Only a few technologies in the past ten years have captivated our interest as much as artificial intelligence (AI). The technology is poised to alter the current corporate landscape significantly. Therefore, the attention is legitimate.
In a recent poll of Fortune 500 CEOs, for instance, it was discovered that 81 percent of them thought AI/Machine Learning technology was “essential” for them.
For CEOs, artificial intelligence was more significant than virtual reality, remote sensing, nanotechnology, 3D printing, and sophisticated robots. People are becoming increasingly aware of the fact that AI is now being employed widely and is not just a science fiction concept.
Are you prepared to embrace the limitless opportunities that AI offers?
In this post, we’ll go over the factors you should consider to ensure a successful implementation and the four steps you should take to get your business data ready for AI.
AI Implementation Questions
You should take the following factors into account as you set the foundation for a successful AI integration.
Learn about AI and what it can do for your company’s data. Business owners like you risk missing out on many excellent chances if they don’t comprehend the capabilities of AI. Take an online course to keep your knowledge current and make sure you know what it is capable of. Good choices include Udacity’s Introduction to Artificial Intelligence and Columbia Business School’s Artificial Intelligence for Business.
Find the most crucial areas where AI may help your company. It’s not a smart idea to deploy AI without a defined action plan because you’re essentially diving into the unknown. Be sure to point out specific areas and give certain of them priority when talking about how AI can help your organization. There should undoubtedly be business data there.
Ensure that your IT system can withstand the transition. To manage AI, a business needs a strong IT infrastructure, yet many of them don’t have one. For instance, recent research indicated that one of the top five challenges in implementing AI was “Difficulty organizing and interpreting the data,” according to 29% of respondents.
Let’s now examine what it takes to AI-proof your business data.
1. Make sure your business data has labels so AI can understand it
Data analysis without labels is a restricted capability of AI and machine learning (ML). Unsupervised learning in machine learning can do an exploratory analysis of such data, but it cannot yield insightful results. As a result, organizations shouldn’t switch to AI and ML without first labeling their data properly.
For illustration, a company receives many emails and support tickets from customers, all of which are labeled with the type of issue they relate to (delivery issues, refund requests, etc.). The company will guarantee that the insights generated by AI will be valuable by creating a system that automatically labels incoming customer support chats, emails, and phone calls.
2. Every Context Must Be Your Own
Do you know what data to feed AI algorithms? The decision of what information to feed is more complex than it may seem. Therefore, this is something that should be taken with seriousness. For instance, most AI and ML algorithms are good at finding correlations but need to be made aware of the context of the data.
They cannot identify whether the information is essentially important or irrelevant as a result. Here’s an illustration of how “context” could prevent AI and ML from producing valuable solutions:
An online store’s recommendation tool oversells a particular product. Specialists investigated the issue and found that this product was heavily promoted six months prior, which is why historical data revealed a significant increase in sales from current customers; additionally, the promotion was based on the “discount” rather than the product’s actual usefulness to the customer.
You should provide AI with context and facts in order to avoid experiencing similar issues. In this instance, it will comprehend the context of the data and make sure the answers it generates are pertinent.
Frequently asked questions:
Which data source is the most effective for AI systems?
As a primary source of data, one can also use the information from CRM and ERP systems (Enterprise Resource Planning). On the other hand, secondary data sources include things like journal articles, government reports, staging websites, and publications from unaffiliated research facilities.
What techniques are used to prepare the data?
Data preparation includes all aspects of data pretreatment, profiling, cleansing, validation, and transformation. It frequently includes entails combining data from various internal systems and outside sources.
How do you begin processing data?
The initial stage in data processing is data collection. Information is retrieved from a variety of sources, including data lakes and warehouses. It is essential that the data sources offered to be trustworthy and well-constructed in order to guarantee that the data obtained (and later used as information) is of the highest quality possible.
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