A Practical Framework for Enterprise AI Use Case Assessment
Moving Beyond the Initial Excitement
Large language models (LLMs) have significantly changed how businesses develop software, automate processes, and interact with customers. For enterprise architects and senior technology leaders, the real challenge isn't just adopting Artificial Intelligence (AI), but determining where it offers significant value beyond traditional IT systems. While the initial enthusiasm for advanced AI is understandable, it's crucial to recognize that not every problem requires an AI-driven solution. Many business challenges are effectively addressed through well-established methods using defined logic, structured databases, and rule-based workflows. The true power of AI lies in its ability to handle complex situations involving unstructured data, subtle human-like interactions, and adaptive decision-making.
When thinking about when to use what, I was always wondering if there is a compelling way to derive a "usefulness score" from use-cases based on their attributes with regards to optimizing their human interaction. In this article I summarized my thoughts around a structured and pragmatic method for evaluating potential AI applications within an organization. By systematically assessing how specific tasks relate to human-machine interaction (HMI), the principles of cognitive processing (as defined by the CHC theory), and the inherent capabilities and limitations of LLMs, you can start to make more informed decisions about when and where to use AI and LLMs in particular. This approach allows for the prioritization of AI investments that promise clear and measurable business advantages, avoiding the drawbacks of implementing AI simply for the sake of novelty without a clear benefit. Or: Preventing JACB (just another chat bot).
Why Human-Machine Interaction, Cognitive Processing (CHC Model), and LLM Capabilities are Important Considerations
I am speculating that judging whether AI is the right solution for a particular business problem, a thorough analysis requires examining the task from at least three different angles. And let me be clear that this is a thought experiment and not a scientific study based approach. I am trying to present a practical scoring approach that combines UX with cognitive abilities and the potential of LLMs capabilities.
Human-Machine Interaction (HMI): This focuses on how users will work with the proposed system. Traditional IT solutions often use structured input methods like forms and data entry fields. In contrast, LLMs are excellent at enabling more natural and intuitive interactions through voice, chat, or the processing of unstructured data like documents and emails. Understanding the required level of interaction complexity and the nature of the data involved is vital in deciding if an LLM-powered solution offers a considerable advantage in terms of user experience and efficiency.
Cognitive Processing (CHC Model): Based on the Cattell-Horn-Carroll (CHC) theory, a well-regarded psychological framework, we can classify human intelligence into various cognitive abilities. By linking enterprise tasks to specific CHC abilities such as knowledge retrieval, pattern recognition, problem-solving, and auditory processing, we can understand whether AI can effectively support or even replace human effort. For example, tasks needing extensive pattern recognition in large datasets are well-suited for AI, while those requiring sensitive emotional understanding might still need human involvement.
LLM Capabilities and Limitations: While LLMs have impressive abilities in processing natural language, understanding meaning, and creating coherent responses, it's important to be aware of their limitations. They can struggle with tasks needing precise factual accuracy, complex real-world reasoning, and maintaining long-term memory or context over extended interactions. A clear understanding of these strengths and weaknesses is essential in deciding whether an LLM is the appropriate technology for a given application. Over-reliance on LLMs for tasks demanding absolute accuracy or intricate logical deduction could lead to unreliable results.
Assessing Enterprise LLM Use Cases
Let's take a look at some of the use-cases that come to mind when thinking about integrating AI into traditional systems. I am using examples that represent my experiences, knowledge. You will most likely find others but I wanted to have a set as example.
Conversational IT Interfaces (e.g., Voice banking for elderly): Imagine a world where interacting with technology is as simple as having a conversation. This use case explores the power of voice and natural language to create more intuitive IT interfaces, particularly for users who may struggle with traditional app-based or text-heavy systems. By enabling natural dialogue, these interfaces can make complex tasks like banking more accessible to a wider range of users, simply by understanding and responding to spoken commands.
Data Migrations & Mining (e.g., Legacy Host → SAP ERP): For many enterprises, the journey to modernizing their IT infrastructure involves the complex and often arduous task of moving historical data from outdated legacy systems to contemporary solutions like SAP ERP. This use case focuses on how AI can streamline this process by automating the extraction, transformation, and loading of data, significantly reducing the manual effort and potential for errors typically associated with such large-scale migrations.
Insurance Underwriting (Product Recommendations Based on Profile & Damage History): The insurance industry relies heavily on accurate risk assessment to tailor policies to individual customers. This use case introduces an AI-powered system designed to enhance underwriting by analyzing customer profiles, past claims history, and broader industry trends to intelligently suggest policy adjustments and personalized recommendations, ultimately leading to more accurate and relevant coverage.
Legal & Compliance Automation (e.g., Court complaint handling): Legal teams often face a high volume of court complaints that require careful review, analysis, and response. This use case explores how AI can automate aspects of this process by leveraging LLMs to quickly analyze new complaints, extract key information such as involved parties, allegations, and deadlines, and even suggest initial responses or relevant legal precedents. This automation can significantly reduce the manual effort involved in initial complaint processing, allowing legal professionals to focus on more complex strategic aspects of litigation.
IT Ticket Resolution (Enterprise Help Desk Automation): The sheer volume of IT support tickets can overwhelm even the most efficient service desks. This use case examines how AI, driven by LLMs, can provide significant relief by automatically classifying incoming tickets, suggesting effective solutions based on past incidents and knowledge bases, and even providing automated responses for common issues, thereby reducing the workload on human agents and improving response times.
Business Intelligence (BI) Report Generation: Business analysts often spend considerable time and effort crafting SQL queries and meticulously formatting reports to extract valuable insights from company data. This use case presents a vision where AI, powered by natural language processing, allows business users to simply ask questions in plain language and receive automatically generated queries, dashboards, and visualizations, democratizing access to data-driven decision-making.
Manufacturing Quality Control: Ensuring high-quality products is paramount in manufacturing. This use case delves into how AI can enhance quality control processes by analyzing diverse data sources, including structured sensor readings and unstructured technician reports, to proactively detect potential quality issues, predict failures, and ultimately optimize production processes for fewer defects and reduced downtime.
Regulatory Compliance Monitoring (Finance, Healthcare, Manufacturing, etc.): In today's complex regulatory landscape, organizations across various industries face the constant challenge of staying compliant with evolving rules and laws. This use case explores how AI can act as a vigilant monitor, continuously tracking regulatory updates, analyzing potential compliance risks within company documents and operations, and generating actionable plans to ensure adherence and avoid costly violations.
Customer Sentiment & Brand Reputation Analysis: Understanding how customers feel about a brand is crucial for any business. This use case highlights the power of AI to analyze vast amounts of online customer feedback from diverse sources like social media, reviews, and support channels, identifying sentiment trends, extracting key topics of discussion, and flagging potential risks to brand reputation, enabling faster and more informed responses.
Code Refactoring & Legacy Modernization: Many enterprises grapple with aging legacy codebases that are difficult and expensive to maintain. This use case introduces the potential of AI to assist developers in the complex tasks of code translation, refactoring to improve efficiency and readability, and overall modernization of these systems, ultimately accelerating the modernization process and reducing long-term maintenance costs.
HR Policy & Employee Handbook Assistant: Navigating company HR policies and employee handbooks can often be a source of frustration for employees. This use case envisions an AI-powered assistant that can provide instant and accurate answers to employee questions in natural language, drawing directly from company policy documents, thereby reducing the burden on HR departments and empowering employees with self-service access to important information.
Contract Review & Risk Analysis: Reviewing and analyzing contracts for potential risks and obligations is a critical but often tedious task for legal and business teams. This use case explores how AI can significantly expedite this process by automatically extracting key terms and clauses, identifying potentially risky language, and suggesting modifications, leading to faster contract cycles, reduced legal risk, and improved negotiation outcomes.
Enterprise Training & Knowledge Management: Equipping employees with the right skills and knowledge is essential for organizational success. This use case presents the concept of AI-driven training platforms that can personalize learning experiences by generating tailored learning paths, interactive quizzes, and relevant content based on individual roles and performance, ultimately improving knowledge retention and lowering training costs.
The U-A-D Scoring Framework for Enterprise AI Use Case Evaluation
To turn these considerations into a practical evaluation, I suggest a scoring framework based on three key factors: Usability Improvement (U), Automation Potential (A), and Decision-Making Enhancement (D). Each of these factors is rated on a scale of 1 to 5, where 1 indicates minimal impact and 5 signifies a significant positive impact. The total of these three scores gives a Total Impact Score (with a maximum of 15), which can be used to prioritize AI adoption efforts. Ultimately, this idea is supposed to helps enterprise architects and senior developers to prioritize AI investments, ensuring they focus on use cases that offer the best return on effort, with AI solving real business problems, rather than just adding complexity.
Let's look at the first use-case and do the exemplary scoring:
Conversational Banking for Elderly Users
Use Case: Voice-enabled banking for elderly customers who struggle with mobile apps.
HMI: Voice-based interaction (speech-to-text + intent recognition)
Cognitive Processing: Auditory processing, simple task execution
LLM Capabilities: Speech-to-text, task automation, natural conversation
Score: Usability: 5 | Automation: 4 | Decision-Making: 2 → Total: 11
AI Value: Accessibility, inclusion, simplified banking interactions.
I've gone ahead and did the scoring for all the use-cases:
Insights from the Scoring
High-Scoring Use Cases (12-15) Are Strong Candidates for Enterprise LLM Adoption
Usability-Driven Use Cases (High U Score)
Automation-Heavy Use Cases (High A Score)
Use cases scoring between 12 and 15 emerge as strong candidates, often demonstrating a potent combination of high automation and enhanced decision-making capabilities. Examples include Regulatory Compliance Monitoring and Insurance Underwriting, where AI excels at automating complex analyses and improving risk assessments. Similarly, AI-powered BI Report Generation and IT Ticket Resolution showcase the ability of LLMs to accelerate decision-making through automated analytics and reduce operational costs through significant automation. Finally, Contract Review highlights the potential for LLMs to alleviate legal workloads by automating risk assessment and ensuring compliance.
Sensitivity Analysis
The sensitivity analysis, involving the creation of three distinct scenarios with varied weights for Usability, Automation, and Decision-Making, helps us understand the robustness of our initial use case scoring. By shifting the emphasis across these key dimensions, we can observe how the relative ranking of different AI applications changes. This process is crucial because it acknowledges that different organizations, or even different departments within the same organization, may have varying strategic priorities. For instance, one might prioritize improving user experience (Usability), while another might focus on increasing efficiency (Automation) or enhancing strategic insights (Decision-Making). The sensitivity analysis allows for a more nuanced understanding of which use cases hold the most promise under different strategic lenses, ultimately leading to more informed investment decisions in enterprise AI.
Use Cases with High Decision-Making Potential (High D Score): Use cases like "Decision Support & Recommendations," "Business Intelligence Report Generation," "Regulatory Compliance Monitoring," and "Contract Review & Risk Analysis" tend to rise to the top when Decision-Making is given a higher weight. This suggests that if the primary goal is to enhance decision-making through AI, these use cases should be prioritized.
Use Cases with High Automation Potential (High A Score): "IT Ticket Resolution," "Data Extraction & Migration," "Manufacturing Quality Control," "Regulatory Compliance Monitoring," "Code Refactoring & Legacy Modernization," and "Contract Review & Risk Analysis" perform strongly when Automation is prioritized. This indicates that if the focus is on reducing manual effort and improving efficiency, these use cases are good candidates.
Use Cases with High Usability Improvement Potential (High U Score): "Conversational IT Interfaces" and "HR Policy & Employee Self-Service Assistant" show significant improvement in ranking when Usability is given more weight. This highlights their potential to enhance user experience and accessibility.
Stable Use Cases: Some use cases maintain relatively high rankings across different scenarios. For example, "Regulatory Compliance Monitoring" and "Contract Review & Risk Analysis" appear consistently in the top tier when either Automation or Decision-Making is emphasized. "IT Ticket Resolution" also remains a strong contender when Automation or Usability is prioritized.
Less Stable Use Cases: Use cases with a more balanced distribution of scores across U, A, and D might see more significant shifts in their ranking depending on the weighting. For instance, "Customer Sentiment Analysis" and "Enterprise Training & Knowledge Management" show more variability in their position.
The stability of the use case scores is dependent on the specific weights assigned to Usability, Automation, and Decision-Making. Organizations need to define their strategic priorities clearly. If the goal is primarily to improve user experience, then use cases with high Usability scores should be prioritized. If the focus is on efficiency and cost reduction, then use cases with high Automation potential are key. And if the main objective is to leverage AI for better insights and strategic decisions, then use cases with high Decision-Making scores should take precedence. The framework allows for flexibility in prioritization based on these organizational goals.
Why Traditional IT Systems Won’t Disappear But Must Evolve
Enterprise IT systems have long relied on
Well-defined workflows (e.g., ERP transactions, financial reconciliations)
Structured data operations (e.g., SQL-based reporting, batch processing)
Deterministic decision-making (e.g., loan eligibility, insurance pricing)
However, as organizations digitize more complex processes, traditional IT often
Unstructured data interpretation (e.g., analyzing contracts, emails, voice recordings)
Natural human interactions (e.g., understanding customer intent, legal disputes)
Flexible decision-making (e.g., adaptive pricing, regulatory risk assessment)
This is where
Applying This Framework to Your Enterprise AI Strategy
The strategic application of this framework is crucial for guiding your enterprise AI efforts. It highlights that traditional IT systems are still the best option for tasks with clear rules, precise calculations, and structured data processing. On the other hand, the real benefit of LLMs comes when tackling challenges that involve interpreting unstructured information, enabling natural language interactions, and requiring complex, adaptive decision-making. By carefully using the U-A-D scoring mechanism, organizations can effectively prioritize their AI investments, focusing resources on use cases scoring 12 or higher, which suggests a strong potential for significant impact. For tasks below this level, a more nuanced, combined approach using AI with traditional methods might be needed. The main aim is to move past the general excitement around AI and use a structured, analytical method to ensure that AI projects provide real business value instead of becoming expensive and unnecessary undertakings. As you think about the future of AI in your company, consider your current evaluation methods and identify the use cases with the most potential for significant change.