A few years ago, I was listening to a vendor pitch with a group of enterprise IT veterans.
copyright by enterprisersproject.com
The sell focused on features of an intrusion detection software product and the value of artificial intelligence . The vendor said AI techniques allowed the product to automatically detect threats.
Discussing the pitch afterward, the general sentiment of the group was “I don’t buy it. There is no such thing as magic!” I agreed. As a student of enterprise software, I understood two key things:
- Vendors hype the “next great thing” to make it seem more valuable.
- Software is very specific. Code has specific instructions and performs exactly as told.
Based on our past software experience, the vendor pitch seemed like snake oil. In our world, software did not figure things out for you. It just followed the rules set for it.
We did not understand the power of AI, which is turning traditional software development on its head. I’m now convinced that using AI will quickly become as common as today’s use of databases. Far from viewing it as snake oil, I now understand that AI is essential to bring businesses to the next level.
How to select the right AI project – and beat resistance
Selecting the right AI pilot project is a vital first step because success with AI is different than with traditional software development. Rather than software performing exactly what it’s told to do, an AI system learns what it needs to do. That is the opposite of what we are used to, and that creates resistance to investing in AI.
Start by finding examples of outcomes you want (and you need many of these examples) so the software can learn what it needs to do. This means you must be prepared to explore and be comfortable not knowing why or how something works.
The best way to curb your own resistance to AI is to work on a use case. For example, a recommendation engine project served as a starting point for one company’s AI experience. Initially, it was hard to get product managers to attend meetings and provide feedback. But as the project progressed and the managers understood the approach, they all identified multiple opportunities within their own area of responsibility and sought to launch their own AI efforts.
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They began to recognize how machine learning could be useful in their roles. This is why selecting the initial AI project is vital. It demonstrates the power of AI and machine learning and how it can be applied broadly across the business.
AI is about changing routines, and that will always meet resistance. The recommendation engine successfully connected technology and people to show how AI can help people achieve objectives.[…]
read more – copyright by enterprisersproject.com
A few years ago, I was listening to a vendor pitch with a group of enterprise IT veterans.
copyright by enterprisersproject.com
Discussing the pitch afterward, the general sentiment of the group was “I don’t buy it. There is no such thing as magic!” I agreed. As a student of enterprise software, I understood two key things:
Based on our past software experience, the vendor pitch seemed like snake oil. In our world, software did not figure things out for you. It just followed the rules set for it.
We did not understand the power of AI, which is turning traditional software development on its head. I’m now convinced that using AI will quickly become as common as today’s use of databases. Far from viewing it as snake oil, I now understand that AI is essential to bring businesses to the next level.
How to select the right AI project – and beat resistance
Selecting the right AI pilot project is a vital first step because success with AI is different than with traditional software development. Rather than software performing exactly what it’s told to do, an AI system learns what it needs to do. That is the opposite of what we are used to, and that creates resistance to investing in AI.
Start by finding examples of outcomes you want (and you need many of these examples) so the software can learn what it needs to do. This means you must be prepared to explore and be comfortable not knowing why or how something works.
The best way to curb your own resistance to AI is to work on a use case. For example, a recommendation engine project served as a starting point for one company’s AI experience. Initially, it was hard to get product managers to attend meetings and provide feedback. But as the project progressed and the managers understood the approach, they all identified multiple opportunities within their own area of responsibility and sought to launch their own AI efforts.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
They began to recognize how machine learning could be useful in their roles. This is why selecting the initial AI project is vital. It demonstrates the power of AI and machine learning and how it can be applied broadly across the business.
AI is about changing routines, and that will always meet resistance. The recommendation engine successfully connected technology and people to show how AI can help people achieve objectives.[…]
read more – copyright by enterprisersproject.com
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