To paraphrase the American writer Mark Twain: “reports of the death of AI are greatly exaggerated.”
Lately, a number of press articles and market commentaries have claimed that the boom in investment in Artificial Intelligence development is heading for a sharp fall — much like the bursting of the dot-com bubble in the early 2000s.
There’s been talk of oversaturation in the market for AI tools, dizzyingly high expectations about the value of AI to solve business and civic issues, and endless exaggeration that AI will be a cure-all for the wide spectrum of life’s problems.
Some reports point to high-profile companies questioning the return on investment of their AI strategies and even reversing out of AI initiatives. Others have expressed discomfort that AI development seems to be largely in the hands of a small number of so-called ‘tech bros’ who have unclear objectives and appear unconcerned about the potential job losses and societal change that may occur because of the technology.
Look closer, however, and you’ll see that media attention has mostly centred on the consumer-focused generative AI market, such as ChatGPT, Copilot and Gemini. What is not being discussed is the quiet upsurge in AI adoption within data management operations, where it is increasingly deemed essential for data-driven organisations seeking to remain competitive.
Data is one of your most valuable resources as a business owner — it helps you make informed decisions; provides insights into business and market performance; aids in offering a more personalised service to your clients and can even empower you to anticipate shifting market trends.
Given this, it almost goes without saying that those who are best able to exploit their business’s expanding pool of data will profit the most. And when we consider that AI has the capability to tackle multiple data management tasks at the speed of light, without needing a rest, the commercial benefits for businesses that harness its power are clear.
For example, AI can help drive efficiency. It can streamline tricky and time-consuming data operations like data cleansing, analysis and regulatory compliance. And, when implemented appropriately, AI can help to reduce human error, as well as the costs and reputational risks associated with getting things wrong.
Most importantly, AI can augment your team. It can be their always-on helper equipped to rapidly learn and perform routine and repetitive data-intensive roles with a high level of accuracy. This allows your staff to focus on higher value tasks and allows your business to scale quickly and securely.
That being said, the old principle of ‘garbage in, garbage out’ still applies: AI can only deliver meaningful insights when it is fed accurate, consistent and trusted data. Relying on faulty output is a massive corporate risk, in terms of poor customer outcomes, regulatory sanction and jeopardised business reputation. It’s key to grasp that AI can only do a ‘good job’ if the data it works from is clean and simple. But ultimately, AI is here to stay. Look past the general hype and focus on purposeful AI – the kind that is being calmly applied in data management to generate real-world outcomes as a pathway to improved business performance.
