Understanding AI: A Simple & Detailed Guide

by Richard Farrell

In today’s rapidly evolving digital landscape, Artificial Intelligence (AI) is more than just a buzzword; it’s a transformative force reshaping industries and creating new opportunities, and the easiest way to scale up handling large amounts of complex historical data, fast.

For businesses seeking to achieve true digital transformation, understanding and strategically applying AI is no longer optional, but essential. This guide will demystify AI, explore its practical applications, and highlight how it can empower your organisation to thrive.

In this article, we’ll answer the following questions for you:

• So, what is AI?
• How does AI learn and work?
• Common AI terminologies
• AI and digital transformation: What can AI do for your business?
• Can you tailor machine learning to your business needs?
• What are the typical use cases for AI?
• The downside of AI
• How can we help your business?

So, what is AI?

AI utilises computers and machines to replicate intelligent human brain behaviour, enabling predictions, problem-solving, faster response times, and increased efficiency. This includes a wide range of abilities such as learning from experience, understanding natural language, recognising patterns, making decisions, and solving problems.

There are many different concepts and applications of AI, including machine learning (ML), deep learning, natural language processing (NLP), robotics, and generative AI. For applications, AI can be used from speech and image recognition, to automatic translation systems, self-driving cars, problem-solving, and many more.

In addition, AI, as it exists today, is quite different from the sensationalised visions often depicted. Present-day AI is more accurately described as a set of tools which excel at analysing large datasets, spotting patterns, and automating intricate tasks with efficiency and precision that far surpass human abilities, primarily focusing on addressing distinct challenges and enhancing human skills.

How does AI learn and work?

The magic of AI often lies in its ability to “learn”. Unlike traditional software that follows rigid, pre-programmed instructions, AI systems are designed to improve their performance as they are exposed to more data. This learning process is primarily achieved through a subset of AI known as Machine Learning (ML).

And from here, it is essential to note that AI and ML are distinct. Despite often being used interchangeably, ML is just a subset of AI, where machines learn from data to improve performance without being explicitly programmed. In short, AI is the goal, ML is the method.

ML involves training algorithms on large datasets. An algorithm is a set of rules or instructions that an AI system follows to perform a task. During training, the algorithm sifts through the data, looking for patterns, correlations, and relationships. The learning process of AI goes through 6 steps:

• Data Collection: AI systems require extensive data, from customer records, images, text documents, or product sales history.
• Training the Model: Using algorithms, the AI system is trained on this data. For instance, if you’re training an AI to recognise dogs in photos, it would be shown thousands of images labelled “dog” or “not dog.”
• Pattern Recognition: Over time, learning to recognise patterns adjusts and improves, identifying the characteristics that define a “dog” image, for example.
• Prediction or Decision-Making: Once trained, the AI can apply what it has learned to new data. That’s why AI for business can predict what a customer wants to buy next or detect potential fraud in a transaction.
• Continuous Learning: Modern AI often continues to learn and improve as it processes new data – this is known as adaptive learning.

Common AI terminologies

To understand AI, there are some other common terms that you need to know:

• Deep Learning: A type of machine learning using neural networks with many layers, often used in image and speech recognition.
• Natural Language Processing (NLP): Enables machines to understand and respond to human language.
• Generative AI: A category of AI that creates new content (text, images, audio, code, etc.) based on learned patterns. Examples: ChatGPT (text), DALL·E (images), Jukebox (music), Codex (code). This term is used when referring to the broader capability of AI to generate content across various formats.
• Large Language Models (LLMs): LLMs is a type of Generative AI specifically trained on vast amounts of text data to understand and generate human-like language. Focused on language tasks like chatting, summarising, translating, coding, etc. Examples: GPT-4, Claude, LLaMA, Gemini.
  
  In short, Generative AI is the ‘umbrella term’ and the LLM is one powerful tool under it, focused on language. In our Liberty Converse chatbots, Generative AI is the overall capacity that enables the bot to generate human-like conversations. The LLM is the specific engine doing the heavy lifting; it understands the user’s input, reasons about it, and generates appropriate responses.
• Chatbot: An AI-powered software application designed to simulate human conversation, typically via text or voice.
• Predictive Analytics: This means using AI and statistical algorithms to analyse historical data and predict future outcomes. For example, our Tenant Hub utilises predictive analytics, enabling you to identify at-risk tenants and proactively engage them with early warnings and tailored support.

AI and digital transformation: What can AI do for your business?

Integrating digital technologies to fundamentally rethink and reshape business operations, culture, and customer experiences defines digital transformation. AI acts as a key driver of digital transformation rather than a mere tool, unlocking previously unattainable capabilities.

AI can do a lot of things for your business:

• Automate repetitive tasks and processes: AI can take over mundane, repetitive, and rule-based tasks, freeing up your human workforce to focus on more strategic, creative, and value-added activities.
• Enhance operational efficiency: AI can identify bottlenecks, predict maintenance needs for machinery, optimise logistics routes, and manage inventory more effectively, leading to significant cost reductions and faster turnaround times.
• Improve decision-making with data-driven insights: AI can process and analyse vast amounts of data in real-time, uncovering patterns and insights that would be impossible for humans to detect. This leads to more informed, accurate, and timely decisions across all business functions.
• Personalise customer experiences: AI-powered tools can analyse customer behaviour, preferences, and historical interactions to deliver highly personalised experiences. This includes tailored product recommendations, customised marketing campaigns, proactive customer support, and intelligent chatbots for contact centres.
• Strengthen security and fraud detection: AI’s ability to detect anomalies and patterns makes it invaluable for identifying and preventing fraudulent activities and cyber threats in real-time. This is crucial for financial services, insurance, and any business handling sensitive data.

Can you tailor machine learning to your business needs?

The answer is yes! An AI/ML model is a complex set of algorithms or neural networks that you or I would struggle to understand, which reflect the behaviour of a human brain. You train your model using historical data to replicate a decision that an expert would make when provided with the same information.

There are many widely available ML models to solve complex data issues available on the Internet. Once trained, use the model to ‘reason’ over data that it hasn’t seen before to deliver a prediction, resulting in smarter decision-making.

The prediction generated can then be returned to an application, process flow or third-party system through an API interface to determine the next step in the process.

Making AI available to all

This is now being turned on its head. The technology is more widely available and much easier to use. The power of AI is being given to business users to understand and solve data problems, addressing many more organisational needs.

People can build and train their machine learning models using their own data to predict future outcomes and act on them. Ideally, all directly from within your applications and process flows. With generative AI and LLMs, you can now rapidly build and train sophisticated machine learning models using your own data to predict future outcomes and drive immediate action. A secure platform approach from Netcall makes this accessible directly within your existing applications and workflows, ensuring your AI initiatives are both fast and safe.

While AI is available to all, the approach for each is different. Read our blog about AI Innovation vs Intervention to know your answer to whether you are innovating, or are you just caught up in the purgatory of inventing something “new”.

What are the typical use cases for AI?

The use cases of AI are endless, from predicting the likelihood of a customer or patient missing an appointment to maximising appointment attendance, to improving the Tenant Satisfaction Measure, so that you can offer support earlier.

For Customer Service & Experience:

• Chatbots and virtual assistants provide 24/7 instant support and resolve routine issues.
• Personalised recommendations for products, services, or content based on individual preferences and past behaviour.

For Operations & Efficiency:

• Process automation (RPA & Intelligent Automation) automates repetitive, rule-based tasks in finance, HR, legal, and other departments (e.g., data entry, invoice processing, report generation). Explore how automation helps insurers revolutionise their claims processes more: An insurance claims process fit for Gen-Z – 3 tips
• Predictive maintenance involves predicting equipment failures before they occur, enabling proactive maintenance and reducing downtime.

For Finance & Risk Management:

• AI can identify unusual transaction patterns that indicate fraudulent activity in banking and insurance. It is also involved in risk assessment by analysing vast datasets to assess creditworthiness and predict loan default risk.

For Public Sector & Local Government:

• An AI-powered platform like Netcall’s Liberty for Local Government can help local authorities reduce licence costs, improve citizen experiences, remove data silo and enhance staff productivity.
• Authorities can also optimise public services, from waste collection routes to emergency response planning with the help of AI.

The downsides of AI

• Bias and discrimination
• Ethical concerns and lack of transparency
• Privacy concerns
• Security risks
• Over-reliance and loss of human skills

A responsible approach to AI involves understanding and mitigating these risks.

How can we help your business?

Our Liberty platform is infused with business-ready AI, putting safe and simple-to-use capabilities in your hands. So you can accelerate workflows and energise engagement. Utilise AI to deliver immediate benefits across your entire organisation and create an AI strategy that gives you a competitive edge.

We understand that embracing AI doesn’t have to be complex or daunting. Our focus is on delivering practical, ethical, and impactful AI solutions that directly address your business needs.

We don’t just provide technology; we empower you to build a cohesive AI strategy. Our range of platforms, from low-code applications and contact centre solutions to intelligent document processing and process mapping and improvement tools, has the capabilities to integrate AI intelligently, leveraging its power to gain a competitive advantage, innovate faster, and meet the evolving demands of your market.

Netcall’s Liberty puts safe and simple-to-use AI in your hands, ensuring you can harness its power responsibly and effectively for sustainable growth.

To learn more about our approach to artificial intelligence and discover how a high-impact Enterprise AI Platform can benefit your organisation, contact us for a demo today.