What is Applied Artificial Intelligence? Definition, Key Concepts, and Tech

Applied AI refers to the practical use of artificial intelligence in order to solve specific business challenges. By integrating Applied AI systems into their operations, businesses can gain a competitive edge through automating repetitive tasks, getting data-driven insights, and delivering personalized customer interactions.

In this article, we explore the benefits and key concepts of Applied AI, explain how to effectively implement an Applied AI project in an organization, and provide insights into the practical applications of AI across various industries, showcasing real-world use cases and success stories.

At Dexola, we have extensive experience developing Applied AI solutions tailored to our clients’ needs, from Web3 solutions to crisis relief projects, which we describe in more detail below. 

An interface of the Norway Assistant chatbot developed by Dexola.

Highlights:

1. Applied AI transforms industries with practical applications in finance, healthcare, retail, and other sectors.
2. Applied AI has gone through pivotal milestones, from Alan Turing’s early theories to advanced AI systems, shaping today’s technology landscape.
3. Successful AI project implementation implies a strategic approach to defining AI project goals and scope.
4. Integrating AI with quantum computing, IoT, and blockchain will lead to innovations in personalized medicine, smart cities, autonomous transportation, and sustainable energy management.

Building Blocks of Applied AI: Key Concepts and Technologies

Applied Artificial Intelligence (AI) is the practical implementation of AI technologies. Applied AI focuses on creating functional applications that solve real-world problems across various industries.

Understanding of Applied Artificial Intelligence involves concepts such as machine learning (ML), deep learning, natural language processing (NLP), computer vision, and robotics.

Below is an overview of these key technologies that form the basis of Applied AI solutions:

Technology	Description	Applications
Machine learning (ML)	Algorithms that enable systems to make predictions or decisions based on data without being explicitly programmed.	Predictive analytics, recommendation platforms, anomaly detection
Deep learning	A subset of ML that uses neural networks with many layers (deep networks) to model complex patterns and representations in data.	Image and speech recognition, natural language processing, autonomous driving
Natural language processing (NLP)	Techniques that allow machines to understand, interpret, and respond to human language.	Chatbots, sentiment analysis, language translation, text summarization
Computer vision	Methods that enable computers to interpret and make decisions based on visual data from the world.	Object detection, facial recognition, medical image analysis, automated inspection systems
Robotics	Integration of AI with physical machines to perform tasks that typically require human intervention.	Assembly line automation, medical surgeries, autonomous robots, warehouse management

Principles of Applied AI

Applied AI operates on a set of core principles that guide its development and deployment. Understanding these principles makes it easier to grasp the Applied AI meaning. 

Here are the key principles of Applied AI:

1. Data-driven decision-making. The cornerstone of applied AI is its reliance on data. AI systems analyze large datasets to extract insights and make informed decisions.
2. Scalability. Applied AI solutions must handle increasing amounts of data and complex tasks. Scalability ensures that AI systems grow with organizational needs.
3. Adaptability. AI systems must be adaptable to new data and changing environments. This involves continuous learning and updating models to maintain their accuracy and relevance.
4. Transparency and explainability. AI decision-making processes should be understandable to humans, building trust in critical applications.
5. Ethical considerations. AI development must adhere to ethical guidelines to ensure fairness, privacy, and accountability.
6. Interdisciplinary collaboration. Successful Applied AI projects often require collaboration with specialists across various disciplines. They include data science, domain, and business strategy experts and Applied AI engineers.
7. User-centric design. Applied AI solutions should be designed with the end user in mind. AI solutions should be intuitive, address user needs, and enhance adoption and effectiveness.
8. Performance and efficiency. AI systems should be optimized for performance and efficiency. This means they should deliver accurate results quickly and with minimal resource consumption.
9. Continuous improvement. Applied AI is an iterative process. This involves continuous improvement and refinement of models and algorithms.
10. Compliance and security. AI systems must comply with data protection laws and regulations. Security measures should also be put in place to prevent unauthorized access.

Why Businesses Switch to Applied AI

Applied AI has plenty of benefits for business. Let’s look at some of them in detail:

Enhanced efficiency and productivity

AI can automate mundane and repetitive tasks. This frees teams for strategic activities and leads to increased productivity and efficiency. AI can also identify bottlenecks in processes, streamlining operations and reducing waste.

Improved decision-making

AI processes vast amounts of data to uncover patterns and trends. This provides valuable insights for informed decisions. AI forecasts future trends and outcomes based on historical data, enabling proactive decision-making.

Cost reduction

Automation and process optimization reduce operational costs, minimizing manual labor and errors. AI can distribute resources to reduce overhead costs and improve operational efficiency.

Enhanced customer experience

AI analyzes customer data to provide personalized recommendations and services. This increases customer satisfaction and loyalty.

Innovation and competitive advantage

AI helps businesses identify new market opportunities. AI adoption enables companies to develop innovative products to meet evolving customer needs.

Scalability

AI solutions scale with business growth. They can manage increasing data volumes and complex tasks without proportional cost increases. AI can also assist in global expansion by providing insights into new markets.

Enhanced accuracy and quality

AI systems can execute tasks with high accuracy and consistency. This reduces errors and improves output quality. AI-powered inspection systems can detect product defects and anomalies, ensuring high-quality control standards.

Risk management

AI analyzes transaction patterns to detect and prevent fraud in real-time. This protects businesses from financial losses. AI also enhances cybersecurity by detecting and responding to threats faster.

Practical Applications of Applied AI in Various Industries

Applied AI is transforming industries by introducing innovative solutions. These solutions enhance efficiency, accuracy, and customer experience.

The table below shows specific use cases of applied AI in different sectors, its business impact, and Applied AI examples.

Industry	Use Case	Business Impact	Examples
Finance	Fraud detection	Reduces financial losses and increases security	AI algorithms analyzing transaction patterns to detect fraud
	Algorithmic trading	Enhances trading efficiency and profitability	AI systems executing trades based on market data
	Customer service	Improves customer support and reduces costs	Chatbots handling customer inquiries
	Credit scoring	Increases accuracy in creditworthiness assessment	AI models evaluating credit risk
Healthcare	Diagnostic imaging	Enhances diagnostic accuracy and speed	AI interpreting medical images
	Personalized medicine	Tailors treatments to individual patient needs	AI analyzing genetic and clinical data
	Patient monitoring	Improves patient care and early intervention	Wearable devices with AI monitoring vitals
	Administrative workflow automation	Increases operational efficiency	AI automating billing and scheduling tasks
Retail	Demand forecasting	Optimizes inventory and reduces stockouts	AI predicting product demand
	Inventory management	Enhances stock control and reduces waste	AI optimizing stock levels
	Customer recommendations	Increases sales and customer satisfaction	AI suggesting products based on browsing history
	Personalized marketing	Improves marketing effectiveness	AI creating targeted marketing campaigns
Manufacturing	Predictive maintenance	Reduces downtime and maintenance costs	AI predicting equipment failures
	Quality control	Ensures high product quality and compliance	AI inspecting products for defects
	Supply chain optimization	Enhances supply chain efficiency	AI optimizing logistics and inventory flow
	Robotic process automation	Increases production efficiency	AI-powered robots handling assembly tasks
Transportation	Route optimization	Reduces delivery times and fuel consumption	AI optimizing delivery routes
	Autonomous vehicles	Improves safety and operational efficiency	Self-driving cars and trucks
	Predictive maintenance	Reduces vehicle downtime and maintenance costs	AI predicting maintenance needs for vehicles
	Traffic management	Reduces congestion and improves traffic flow	AI controlling traffic signals and monitoring traffic patterns

A Brief History of Applied AI

The timeline below highlights the key events and breakthroughs that have shaped the field of Applied AI.

1950s: The Birth of AI

• 1950. Alan Turing introduces the concept of machine intelligence in his seminal paper “Computing Machinery and Intelligence”.
• 1955. A team of young researchers coin the term “Artificial Intelligence” at a Dartmouth College summer workshop. This event is considered the birth of AI as an academic discipline.

1960s: Early developments

• 1961. General Motors introduced and deployed the first industrial robot, Unimate. This event marked the beginning of AI in manufacturing.
• 1964. Joseph Weizenbaum developed ELIZA. This early natural language processing program could simulate a conversation with a human. Eliza laid the groundwork for future chatbots.

1970s: Expansion and recession

• 1972. The logic programming language PROLOG was developed. It became a key tool for AI research and applications.
• 1974-1980. The first “AI winter” occurs, a period of reduced funding and interest in AI due to unmet expectations and slow progress.

1980s: Expert systems and renewed interest

• 1980s. The introduction of expert systems brings AI into practical applications in various industries. MYCIN for medical diagnosis is one of the examples.
• 1987-1993. The second “AI winter” takes place. It marked another decline in AI research funding and commercial interest.

1990s: Machine learning and data mining

• 1997. IBM’s Deep Blue defeats world chess champion Garry Kasparov. This showcased AI’s capability to solve complex problems.
• 1998. The launch of the first recommendation systems. For example, Amazon applied AI to personalize customer experiences.

2000s: Big data and AI renaissance

• 2006. Geoffrey Hinton, Simon Osindero, and Yee-Whye Teh published a paper on deep learning. This revitalized the interest in neural networks.

2010s: AI goes mainstream

• 2011. IBM’s Watson DeepQA computer wins the game show Jeopardy!, showcasing advancements in natural language processing and machine learning.
• 2012. AlexNet, a deep-layered convolutional neural network, wins the ImageNet competition, significantly improving image recognition capabilities.
• 2017. Google’s DeepMind demonstrates AI’s potential with the success of its computer Go program AlphaGo, which defeated a professional Go player.

2020s: Applied artificial intelligence expands into everyday life

• 2020. AI has become integral to various applications. These include autonomous vehicles, virtual assistants (like Siri and Alexa), and healthcare diagnostics.
• 2022. OpenAI’s ChatGPT and similar models showcase the practical application of AI in conversational agents, content generation, and more.

Today, applied AI has a significant economic impact. The global AI market is projected to reach $1,811.75 billion by 2030, according to a report by Grand View Research. Applied AI is expected to continue its rapid growth at a forecasted CAGR of 36.6% from 2024 to 2030.

Applied AI Development Scope

It is crucial to define the scope and goals of an applied intelligence project for its success. This process involves several key steps:

1. Identify the problem or opportunity

Identify the specific problem or opportunity the AI solution aims to address. Understand current challenges like inefficiencies, high costs, or unmet customer needs. Clearly articulate the problem to ensure the AI project aligns with business objectives and delivers value.

2. Define project objectives and success metrics

Once you define the problem or opportunity, the next step is to set clear objectives for the AI project. Objectives should be specific, measurable, achievable, relevant, and time-bound (SMART). 

Examples of possible business objectives:

• Improved customer service.
• Increased sales.
• Operational efficiency.
• Fraud detection.
• Predictive maintenance.

Success metrics might include improved efficiency, cost savings, and enhanced customer satisfaction. These metrics provide a basis for assessing if an AI solution meets the desired outcomes.

3. Assess data requirements and availability

Identify the types of data needed, such as structured or unstructured data. Assess the availability and quality of existing data sources. Ensure the data is relevant, accurate, and sufficient for training AI models.

4. Determine technical feasibility and resources

Assess available technologies and tools to define technical feasibility. Consider required computational resources like hardware and software infrastructure. Determine the expertise needed for development and implementation.

5. Define project deliverables

Specify tangible outcomes or outputs the project will produce. Examples include prototypes, data models, analytical reports, or a fully operational AI system. This ensures stakeholders understand the project’s goals and provides benchmarks for success.

6. Establish a roadmap and timeline

Outline the key phases of the project. The roadmap should include data collection, model development, testing, and deployment. Set realistic milestones and deadlines to track progress. Review and adjust the roadmap regularly to address challenges or changes in scope.

7. Engage stakeholders and define roles

Engage stakeholders throughout the project. Stakeholders may include business leaders, domain experts, IT staff, and end users. Clearly define roles and responsibilities to coordinate efforts and manage expectations. Focus on effective communication to align the AI solution with organizational goals.

At Dexola, we engineer advanced solutions for products and enterprises using sophisticated large language models from OpenAI, Anthropic, and Meta. This paradigm not only optimizes and accelerates workflows but also manages previously unsolved problems, boosts operational bandwidth, and ultimately drives revenue growth. Our bespoke LLM services enable clients to harness state-of-the-art technology for significant gains in efficiency and profitability.

Use Cases of Applied AI: Dexola’s Experience 

Below, we delve into Applied AI examples from a few industries to show how real-world Applied AI use cases can address specific problems and offer innovative solutions.

Web3 and Blockchain: Smart Contract Analysis

Problem. Blockchain and Web3 ecosystems face challenges in ensuring the security and reliability of smart contracts.

Solution. AI can analyze smart contracts to identify vulnerabilities and predict potential security risks. Machine learning algorithms can also monitor blockchain transactions to detect and prevent fraud.

Use case. An AI system developed by a blockchain security company analyzes smart contracts for vulnerabilities before deployment. This leads to reduced risk of exploits.

NFT Market: Market Analysis and Pricing

Problem. Understanding market trends and accurately pricing NFTs can be challenging due to the volatility and uniqueness of the NFT market.

Solution. AI models can provide insights into NFT pricing and market behavior.

Use case. An NFT marketplace uses AI to analyze historical sales data and market trends, providing artists and collectors with pricing recommendations for NFTs and other digital assets.

Social Impact: Crisis response and relief

Problem. It is challenging to provide timely information and support to large populations, especially in underserved or crisis-stricken areas.

Solution. AI-driven chatbots can provide information and resources to individuals in need. They can be programmed to handle various inquiries, offer mental health support, and connect users with essential services.

Use case. Dexola developed a chatbot that guides Ukrainian refugees in Norway by providing real-time assistance with paperwork, housing, and other essential services. The chatbot helps refugees navigate the challenges of settling in a new country by offering vital information from credible sources to ease their adaptation.

Final Words

The transformative power of AI is evident in real-world applications that solve complex problems and improve operational outcomes. By adopting applied artificial intelligence, businesses can stay ahead in a competitive landscape.

The continued development and integration of AI will drive further innovation and transformation across all sectors of society. Applied AI will play a critical role in areas such as personalized medicine, smart cities, autonomous transportation, and sustainable energy management.

Looking further ahead, the integration of AI with other emerging technologies, such as quantum computing, Internet of Things (IoT), and blockchain, will likely unlock new possibilities. 

If you need a detailed explanation of applied AI and how it can benefit your organization, contact our team at Dexola. Dexola has a track record in developing tailored AI solutions. We’ll help leverage AI to achieve your business objectives.