Artificial intelligence (AI) has shifted from a bold experiment to a strategic necessity. Recent studies show that 88% of C-level decision-makers want to accelerate AI adoption in 2025. Yet, just a fraction of AI initiatives delivers the expected results.

An inmate at a New York correctional facility, Glenn Rodriguez, was due for parole soon. The man had demonstrated excellent behavior and eagerly anticipated his release to embark on a fresh start. To Glenn’s dismay, the board denied him parole.

You can view artificial intelligence as an umbrella term encompassing different types of algorithms that can autonomously perform complex tasks that normally require human intelligence. For instance, AI models can analyze historical data to make predictions for the future. They can detect anomalies in a machine’s functioning before it breaks down. AI can even drive vehicles. And depending on which AI subtype you are looking at, it can compose music and generate artwork.

Everyone wants AI, but few know where to start. Enterprises face analysis paralysis with implementing AI effectively when massive, expensive models feel like overkill for routine tasks. Why deploy a $10 million solution just to answer FAQs or process documents? The truth is, most businesses don’t need boundless AI creativity; they need focused, reliable, and cost-efficient automation.

Automated data collection is the process of harvesting data automatically from various sources without human intervention and storing it at the corresponding location in your company’s database/system.

Understanding AI types and subsets—and their applications in business