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May 8
Sand Technologies
AI has been a part of enterprise technology for decades, quietly driving automation, analytics and prediction behind the scenes. Its roots stretch back to 1956, when researchers first explored the idea of machines that could mimic human intelligence. Since then, it has primarily worked in the background, solving well-defined problems with data.
With the rise of Generative AI, AI’s role has shifted from a behind-the-scenes engine to a more visible, creative force sparking new conversations across industries. Built on decades of progress, GenAI has rapidly been expanding what’s possible when it comes to generating original content and not just analyzing existing data.
Yet, as its influence grows, so does the confusion. Despite their fundamental differences, many still use “AI” and “GenAI” interchangeably. Understanding these distinctions is key, as they have real implications for businesses, industries and how we think about intelligence itself.
Since its inception, artificial intelligence has evolved into a multi-billion-dollar industry, projected to reach US$244.22 billion by 2025. Today, AI is embedded across nearly every sector — from insurance to water utilities — driving transformation, efficiency and innovation at scale. But what exactly is AI?
AI is technology that enables machines to make sense of the world, not just follow instructions, but learn, adapt and respond. Unlike traditional programs, AI systems can spot patterns, adjust to new information and improve over time without being explicitly told how to do everything.
At its core, AI is built on algorithms trained on large datasets. These algorithms take in vast amounts of information, find patterns and use those patterns to make predictions or take action. They can also mimic aspects of human thinking, like recognizing faces in photos, translating languages or predicting trends in sales.
What makes AI powerful isn’t just speed; it’s adaptability. With the correct data and design, an AI system can become a powerful tool for solving complex problems, enhancing productivity and unlocking new ways of working across industries.
Generative AI is a form of artificial intelligence that focuses on creating new, original content. While traditional AI is typically used for tasks like classification or prediction, Gen AI takes data and produces new outputs, such as text, images, music or even video.
These systems operate by learning from vast datasets and understanding their patterns. Once trained, they can generate new examples that resemble the data they’ve learned from but are unique in their own right. Popular models like Generative Adversarial Networks (GANs) and large language models, such as GPT, are examples of generative AI that can create everything from realistic images to coherent, human-like text.
Gen is already driving transformation across industries. Over 71% of companies worldwide use GenAI in at least one function. In entertainment, Gen AI is helping scriptwriters brainstorm ideas or even write dialogue. GenAI is a tool that not only automates but also inspires creativity, opening up new possibilities for innovation across sectors.
Traditional AI is trained through two primary methods: supervised and unsupervised learning. In supervised learning, the system learns from already categorized data, identifying patterns between inputs and their corresponding outputs. In contrast, unsupervised learning focuses on uncovering patterns within data that isn’t labeled, often using techniques like clustering or detecting anomalies.
Generative AI takes a different path, relying heavily on Generative Adversarial Networks (GANs). A GAN has two neural networks: the generator, which creates content, and the discriminator, which evaluates its authenticity. It is a constant back-and-forth, refining the output until the system can generate realistic, creative results. It is a unique blend of competition and collaboration that drives innovation and creativity.
Not all AI systems are created equal. Traditional AI typically excels at tasks that involve classification, prediction or optimization. It’s the engine behind systems that recognize moments in videos, forecast trends or recommend products based on past behaviors. While these outputs are incredibly useful, they are typically confined to structured, predefined tasks.
On the other hand, Gen AI goes beyond analysis and begins to make way for creation. Whether crafting compelling text, generating lifelike images or composing original music, generative AI can create something entirely new.
But its impact isn’t limited to creative industries; Gen AI is also revolutionizing customer service. By generating personalized responses, automating interactions and even creating custom solutions on the fly, it’s enhancing the customer experience in ways traditional AI can’t match.
When it comes to data, traditional AI and generative AI use it in very distinct ways. Traditional AI relies on data to learn patterns, predict outcomes and make decisions based on existing information. AI functionalities such as predictive analytics are highly efficient at analyzing past data to provide insights and make recommendations. For example, predictive analytics is used in healthcare to identify and predict public health trends.
Gen AI pushes beyond analysis by using data to produce new content from lifelike artwork to functional code. Its ability to create depends heavily on the data it learns from, which makes thoughtful data selection and curation essential. The richer and more diverse the training data, the more refined, creative and accurate the outputs tend to be.
Even as technology rapidly advances, one thing remains constant: AI and Gen AI still depend on human oversight in different ways. Traditional AI often requires humans to provide labeled data and fine-tune the system’s performance as it learns. Even after deployment, human input ensures that the AI remains aligned with business objectives and ethical guidelines.
On the contrary, Gen AI places a unique emphasis on human direction. While the technology can produce content independently, humans still play a crucial role in guiding the creative process. For instance, a writer may provide the initial input, but their direction shapes the final result. Beyond creativity, human oversight also helps mitigate risks and ensures the generated content aligns with brand standards and societal expectations.
Traditional AI is a workhorse. It’s embedded in systems that run quietly but efficiently, often powering our tools without our noticing. One of the most common examples is recommendation engines. Whether browsing a streaming platform or an online store, traditional AI helps surface the content or products you’ll most likely engage with.
In insurance and finance, AI algorithms analyze patterns to detect unusual transactions in real time, flagging potential fraud before it escalates. Studies show that AI-powered fraud prevention tools have led to a 40% increase in detection accuracy for businesses.
Across the water sector, utilities are turning to AI and IoT to stay ahead of system failures. By using real-time data to detect leaks early, prevent outages and optimize water distribution, these utilities can save up to £1.3 million annually and ensure they can offer their customers more service reliability.
In the energy sector, AI and precision analytics enable city planners to pinpoint optimal sites for expanding EV charging infrastructure. By combining machine learning with geospatial insights and zoning data, these solutions help design charging networks that serve users efficiently while keeping capital costs in check.
AI’s applications are broad and cross-industry. But the goal is consistent across all these examples: streamline decision-making, reduce inefficiencies and make systems smarter and faster.
In the education sector, Gen AI transforms customer service by automating inquiries, cutting costs and enhancing response times, all while reducing escalations and improving CSAT scores. One edtech provider, for example, successfully used Gen AI to resolve more than 80% of queries without human intervention, setting the stage for scalable growth.
The telecom industry has been increasingly using Gen AI to enhance customer experiences and improve network planning and operations. For example, Telkomsel, one of the world’s largest mobile operators, is leveraging Gen AI to forecast network demand and help operators optimize site deployments.
Another great example can be seen in the healthcare industry, where governments and medical professionals are using Gen AI to improve patient care and expand access to quality healthcare. Irrespective of the sector, Gen AI’s value lies in its ability to automate complex tasks, unlock new forms of creativity, and generate insights at scale.
Both traditional and Gen AI are transforming the way organizations operate. Together, they help businesses make faster decisions, work more efficiently, lower costs and deliver more value to customers.
Still, both technologies face limitations. Both types of AI rely heavily on high-quality, representative data. Without it, outputs can be biased, inaccurate or misleading. Gen AI, in particular, raises concerns around transparency, ethical use and intellectual property. It’s also important to note that scaling AI across an organization requires the proper infrastructure, transparent governance and skilled oversight.
Despite current challenges, the future of AI is full of possibilities. The question now is whether traditional and generative AI will continue to serve distinct roles or eventually converge into more general systems. We’ll likely see a mix of coexistence where each is applied to its strengths and continued innovation toward more flexible, unified models.
To stay ahead, businesses must lay strong foundations: quality data, clear governance, and active human oversight. Whether leveraging traditional AI, generative AI, or both, aligning technology with real-world goals while prioritizing innovation and responsibility is key.
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