The advantages of the AI industry are completely clear in front of enterprise leaders, but sometimes the execution of technology leads to some issues. You might have heard about different brands adopting this advantageous technology and increasing their revenue. But, in reality, according to the IDG survey, only 1 out of 3 artificial intelligence projects actually succeed.
In this post, we are going to cover key challenges faced by the enterprise to adopt AI model and method to overcome these challenges.
Challenge 1: Use Cases and Ownership
Investing in this technology sector is a big decision, every investor wants to see significant growth within six months of adoption. For this, the business enterprise needs to pick the right business use case to fully optimize AI. Almost all the enterprise make the mistake of using technology for smaller projects. However, selecting small projects is a smart move, but it won’t show any significant change.
The basic solution here is to select your project which is comprised of tasks that are core elements of your revenue generating stream. You have to recognize the accurate use case to test and showcase the strength of artificial intelligence technology. You can follow some guidelines like;
- A select task which is a part of the decision-making process in searching and analyzing the huge amount of data.
- A task which has a low tolerance for errors.
- Pick tasks which are of repetitive nature, but have to perform at scale nevertheless.
Challenge 2: Data Infrastructure
No AI solution can be created without proper data. The data has to clear, well structured and easy to access. However, enterprise do have piles of data, but they sometimes fail on following fronts;
- When the data has been collected using different business functions and sources, then the problem is the absence of a single unified repository from where this data can be accessed accrues.
- The problem of unstructured data is very common. The lack of context and category of data makes data redundant for machine learning.
- The missing or incomplete data is yet another problem. These inconsistencies result in skewed or faulty learning, which ultimately leads to failed solutions.
AI solutions are formulated for decision making, future predictions, analytic, etc., This requires complete availability of clean and effective data. That’s why it’s important to draw the complete range of data to create business solutions. If an enterprise doesn’t have ready data, then they should take time to put together clean and organized datasets to ensure success.
Challenge 3: Skilled Team
The success of the AI project highly depends on your team. If you have a team of skilled professionals, then your project will be successful. But, as the knowledge of practical artificial intelligence is limited that’s why it’s difficult to find people with the right skills. According to the State of Artificial Intelligence report, 2017 by Teradata, 34 percent of enterprises state that lack of talent is a key barrier to AI adoption.
To solve this problem, an enterprise needs to tie hands with single stacks holders. They can recognize the data science and allied skulls gap between the enterprise. To achieve that it would be great to work with technology partners who offer skilled AI.
So, guys, if you want to adopt this interesting technology, then you have to overcome these challenges with simple solutions.