What Is Artificial Intelligence? Definition, Examples & Use Cases

What Is Artificial Intelligence?

Artificial intelligence (AI) enables software to perform tasks that normally require human intelligence—such as understanding language, recognizing images, making decisions, and learning from data. By combining advanced algorithms, vast datasets, and scalable computing, AI models improve automatically over time. To see how AI drives efficiency end-to-end, start with our main pillar on AI Business Automation: Boost Efficiency & Drive Growth.

A Brief History

  1. The Early Days (1950s–1970s)
    Alan Turing’s rule-based expert systems paved the way for today’s compliance engines in ai legal & compliance automation.

  2. Machine Learning Emerges (1980s–1990s)
    Statistical methods and faster processors unlocked data-driven insights—foundational to ai business intelligence.

  3. Deep Learning Revolution (2010s)
    Neural networks with many layers drove breakthroughs in vision and language, powering modern chatbots & virtual agents.

  4. The Era of Generative AI (2020s)
    Large-scale language and image models now generate text, code, and art on demand—transforming content creation & copywriting.

Key Technologies

  • Machine learning: Models that learn from examples—used in predictive analytics to forecast demand and detect anomalies.

  • Deep learning: Complex neural nets for tasks like image recognition in it & cloud management.

  • Natural language processing: Interpreting and generating text, essential for modern customer service automation.

  • Robotic process automation: Software bots handling routine tasks; when enhanced with AI decisioning, you get the end-to-end operations & logistics flows.

  • Document automation: Extracting structured data from forms and contracts—see examples in finance & accounting automation.

How Companies Use AI Today

Benefits vs. Challenges

Benefits

Challenges

  1. Ensuring clean, compliant data (critical for document automation)

  2. Mitigating bias in models

  3. Integrating AI with legacy systems via RPA + AI workflows

  4. Closing internal skills gaps

Looking Ahead

Next Steps

  1. Explore each roadmap—from chatbots & virtual agents to supply chain & inventory management—to identify quick-win pilots.

  2. Use our free templates and calculators to measure ROI on a small automation project.

  3. Scale responsibly, integrating AI into core workflows like lead generation & sales outreach and finance & accounting automation.

Further Reading

  1. “Artificial Intelligence,” Wikipedia: https://en.wikipedia.org/wiki/Artificial_intelligence

  2. Bernard Marr, “What Is AI? Everything You Need to Know About Artificial Intelligence,” Forbes, Jan 2025

  3. McKinsey & Company, “The State of AI in 2025”

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