AI – A Catalyst for Global Innovation

Artificial Intelligence (AI) is no longer the stuff of science fiction. Built into mobile phones, other devices and many different types of software applications, today it is an intrinsic part of our lives and the organisations that we work for across industries and sectors. AI is giving us new capabilities, and sometimes solutions to problems that we did not even know we had. The question is — why is AI such a boon to innovation?

In this article I quote extensively from the AI for Innovation chapter of my book The Autonomous Enterprise – Powered by AI, published by BCS. In the book, I write that AI is a general purpose technology that ranks alongside the advent of the digital computer. This has led to the expectation that soon it will impact many aspects of our business and personal lives to the same degree as the personal computer, mobile phone or web. This pace of development and adoption is being boosted by four major drivers:

• The capital markets: these have been alive to AI’s capabilities, so funds have been diverted to drive further research. Goldman Sachs forecasts that AI investment will approach $200 billion globally by 2025
• Developments: advances in deep learning have led to some eye-catching inventions like autonomous cars. More recently, Large Language Models (LLMs), like ChatGPT, have become widely accessible. These developments connect with a broad population of opinion formers
• An invention that is a method of invention: AI is a tool for innovation in its own right, so it is difficult to dismiss or pigeon-hole it as ‘not for us’
• Ubiquity: the greater ubiquity in our everyday lives, hugely helped by ChatGPT and its recent phenomenal success, has raised awareness among private and public sector decision makers.

These drivers and others are causing a snowball effect and the expectation that AI can address some of society’s biggest issues: managing an aging population; the transition to low carbon energy; better governance and better quality education. The ways that AI can contribute to the solution to some of these long-term problems include:

• Helping people generate new ideas, or generating new ideas
• Enabling disruptive business models
• Enhancing many types of products and services
• Boosting creativity

New requirements and new solutions

Business innovation of any type usually falls into one of two categories: new ways of dealing with existing requirements, or ways to satisfy an unrecognised need. At the heart of AI lies its ability to analyse colossal amounts of data and find information that points to a new way of doing things or a previously unknown requirement.

For example, AI can help by looking at supply chain data; if you have access to large enough data sets then you can, potentially uniquely, determine how supplies can be refreshed more efficiently. This is difficult for a human mind because we cannot see patterns in very large amounts of data, but AI can, thereby solving an existing problem in a new way. For example, a well-known UK-based online grocer conducts millions of demand and supply forecasts a day to ensure the freshness of its stock, minimising food waste.

An example of how AI is satisfying a previously unknown need is the way that it is used to find patterns in millions of data points captured from desktops to recreate business processes virtually. This allows issues in process flows to be identified and rectified. An international agribusiness that has used AI-driven process intelligence solutions for this purpose reports saving circa 15 years’ worth of manual effort. Without AI’s ability first to help with image recognition and capture, and secondly to join the dots and discover patterns in the data, the company would have needed many process optimisation and subject matter experts to work across operations teams that are located on different continents, working in many different time zones. It would have also involved taking people away from their daily work so that the information could be captured manually first, and then reviewed and analysed.

Enabling disruptive business models

AI puts a step-change into the factors that disrupt business models with some examples that are already emerging. From the evidence already available there are at least four types of models that are actively enabled by AI, with many more to come:

• Micro outsourcing: AI will increase disruption by helping companies dramatically reduce transaction or production costs, and increase capacity. An example is AI composing music and sound tracks for videos. Providing these services at a feasible cost has been difficult in the past but now AI can be used to both understand the brief, in this case the type and style of music, and produce it
• Changing commercial boundaries: disruptive models over the last two decades have challenged traditional boundaries between companies and their various stakeholders, and the rise of AI is expected to accelerate this disruption. Specifically, customers are likely to demand better service pre-commitment, while competitors will leverage AI to offer high-quality services at lower costs, possibly through 'freemium' models
• Changing physical or geographic boundaries: AI is making this possible in many ways, including its use to optimise 3D printed products that can be highly customised and produced locally to customer order. Another use is its ability to improve voice activation and recognition, removing the reliance on physical devices such as a keyboard or remote keypad
• High value technical advances: the most disruptive models will be those with a value proposition which is a leap in the eyes of the customer. Typically these will be the large scale moon-shot plays and there will probably be a relatively small number of organisations that end up with commercially transformative products. We know that the likes of self-driving cars, user interfaces for computers, and customer service platforms will be continually enhanced.

They will catch the headlines and a small number of organisations will probably transform the way we live.

Enhancing products and services

In many business settings the ideas with the best returns will probably be based on AI in combination with other technologies. One such area is intelligent process automation that uses AI with technologies such as Robotic Process Automation (RPA) (some of which use computer vision) and/or APIs. There is an increasing role for AI to combine with these technologies including as part of intelligent document processing and/or chatbots.

Opportunities exist with software solutions today to capture information about a process, re-engineer it and improve it, embed AI within data entry forms and then use a combination of AI, APIs and RPA to automate it.

Boosting creativity

There is a place within the innovation cycle for creativity in the usual artistic sense, and experiments show that AI can contribute to these activities as well. This has been proven most recently by the capabilities and huge uptake of ChatGPT and DALL·E 2 to create new content as well as images and art.

Artificial Intelligence is a groundbreaking tool for innovation — an invention that is a method of invention. As the examples show, AI is paving the way for innovations that were once deemed unattainable. As AI's development continues, its convergence with various sectors promises more potential and novel innovations to come.