Despite all your efforts, the majority of the AI initiatives have failed. You have a leading technology service provider at standby. You have the best resources employed in the projects. You have planned out the AI initiatives down to the last T. Yet, it fails. How? In this Harvard Business Review article, Terence Tse and his co-authors explain why your AI projects are failing.
Mistakes in AI Initiatives
AI tools can produce results only when they are compatible with your legacy systems. Also, you need a team that has its fundamentals right. The team members should be qualified to help in ‘building, integrating, testing, releasing, deploying, and managing’ your system.
Instead of being demotivated by multiple failures, you must find out what works in favor of your AI initiatives. Start by creating an environment that facilitates the project. Also, assemble a good team.
Your production environment must fulfill the following benchmarks:
- Maintain thorough data hygiene to help your AI systems running smoothly. Incomplete or corrupt information can pull them to the ground.
- You need a structure around the projects to work. However, businesses are changing at a rapid pace. Ensure that you have a production environment that can inculcate your requirements sooner.
- Teams must work with several systems in these AI initiatives. Also, ensure that you regularly update these to keep the lights on as well as for the newer plans.
Perks of a Good AIOps Team
Do you have skilled resources to carry on the weight of these new projects alone? If not, have you thought of outsourcing some of the skills? Since you are pinning a large amount of money and expectations into these projects, it is best to hire the best resources. Here are some ‘tradeoffs’ for in-house and outsourced projects:
- The best part of doing it in-house is that you decide everything. You do not have to devote a considerable amount of time negotiating contracts. On the flip side, you must cut through the loops of administrative mayhem to set things in motion. You are also burning your pocket and resources.
- Having an AIOps service provider allows you to use their expertise right from the start. Years of experience would enable them to set up systems that fit well with your existing tools. You have access to their readymade infrastructure and production environment. Additionally, your internal resources can work on other projects to keep the lights on. The issue with vendor-based AI initiatives is that you lose some of that proprietary rights over your innovation. Also, you must pay up for those expert opinions or compromise on quality products.
To view the original article in full, visit the following link: https://hbr.org/2020/06/the-dumb-reason-your-ai-project-will-fail