How to Identify Good AI Use Cases in Maritime
AI is becoming easier to access.
The difficult part is no longer finding an AI model.
The difficult part is deciding where AI actually belongs in a maritime operation.
A shipping company can identify dozens of possible AI applications.
Document processing.
Procurement.
Maintenance.
Fleet analytics.
Compliance.
Risk management.
Reporting.
Crew operations.
Port calls.
But not every problem needs AI.
Some problems are better solved with a simple workflow change.
Others need system integration.
Some need better data.
Some need conventional automation.
And only some genuinely benefit from AI.
This makes AI use case selection one of the most important steps in any maritime AI strategy.
The goal should not be to find the most impressive AI demonstration.
The goal should be to find a problem where AI can produce a measurable operational improvement.
What Is an AI Use Case?
An AI use case is a specific business or operational problem where artificial intelligence can perform, assist with, or improve a defined activity.
A good use case is more specific than:
"We want to use AI in procurement."
A better definition would be:
"Procurement employees spend significant time reading supplier quotations and manually extracting information before comparing offers."
Now the potential AI opportunity becomes clearer.
AI could potentially:
Read quotations
Extract relevant information
Structure the data
Normalize terminology
Support comparison
Highlight differences
The use case is no longer "AI in procurement."
It is a specific workflow problem.
That distinction is critical.
Start With the Problem, Not the Technology
One of the most common mistakes in AI projects is starting with the technology.
A company discovers a powerful AI model.
Then it asks:
"Where can we use this?"
A better approach is the opposite.
Start with the operation.
Ask:
What takes too much time?
What requires repetitive manual work?
Where do employees read large amounts of information?
Where are decisions delayed?
Where do errors occur?
Where is information difficult to find?
Where are people constantly comparing information?
Where are patterns difficult to identify?
Then ask:
Could AI improve this process?
This keeps the business problem at the center.
Step 1: Identify Information-Heavy Work
AI becomes particularly interesting when employees spend significant time processing information.
Look for activities involving:
Reading
Classification
Extraction
Comparison
Summarization
Search
Interpretation
Pattern recognition
Maritime organizations generate information through many channels.
Emails.
PDFs.
Reports.
Quotations.
Invoices.
Technical documents.
Inspection records.
Operational reports.
The more information intensive the workflow, the more likely there may be an opportunity for AI assistance.
But information volume alone is not enough.
There also needs to be a meaningful business outcome.
Step 2: Find Repetitive Work
Repetition is another useful signal.
Consider a procurement employee receiving dozens of supplier quotations.
Each quotation may require the employee to:
Open the document.
Find the relevant items.
Identify prices.
Check quantities.
Review delivery information.
Compare suppliers.
Enter information into another system.
If this process happens repeatedly, there may be an opportunity for automation and AI.
AI could potentially handle some interpretation.
Automation could move the extracted information into the next workflow step.
The employee could focus on reviewing the result and making the decision.
This creates a useful combination:
AI for interpretation + automation for execution + human judgement for decisions.
Step 3: Look for Unstructured Data
Not every AI opportunity involves unstructured information.
But unstructured data is particularly interesting.
Examples include:
Emails
PDFs
Reports
Quotations
Free text
Technical documents
Inspection notes
Traditional software works well when information already exists in structured fields.
For example:
Vessel ID = 12345
Supplier = ABC Marine
Amount = 5,000
But a PDF quotation may contain the same information in a format that a database cannot immediately use.
AI can potentially help convert that unstructured information into structured data.
That makes document heavy maritime workflows strong candidates for investigation.
Step 4: Identify Decisions That Depend on Large Amounts of Information
Another strong signal is a decision that requires people to examine information from multiple sources.
For example:
A fleet manager wants to understand why maintenance costs have increased.
The information might exist across:
Maintenance records
Procurement data
Equipment history
Supplier information
Vessel data
Financial records
A person may need to search several systems and combine the information manually.
AI could potentially help summarize and connect relevant information if the underlying data is accessible and reliable.
The opportunity is therefore not simply:
"Use AI for maintenance."
It is:
"Help decision makers understand maintenance patterns across multiple sources of information."
Step 5: Look for High-Volume Processes
Scale matters.
A task performed once a month may not justify an AI project.
A task performed hundreds or thousands of times may be a different story.
Ask:
How often does this task happen?
Then:
How much human effort does each occurrence require?
A simple calculation can help.
Suppose:
500 documents are processed each month.
Each document takes 10 minutes to review.
The organization spends approximately 83 hours per month on that activity.
That creates a measurable baseline.
An AI solution can then be evaluated against that baseline.
The exact value will depend on the workflow and implementation.
But the principle is important:
Measure the problem before measuring the technology.
Step 6: Check Whether the Data Exists
A promising AI idea can fail because the required data does not exist.
Suppose a company wants predictive maintenance.
The idea sounds attractive.
But ask:
Do we have historical maintenance records?
Do we have equipment information?
Do we have enough relevant operational data?
Is the data consistent?
Can we access it?
Is it linked to the right equipment?
Is there enough historical information to identify useful patterns?
If the answer is no, the organization may need to solve a data problem before solving an AI problem.
This is why AI readiness is partly a data readiness question.
Step 7: Check Data Quality
Having data is not enough.
The data needs to be usable.
Consider supplier names.
One system may contain:
ABC Marine Ltd
Another:
ABC Marine
Another:
ABC Marine Services
Are these the same supplier?
The answer may be obvious to an employee.
A machine may not know without appropriate data processing and context.
The same problem can occur with:
Vessel names
Equipment names
Spare parts
Ports
Units
Dates
Product descriptions
Supplier information
Data quality therefore matters before AI is introduced.
Step 8: Understand the Existing Workflow
An AI solution should fit into the workflow.
Consider document processing.
The current workflow may be:
Email → PDF → Employee Review → Spreadsheet → Approval
An AI solution might extract information from the PDF.
But what happens next?
If the employee still has to manually copy everything into another system, much of the potential value remains unrealized.
A better design could be:
Email → AI Extraction → Validation → Structured Data → Workflow → Human Approval
The technology is valuable because it changes the workflow.
Not simply because it reads the document.
Step 9: Ask Whether AI Is Actually Necessary
This is one of the most important questions.
Sometimes the answer is no.
Suppose the rule is:
"If a purchase order exceeds a defined amount, send it to a specific approver."
That is a deterministic rule.
Traditional automation can handle it.
AI is not necessary.
Now consider:
"Read a supplier quotation, identify the relevant products, compare descriptions, and summarize the differences."
That involves interpretation.
AI may be useful.
A practical rule is:
Simple rules → Automation
Complex interpretation → AI
Many workflows can use both.
Step 10: Consider the Cost of Being Wrong
Not all AI use cases carry the same risk.
This matters in maritime operations.
Consider an AI system summarizing a document.
An error may require human correction.
Now consider a system influencing a critical operational or safety decision.
The consequences may be much greater.
AI use cases should therefore be evaluated based on:
Impact
Risk
Human oversight
Explainability
Data quality
Decision consequences
The higher the potential consequence of an error, the more carefully the system should be designed and governed.
Step 11: Determine Whether Humans Should Stay in the Loop
AI does not have to make the final decision.
A strong use case can simply help a person make a decision faster.
For example:
AI → Extracts quotation information
↓
AI → Highlights differences
↓
Employee → Reviews information
↓
Employee → Approves supplier
The AI performs information intensive work.
The human retains decision responsibility.
This approach can be particularly useful where professional judgement matters.
Step 12: Define the Success Metric
An AI use case should have a measurable outcome.
Ask:
What should improve?
Possible measures include:
Processing time
Manual effort
Error rate
Response time
Information retrieval time
Cost visibility
Decision preparation time
Number of documents processed
Percentage of workflow automated
For example:
Instead of saying:
"We want AI to improve procurement."
Define:
"Reduce the time required to extract and compare supplier quotation information."
Now the project has something measurable.
A Practical AI Use Case Scorecard
A maritime organization can score potential AI use cases against several criteria.
Criterion | Question |
Business value | Does solving the problem matter? |
Frequency | How often does the task occur? |
Manual effort | How much human time is involved? |
Data availability | Does the required data exist? |
Data quality | Is the information usable? |
AI suitability | Does the task require interpretation? |
Integration | Can AI fit into the workflow? |
Risk | What happens if the output is wrong? |
Human oversight | Can people review important outputs? |
Measurement | Can improvement be measured? |
A use case that scores well across these dimensions is generally more promising than one selected simply because it sounds innovative.
Example: Evaluating a Marine Procurement Use Case
Consider supplier quotation processing.
Current Problem
Procurement employees receive quotations through email and spend time extracting information manually.
Information
PDF quotations, emails, item descriptions, prices, quantities, delivery information.
Repetition
The process occurs frequently.
AI Opportunity
Extract and structure quotation information.
Automation Opportunity
Move structured information into the procurement workflow.
Human Role
Review extracted information and make the purchasing decision.
Measurement
Time required to prepare quotation comparisons.
This is a much stronger AI use case than simply saying:
"Let's introduce AI into procurement."
Example: Evaluating Predictive Maintenance
Now consider predictive maintenance.
Problem
The organization wants earlier visibility into potential equipment issues.
Required Data
Potentially:
Equipment history
Maintenance records
Operational data
Sensor information
Failure history
Question
Is enough relevant historical data available?
If yes, the use case may be worth investigating.
If not, the organization may first need to improve data collection and integration.
This example demonstrates an important principle:
A good AI idea can still be premature.
Example: AI for Maritime Documents
Suppose a team processes hundreds of maritime documents.
The documents contain information that employees repeatedly extract.
This could be a strong AI candidate because the workflow combines:
High information volume
Repetition
Unstructured data
Manual extraction
A measurable process
The project can begin with a narrow document type rather than attempting to process every maritime document immediately.
Start with one workflow.
Measure the outcome.
Then expand.
Common AI Use Case Mistakes
Choosing AI Because It Is Trending
A popular technology is not automatically a business solution.
Starting With a Model
Choosing the model before understanding the workflow can lead to technically impressive but operationally weak projects.
Ignoring Existing Automation
Some processes do not require AI.
Ignoring Integration
An AI application that creates another isolated workflow can increase complexity.
Ignoring Data Quality
Poor data can produce unreliable outputs.
Trying to Automate Everything
Some decisions require human expertise.
Measuring AI Instead of the Business Outcome
The number of AI requests or documents processed is less important than the actual operational improvement.
A Five-Question Test
Before moving forward with an AI use case, ask:
1. Is the problem important?
If solving it does not matter to the business, AI will not create meaningful value.
2. Is the work repetitive or information intensive?
These characteristics often indicate potential.
3. Does the required data exist?
Without relevant data, many AI use cases cannot work effectively.
4. Does the task actually require AI?
If simple automation can solve it, use automation.
5. Can we measure improvement?
If success cannot be measured, it becomes difficult to determine whether the project is delivering value.
If the answer to all five is positive, the use case deserves deeper evaluation.
Start With One Workflow
A company does not need to transform every process at once.
A focused pilot can provide useful learning.
For example:
One document type
One procurement workflow
One reporting process
One fleet analysis problem
Then measure the result.
The organization can learn:
What data is required
Where integration is needed
How users interact with AI
Where errors occur
What governance is necessary
Whether the expected value is real
This creates a foundation for responsible expansion.
From AI Experiments to AI Strategy
Successful AI adoption is not a collection of unrelated experiments.
Over time, individual use cases should connect to a broader technology strategy.
For example:
Document Intelligence
↓
Procurement Intelligence
↓
Operational Data
↓
Fleet Analytics
↓
Decision Support
This creates an increasingly connected intelligence layer across the organization.
But the foundation remains the same:
Understand the workflow.
Connect the data.
Identify the right use case.
Apply AI where it adds value.
Measure the outcome.
The Bigger Picture
The maritime industry has no shortage of potential AI applications.
The challenge is choosing the right ones.
The strongest AI use cases are not necessarily the most futuristic.
They are often practical problems that employees already experience every day.
A team spends too much time reading documents.
A manager struggles to compare information across systems.
A procurement employee manually reviews supplier quotations.
A fleet team spends hours identifying patterns.
A report requires information to be collected from several systems.
These are real problems.
And real problems provide a much better starting point for AI than technology trends.
The goal should not be:
"How much AI can we deploy?"
It should be:
"Which decisions and workflows can we improve with AI?"
That is where the real opportunity begins.
FAQ
1. What makes a good AI use case in maritime?
A good AI use case usually addresses a meaningful business problem involving repetitive or information intensive work, has suitable data, can fit into an existing workflow, and has a measurable outcome.
2. How do shipping companies identify AI opportunities?
Companies can start by mapping workflows and identifying repetitive tasks, information heavy processes, difficult analysis, manual document processing, and decisions that require information from multiple sources.
3. Does every automation opportunity require AI?
No. Simple rule based processes can often be handled through conventional automation. AI is more relevant when a workflow requires interpretation, classification, pattern recognition, or analysis of complex information.
4. Why is data important for maritime AI use cases?
AI requires relevant information to produce useful results. Data availability, quality, consistency, accessibility, and context can significantly affect the feasibility of an AI use case.
5. What are common AI use cases in maritime?
Potential use cases include document processing, marine procurement, predictive maintenance, fleet analysis, compliance support, operational reporting, risk analysis, and natural language access to maritime data.
6. Should humans review AI outputs in maritime operations?
For many important workflows, human review can be appropriate, particularly where decisions involve significant operational, financial, safety, or compliance consequences.
7. How should a maritime company measure an AI project?
Measurement should focus on the business outcome, such as processing time, manual effort, error reduction, decision preparation time, information retrieval time, or another clearly defined operational metric.
8. Should a shipping company start with one AI use case?
A focused starting point can be practical. Testing one well defined workflow allows an organization to evaluate data, integration, user adoption, risk, and measurable value before expanding.
