can create unnecessary pressure when leadership announces:
“We need to find an AI project.”
Suddenly every department begins searching for something that can be labeled AI.
Ideas emerge because they involve artificial intelligence rather than because they address meaningful organizational needs.
That is backwards.
The objective is not to create an AI project.
The objective is to improve the organization.
AI is one possible tool for doing that.
A much stronger starting point is:
“What is something important that we need to do better?”
Then determine whether artificial intelligence belongs in the solution.
Problems Before Products
AI vendors understandably begin conversations with their technology.
Organizations should begin with their problems.
That distinction protects organizations from becoming overly influenced by impressive demonstrations.
A vendor may show an AI system capable of analyzing thousands of documents in seconds.
Impressive.
But does your organization have a document-analysis problem?
Another platform may generate sophisticated forecasts.
Useful.
But is forecasting currently limiting your organization?
Another system may automate customer interactions.
Interesting.
But are customer interactions actually where your greatest opportunity exists?
Technology capability does not automatically equal organizational value.
The sequence should remain:
Problem → Desired Outcome → Requirements → Possible Solutions → Appropriate Technology
Not:
AI Product → Find Something to Use It For
Start With Organizational Priorities
The strongest AI opportunities often connect directly to priorities the organization already has.
What is leadership trying to accomplish this year?
Perhaps the organization wants to:
increase revenue,
improve profitability,
reduce employee turnover,
increase production capacity,
improve customer service,
reduce downtime,
improve quality,
expand services,
reduce administrative burden,
strengthen forecasting,
manage inventory more effectively,
improve employee development,
or make better use of organizational data.
These priorities create useful boundaries.
Instead of asking every possible question about AI, ask:
“Where could AI help us make progress on something we already care about?”
That keeps AI connected to strategy.
Listen for Repeated Complaints
Employees often identify potential AI opportunities without realizing it.
Listen for statements such as:
“This takes forever.”
“We have to do this manually.”
“I spend half my day looking for information.”
“We never know until it's too late.”
“I have to check every one of these.”
“We enter the same information twice.”
“Only one person knows how to do this.”
“We have all this data, but we don't really do anything with it.”
“I wish we could predict this.”
“Customers ask us the same questions constantly.”
“There has to be an easier way.”
These statements are signals.
They identify friction.
And friction is often where technology can create value.
Look for High-Volume Work
A task does not need to consume hours each time it occurs to represent a significant opportunity.
Frequency matters.
Suppose an administrative task requires only five minutes.
If it occurs twice per year, improving it probably does not matter much.
If 100 employees perform it 10 times every day, it becomes a very different opportunity.
Organizations should therefore consider:
Time per task × Frequency × Number of people
A small improvement multiplied across hundreds or thousands of occurrences can create significant value.
This is why repetitive, high-volume work deserves attention.
Look for Information-Heavy Work
Artificial intelligence can be especially useful when employees must process more information than they can reasonably evaluate manually.
Examples might include:
large spreadsheets,
customer histories,
maintenance records,
financial transactions,
survey responses,
documents,
support requests,
production records,
sales histories,
inspection reports,
quality data,
or written comments.
Ask:
Where do employees have too much information to examine effectively?
The opportunity may not be to remove the employee from the process.
It may be to help the employee determine:
What deserves my attention?
That is an important form of AI assistance.
Look for Patterns Humans May Miss
Humans are excellent at many forms of judgment.
We are less effective at consistently identifying subtle patterns across enormous datasets.
AI may help organizations identify:
changing customer behavior,
sales trends,
unusual transactions,
quality patterns,
equipment behavior,
seasonal changes,
retention risks,
operational anomalies,
or emerging changes in performance.
This creates a valuable question:
What would we want to know earlier if we could?
Maybe:
A customer is beginning to disengage.
Sales are shifting unexpectedly.
A piece of equipment is behaving unusually.
Demand is changing.
Inventory risk is increasing.
A program outcome is deteriorating.
A business metric has moved outside its normal range.
AI can sometimes turn historical data into an early-warning capability.
Look for Prediction Problems
Organizations constantly make predictions, even when they do not call them predictions.
How much will we sell next month?
How many employees will we need?
Which customers are likely to return?
How much inventory should we order?
When might this equipment require maintenance?
Which leads are most likely to convert?
Which accounts require attention?
How much demand should we expect?
Often these decisions rely heavily on experience and intuition.
Human judgment remains valuable.
But AI may provide an additional source of evidence.
A good AI opportunity may therefore begin with:
“What are we already trying to predict?”
Look for Knowledge Problems
Sometimes the problem is not that the organization lacks information.
It is that employees cannot access it easily.
Policies exist.
Procedures exist.
Training documents exist.
Customer histories exist.
Technical manuals exist.
Past reports exist.
Institutional knowledge exists.
But finding the right information takes too long.
Employees repeatedly ask the same people the same questions.
New employees struggle to learn organizational knowledge.
Experienced employees become bottlenecks because everyone depends on what they know.
AI may help organizations make knowledge more accessible.
But remember the readiness principle:
AI cannot reliably retrieve knowledge the organization has never captured or cannot identify.
Knowledge access begins with knowledge management.
Look for Repetitive Creation
Many jobs require employees to repeatedly create similar outputs.
Emails.
Reports.
Summaries.
Meeting notes.
Job descriptions.
Proposals.
Customer responses.
Training materials.
Internal documentation.
Marketing content.
Presentations.
Policies.
Instructions.
Artificial intelligence may accelerate portions of this work.
But organizations should distinguish between:
drafting
and
final responsibility.
AI may create the first version.
A person may still need to provide context, judgment, accuracy, organizational voice, and approval.
The objective may be to eliminate the blank page, not the human.
Look for Classification and Sorting Problems
Organizations frequently spend significant employee time determining:
What is this?
Where does it belong?
Who should receive it?
How urgent is it?
Does it require attention?
Examples include:
customer inquiries,
support tickets,
documents,
applications,
survey responses,
maintenance requests,
emails,
quality issues,
and incoming forms.
AI can potentially help classify, categorize, prioritize, and route information.
These applications may appear less exciting than highly visible generative AI systems.
Operationally, they can be extremely valuable.
Look for Comparison Problems
Employees often spend time comparing information.
This contract against the previous contract.
Actual sales against forecast.
This month's performance against last month.
One supplier against another.
This policy against new requirements.
One document against another.
Production performance across shifts.
Customer behavior across periods.
AI and analytical tools can help identify differences, patterns, anomalies, and changes.
Ask:
Where are employees repeatedly trying to figure out what changed?
That can reveal valuable use cases.
Look for Monitoring Problems
Some organizational tasks involve watching something continuously and waiting for a meaningful change.
Inventory levels.
Sales trends.
Equipment conditions.
Website activity.
Customer behavior.
Financial transactions.
Quality metrics.
Operational performance.
Employees may review information periodically because continuous human monitoring is unrealistic.
AI can potentially help monitor information and alert people when something deserves attention.
The objective is not necessarily:
AI handles everything.
It may simply be:
AI watches. A person investigates.
That can be an extremely powerful partnership.
Look for Bottlenecks
A process may depend heavily on one person.
Everything waits for:
a manager,
an analyst,
an experienced technician,
an administrative employee,
a subject-matter expert,
or someone who understands a particular system.
Ask:
Why is this person the bottleneck?
Maybe they possess knowledge no one else has.
Maybe they manually review information.
Maybe they create routine reports.
Maybe every exception requires their attention.
AI may not eliminate the need for their expertise.
It may allow that expertise to be used more strategically.
For example, AI might handle routine cases while the expert focuses on exceptions.
That can increase capacity without removing human judgment.
Ask Employees One Powerful Question
One of the simplest ways to identify AI opportunities is to ask employees:
If you could make one part of your job easier, what would it be?
Do not mention AI initially.
Listen to the problem.
An employee may say:
“I wish I didn't spend every Monday morning creating this report.”
“I wish I could find customer information faster.”
“I wish we knew earlier when orders were slowing down.”
“I wish someone else could handle all these routine questions.”
“I wish I didn't have to read 200 comments to understand what customers are saying.”
“I wish we could predict how busy we're going to be.”
Now the organization has something useful.
The next question becomes:
Could AI help?
That sequence keeps the technology connected to real employee needs.
Not Every Problem Is an AI Problem
This may be one of the most important lessons in the entire guide.
Sometimes the best solution is:
better training,
clearer communication,
process redesign,
new equipment,
better management,
standard software,
a spreadsheet,
a database,
an automation rule,
or simply stopping unnecessary work.
Organizations should not use artificial intelligence when a simpler solution will work better.
This is not a failure of AI strategy.
It is evidence of good organizational judgment.
The Simplest Effective Solution Usually Wins
Imagine employees manually transfer information from one system into another.
AI could potentially read the information, interpret it, and enter it automatically.
But perhaps the two systems already have a standard integration feature that no one enabled.
Use the integration.
Or imagine employees struggle to locate a particular policy.
An AI knowledge assistant could solve the problem.
But perhaps the organization's shared drive is so disorganized that employees cannot identify which policy is current.
Organize the documents first.
AI should not add unnecessary complexity.
The goal is improvement, not technological sophistication.
Evaluate the Consequence of Error
Some AI opportunities may offer significant value but also carry significant risk.
Ask:
What happens if the AI is wrong?
If AI suggests a poor headline for an internal newsletter, the consequence is probably small.
If AI produces an incorrect sales forecast, the consequence may be more significant.
If AI influences hiring, healthcare, lending, safety, education, or legal decisions, the potential consequence may be substantial.
Risk does not automatically eliminate the use case.
It changes how the use case should be designed, tested, governed, and reviewed.
A useful principle is:
High potential value + high consequence of error = proceed carefully.
Consider Data Availability Early
A use case may sound excellent until someone asks:
“Do we have the information needed to do this?”
Suppose an organization wants to predict customer attrition.
That may require historical information about:
customer purchases,
frequency,
changes in behavior,
service interactions,
contract status,
or other relevant indicators.
If the organization has none of that information, the project may not be ready.
This does not mean abandoning the idea forever.
The first step may simply become:
Begin collecting the information we will eventually need.
Sometimes an AI opportunity reveals a data-development opportunity.
Consider Whether the Problem Occurs Often Enough
A technically possible AI application may not justify implementation.
Suppose AI could reduce a particular task from four hours to one hour.
That sounds valuable.
But if the task happens once every three years, the organization may spend more time implementing the solution than it saves.
Now suppose the same task occurs every week.
That is very different.
Good AI use cases often involve problems that are:
frequent,
expensive,
time-consuming,
high-volume,
strategically important,
or difficult for humans to handle consistently.
Consider Whether Improvement Can Be Measured
A strong AI use case should have some way to determine whether it worked.
For example:
Problem
Employees spend too much time preparing reports.
Measure
Average hours required per report.
Problem
Forecasting is unreliable.
Measure
Forecast accuracy.
Problem
Customers wait too long for responses.
Measure
Average response time.
Problem
Employees manually review thousands of records.
Measure
Employee hours required and percentage of important cases correctly identified.
Problem
Equipment problems are discovered too late.
Measure
Unplanned downtime or advance warning time.
The measure does not need to be perfect.
It needs to help answer:
Did this become better?
Define the Problem Before Defining the Solution
A useful problem statement can follow a simple structure:
Our organization currently experiences [problem], which results in [impact]. We want to improve [desired outcome], and we will know we have improved when [measure].
For example:
Our customer service employees spend significant time searching across multiple documents for answers, which increases response time. We want employees to locate reliable information faster, and we will measure improvement through average response time and employee time spent searching.
Notice that the statement does not mention AI.
That is intentional.
The problem should remain valid even if AI is not the eventual solution.
Avoid Problems That Are Too Broad
Some problem statements are almost impossible to act upon.
“We need to improve productivity.”
“We need to use our data better.”
“We want better customer service.”
“We need more sales.”
These are organizational objectives, not yet useful AI problems.
Narrow them.
Instead of:
“Improve productivity.”
Try:
“Our sales team spends approximately six hours each week manually preparing account activity summaries.”
Instead of:
“Use our data better.”
Try:
“We have five years of customer purchase history but do not currently identify customers whose purchasing behavior is declining.”
Specific problems create testable use cases.
Separate the Problem From the Assumed Solution
Employees may bring ideas such as:
“We need an AI chatbot.”
“We need predictive analytics.”
“We need an AI assistant.”
“We need an AI dashboard.”
Before evaluating the proposed solution, ask:
What problem would that solve?
Maybe the chatbot idea exists because employees cannot find information.
Maybe the dashboard idea exists because managers receive information too late.
Maybe predictive analytics is being proposed because forecasting is unreliable.
Understanding the underlying problem opens the door to multiple solutions.
That prevents organizations from becoming attached to a technology before understanding what they actually need.
Create an AI Opportunity List
Organizations can begin collecting potential AI opportunities in a simple format.
For each idea, document:
the problem,
who experiences it,
how frequently it occurs,
the current impact,
the desired improvement,
the information required,
potential risks,
and how success could be measured.
Do not worry initially about ranking everything perfectly.
The first objective is to create visibility.
You may discover that 30 possible AI projects exist.
That does not mean you should pursue 30 projects.
It means you now have choices.
Prioritize Value, Feasibility, and Risk
Once opportunities have been identified, evaluate each one across three dimensions.
Value
If this works, how meaningful will the improvement be?
Consider:
time savings,
cost reduction,
revenue opportunity,
customer experience,
employee experience,
quality,
risk reduction,
or strategic importance.
Feasibility
How realistic is implementation?
Consider:
data availability,
technology requirements,
employee capability,
process clarity,
cost,
integration,
and organizational capacity.
Risk
What happens if the AI fails or produces an incorrect result?
Consider:
financial consequences,
privacy,
security,
safety,
legal implications,
reputation,
fairness,
and impact on people.
The strongest early opportunities are often:
high value, reasonably feasible, and relatively low risk.
Those are excellent places to learn.
Do Not Start With the Hardest Problem
Organizations sometimes believe their first AI initiative should be transformational.
That can be a mistake.
The most ambitious use case may require:
multiple system integrations,
large amounts of sensitive data,
significant process redesign,
specialized expertise,
high employee adoption,
and substantial investment.
That may eventually be worthwhile.
But it may not be the best place to begin.
Early AI projects should help the organization learn how to:
select technology,
prepare data,
train employees,
establish governance,
measure results,
and manage change.
A successful smaller project builds confidence and capability for larger projects later.
Look for a Meaningful Win
The best first use case is not necessarily the easiest one.
If the problem is so trivial that no one cares whether it improves, the pilot may prove very little.
Look for something meaningful enough that employees and leaders will notice.
A useful first project might:
save employees several hours each week,
improve access to important information,
identify a risk earlier,
improve a recurring forecast,
reduce a frustrating administrative task,
or provide insight the organization currently does not have.
The ideal starting point sits between:
too trivial to matter
and
too complicated to succeed.
Create an AI Opportunity Scorecard
Organizations can use a simple 1-to-5 rating for each potential use case.
Rate:
Organizational Value
1 = Minimal value
5 = Significant value
Frequency
1 = Rare
5 = Very frequent
Data Readiness
1 = Required data unavailable
5 = Required data readily available and reliable
Technical Feasibility
1 = Very difficult
5 = Relatively straightforward
Workforce Readiness
1 = Significant adoption barriers
5 = Employees are prepared and interested
Measurability
1 = Difficult to evaluate
5 = Clear success measures
Risk
1 = High consequence of error
5 = Low consequence of error
This does not need to become a complicated mathematical model.
Its purpose is to force structured discussion.
A project that sounds exciting may become less attractive when the organization realizes the necessary data does not exist.
A relatively simple project may rise to the top because it creates meaningful value with manageable risk.
The AI Opportunity Conversation
A productive AI opportunity discussion can revolve around seven questions:
1. What problem are we trying to solve?
Be specific.
2. Who experiences the problem?
Identify employees, customers, departments, or stakeholders.
3. How significant is the problem?
Consider frequency, cost, time, frustration, risk, or strategic importance.
4. What would better look like?
Define the desired outcome.
5. What information would be required?
Identify relevant data and knowledge.
6. What happens if AI is wrong?
Understand the risk.
7. How would we measure success?
Establish the evidence needed to justify expansion.
Only after answering those questions should the organization begin seriously evaluating AI solutions.
What Good AI Problem Selection Looks Like
Organizations that are effective at identifying AI opportunities typically:
begin with organizational priorities,
involve employees who understand the work,
identify specific rather than abstract problems,
look for repetitive and information-heavy work,
examine prediction and monitoring needs,
identify bottlenecks and knowledge gaps,
consider simpler solutions before AI,
evaluate data availability early,
consider the consequence of error,
define measurable outcomes,
compare opportunities based on value, feasibility, and risk,
and resist the temptation to begin with the most complicated project.
This discipline dramatically increases the likelihood that AI investment produces meaningful results.
AI Opportunity Self-Check
Consider each statement based on the specific opportunity you are evaluating, not the potential of AI in general.
Select the response that most accurately reflects what your organization currently knows about the proposed use case.
We can clearly describe the problem without mentioning AI.
The problem connects to an organizational priority.
Employees who experience the problem have helped define it.
We understand how frequently the problem occurs.
We understand the current cost, time, frustration, or risk associated with the problem.
We can describe what improvement would look like.
We have considered whether a simpler non-AI solution could address the problem.
We understand what information would be required.
We have reasonable access to the required data.
We understand the major limitations of that data.
We understand what could happen if the AI is wrong.
The potential value is proportionate to the risk.
We can identify the employees who would use or be affected by the solution.
The organization has enough technical capability to test the idea.
We can establish a measurable baseline.
We know how we would evaluate success.
The project is small enough to test without creating excessive organizational risk.
The problem is important enough that a successful solution would matter.
Someone is willing to own the pilot.
We are willing to stop the project if evidence shows that it does not create sufficient value.
The opportunity may be a strong candidate for a manageable AI pilot. The problem, potential value, required information, ownership, risk, and measures of success are reasonably well understood.
The opportunity may have potential, but additional discovery is needed before implementation. Focus on clarifying the problem, data, affected employees, risks, baseline, and expected results.
The opportunity is not yet sufficiently defined for an AI pilot. Continue exploring the underlying problem before selecting technology or committing significant organizational resources.
A promising AI opportunity begins with a meaningful organizational problem. The goal is not to produce a Yes response to every statement before learning begins, but to understand enough about the problem, value, risk, information, and ownership to run a responsible test.
Practical Next Steps
Organizations can begin identifying AI opportunities without purchasing anything.
Ask leadership for the organization's top priorities.
Connect AI exploration to work that already matters.
Ask employees what frustrates them.
Listen for repetitive, manual, slow, and information-heavy work.
Identify where people spend time searching.
Knowledge-access problems can be significant opportunities.
Identify what the organization wishes it knew earlier.
These may reveal forecasting, monitoring, or predictive opportunities.
Map high-friction processes.
Look for bottlenecks, handoffs, repetitive work, and decision points.
Create a list of candidate problems.
Do not evaluate everything immediately.
Eliminate problems with obvious simpler solutions.
Use the simplest effective approach.
Evaluate data availability.
Determine whether the necessary information exists.
Assess risk.
Consider the consequence of an incorrect AI output.
Prioritize a few opportunities.
Look for high value, reasonable feasibility, and manageable risk.
Then choose one.
The Problem Is More Important Than the AI
Artificial intelligence creates an unusual temptation.
Because the technology is impressive, organizations naturally focus on what it can do.
But organizations do not create value simply by using impressive technology.
They create value by solving meaningful problems.
A modest AI application that saves employees hundreds of hours may be more valuable than a sophisticated AI initiative that produces little operational improvement.
A simple predictive model that identifies declining customer behavior may create more value than an expensive AI platform employees rarely use.
An internal knowledge tool that helps employees find reliable information may matter more than an AI demonstration that impresses visitors.
The technology should disappear behind the outcome.
Eventually, the best AI applications may not even feel particularly remarkable.
They will simply become:
how the organization works.
That is the objective.
Not AI for the sake of AI.
Not innovation for appearances.
Not technology because competitors are talking about technology.
Instead:
A real problem.
A meaningful opportunity.
An appropriate solution.
A measurable result.
Do not begin by searching for something artificial intelligence can do.
Begin by finding something your organization genuinely needs to do better.