Artificial intelligence has unquestionably become a buzzword, not just within the business realm but also in public discourse. Deservedly so, as we encounter AI daily, sometimes without even realizing it. Examples include speech-to-text applications, like Google Translate, the Google search engine, and Spotify’s recommendations.
In this guide, we will show you how to integrate AI into your business while shedding light on its legal challenges.
The reach of AI technology
The field of AI is rapidly advancing and is projected to contribute $15.7 trillion to the global economy by 2030. As of mid-2023, the United States alone had nearly 15,000 registered startups focused on AI, while the worldwide count exceeded 60,000 such businesses.
For more data confirming its scale, visit our article on AI statistics and facts for 2024.
What is AI?
Firstly, you have to understand what does AI stand for, recognizing that the term is frequently misapplied to denote conventional algorithms.
What is artificial intelligence, then? AI refers to the simulation of human intelligence processes and the ability of machines to solve problems. It involves computers’ capacity to interpret intricate data, identify patterns, and make decisions – a task previously exclusive to humans. Significantly, these algorithms can learn over time, addressing challenges across various business domains.
The definition of AI
Artificial Intelligence (AI): Algorithms that mimic, at least to some extent, intelligent or human behavior.
Business use cases of AI
Discussing business broadly, without delving into specific domains, we can identify several key areas where the general application of AI is particularly noteworthy and has the potential to revolutionize your business.
Let’s talk about business now and how to put artificial intelligence to use. We distinguish the following areas for general application of AI and in a moment we will explain how does AI work in industry-specific scenarios and provide examples of artificial intelligence.
Data science aims to reveal concealed patterns, relationships, and insights within the vast amount of daily information that businesses accumulate. In an era marked by escalating data volumes, it plays a crucial role in forecasting, risk minimization, fraud detection, and enhancing operational efficiency.
Data science is eagerly applied for:
- risk analysis and customer profiling in finance; on a side note, remember that in many countries, AI can’t be used to make credit decisions
- drug discovery and personalized treatment in medicine; but, as above, AI can’t make decisions about on treatment undertaken
- predictive maintenance in manufacturing
- traffic management and demand forecasting in transportation and logistics
- targeted advertising and sentiment analysis in marketing
- recommender systems in e-commerce and entertainment
- content and connection suggestions in social media apps
- improving drivers’ safety and autonomous vehicles in the automotive industry.
Computer vision algorithms detect, recognize, and identify individuals, locations, and objects in diverse visual content, encompassing photos, graphics, and videos. They analyze information with speed and precision. Computer vision aims to mimic human sight.
Business use cases of computer vision include:
- detecting microbial colonies in pharma
- cancer diagnosis in healthcare
- quality control in manufacturing
- face detection in social media or photo editing applications
- driver assistance systems in automotive
- access control in security
- health and safety measures control in production
- optical character recognition in document management
- transforming the content for people with vision or hearing impairments.
One of our latest projects utilizing computer vision is the personal protective equipment control system built for a car manufacturer.
Natural Language Processing
NLP algorithms are designed to recognize, comprehend, and analyze human language, making it one of the most intricate branches of AI. This complexity arises from the inherent presence of exceptions and ambiguities in human language. Elements like irony and humor introduce additional challenges, and contextual nuances significantly influence the interpretation of words and sentences. Moreover, the multitude of languages, each governed by distinct rules, requires separate analysis.
NLP is used in business in:
- sentiment analysis in social media monitoring and market research
- customer profiling in retail, banking, or telecommunications
- semantic search in e-commerce, education, or human resources
- text mining and classification in spam filtering and fraud detection.
Predictive modeling is used to predict future events or their outcomes by analyzing past occurrences and current data. Algorithms are trained to uncover patterns and relationships that may not be apparent at first glance. Many calculations can occur in real-time, enabling prediction of behaviors and trends.
Predictive modeling finds applications in diverse scenarios:
- forecasting weather and road conditions
- anticipating energy consumption in the energy sector
- route optimization and maintenance needs prediction in transportation
- risk assessment and calculation in insurance and banking
- predicting employee turnover and identifying high-potential candidates in human resources
- creating TV ratings in entertainment
- analyzing customer behavior in marketing
- forecasting disease outbreaks and resource needed in healthcare
- predicting sales volumes in e-commerce
- forecasting guests and orders in hospitality.
How to start an AI project?
As evident from the business cases mentioned above, AI offers diverse benefits to businesses. However, selecting areas for optimization and initiating an AI project in your company can be challenging. Here are the recommended steps to guide you through the process.
Step 1: Diagnose and analyze the problem
If you’re considering using AI, there must be an identified problem, or you recognize the potential for doing things differently, more efficiently, or faster. At this point, you have to diagnose the challenge and validate your idea. You have to gather as much information as possible about the case, such as:
- existing solutions
- equipment used
- real-life scenarios and processes taking place
- real data obtained during the process.
Step 2: Create a feasibility study
Knowing the problem inside out, you can verify your initial assumptions and hypotheses. The most effective way to do it is by running a feasibility study.
A feasibility study is an assessment conducted to determine a solution’s practicality, viability, and potential success. It typically involves analyzing various economic, technical, legal, operational, and scheduling considerations. The aim is to evaluate whether the project is achievable and advisable.
In NeuroSYS, we can only propose a complete solution with time and cost estimates after a feasibility study.
Step 3: Divide the AI project into smaller chunks
When dealing with complex AI projects, the timeline can extend to several months, and various challenges may arise, particularly concerning data availability and quality. To effectively navigate such projects, we recommend breaking them into subprojects with defined timelines, deliverables, and cost estimates.
In the initial sprints, you can validate the core assumptions, allowing you to make an informed decision on whether to proceed further or conclude the project without significant investment.
This approach minimizes the risk and allows you to estimate the time and resources needed to build a complete solution.
The legal challenges
Finally, it’s crucial to bear in mind that the AI landscape remains relatively unregulated, and legal considerations can significantly impact your project at any stage of its development. Consequently, it’s essential to address legal challenges from the outset and approach them with due diligence.
The imperative nature of the situation is underscored by the announcement that the AI Act will regulate the use of artificial intelligence in the EU. According to the European Commission, this legislation is set to become the world’s first comprehensive AI law. You can find the most recent information on the AI Act in the EU’s entry updated December 2023.
The most important part applies to the systems that will be banned as they pose a threat to EU citizens, such as:
- cognitive behavioral manipulation of individuals
- social scoring: categorizing individuals based on their behavior, socioeconomic status, or personal traits
- biometric identification and categorization
- real-time and remote biometric identification systems.
Read more about the legal challenges of AI.
AI meaning in the current business landscape cannot be undervalued. Its applications are vast and can solve many problems faced by both organizations and people. What is AI technology exactly capable of? The time will undoubtedly reveal more astounding use cases that no one has dreamed of.
However, it’s crucial to remember that artificial intelligence projects are highly unpredictable and hazardous. That’s why we have outlined steps to help you start one while minimizing the associated risks. Even if you conduct the project in-house, it is always advisable to hire AI consultants before you start.