Artificial intelligence and machine learning: what’s the difference?

How marketing can benefit from AI or ML

By Alexander Löwe Last updated Dec 4, 2023

The terms artificial intelligence (AI) and machine learning (ML) are frequently used in the marketing industry and unfortunately often mistakenly equated.

Index

Differences between artificial intelligence and machine learning

Category	Artificial intelligence (AI)	Machine learning (ML)
Definition	AI refers to the creation of systems that are capable of performing tasks that normally require human intelligence. This includes speech recognition, decision making, visual perception and translation between languages.	ML is a branch of AI that focuses on teaching computers to learn from data and make predictions or decisions. ML systems improve their performance by training them with more data.
Scope	AI has a broader scope and encompasses everything a computer does to act intelligently, whether through rule-based systems, machine learning, or other methods.	ML is more specific and focuses on the development of algorithms that can learn and make predictions.
Learning ability	AI systems can either be rule-based and without the ability to learn, or they can use machine learning to learn and improve.	ML systems are designed to learn and improve over time.
Goal	The aim of AI is to create intelligent systems that can perform tasks that normally require human intelligence.	The aim of ML is to enable systems to learn from data and make predictions or decisions.

Applications of artificial intelligence (AI) and machine learning (ML) in the corporate world

Artificial intelligence (AI) and machine learning (ML) are having a profound impact on the corporate world and have become key elements for the success and efficiency of companies in a wide range of industries.

Applications of artificial intelligence

Artificial intelligence (AI) refers to the use of machines and systems that are able to carry out activities that usually require human intelligence. This definition may seem broad, but in a business context, AI usually refers to technologies that are able to recognize environmental factors, act autonomously and thereby increase the probability of achieving defined goals independently and efficiently.

Core applications

Analysis and conclusion
Planning
Learning
Decision-making and decision-making
Optimization

Applications of machine learning

In machine learning, the focus is on the methods – the mathematical models and algorithms – that enable a computer system to learn. It deals with how to use large amounts of data from various sources in such a way that a machine can use the information to learn through experience.

Before machine learning was introduced, developers taught computers how to work with data by programming complex sequences of instructions. Today, the traditional approach would require writing millions of lines of code to accomplish the same flexible and complicated tasks that are possible with machine learning. Each new and unknown problem would have required a programmer to write new code.

Core applications

Deep learning
Deep neural networks
Insights from innovation learning (innovation insights learning)
Adversarial learning

Synergy of artificial intelligence and machine learning

AI and ML are two technologies that work hand in hand to optimize processes and drive innovation. This comprehensive table highlights how these technologies are used in different industries, performing both independent and overlapping tasks.

Scope	Task	AI task	ML Task
Customer service and chatbots	Automated response to FAQs	Chatbots answer customer questions	Analyzing data to improve responses
	ML-based customer interaction	Interaction with customers	Prediction of customer inquiries
	Personalization of customer service	Customization of the interaction	Analysis of behavior and preferences
Sales and marketing	Personalized advertising	Targeted advertising	Analysis of customer preferences
	Predictive analytics for purchasing behavior	Adaptation of sales strategies	Prediction of purchasing behavior
	Lead generation	Identification of potential customers	Scoring of leads
Production & supply chain management	Optimization of the supply chain	Automated warehouse management	Increasing efficiency through data analysis
	Forecasting delivery times	Provision of delivery time information	More accurate prediction of delivery times
	Production planning	Automated production planning	Optimization of production plans
Financial services	Fraud detection	Identification of suspicious activities	Pattern recognition for fraud prevention
	Algorithmic trading	Execution of trading transactions	Optimization of trading strategies
	Credit risk assessment	Assessment of creditworthiness	Analysis of financial data and risk assessment
Healthcare	Image analysis	Analysis of medical images	Improvement in diagnostic accuracy
	Personalized treatment plans	Creation of treatment plans	Analysis of patient data
	Predictive analytics	Prediction of disease outbreaks	Identification of patient needs
Human resources and talent acquisition	Automated CV analysis	Scanning applicant profiles	Assessment of qualifications and experience
	Predicting employee turnover	Identification of fluctuation risks	Analysis of employee data
	Personalized employee development	Design of further training plans	Analysis of employee performance
Research and development	Data analysis for research purposes	Accelerating scientific discoveries	Analysis and interpretation of research data
	Drug development	Identification of potential drug candidates	Analysis of data for drug development
	Optimization of material sciences	Support in the development of new materials	Analysis of material properties
Language and text processing	Voice assistants	Speech recognition and processing	Improving speech recognition accuracy
	Automated translation services	Provision of translations	Optimization of the translation accuracy
	Sentiment analysis	Analysis of customer ratings	Recognizing opinions and moods
Security and monitoring	Face recognition	Identification and tracking of persons	Improved detection accuracy
	Predicting security threats	Identification of potential risks	Analysis of data for risk prediction
	Network security	Protection against cyber attacks	Detection of anomalies and threat patterns
Retail trade	Personalization of the customer experience	Analysis of shopping habits	Personalization of offers
	Optimization of stock levels	Forecast of demand	Optimization of stock levels
	Price optimization	Dynamic pricing	Analysis of market conditions and demand

Alexander Löwe

Alexander is co-founder of the 111 Percent Knowledge Center, the we dot agency, as well as other ventures. He lives for entrepreneurial visions – at the age of 6, he founded a jam manufacturing company in South America with his mother. Alexander's talents include his holistic and strategic mindset and curiosity for economics as well as new technologies. Branding, user experience and marketing strategies are among his expertise.