Beyond the generative AI hype
The potential of AI to be a force for good is huge, whether for organizations looking to support climate goals or for individuals seeking to make healthier lifestyle choices. Yet while many of us are alive to the opportunity, we are not always clear on the practicalities of the technology that sits behind this. Often, media focus is on generative AI rather than wider AI technologies and use cases that are revolutionizing the world around us. AI and generative AI appear to be used interchangeably as if they are one and the same. But should it be?
In this blog we explore different terminology, while also looking at the benefits of the different AI applications coming together so AI’s potential can be realized.
AI defined
Artificial Intelligence (AI) refers to technologies that emulate human capabilities such as learning, reasoning, and problem-solving. According to the international vocabulary standard for information technology terms, ISO/IEC 2382-1:2015, AI is:
“The branch of computer science devoted to developing data processing systems that perform functions normally associated with human intelligence, such as reasoning, learning, and self-improvement.”
Or in the words of BSI’s Conor Hogan: “AI isn’t magic, it’s an evolution of computing using maths and a huge scale of compute-power”.
In comparison, generative AI is a subset of AI that focuses on creating new content. It uses models trained on large datasets to generate text, images, code, and more.
While traditional AI might predict your next purchase or flag a fraudulent transaction, generative AI can write an email for you, design a logo, or simulate conversation.
Understanding the AI foundations
Whilst the term Artificial Intelligence was coined in 1956, during the first quarter of the 21st century AI systems have been trained on an increasingly vast amount of information, from which they learn to perform tasks - from classifying emails as spam; to making recommendations for your next film; to creating human-like content in the form of images or written and spoken language. This has been enabled by things like the availability of huge amounts of text in a digital format, the power of computer processing distributed across the cloud, and advances in mapping language into numerical representations for computers to process and self-learn from.
Such developments have meant moving from computers being programmed by humans with rules to enable them to perform activities (computer programming), to the computers self-learning their own rules from sets of data (machine learning) and having the ability to look for patterns and make predictions (such as neural networks). And more recently computers have gained the ability to perform a wider range of tasks, whether that’s processing and responding to text and voice commands (natural language processing) or generating new text and images to answer complex questions (generative AI).
While these are all unique types of AI, they can often be combined to deliver business objectives and achieve greater impact.
AI in action
For example, the retail sector provides a great use case where combining different types of AI delivers solutions that disrupt markets. A Scandinavian SPAR store now allows 24-hour access, supporting differing lifestyles thanks to the combining of AI applications. From operations such as security, loyalty schemes, stock control and self-serve checkouts, AI technologies such as deep learning, object recognition, computer vision, and advanced sensors help make this happen.
Car manufacturing has also been transformed using different AI technologies. For example, robotic welders use reinforcement learning to improve their ability to assemble car doors, computer vision to detect defects such as scratches or dents and predictive maintenance, using data collected from sensors, to work out when they need a tune-up.
A call to leaders
In light of AI increasingly changing business models, getting the right AI applications working seamlessly to support your goals could unlock huge differentiation.
With estimates stating more than 80% of AI projects fail, now’s the time to make sure there is a focus on how AI helps achieve your organization’s strategy, with clear appreciation for the types of AI that could come together to deliver on this.
This includes recognizing that AI literacy should not be confined to within the IT function and that people across your organization, including senior leaders, should have an awareness of what AI is and the outcomes it can achieve.
Organizations who prioritize AI literacy, empower teams to build knowledge of the different AI applications and equip them to communicate the AI requirements clearly, in a non-technical way, can be well-placed to demystify the AI hype and achieve successful AI adoption.
Benchmark your AI opportunities
Our Trust in AI research shows there is an opportunity to improve AI training and internal engagement, with many sectors and markets rating low-medium maturity. Take a look at the opportunity for your organization with our AI maturity dashboards here.
