Call us now:
It’s fair to say that we have been here before with tech bubbles. They are nothing new. As far back as the first Industrial Revolution, when recent tech records began, we have witnessed the rise, fall, and rise again of technology.
As this article nicely points out, as does Gartner in their AI hype curve, there is a rise in AI, followed by a fall, then a steadying out. A curve that was replicated across every recent digital tech life cycle over the past 30 years. the .com, mobile, streaming, social media, Augmented Reality, and now AI. While the likes of Google, Facebook, Netflix, and Amazon were creating markets, financial markets were worried that they all weren’t making money.
The same is true for AI; the money that’s been pumped into AI reflects the bubbles created by previous tech innovation, with the view that out of all the investment pumped in, creating innovation, a winners will drop out the other side of the mincing machine as customers tune themselves to what AI can do, what it cannot do and how it will enable changes in their strategic, financial and operations performance.
But as Gartner points out, it has to move beyond Gen AI, into Domain Specific AI, adopt improvements in Prompt Engineering, orchestration, trust, and move towards continuous improvement for it to deliver ‘hands-off’ benefits to end users.
Today, AI has similarities to drone technology and the military. Until the Ukraine/Russia conflict, packing the average household drone with explosives wasn’t considered, until it was adopted to great effect on the battlefield. Now that the use case has been proven, military drone technology has taken off (quite literally), changing the battlefield of the future. AI requires real-world use cases for it to ‘stick’ with industry, and as reported this week by MIT, we’re some way off from this happening.
@Thaddeus Dziekanowski Brian D. McCarthy Faisal Khan
Veritas GRC
#artificalintelligence #cybersecurity #cyberriskmanagement
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