Learning Objectives

  • Define artificial intelligence in IT 231 terms (not a research paper).
  • Describe impacts on business (productivity, new products, risk) and society (jobs, bias, access).
  • Place AI next to other Unit 10 topics (ML, cloud, IoT) without replacing them.

This chapter opens Unit 10. Later notes cover data science, ML, and neural networks, cloud, green, and virtual computing, big data and blockchain, social media and digital marketing, and IoT.

What AI is here

Artificial intelligence is the field of making computers perform tasks that usually require human intelligence: recognizing speech and images, recommending products, translating language, detecting fraud, or drafting text. Narrow AI (today’s systems) is good at specific tasks. General AI that matches humans across all tasks is not what current business tools are.

Machine learning (next chapter) is a common way to build AI: the system improves from data rather than only from hand-written rules. Neural networks are one ML technique.

Impact on business

  • Productivity: automation of routine classification, customer chat, document drafting, and forecasting.
  • New products: recommendation engines, credit scoring, route planning, personalized marketing.
  • Cost and skill mix: fewer hours on repetitive work; more need for people who can check outputs and manage data.
  • Risks: wrong decisions at scale, privacy, dependency on vendors, and “hallucinated” facts in generative tools.

Managers should treat AI as a tool inside an information system: data quality, process design, and accountability still matter.

Impact on society

  • Jobs: some roles shrink (data entry, basic translation); others grow (AI-supported analysis, oversight, prompt and process design). Displacement is uneven by sector and education.
  • Access: language tools can help Nepali users, but models trained mainly on English data can perform worse on local languages and contexts.
  • Fairness: biased training data can reproduce discrimination in hiring or credit.
  • Public life: deepfakes, misinformation, and surveillance concerns require digital literacy (Unit 9) as well as technology.

Nepal example

Banks, wallets, and telecoms already use scoring and fraud rules that are AI-adjacent. A shop using a chatbot for FAQs still needs a human path for complaints. Policy and Electronic Transactions Act issues (Unit 9) apply to automated decisions that affect customers.

Key Takeaways

  • Course-level AI is narrow, data-driven automation and assistance—not science fiction.
  • Business gains come with operational and ethical risk.
  • Society feels AI through jobs, language access, bias, and information quality.

Discussion Questions

  1. Name one task in a Nepali SME that is a good AI candidate and one that is not.
  2. Why is “the model said so” a weak management control?
  3. How does AI depend on cloud computing and big data without being the same thing?