The excitement built among businesses for AI technologies continues to grow with every passing day. According to a recent prediction of IDC, businesses will spend $78 billion on AI technologies like chat-bots, deep learning, and the required infrastructure in 2022. If this trend turns up right, it will be three times more from $24 billion forecast for the current year, 2019.
The market for AI is evolving quite rapidly. We can easily see that mainstream businesses from across industries are now ready to adopt AI. They are launching the latest pilot projects and applying AI to introduce simplification and more efficiency to their business processes.
But with growing AI trends will come more risks. It will be hard to avoid mistakes exposing an AI solution to risks when the adoption of AI technologies will reach a mass level. Are you also planning to apply AI to your business operations but not aware of what could go wrong? If yes, then this post is just for you. It points out some key mistakes most likely being made by AI developers. The purpose of this post is to help you intelligently make investments on your AI development.
Trying to transform everything with use of AI
AI based technologies are showing unlimited possibilities, but it does not mean you can start thinking about them all. It will not be a wise decision for an AI development and begin transforming everything at your business in a single shot. It’s just impossible and, neither should you try to implement Artificial Intelligence with this approach.
It’s advised you start with low-hanging fruits that you can catch easily without putting too much effort.
Investing in a one-off AI solution
An AI solution not helping you develop an overall process to do AI and is not also the part of subsisting data pipeline is considered as a one-off AI system. It doesn’t go too far! The success is only possible if you think of the sustainability and create a foundation for your AI assets with considering all possibilities for each of the projects.
To be sustainable for your AI implementation, invest in a solution that generates sufficient returns which can be invested again in the future development and scaling. With this approach, a business can obtain an AI solution which has the potential to serve a whole business, not like a new tool for a particular requirement only.
Launching AI development without the right infrastructure
There is everything available for core web and software development technologies in the market. But the case with AI is different. When it comes to the development of AI solutions, a business will have to invest in both core and advanced digital technologies to provide the right infrastructure. And companies having no previous experience in cloud computing, mobile, web, analytics and big-data will face three times more difficulties than those with them. As estimated, 75% of all organizations adopting AI will learn by building their existing digital capabilities.
Starting without data
So far, most of the AI systems are ML systems which require data to do their jobs. On the other hand, in most of the cases, a company would generally use the same publicly available data which is also used by other competitors. The results by the data will be moderate enough to help a company bring in any sort of improvement to processes. To obtain better results than competitors, AI should be based on better data that has already gone through cleaning, normalization, and preparation processes. But before a company gets this sort of data in hand, it will generally need to make big investments in collecting and cleaning the data qualifying for an AI system.
Working with undefined ways of assessing and measuring success
A business should have a proper hypothesis in hands on how investing in an AI will help it have better capabilities for the decision-making, sales, customer-support, etc. The hypothesis should be tested in action and evaluated for its outcomes.
The hypothesis would be nothing but a plan on how the success of an AI solution will be measured in terms of adoption and outcomes. There should be weighty data backing the decision of implementing an AI solution, not just intuitions or rough estimations. Without data, even the smarted AI tool will fail.
Beginning with no clue whether AI will really help or not.
The form of AI we currently have seems to be a century behind what Ironman’s Jarvis can do. But things will evolve with time. There have been a lot of advancements in AI technologies in the last couple of years. Though, it’s not easy to apply it to any given problem. A business must know the current limitations of AI and, what it can deliver before integrating it into existing systems and processes.
No company should adopt AI on the information saying that other companies too are adopting it. You must have reason to do so and, if you have not searched that reason then do not go for it now.
Starting without the having right teams
The success of an AI does not on relying on the best of technologies putting into action but on those who handle them. You will need a team of experts to successfully implement your AI solutions. You will also need data scientists to handle and sort out data supporting your AI system.
Building own AI capabilities for diverse functions
We have learned this from the most popular AI solution of today’s time, IBM Watson that even pre-built AI services take time for implementation. Fortunately, some companies have already started to work on ready-made solutions developed into SaaS offerings, like Salesforce, Dynamics, and Adobe Marketing Cloud. The Machine Learning services from Azure, AWS, and Google are already available for use. With them, a business does not need to build everything from scratch. But if you still do that, you are surely wasting your time and money.