The usefulness and applications of artificial intelligence have really been ramped up, as AI is being deployed across numerous industry sectors to great results. So the question for businesses is no longer whether AI should be adopted, but the best way of adopting AI into current business processes – and that means deciding whether you should buy AI software, or have it built in-house.
It’s not a simple decision, and it can become complicated depending on your business needs. For example, some companies rely on AI for powering their core business features. This includes companies like Uber and their autonomous vehicles, or advanced recommendation engines on websites like Netflix, where the software uses AI to better tailor recommendations to the user.
Amazon has also been investing heavily into AI, and even launched Amazon Go, a chain of physical retail stores with absolutely no checkout aisles. Using a lot of sophisticated technology such as computer vision, sensor fusion, and machine learning, Amazon Go convenience stores literally track every item removed (or placed back) onto the shelves, updating the user’s virtual checkout cart and billing them through their Amazon account.
Of course, these are examples of Fortune 500 companies putting serious money into AI technology, and we want to aim this article towards the average business where AI is useful for data analysis purposes. To get a better idea, you may want to check out some predicted data analytics trends to watch out for in 2020.
So first, let’s break down exactly how AI enhances data analysis for businesses.
How artificial intelligence positively impacts business data analysis
Big data analytics are nothing new, and the concept of data analytics has been around for many years. What’s changed, however, is that big data analytics has been vastly improved by artificial intelligence. Before AI, companies used entire teams of researchers and market analysts to track trends, customer habits, and everything else that big data covers.
Now with AI-driven data analysis, companies can have all of the answers they need pretty much immediately, at their fingertips. Not only does this save a ton of money on hiring research experts, but it provides the most accurate possible results, free of human error.
Artificial intelligence is able to convert information based on historical data into accurate predictions, answering questions such as whether customers will favorably receive new products, the probability of customer churn, but it can also streamline other company activities like accounting, human resources, and financial tracking. As an example to the power of AI-driven big data analysis, AI can be used to scour the web for instances of copyright infringement, such as your trademark logo or copyrighted images and products appearing on other websites.
Building or buying AI for data analysis: Your options
The decision to build or buy AI for your data analysis boils down to how unique your business needs are, in regards to the application of AI. So it’s important to evaluate your core objective in implementing artificial intelligence into your business. Do you want AI to completely transform and become a core asset of your business, or do you only need AI for simple tasks?
For companies that wish to use AI for handling routine activities, it’s preferable to buy artificial intelligence software as a service. Building AI in-house can cost millions of dollars and months of training the machine learning algorithm, and AI service vendors already have all of this ready for you, and can make adjustments to the software to suit your needs in most cases.
This is because the current model of machine learning mostly excels at repetitive administrative tasks, like searching for redundant customer records, verifying shipments from supplier invoices, and predicting customer trends based on things like customer feedback.
When you purchase AI-driven software as a service, the vendor will also be responsible for integrating the AI applications into your existing IT environment, as well as training your IT technicians on how to use the AI software, and additional things like training the machine learning algorithms for your specific business needs.
If you decide to buy AI software as a service, you’ll need to decide whether you want to purchase from a specialized AI vendor, or go with enterprise software that comes equipped with AI-based features and abilities.
AI Specialist Vendor versus Enterprise Software
If you choose a specialist vendor, the advantages often come in the form of cost and time saving. The vendor will already have data scientists and software engineers for training the machine learning models. However, there is risk involved with specialist vendors.
There are tons of AI company startups, and like all startups, many will not last.
On the other hand, you have the option of approaching your existing software vendors, who may have already added AI capabilities to systems that you’re already using. This is a great route to take, because the system already has access to massive data sets, including your own business data history, and this data can be quickly implemented.
Hybrid AI solutions
If it’s too difficult to decide between AI specialist vendors or AI-capabilities from your existing enterprise software, you do in fact have the option of a hybrid solution, in that you can integrate purchased AI technology with your existing software, using open source AI tools like Microsoft’s Cognitive Toolkit, or Google’s TensorFlow. This makes it easier and more cost effective to “build” your own AI.
There are also numerous cloud-based AI services that make it easy for companies to get started on building their own AI applications. This includes companies like Google, Amazon, and SAP, which all offer a variety of AI tools and templates, such as customer service chatbots, image recognition software, and speech recognition processing. These providers often offer a pay-as-you-go model, which means you can rent the computing power necessary for running machine-learning applications (which takes vast amounts of GPU and/or CPU resources, depending on the model training being done).
Final advice on deciding whether to build or buy AI solutions
To get a good idea of exactly how and where you should implement AI into your business for the purpose of data analysis, you should look at competing businesses within your sector. You can also educate yourself further, such as by watching TED talk videos with presentations that focus on AI and big data analysis.