AI is massively overhyped right now and it hasn’t even reached peak hype yet.
It’s not hard to understand why. AI shows tremendous potential and has the breadth to span far more than just IT and business problems. Investors, IT vendors, service providers, and others are all pouring money into AI. General enterprises are under pressure to deploy AI quickly. The hype from media companies, analysts, and proponents of AI makes it seem as if enterprises will miss out if they don’t jump on AI immediately.
There are always surveys and data points to drive the point home, often showing that CIOs/CTOs are saying they are moving quickly into AI and of course are prepared for it. These surveys have a huge pitfall – no CIO or CTO is going to say they are not implementing AI and risk looking behind or uninformed.
To make it worse, there are often ladder climbers or attention getters within any enterprise who are pointing and shouting, saying that AI is a game changer and creating an atmosphere that pressures all parts of the organization to get on the AI bandwagon. Executives read in business publications about AI, as do members of boards, and they add additional pressure to move very quickly into AI.
AI: Pressure and time
All of this pressure means that in many circumstances, AI solutions are being discussed and even implemented without the real due diligence that needs to be done to make using AI successful. Many AI solutions are new, without long track records. Enterprises also need to make sure that they are applying AI in a way that will be more than superficially beneficial. Two mindsets need to be set aside to achieve success. First, the idea that anything is better with AI needs to be discarded. AI is and will be a tremendously useful tool, but that doesn’t mean it should be peanut-buttered across the entire enterprise. Second, is the idea that all data is good data made good just by being fed into an AI. The old saying still applies – garbage in, garbage out.
Looking at parts of the business, including IT, where an AI solution is more mature is a good place to start. Implement AI at small scale first, then expand. Consider what results a given AI solution is claiming to provide, and how valuable those are. Do those results influence business decisions, or are they simply fodder for a big visual dashboard that makes people feel nice about the company? The plans for implementation need to be created based on need, AI maturity in the needed area, and reasonable expected results. Cost/benefit analysis and ongoing costs need to be understood, everything that would be done to implement a technology that isn’t so in-your-face hyped.
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By GlobalDataAI does amazing stuff, and the new use cases are piling up. It’s okay to move quickly to adopt, but don’t just assume value will appear by simply using AI. Do the planning from both technological and business viewpoints and create an AI implementation to be proud of, not one that was driven solely by the fear of missing out.
