AI without the hype

Your guide to implementing AI

AI 101: What you need to know before bringing generative and traditional AI into business operations

A clear definition

AI in business demystified

Artificial Intelligence (AI) is the development of computer systems that can perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and language translation or generation. AI enables machines to learn from experience, adapt to new inputs, and perform human-like tasks with remarkable accuracy and efficiency

For business and industry

Key AI concepts and terms

Machine Learning

Machine Learning refers to algorithms that learn from data to make predictions or decisions without being explicitly programmed. ML enables systems to improve their performance on a specific task with experience.

Deep Learning

Deep Learning is a subset of machine learning that uses neural networks to model complex patterns in data. It powers breakthrough applications in machine vision, language, and more.

Natural Language Processing

Natural Language Processing is an AI technology that enables computers to understand, interpret, and generate human language. NLP is behind apps like chatbots, sentiment analysis, and machine translation.

Computer Vision

Computer Vision is an AI capability that allows computers to interpret and understand visual information from the world. CV enables applications like facial recognition, object detection, and autonomous vehicles.

The current state

AI technology for businesses across industries

Artificial intelligence and generative AI are transforming industries by automating processes, providing insights and enhancing customer experiences.

Across industries, AI is driving efficiencies, reducing costs and unlocking new opportunities for growth and innovation. In finance, AI is being used to detect fraud and make investment decisions. Manufacturing is using AI for predictive maintenance and quality control. The insurance industry is using AI for risk assessment and claims handling.

Generative AI: The general purpose AI

Generative AI

Understanding the business case

Generative AI is a type of AI that creates new content, such as images, text, code or audio, that resembles the data on which it was trained. It learns the underlying patterns in the training data to produce novel, realistic output that is often indistinguishable from human-generated content. This technology has the potential to revolutionise content creation, design, and problem-solving across all industries.

The difference

Generative AI vs. traditional AI in industry

Traditional AI focuses on analysing existing data for predictions, classification, or pattern recognition. It excels at tasks such as fraud detection, image classification, and predictive maintenance. Traditional AI is deterministic, meaning it does not produce new content.

In contrast, Generative AI creates new content based on learned patterns from the training data. It can generate images, text, music, and more, opening up new possibilities for content creation, problem-solving, and personalisation. Alternative to Traditional AI, its non-deterministic. This means the AI can produce different outputs even when given the same input multiple times, resulting in unpredictable outcomes.

When deciding which one to use, choose traditional AI for tasks like prediction, classification, and anomaly detection, whilst Generative AI is better suited for content creation, design, and personalisation.

General Purpose Technology

How Generative AI can impact every aspect of business operations

In AI terms, GPT, as in ChatGPT, stands for "Generative Pre-trained Transformer". But it has a second meaning in a broader sense: "General Purpose Technology". GPTs are technologies that can profoundly impact and transform entire industries and economies. Generative AI, as a GPT, has the potential to revolutionise every aspect of work within an organisation, beyond specific tasks or departments.

Putting traditional and generative AI to work for your business

Generative AI can be used to generate product designs and prototypes, create personalised content for marketing and customer engagement, and develop virtual assistants and chatbots with human-like responses.

Streamline business processes with AI automation

AI can automate repetitive tasks, such as data entry, document processing, and customer support, and various tasks from software development life cycle, improving efficiency and reducing errors. By automating these processes, businesses can improve efficiency, reduce errors, and free up employees to focus on higher-value tasks.

Predictive analytics for the industrial sector

Predictive analytics powered by AI crunches historical data to predict future trends, helping businesses make informed decisions about inventory, pricing, and resource allocation. AI also detects patterns and anomalies in large data sets, uncovering insights and potential problems that humans might miss.

Anomaly detection for the industrial sector

AI can quickly spot anomalies and outliers in large data sets, revealing potential issues or opportunities that might otherwise go unnoticed. This is particularly valuable in fraud detection, quality control and cybersecurity. By identifying suspicious transactions, product defects or network intrusions in real time, AI helps organisations mitigate risk and maintain a competitive edge.

Personalised marketing with AI: increasing engagement and conversion rates

AI analyses customer data to deliver targeted, personalised marketing messages, increasing engagement, conversion rates and loyalty. AI-powered recommendation engines suggest products based on individual preferences, while dynamic content optimisation ensures customers receive the most relevant content.

Intelligent customer support: AI chatbots and virtual assistants

AI chatbots and virtual assistants provide 24/7 support, answering questions, guiding users and processing transactions. They understand natural language, learn from interactions and provide human-like responses, improving satisfaction and reducing costs. For complex issues, AI routes customers to the most appropriate human agent.

Strengthen security and prevent fraud with business-proven AI

AI monitors transactions in real time, identifying suspicious activity and potential fraud to protect businesses and customers. In cybersecurity, AI can analyse network traffic, detect malware and identify potential vulnerabilities, enabling proactive threat mitigation.

Utilising AI technology for object identification and image classification

AI automates visual inspection tasks, such as detecting defects in manufacturing or analysing medical images, improving accuracy and efficiency. This technology can be applied to quality control, inventory management, remote monitoring, and more, reducing costs and improving outcomes.

Harnessing generative AI for internal research and knowledge sharing

AI-powered tools are transforming the way organisations collect, access and share information internally. These systems quickly analyse vast amounts of unstructured data, extract key insights and make them easily accessible to employees. This saves time, promotes knowledge sharing across departments, and ultimately boosts productivity.

„Artificial intelligence is the next phase of the digital revolution. We have helped several organisations harness the power of artificial intelligence to become more efficient, productive and insightful. Our proprietary building blocks accelerate deployment of a practical solution that delivers quantifiable benefits.“

Ignasi Barri Vilardell

Director of digital strategy, Global business development

GFT

Implementing AI in business

A practical 4-step approach

Aligning AI with your business objectives

Start with your day-to-day processes and identify bottlenecks or areas for improvement. Find specific business problems or opportunities that AI can address, playing to the technology's strengths.

Establishing the necessary AI technology infrastructure and data foundation

Every AI strategy needs a data strategy. AI requires high-quality, relevant data, so it's crucial to develop a data strategy that covers data collection, storage, governance, and safety. Ensure that data is accurate, consistent, and accessible to the right teams and systems.

Tackling data privacy and security challenges with AI technology

Because AI relies on vast amounts of data, it is critical to address privacy and security concerns. Ensure that AI systems comply with relevant data protection regulations, such as GDPR or CCPA, and implement robust security measures to protect sensitive information.

Creating an AI-friendly organisational culture

Foster a culture of innovation and continuous learning, encouraging employees to embrace AI and develop the necessary skills through training programmes, workshops and hands-on projects. Encourage cross-functional collaboration to ensure AI initiatives are aligned with business goals.

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