Self-driving cars, intelligent virtual assistants, facial recognition to unlock our phones – we can continually see and experience the transformative power of artificial intelligence (AI) all around us. AI is seamlessly embedded in the tools we use every day, like our fingerprint scanning phones and intuitively recommending streaming services, and it’s impossible to browse news or scroll through our LinkedIn feeds for longer than a few minutes without encountering an article about another astounding advancement in this branch of data science.
There is widespread awareness of AI’s ability to provide newfound insights, solutions, and enhancements to modernize the way a business functions and serves its clients, but how can you start taking advantage of these capabilities? Knowing where to begin in an ever-evolving domain can be intimidating, so what should your first steps be? Here we walk through the initial pieces involved in successfully implementing AI into your business.
A useful first step in adopting AI is simply asking, “What exactly is AI?” As with most buzzwords swirling around in STEM, it can be hard to find a concrete definition of the advancement behind all the hype. AI, in a nutshell, can be described as an application of data science to approximate human intuition and decision making or human sensory functions, like recognizing images or speech. Fortunately, there are tons of resources available online to help expand on this definition. Some examples of proficiencies in AI include predictive analytics of customers and machinery, automation, and monitoring and generating alerts. Being familiar with the types of processing AI can do, including natural language, image, and video, also helps to spark ideas about where you see AI fitting into your business.
Now that you have a grasp of AI’s capabilities, how can these be utilized specifically in your favor to enhance your existing processes or solve persistent problems? The specifics of AI’s best-fit role in a business will vary from industry to industry, but it helps to gather an overview of the technical processes integral to your business and the areas these processes have room for improvement.
Whether it’s intelligently predicting customer behavior, extracting key information from documents, or flagging abnormal activity, AI can improve upon common shortfalls through different probabilistic, statistical, and machine learning methods.
Data is the lifeblood of AI. Just like people require experience and exposure to develop natural intelligence on a given subject, artificial intelligence requires experience and exposure to develop intuition and successfully make decisions – and the more data the better. AI systems are commonly fueled by machine learning models which are able to rapidly iterate on massive data-sets, constantly making subtle improvements. Patterns we observe in data and all around us can be incredibly complex, and encountering a large amount of data allows these models to become robust to the subtleties that make human intuition and sensing so involved, powering more intuitive AI conclusions.
So, what kind of data are we talking? In short, any information that would or could be taken into account in a human’s gut-feel must be collected and digitized. What is the material behind your expert’s intuition? This will all be required by a machine to recreate your intellect. Depending on the task, this could mean huge amounts of images, audio, text, video, or other forms of unstructured data, which has not been organized in a pre-defined manner. The manipulation of this information into a format that is digestible by an algorithm has given rise to fields like computer vision and natural language processing. Developments in these disciplines open doors to mimicking uniquely human processes like recognizing pictures and understanding speech and text. Because vast amounts of data are so important to AI, data enrichment packages from providers like Alteryx can also prove useful by merging what you’ve collected with third-party data to further expand the knowledge base your AI system has access to, especially when modeling customer behavior.
Now that your data has been gathered and digitized, start small to iteratively build out an AI solution and integrate the system with your existing processes. Begin by developing a machine learning model to solve a specific use case to provide business value quickly, as well as gain organizational support for the significance and tangibility of AI.
If you’re ready to get started, we can help you build actionable machine learning insights in as little as 6 weeks with our RapidInsight solution. For additional information on this solution, visit ccganalytics.com/Rapidinsight. To speak with a data science consultant right away, call (813) 968-3238 or contact us at email@example.com.
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