The three most common types of AI in use today – Transcript
In this video series, we’re looking at how AI is deployed across the enterprise and making the case for managing it as an asset. This isn’t just about cost and risk—it’s about harnessing innovation and enabling our organisations to move faster and more efficiently. In the previous video, we explored how AI adoption currently shows all the classic signs of IT sprawl: plenty of experimentation but limited foresight around return on investment and scant governance structures. Today, I’d like to examine which types of AI are actually in use.
According to ITAM Forum research, three main forms of AI stand out. Let’s look at them in ascending order of adoption.
The three most common types of AI
1. Predictive Analytics & Machine Learning (around 14–15%)
This involves using algorithms to anticipate future outcomes based on historical data—an approach applicable to everything from predicting faults on an assembly line to identifying emerging patterns in a supply chain. Consider the work done by Rentokil Initial: they’ve employed facial recognition technology for rodents, using video footage on client sites to proactively detect a rat infestation before it becomes a pressing issue. Rather than waiting for the next scheduled site visit, their team can intervene early, reducing disruption and improving the client’s overall experience. It’s this kind of proactive approach that predictive analytics makes possible, helping organisations move from reactive fire-fighting to future-focused prevention.
2. Natural Language Processing (NLP) (around 17%)
NLP capabilities allow systems to understand, interpret, and respond to human language. You’ll often see this in chatbots handling basic customer queries or providing HR guidance by parsing and referencing policy documents. Whether it’s assisting customers through support channels or automatically analysing legal contracts, NLP lets organisations engage with text-based data in a more meaningful, agile manner. Over time, this can streamline operations, reduce manual workloads, and help teams focus on higher-value tasks.
3. Generative AI (around 29–30%)
Generative AI—think ChatGPT, Copilot, and similar technologies—is currently enjoying the highest adoption rate. It’s used to create content, write code, and even generate entirely new visual and audio experiences. A compelling example is the fashion retailer Mango. They’ve combined photography of their garments with AI-generated models and backgrounds, effectively producing an entire marketing campaign without the need for traditional photoshoots. Gone are the logistics of models, photographers, and expensive locations. Instead, the design and launch of new campaigns can happen at remarkable speed. While some worry this might displace traditional roles, we must also recognise that new digital skill sets and positions will emerge as part of this evolution. The result is a supply chain for advertising that can adapt instantly to market demands, staying nimble and competitive.
These three use cases—predictive analytics and machine learning, natural language processing, and generative AI—illustrate the wide-ranging impacts AI is having across the enterprise.
In the next video…
In the next video, we’ll explore some of the barriers organisations face when implementing and experimenting with AI. Ultimately, understanding these constraints will help us design better governance frameworks and maximise the value AI can bring.