The data is going to pay off!
All your corporate data put together with AI will revolutionise your business, create new business models and growth opportunities, and make your workforce more efficient!
If the AI is implemented properly, and all data made available to it, the sandwiches from the cafeteria will taste better and have fewer calories! Employee restrooms will only have a refreshing lemon scent! Executive bonuses will be guaranteed!
Data day reality
If that sounds way overblown, well it is. Though sadly it’s not too far off from some of the marketing that is appearing right now for AI. Companies are being told that they must implement AI and they must implement it as soon as possible, because if they don’t implement AI, their competitors will and then their competitors will “win”.
It’s not a stretch to think that falling behind on technology adoption may be a competitive disadvantage. That makes the statements about AI feel real and feel urgent. But the thing missing from that statement is – AI for what, exactly? There lies the biggest issue with most AI pitches – a lack of specifics and/or claims that seem plausible but miss the bridge from here to there.
It’s hard to blame non-technical people including senior executives who are pushing for more AI. They sense correctly that AI will be important, and the urgency created by the hype leads them to feel the need to do something.
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By GlobalDataFocus on the fundamentals
Some of the claims made about AI harken back to one of the major hype trends, namely, ‘big data’. All you need to do is implement a big data solution and answers will pop out of the machine, because data is a business elixir.
Does this mean big data was useless? No, it certainly wasn’t. But it didn’t become useful until everybody figured out what queries to run against the data and what questions to ask. As it turns out, massive quantities of data alone is like wandering into the library blindfolded.
To cut through the proverbial fatback and get down to the meat of what AI can do, decision makers should focus on the fundamentals. That means exploration and planning, first finding use cases for AI that have ready-made solutions.
For instance, popular collaboration software such as Zoom or Cisco Webex all have several AI features, such as automatic transcripts, automatic meeting summaries, adding markers to meeting recordings and the like. Chatbots for use in support for internal and external customers is another good use case. There is a ton of good use cases that companies can take advantage of right now, without having to build their own application from the ground up. Take the time to find good applications of AI within the business and then look at feasibility and ROI.
Just like with any new technology, AI is at the top of its hype cycle when its considered blasphemy to even suggest slowing down on AI initiatives. But it’s important to not get lost in the noxious smog emanating from this kind of hype. Do the same justifications you would on any technology, cost vs. time vs. payback and run a small-scale proof of concept to further define what you want to get out of your AI solution. In the meantime, show anyone who claims that data = success the door unless they can provide an example relevant to the business.
