OpenAI’s ChatGPT has taken the world by storm. Since its launch late last year, it’s become the fastest-growing app in history, reaching 100 million users in only two months.
This new tool used across the globe has democratised AI in a specific and accessible context, allowing the average web user to explore and play around with AI. Its launch has led to widespread excitement and much discourse about how it will revolutionise tasks, jobs, functions and industries.
In fact, OpenAI has most recently launched GPT-4, which exhibits human-level performance, being even more reliable and creative than GPT 3.5, which ChatGPT was built on.
Is ChatGPT a peek into the future, or is the future already here?
Prior to the development of ChatGPT, creating an AI model that could understand and respond to natural language was a complex and technical process. Certainly, for many, ChatGPT is a peek into the future. But for companies who are further along in their AI and machine learning (ML) journey, it can be argued that the future is already at our doorsteps.
The issue is that this ‘future’ just hasn’t been evenly distributed. In fact, theCommittee for Economic Development of Australia recently launched a report which found that AI is still in the early phases of implementation in many Australian companies, with only 34% of firms using it across their operations.
The quiet revolution of “boring AI”
With its cutting-edge ability to generate human-like text, ChatGPT is certainly a novel and engaging tool individuals can use to generate conversational answers. However, at a societal or industrial level, there are far greater challenges to solve. We know that more needs to be done to accomplish global change, whether that’s helping companies get to net-zero emissions or speeding up new drug discoveries. There are technologies already advanced enough to transform every job and organisation, and a number of organisations around the world are using them.
However, the majority of organisations are currently still using these technologies in quite rudimentary ways. For instance, they may be using AI and ML to look at data to understand past events, with data analysts still carrying out predictive modelling.
Many businesses are currently using data for hindsight rather than foresight. There is a slow revolution underway that will see business leaders shift their organisations to utilise AI and data to work their way up to automated decision making. As AI and ML mature, huge pain points will be fully automated – removing mundane, repetitive and time-consuming tasks from us humans. Realising this, Atlassian leveraged data and AI to remove barriers of infrastructure and scale, allowing a more data-driven service to their support staff and customers. As a result, Atlassian was able to reduce two thirds of their operation costs while achieving 2x faster time-to-market of new features. This is the real revolution – the quiet revolution of “boring AI”– happening behind the scenes, without much fanfare, but having a profound impact on how work is done in many industries.
“Boring AI” refers to the use of AI to automate and optimise routine tasks, improve operational efficiency, and ultimately drive business value. By delegating all the time-consuming, repetitive tasks to a super-efficient AI system, organisations can free up time for people to focus on what they do better than machines: creative problem-solving, strategic thinking, and innovation.
Unlike the AI behind GPT-4, which often grabs headlines with its ability to generate creative output, boring AI is focused on transforming the underlying bolts and nuts of business operations. This type of AI is empowering companies to build better products, reduce costs and optimise operations. Australian software company, Bigtincan, has improved its product development cycle using AI-fueled automation and faster data processing capabilities. In doing so, Bigtincan was able to create an integrated data engineering function, deliver a consistent data pipeline and improve reporting efficiency by over two times.
Such ‘less exciting’ uses of artificial intelligence often get overlooked, but have the potential to save money and reduce errors – as well as free up time spent on labour-intensive, repetitive work. A 2023 report by CSIRO found that Australian decision-makers whose businesses adopted AI-enabled solutions reported average time savings for existing processes of 30%. Additionally, respondents reported an average of $361,315 of incremental revenue generated by such AI-related initiatives implemented.
In Australia, IDC has predicted that AI system spending will grow to $3.6 billion in 2025. This is a compounded annual growth rate of 24.4% from 2020-25. This investment has increased as organisations seek to improve employee efficiency and speed up decision-making in the face of economic headwinds.
Most critically, the technologies behind boring AI are maturing and are applicable to any industry. It has also successfully made its way into highly regulated industries like banking, healthcare, insurance and manufacturing, where the use of AI is understandably subject to strict rules and regulations.
Australian bank, NAB, has made the most of implementing AI in its strategy to meet a growing need for data to help deliver personalised experiences, detect and prevent fraud and scams, optimise digital engagement and manage risk. The bank has done so by combining a data warehouse and data lake to support its data workloads, including real-time analytics and AI driven recommendations.
Maximising the value of AI
In order for businesses to undergo this significant transformation, they need to get a handle on vast amounts of data and invest in the right data architecture that can make sense of it. Then, AI and ML can be effectively performed on top of it. That’s the critical part of the discussion that is often overlooked.
In fact, a Databricks and MIT Technology Review Insights report revealed that 78% of APAC CIOs identified problems with data are more likely than any other factors to jeopardise the achievement of their AI and ML goals by 2025. This means that businesses that are able to get a handle on the breadth and quality of their entire datasets are freed up time to innovate.
The challenge – and solution – lies in the organisations’ data architecture. The emergence of modern data architecture like the data lakehouse, which combines the best features of data lakes and data warehouses, means organisations can employ a unified platform that is able to perform business intelligence, streaming and ML. By leveraging data on a unified platform, companies can apply AI to the data to extract maximum value from “boring AI” and drive real business impact.
For businesses wanting to unlock AI to automate and optimise routine tasks, improve operational efficiency, and ultimately drive business value, choosing a unified data architecture is paramount to their success.
While ChatGPT has shown itself to be an impressive feat for the technology industry from a universal standpoint, as it stands its impact may be more wide reaching than far reaching. When diving deeper into the use cases for AI, it can be argued that ‘boring AI’ doesn’t live up to its name, as it works to solve the most interesting and most impactful challenges in our lives.
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