How can you unlock the potential of AI in your business strategy?
In this article, we will provide you with a framework that will help you tackle this goal systematically.
Framework for discovering AI potential in your business
Let’s explore a framework for identifying potential AI applications. This framework can uncover fresh perspectives on creating business value with AI.
This framework is inspired by the paper “What Can Machines Learn, and What Does It Mean for Occupations and the Economy?” by Erik Brynjolfsson, Tom Mitchell, and Daniel Rock. Andrew Ng popularised this framework with modern AI.
We will study this framework using the example of the job of a compliance officer at a bank working with SMEs (small- and medium-sized enterprises). Compliance costs for financial institutions are over $206.1 billion per year.
Analyzing jobs to identify potential AI applications
The basic idea is that AI doesn’t automate full jobs—it automates specific tasks. A job consists of many tasks. Here, “job” can refer to a regular profession (such as Lawyer, HR manager, Merchant, etc.) or to a job in terms of the “jobs to be done” (JTBD) framework.
Here is how you can identify potential AI applications within a job:
- Identify the tasks within a job
- Analyze each task
- Evaluate potential AI solutions
- Evaluate economic benefits
- Evaluate risks
Let’s discuss each step in more detail and see how it applies to a real-life example.
How to identify tasks within a job
Consider a bank compliance officer who monitors the transactions of small and medium businesses.
To understand the main tasks a compliance officer is doing it is best to spend a few hours (or days) with several officers to observe their typical activities and ask questions. The expected result of such analysis is a list of the most frequent and important tasks.
Here is a brief overview of the task flow of a compliance officer:
- The officer works with a special web application that provides a queue of alerts about transactions marked as suspicious by the internal automation
- All client information (business profile, uploaded documents, history of transactions, previous alerts, etc.) is available through this web app
- The officer can chat with the client to request additional information
- The officer has access to external sources to check clients’ information
- The officer has to make all decisions strictly based on the specific compliance policies
- The output of the work is the approval or rejection of suspicious transactions and explanatory comments related to these decisions.
The most important tasks of compliance officers:
- Determining the category of customer documents (invoice, rent agreement, permissions, etc)
- Checking for each document:
- Is it real or fake?
- What is the essence?
- How is it related to the customer’s business?
- Who are the counterparties?
- Does it comply with a category-specific policy?
- Checking for each transaction:
- Is it an expected transaction for the type of business?
- Is it a usual amount?
- Does it correspond to the evidence in the customer’s documents?
- Requesting additional documents from the customers if it is impossible to make a decision
- Deciding to approve or reject the transaction
- Talking to the customers to explain the decision about their transactions
- Checking the customer business evidence in external sources
- Tax authorities
- Web presence
- Google reviews
- etc
How to analyze tasks to identify opportunities for applying AI
In the last step, we divided an officer’s work into repeatable tasks. Now we can analyze each to identify opportunities for applying AI.
For each task:
- Evaluate potential AI solutions
- Evaluate economic benefits
- Evaluate risks
You can do these analyses based on your own AI knowledge or in collaboration with your AI team.
When evaluating the potential AI solutions it’s worth using all available sources:
- Papers on relevant topics
- Previous experience of the AI team
- Brainstorm sessions with the team and business stakeholders
- Consultation with domain experts
- Basic analysis of existing data
- Analysis of existing solutions in production
When evaluating the economic benefits of an AI solution, it’s useful to clearly articulate how this solution creates value for the business and its users. Usually, it will be something from one of the following categories:
- Increased revenue
- Lower costs
- Less time to accomplish the task
- Higher accuracy
- Faster processing
- Improved customer service
When evaluating the economic benefits and risks, use rough grading scores (low/medium/high) with some explanations for each choice. Such grading will require judgments from the business stakeholders and will help to align the team.
In our examples we will use a very simple grading:
- Low value means that even with a perfect solution, we won’t see any difference in business revenues or costs.
- Medium value means that it is worth experimenting with the solutions to understand how AI could influence business metrics.
- High value means that the AI solution will definitely accomplish the task more effectively.
Let’s examine two examples:
- What is the essence of a document
- Determining the category of customer documents
Task: What is the essence of a document
Clients can provide many types of documents: invoices, rent agreements, certificates, business contracts, licenses, etc.
For each transaction, the officer must download and open all documents one by one, look through their content, and analyze the important details.
For example, for an invoice, it’s important to take into account:
- Counterparties
- Date of the invoice and due date
- Amount
- Purpose of the invoice
- Terms of payment
Based on these fields, the officer decides if it is a valid invoice using the bank compliance policy. For instance, the invoice must be related to the bank client, the purpose of the invoice should correspond to the nature of the client’s business, it should have the correct dates and currency, etc.
This task is very time-consuming because documents could be dozens of pages, they can have poor quality (for example, it could be just a screenshot of a handwritten invoice), and there could be a lot of documents per client.
How AI can be applied
- OCR (optical character recognition) can be used to extract text from scanned documents
- LLMs can be used to extract key information, interpret the context of the document, summarize it, and answer questions based on the document’s content.
Business value
- Less time to accomplish the task: AI reduces the manual effort required to understand the document.
- Higher accuracy: By accurately extracting and summarizing key information, AI supports more informed decisions.
- Medium value:
Understanding long documents take a significant share of an officer’s time. For example, a 20-page contract could require around five minutes. AI can reduce this time to less one minute by providing a structured summary of the document.
Severity of risk
- Medium risk
If the AI misinterprets nuanced or context-dependent information, it could lead to incorrect conclusions or actions by the officer.
Task: Determining the category of customer documents
When a client uploads a new document, the officer must determine its category (invoice, rent agreement, license, etc.).
The category will be used to guide the verification process because each category has its own compliance policy. Category information is entered through a special web interface and stored for further use.
How AI can be applied
- Natural language processing (NLP), LLMs, OCR, and image recognition technologies can be used to automatically classify documents based on their content.
Business value
- Less time to accomplish the task: AI reduces the time spent reading, sorting, and processing the documents.
- Low value
Officers can do the classification almost instantly. Basic automated classification is already in place.
Severity of risk
- Low risk
Incorrect classification can be fixed during manual review
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Analysis of all tasks
Here is a summary table for all the main tasks of a compliance officer:
| Task | How AI can be applied | Business value | Severity of risk |
|---|---|---|---|
| Determining the category of customer documents | Natural language processing (NLP), LLMs, OCR and image recognition technologies can be used to automatically classify documents based on their content | Less time to accomplish the task: AI reduces the time spent reading, sorting, and processing the documents. Low value: Officers can do the classification almost instantly. Basic automated classification is already in place. | Low risk:Incorrect classification can be fixed during manual review |
| Is the document real or fake? | Computer vision models can analyze document features to detect anomalies or signs of forgery that might indicate falsification. | Higher accuracy: enhance forgery detection. Low value:Forgeries are very rare, manual processing is enough. | Low risk: False positives can be mitigated during manual review |
| What is the essence of a document? | OCR can be used to extract text from scanned. LLMs can be used to extract key information, interpret the context of the document, summarize it, and answer questions based on its content. | Less time to accomplish the task: AI reduces the manual effort required to understand the document. Better accuracy: By accurately extracting and summarizing key information, AI supports more informed decisions. Medium value: Understanding long documents takes a lot of time. | Medium risk: If the AI misinterprets nuanced or context-dependent information, it could lead to incorrect conclusions or actions by the officer. |
| How is the document related to the customer’s business? | AI systems can compare a document details to business profiles to assess its relevance. | Higher accuracy: Improving the relevance of compliance checks. Medium value: Understanding long documents takes a lot of time. | Medium risk: AI could mistakenly associate documents with incorrect business contexts, leading to inappropriate compliance actions. |
| Identifying the counterparties in the document | LLMs can extract named entities and their roles within documents. | Less time to accomplish the task: AI reduces the need to manually read documents and fill forms. Low value: It’s easy for officers to do this task manually. | Medium risk: Errors in entity recognition could misidentify or omit critical parties involved, affecting compliance integrity. |
| Does the document comply with a category-specific policy? | Rule-based AI systems can verify whether documents adhere to specific regulatory frameworks and industry standards. | Less time to accomplish the task: AI reduces the manual effort required to analyze the document. Better accuracy: reducing human errors in complex policy criteria evaluation. High value: Making decisions about a document can take a lot of time. | High risk: Incorrect policy interpretations could lead to regulatory risks for the business. |
| Is this transaction expected for this type of business? | ML models can compare transactions to typical business activities. | Less time to accomplish the task: AI reduces manual document lookups and speeds up the compliance workflow. Medium value: Understanding the business profile is a time-consuming activity. | Medium risk: As AI relies on historical data, it may not predict unconventional-yet-legitimate business practices, potentially flagging them as suspicious, leading to inappropriate compliance actions. |
| Is the transaction amount normal for this kind of business? | Algorithms can analyze historical data to flag transactions that deviate from established norms. | Higher accuracy: Predictive ML models can flag unusual transaction amounts. Low value: Basic automated checks are already in place. | Medium risk:AI might flag normal transactions as suspicious if they deviate from typical patterns, leading to unnecessary investigations. |
| Does the transaction correspond to the evidence in customer documents | Transaction details can be automatically matched with corresponding customer documents using the results of a named entity extraction system. | Less time to accomplish the task: AI reduces manual document lookups and speeds up the compliance workflow. Medium value: Understanding the business profile is time-consuming. | Medium risk: AI could misassociate transactions and customer documents leading to inappropriate compliance actions. |
| Deciding to approve or reject the transaction | Automated approvals (rule-based system) can be based on predefined criteria. | Faster processing: AI can automate routine decision-making, speeding up transaction approvals and reducing bottlenecks. Medium value: A basic rule-based system is already in place. | High risk: Over-reliance on AI decision-making can reduce human oversight and increase the risk of errors. Customer satisfaction could deteriorate. |
| Requesting additional documents from the customer | Automated systems can trigger requests for additional documentation given the analysis of existing data. | Faster processing: Automated document requests streamline interactions and ensure necessary documentation is collected without delay. High value: The faster the process goes for users, the higher their satisfaction. | Medium risk: Automated requests could be triggered inappropriately, leading to customer dissatisfaction or data overload. |
| Talking to customers to explain the decision about their transactions | LLM-powered chatbots can handle routine inquiries and provide explanations regarding transaction decisions. | Improved customer service: AI-driven chatbots can provide instant responses to customer queries, improving satisfaction and engagement. High value: The faster the process goes for users, the higher their satisfaction. | High risk: AI-generated responses may contain hallucinations, incorrect information, leak personal data, lack empathy, or fail to address specific customer concerns adequately, potentially harming customer relations and causing legal risks for the business. |
| Checking the customer business evidence with external data | External databases and APIs (like tax records) can provide data to verify and enhance customer information. LLMs can summarize content of web pages. | Lower costs: Automating the verification process with AI reduces the need for extensive manual background checks, cutting operational costs. Medium value: The manual process consumes a lot of time. | Low risk: AI could rely on outdated or incorrect external data, leading to inaccurate assessments of compliance status. |
Choosing the scope of the project
Deciding on how and where to apply AI differs case by case.
Consider some basic criteria:
- Solving the task should bring business value (decrease costs, increase revenue, etc.)
- AI solution should be feasible
- The risks of applied AI should not be too high
- The costs of the solution should be less than the business benefits
Evaluating the feasibility of the AI solutions
The solution can likely be delivered if:
- Your AI team successfully solved a similar problem
- Other teams of competitors successfully solved a similar problem with AI
- There are plenty of papers on the topic
- There are cloud services that provide a similar solution
- There are open-source libraries that provide a similar solution
- All the necessary data is available
In some cases, the team could face difficulties. It will be best to start with simple and quick prototypes to gain a better understanding of the complexity, costs, and risks.
If external tools/services/libraries are available, it is worth using them to quickly get a feel of the potential business value.
Analyzing costs
To estimate the costs of an AI solution, consider the following:
- Compute costs for using large GenAI models or cloud services
- Efforts of the AI team
Evaluate compute costs per unit of work. In our case, it could be the cost of processing one document or transaction. Such evaluation will allow us to compare the time and costs required for manual and automated processing.
Team efforts will depend on the specifics of the subject area, the qualifications of the team, and the availability of off-the-shelf solutions. Rely on the team’s experience: if they have previously solved similar problems, expectations about the results will be more realistic.
Let’s apply this logic to our examples.
Task: What is the essence of the document
- Accomplishing the task should bring business value itself regardless of AI: medium value is expected – it takes less time to accomplish the task and it will provide higher accuracy.
- AI solutions are feasible and can bring business improvements in comparison to the status quo: it’s easy to find lots of examples of similar solutions (named entity recognition, summarization, OCR).
- The risks of applied AI are not too high.
- Costs of the solution should be reasonable: compute costs can be very low when using cloud services or hosted models.
Summary for this task: we should include it in the scope of the project.
Task: Determining the category of customer documents
- Solving the task should bring business value regardless of AI: low value – officers can do the classification almost instantly. Basic automated classification is already in place.
- AI solutions are feasible and can bring business improvements in comparison to the status quo: it’s easy to find many examples of similar solutions (document classification).
- The risks of applied AI are low.
- Costs of the solution should be reasonable: compute costs can be very low when using cloud services or hosted models.
Summary for this task: we should not include it in the scope of the project.
Appropriate tasks for the project
Based on the logic described above we can finalize the scope of the project:
- What is the essence of a document
- How is a document related to the customer’s business
- Is this transaction expected for this type of business
- Does the transaction correspond to the evidence in customer documents
- Requesting additional documents from the customers
- Checking the customer business evidence
What’s next
We’ve chosen the tasks of the compliance officer where AI is most promising. The next step should be to outline the architecture of the AI solution. This is a critical point – now we have to go back to the officers and determine how the solution will be integrated into the current web interface to gain the highest business value.
It’s necessary to understand
- How the officer will access the results of the AI system
- How the results will be presented in the interface
- How the officer’s feedback about the AI-generated results will be incorporated into the workflow
It’s best to address these questions before developing the AI solution because the desired UX is crucial and can shape the underlying solution.
For instance:
- If the summary of the document is too long and unstructured, then it won’t save time for the officer. It’s better to talk to the officers to determine the best format for document summaries.
- If officers have to wait for the AI models to process the documents every time they want to access the extracted information, then they will lose precious time. It’s better to process the documents in the background as soon as they are uploaded by the customers.
- If the officer needs to use some unfamiliar external system (for example Jupyter Notebook) to access the extracted information, then it could undermine their motivation to use the AI. It’s better to integrate the AI-generated results directly into the web application that the officers use.
- If the officer is unable to flag errors in the AI-generated results or dispute them, or if the officer can’t understand the reasoning behind the AI’s decision-making, then they won’t trust the AI solution and the whole project might fail.
Summary
To discover potential AI applications, you can use a simple framework:
- Identify the tasks within a job
- Analyze each task
- Evaluate the potential AI solutions
- Evaluate the economic benefits
- Evaluate the risks
To understand the main tasks of a job it is best to spend a few hours (or days) with professionals who do the job to observe their typical activities and ask questions.
When evaluating potential AI solutions, it’s worth using all available sources like papers on relevant topics, previous experience of the AI team, brainstorming sessions with the team, etc.
When evaluating the economic benefits it’s useful to clearly articulate how this solution creates value for the business and users (increased revenue, lower costs, less time to accomplish the task, etc.).
When evaluating the economic benefits and risks of AI solutions, use rough grades low/medium/high with a brief explanation of the choice.
Basic criteria for choosing the scope of a project:
- Solving the task should bring business value
- The AI solution should be feasible
- The risks of applied AI should not be too high
- The costs of the solution should be less than the business benefits
Before outlining the architecture of the AI solution, it’s necessary to understand:
- How the results of AI automation will be accessed
- How they will be presented to the users
- How user feedback about the AI-generated results will be processed
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