One of the emerging paradoxes of artificial intelligence is that its greatest value for business processes includes both processes that are “routine” or “repetitive,” or, on the other hand, complex.
But the key lies in relative value. High-value AI use cases typically involve complex decision-making, pattern recognition, predictive analytics, or end-to-end process automation.
Lower-value use cases are often repetitive, routine, or administrative tasks where AI provides incremental, but not transformative, improvements.
So even if some might think AI will be most disruptive for lower-skill occupations and tasks, it seems highly likely that the bigger amount of disruption will come for job functions that are cognitive, across most industries.
Industry | High-Value AI Use Cases | Lower-Value AI Use Cases |
Healthcare | Diagnostics (image/lab analysis), personalized treatment, drug discovery, operational optimization 1,2,3 | Routine admin tasks (e.g., appointment reminders), basic data entry |
Finance | Fraud detection, algorithmic trading, credit risk assessment, automated loan processing 1,2 | Simple customer queries, basic transaction categorization |
Manufacturing | Predictive maintenance, quality control, supply chain optimization 2,3 | Standard inventory updates, basic production scheduling |
Retail | Personalized recommendations, demand forecasting, dynamic pricing, customer support bots 2,3 | Standard order processing, generic marketing emails |
Software Development | Code generation, intelligent code completion, automated testing/debugging, documentation generation 4,5 | Simple script writing, basic code formatting |
Education | Adaptive learning platforms, personalized content, automated grading 1,3 | Scheduling classes, distributing standard materials |
Business Operations | Hyperautomation (end-to-end process automation), advanced analytics, generative content for marketing/HR 6 3 | Expense report filing, simple workflow notifications |
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