Artificial intelligence (AI) has become a buzzword in various fields, from healthcare to manufacturing. AI technology is designed to help make decisions and perform tasks that have traditionally required human intervention. Many industries are now capitalizing on this innovative technology to improve efficiency, increase productivity, automate routine tasks and reduce operational costs.

However, not all software or technologies can be categorized as AI. In fact, it’s important to distinguish what constitutes an artificial intelligence-based technology from what does not qualify as AI.

AI involves machines or systems that can learn from experience and adjust their responses accordingly without explicit instructions. The goal is to create self-learning algorithms that mimic the cognitive functions of humans such as perception, decision making and learning – among many others. Examples of common AI applications include speech recognition systems like Siri or Alexa; facial recognition software used by social media platforms; autonomous vehicles with computer vision capabilities; natural language processing tools for customer service chatbots; recommendation engines for e-commerce sites – just a few examples among many more.

In contrast, technologies that don’t fall under the umbrella of Artificial Intelligence may offer automated functionality via pre-built workflows but do not possess advanced predictive analytics or machine learning features prevalent in modern-day Artificial Intelligent Apps/Tools.

Here are some examples of such technologies:

Here are some examples of such technologies:

Automated Decision Systems

Automated Decision Systems

Automated decision systems rely upon rule-based logic explicitly defined through boolean conditionals like “If A then B”. They lack sophisticated Machine Learning models developed based on historical patterns derived statistically like Neural Networks or Support Vector Machines (SVM). An excellent example could be a Home automation system where dependency triggered lighting modules exist based on when anyone enters a room using traditional infra-red sensors rather than understanding how long someone stays at home and which appliances they use most frequently over time.

Workflow Automations

Workflow Automation implies pre-defined business processes mapped out visually/logically ahead of time rather than being driven dynamically by intelligent algorithms infused into the process mapping toolset. It enables a repetitive task to be executed in an automated and programmed way, but not necessarily does it entail self-learning analytics for ad-hoc decision support.

Business Process Management (BPM)

As the name suggests, BPM is used to manage an organization’s business processes by visualizing them into a workflow. These workflows can enable collaboration across departments and help companies become more efficient. However, this technology solely facilitates mapping out these workflows and lacks Machine learning models that empower deep analysis or predictions on top of that as AI systems do.

Robotic Process Automation

RPA allows software robots called bots to perform tasks such as data entry and manipulation in front-end applications with web scraping functionality. These technologies are rule-based and programmed towards identifying fields based on screen scraping techniques rather than real-time cognitive intelligence . In essence, they may automate manual jobs performed repetitively; however, their capabilities don’t include cognitive abilities like machine learning or computer vision which are attributes of Artificial Intelligence Systems.

Chatbots/Virtual Assistants limited functionalities

Chatbots represent a set of predetermined responses generated based on pre-defined statements within the knowledge management system designed by human subject matter experts or developers. They aim at automating customer queries solving up-to-date questions contextualized upon core behavioral triggers – thus predefined answers quickly render necessary information-solving user asked queries efficiently, But lack predictive learning skills present in modern natural language processing(NLP) techniques incorporated into intelligent virtual assistants.


In summary ,Technology has evolved significantly over time bringing new terminologies while blurring definitions concurrently also creating many technical buzzwords around specific domain context- sometimes deliberately misleading customers seeking intelligent solutions for their business problems.. While there still exists some very sophisticated automation tools available now dubbed “Intelligent” but are misconceived with true AI-powered solutions. By understanding what counts as true AI features versus merely automated feature sets enabled by conventional RPA platforms/bots through Workflow Automations/BPM/Rule-Based Decision Automations , chatbots- you will be better placed to identify intelligent software/technologies that helps your business encompass a wide array of new opportunities enhanced by advanced learning prediction algorithms adaptive decision making.