Artificial Intelligence (AI) Vs Conventional Programming: Which business case is better solved?

This article will discuss which business case is better solved by artificial intelligence (AI) than conventional programming in detail with applications of them.

In the rapidly evolving landscape of technology, artificial intelligence (AI) has emerged as a transformative force, offering solutions that were previously unimaginable.

One of the key distinctions that sets AI apart from conventional programming is its ability to handle complex, data-driven tasks with remarkable efficiency and adaptability.

Here we will delve into various business cases where AI excels over conventional programming, exploring real-world examples and applications that showcase the undeniable advantages of AI.

In many cases, modern applications combine aspects of both AI and conventional programming. For instance, a system may use conventional programming for certain tasks and leverage machine learning algorithms for others, creating a hybrid approach.

The choice between AI and conventional programming depends on the nature of the problem, the available data, and the desired level of adaptability and learning.

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What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) refers to the field of computer science and technology that focuses on creating systems, programs, or machines capable of performing tasks that typically require human intelligence.

AI aims to develop algorithms, models, and systems that can replicate or simulate various aspects of human intelligence, such as reasoning, problem-solving, learning, perception, and language understanding.

Related Article: Top 21 Artificial Intelligence Projects: Ultimate Guide

What is a Conventional Programming?

Conventional programming, often referred to as imperative or procedural programming, is a programming paradigm that uses explicit statements to describe a sequence of actions that the computer must take to accomplish a specific task.

In conventional programming, the focus is on providing step-by-step instructions to the computer on how to perform a task or solve a problem.

Key characteristics of conventional programming include:

  1. Imperative Statements: Programs consist of sequences of explicit commands that tell the computer what to do. These commands are written in a programming language and are executed in the order in which they appear.
  2. Variables and Data Structures: Conventional programming involves the use of variables to store and manipulate data. Data structures, such as arrays or lists, are used to organize and manage collections of data.
  3. Control Structures: Conditional statements (if-else) and loops (for, while) are commonly used in conventional programming to control the flow of execution based on certain conditions.
  4. Procedures and Functions: Modularization is achieved through the use of procedures or functions, allowing developers to break down a program into smaller, more manageable parts.
  5. State Changes: Programs in conventional programming often involve changing the state of variables or data structures to achieve the desired outcome.

Languages like C, Fortran, and Pascal are examples of conventional programming languages.

In contrast to conventional programming, there are other programming paradigms, such as object-oriented programming (OOP), functional programming, and logic programming, each with its own approach to structuring and solving problems.

While conventional programming has been a dominant paradigm, modern programming often involves combining elements from various paradigms to create more flexible and efficient solutions.

Object-oriented features, functional programming concepts, and other techniques may be integrated into conventional programming languages to improve code organization, reusability, and maintainability.

Key Components of Artificial Intelligence (AI)

Here are the few top Key components and the foundation of Artificial Intelligence (AI) used to build and develop the AI applications and projects:

1. Machine Learning:

Machine learning is a subset of AI that involves the use of algorithms and statistical models to enable computers to improve their performance on a specific task through learning from data.

It encompasses techniques like supervised learning, unsupervised learning, and reinforcement learning.

2. Neural Networks:

Neural networks are computational models inspired by the structure and function of the human brain.

Deep learning, a subset of neural networks, has gained prominence in recent years for its ability to process vast amounts of data and solve complex problems, such as image recognition and natural language understanding.

3. Natural Language Processing (NLP):

NLP is a branch of AI that focuses on the interaction between computers and human language.

It involves tasks like language understanding, generation, translation, sentiment analysis, and chatbot development.

4. Computer Vision:

Computer vision is the field of AI that enables computers to interpret and understand visual information from images and videos.

This technology is used in applications like facial recognition, object detection, and autonomous vehicles.

5. Robotics:

AI-driven robotics involves the development of intelligent machines or robots that can perform tasks autonomously or semi-autonomously.

These robots can be used in manufacturing, healthcare, exploration, and various other domains.

6. Expert Systems:

Expert systems are AI programs designed to emulate the decision-making abilities of a human expert in a specific domain.

They use a knowledge base and rules to provide advice or make decisions.

7. Speech Recognition:

Speech recognition technology enables computers to convert spoken language into text.

That makes it useful for applications like voice assistants, transcription services, and voice-controlled devices.

8. Reinforcement Learning:

Reinforcement learning is a type of machine learning where an agent learns to make a sequence of decisions by interacting with an environment.

It is often used in scenarios where an AI system must learn to achieve specific goals through trial and error.

9. AI Planning and Reasoning:

AI planning and reasoning involve creating systems that can think logically, make inferences, and solve complex problems.

These capabilities are vital for applications like automated decision support systems.

Related Article: Top 15 Artificial Intelligence Tools for Beginners

Artificial Intelligence (AI) Vs Conventional Programming

Artificial Intelligence (AI) and conventional programming represent two different approaches to solving problems and building applications.

Here are key differences between AI and conventional programming:

1. Problem-Solving Approach:

  • Conventional Programming: In conventional programming, developers explicitly write code to instruct the computer on how to perform a task or solve a problem. The logic is explicitly defined, and the program follows a predetermined set of instructions.
  • AI: In AI, the focus is on creating systems that can learn from data and make decisions or predictions without being explicitly programmed. AI systems often involve machine learning algorithms that can adapt and improve their performance over time.

2. Rule-Based vs. Data-Driven:

  • Conventional Programming: Conventional programming relies on predefined rules and logic. Developers anticipate different scenarios and write code to handle each case explicitly.
  • AI: AI systems, particularly in machine learning, learn patterns and rules from data. Instead of explicitly programming rules, these systems learn from examples and generalize patterns to make predictions or decisions on new, unseen data.

3. Flexibility:

  • Conventional Programming: Conventional programs are static and require manual updates by developers to accommodate changes in requirements. They operate based on predefined instructions.
  • AI: AI systems, especially those using machine learning, can adapt to new data and changing conditions without manual intervention. They exhibit a level of flexibility and can improve their performance as they encounter more data.

4. Understanding and Adaptation:

  • Conventional Programming: Programs don’t inherently understand the data they process. They follow predefined logic, and any adaptation requires manual coding.
  • AI: AI systems, particularly in machine learning, can understand patterns and features in data, allowing them to make predictions or decisions without explicit programming for each scenario. They can adapt and improve their performance based on experience.

5. Examples:

  • Conventional Programming: Writing algorithms to sort a list, perform mathematical calculations, or implement specific business rules.
  • AI: Natural language processing, image recognition, speech recognition, recommendation systems, and autonomous vehicles are examples of AI applications that involve learning from data.

Related Article: Top 10 Practical Applications of AI in the World?

Top Business case(s) to solved by Artificial Intelligence (AI) than Conventional Programming

Here are the few Top Business case(s) used to solved Artificial Intelligence (AI) than Conventional Programming in the different industries:

1. Predictive Analytics and Forecasting

Predictive analytics is a crucial component of decision-making in numerous industries, from finance to healthcare.

AI outshines conventional programming when it comes to handling vast datasets and identifying intricate patterns.

For instance, financial institutions rely on AI algorithms to predict market trends and optimize investment strategies.

Companies like Wealthfront utilize AI to create personalized investment portfolios for their clients by analyzing historical data and market conditions.

In healthcare, AI-driven predictive analytics aids in early disease detection and treatment planning.

IBM’s Watson for Oncology, for example, uses AI to analyze vast amounts of medical literature, patient records, and clinical trial data to assist oncologists in making informed decisions about cancer treatments.

2. Natural Language Processing (NLP)

NLP is an area where AI shines brilliantly. While conventional programming can handle simple text processing tasks, AI-driven NLP solutions excel at understanding and generating human language.

Chatbots and virtual assistants, such as Amazon’s Alexa and Apple’s Siri, are prime examples of AI applications in NLP.

They can understand spoken language, interpret user intents, and provide relevant responses, making them indispensable in customer service and smart home applications.

In customer support, companies like Zendesk utilize AI-driven NLP to automatically categorize and route customer inquiries, reducing response times and improving overall customer satisfaction.

3. Computer Vision

Computer vision is the domain of AI that enables machines to interpret and understand visual information from the world around them.

Traditional programming approaches struggle to handle the complexity of visual data, but AI excels at it.

Industries such as healthcare, automotive, and retail benefit greatly from computer vision applications.

In healthcare, AI-powered diagnostic tools like Google’s DeepMind can analyze medical images such as X-rays and MRIs, detecting abnormalities and assisting radiologists in providing more accurate diagnoses.

In the automotive sector, self-driving cars rely on computer vision systems to perceive their surroundings, make driving decisions, and avoid accidents.

Companies like Tesla and Waymo employ AI-driven computer vision extensively to enhance vehicle safety and autonomy.

Related Article: Top 20 Popular Computer Vision Tools in 2023

4. Recommender Systems

AI-based recommender systems have transformed the way businesses engage with their customers.

These systems analyze user behavior and preferences to provide personalized recommendations, improving user satisfaction and boosting sales.

A classic example is Netflix, which employs AI algorithms to suggest movies and TV shows based on a user’s viewing history and preferences.

Amazon also uses AI for product recommendations, increasing cross-selling and upselling opportunities.

5. Fraud Detection and Cybersecurity

The ever-evolving landscape of cyber threats demands intelligent, adaptive solutions, which is precisely what AI brings to the table.

Traditional rule-based systems in cybersecurity often struggle to keep pace with evolving threats, while AI can continuously learn and adapt.

Financial institutions employ AI for fraud detection, analyzing transaction patterns and identifying anomalies in real-time.

Companies like PayPal and Square leverage AI algorithms to prevent fraudulent transactions and protect their users’ financial assets.

Related Article: Top 7 Application of Artificial Intelligence in Finance.

6. Autonomous Robots

AI plays a pivotal role in the development of autonomous robots, enabling them to perceive their environment and make decisions on the fly.

Robots like Boston Dynamics’ Spot are equipped with AI-driven algorithms that allow them to navigate complex terrains, assist in inspections, and even perform tasks in hazardous environments.

Related Article: What is Self Driving Car? – Features, Applications

7. Language Translation

AI-driven language translation services have revolutionized global communication.

Platforms like Google Translate and DeepL employ neural machine translation models that learn from vast multilingual datasets.

These models outperform traditional rule-based translation systems by producing more accurate and contextually relevant translations.

8. Content Generation

AI-driven content generation has gained prominence in various industries, including journalism, marketing, and e-commerce.

GPT-3, developed by OpenAI, is a prime example of a language model that can generate human-like text across a wide range of topics.

Content marketers use AI to produce blog posts, social media content, and product descriptions, saving time and resources.

9. Healthcare Diagnosis and Drug Discovery

In the realm of healthcare, AI demonstrates its superiority by expediting diagnosis and drug discovery.

Researchers are using AI to analyze genomics data, predict disease outbreaks, and identify potential drug candidates.

For example, AI-based platforms like Atomwise leverage deep learning algorithms to discover new drug compounds more efficiently.

Related Article: Top 20 Application of Artificial Intelligence in Healthcare.


The above analysis and text show the difference between AI and conventional programming it tells which business case is better solved by artificial intelligence (AI) than conventional programming.

Artificial intelligence has ushered in a new era of innovation and problem-solving, particularly in cases where traditional programming falls short.

Its ability to process vast amounts of data, understand natural language, interpret visual information, and adapt to changing circumstances makes AI an invaluable tool across industries.

As technology continues to evolve, we can expect AI to play an increasingly prominent role in solving complex business challenges and shaping the future of automation and decision-making.

Businesses that embrace AI stand to gain a competitive edge, enhancing efficiency, improving customer experiences, and driving growth in an ever-evolving digital landscape.

This answers Harnessing the Power of Artificial Intelligence and shows Which business case is better solved by Artificial Intelligence (ai) than Conventional Programming?. Keep Reading.

Related Article: Top 10 Practical Applications of AI in the World?