How Businesses Use AI

Bold Predictions

It’s the time of year when people make bold predictions about how AI is going to change the world. Optimists champion designer proteins that fight diseases, robots that clean oceans and visualisation to cure blindness. But what are businesses actually using AI to do?

We have a lot of data – equivalent to 800,000 libraries of Congress by 2027, according to IDC Global Datasphere. Over 80% of it is unstructured and half is either audio or visual. Hence the focus on multimodal AI, in which inputs and outputs are from a variety of media. Think text-to-speech, speech-to-audio and audio-to-video. Yet regardless of source, extracting information from data and applying it productively is the core of all AI use cases.

Common Workflows

30% of all data generated is in the healthcare sector. As a result, both AI hardware and software providers are focused on delivering services to the industry. These support medical imaging and devices, drug discovery, genomics and digital health. That last category includes customer facing technology, records, and deciphering technical papers.

Other industries at the forefront of AI adoption are automotive, energy, financial services, higher education, manufacturing, retail and telecommunications. While autonomous vehicles grab the headlines in automotive, AI is as prevalent for natural language processing in customer service, demand prediction and process efficiency. Generative AI enables synthetic data and digital twins to accelerate and innovate vehicle design.

As we look across industries we see similar workflows emerging. In energy, AI simulates geological formations to predict the presence of fossil fuels, optimises drilling and predicts maintenance. Prediction plays an important role in financial services in loss estimation and fraud prevention, and in manufacturing it helps control inventory and monitor machinery. Prediction creates a wealth of cost efficiencies and demand generation opportunities, without threatening a large number of existing jobs.

This is one reason why only 10% of enterprises responding to an a16z survey said that cost saving is their top priority when using AI. Simulation, prediction, personalisation, report summarisation and customer assistance are workflows common to most industries.

More of the Same

For all the science fiction outcomes that grab the headlines, a standard list of AI workflows includes virtual assistants and chatbots, knowledgeable co-pilots, code review and generation, content generation, data analysis and reporting, and language translation. Of those chatbots, co-pilots and code generation are most common.

Chatbots use pre-trained models to answer general queries. While the aforementioned a16z survey revealed that the majority of enterprises are using three or more models, over two-thirds of large language models in production are based on OpenAI.

At the end of 2024, OpenAI revealed that its reasoning models perform at the upper levels of human achievement in coding and PhD exams. Reasoning requires large amounts of computational power and energy, and happens during inference, pushing the cost onto end users. This is a path to profitability for OpenAI, but may limit early access to deep-pocketed enterprises.

To make money from AI, businesses need to bring their intellectual property to bear. This means training and running models on proprietary systems. This is where we transition from chatbot to co-pilot, with the latter being an assistant performing tasks in a specific domain.

GitHub has a successful co-pilot, which Microsoft is attempting to replicate in Excel. Construction companies train models to provide on-demand health and safety education, while retail stores generate avatars to guide customers with predictive analytics to optimise the buying journey. Co-pilots squeeze more from existing technologies and the trend is to focus on new opportunities rather than cost cutting.

Predict, Create and Serve

The most common use cases for generative AI are chatbots, co-pilots and code generation. The aim is to increase the quantity and speed of workloads, generating demand with superior and personal service, and design new products through simulation and testing. The intention is that staff without technical training will be able to use AI models to test and create products, predict and generate demand, and serve and maintain customers.

Yet this view of AI as an accelerator for what we already do today is limiting. While it makes sense that enterprises would look to do more for less with new technologies, the emerging enterprises of tomorrow will use AI to reinvent industries. We’ll look at examples of how this may unfold next week.

Questions to Ask and Answer

1. Is my business making the most of in-house data?

2. Would sales and support benefit from personalised client information?

3. Can prediction improve cash flow, inventory control and demand generation?

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