Late to the Enterprise AI Party? 3 Ways to Get Started

Even as artificial intelligence becomes a cornerstone of current business, many enterprises are still struggling with how to get started.
Those looking at what AI-driven companies like Amazon, Microsoft and Google Cloud are doing may be concerned that they don’t have the deep pockets or best-trained staff to emulate these leaders.
The good news is, thanks to advances in hardware and software, virtually any company can get started with an AI project. And they’d be in good company — the global AI market is expected to grow from $93.5 billion in 2021 to $641.3 billion in 2028.
Ideal first steps for those companies looking to grow their business are pursuing three of the most common applications — chatbots, image classification and price prediction.
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1) All the Talk about Chatbots: AI Conversations on the Rise
Chatbots are AI-powered customer service agents. Ask a chatbot a question and it will check across numerous systems to give the customer an answer.
While chatbots have previously struggled to gain consumer favor, today they help improve customer service and satisfaction, as well as save companies considerable money. Juniper Research projects that chatbots are saving enterprises up to $8 billion a year.
Ping An, a major financial services provider based in China, was an early pioneer in using chatbots. Using AI to develop and train conversational chatbots with higher levels of understanding and accuracy, it’s able to address millions of customer queries a day – delivering not just substantial cost savings, but equally important, the ability to improve customer service through reduced call-center wait times.
Core Areas for Conversational AI Applications

Automatic speech recognition, or ASR, is at work when we speak to virtual assistants in our homes or to our phones so they can convert text to type.
Natural language processing, or NLP, takes ASR a step further, and is used to build applications to provide seamless human-technology interactions.
Text-to-speech with voice synthesis enables a chatbot to answer a customer’s question.

Deploying a successful chatbot requires speed, accuracy, customizable speech and language – and it needs to be scalable so that it can serve hundreds or thousands of customer requests if needed.
Sounds straightforward, so what’s the rub? It’s not a one-and-done process. Developing accurate, speedy software requires constant tweaking, which can severely tax data science teams if they’re doing it all manually. Thankfully, there are an increasing number of software tools that can reduce the time it takes to develop a powerful chatbot — what used to take months can now be done in days.
Teams can also develop the skills to build a chatbot before embarking on building one from scratch, with pre-trained models available to provide a head start.
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2) Seeing the Full Picture with Image Classification
Computer vision, also known as image classification, is the process of grouping and sorting images using AI to increase accuracy, improve safety and accelerate new projects. For instance, planning trips, or timing for traffic lights — all cases require real-time awareness and solutions, based on constantly changing data points. Computer vision helps the physical world meet the virtual world.
Deploying image classification requires a trained AI model that is ready to run inference workloads in production to make predictions.
These three stages of segmentation, classification and detection come together as the system runs inference — in just milliseconds.

A typical image classification system will include image segmentation.
The parts of an image are classified into categories.
Any detected anomalies are flagged to operators.

Medical imaging, autonomous vehicles and traffic control systems are three areas in which image classification helps industries improve security, safety and precision. To meet these goals, AI inference needs to run fast, achieve accurate results and be regularly retrained.
Enterprises can develop the skills to build an image classification system in hosted labs that explore how to create an end-to-end data science workflow and deploy the model in production when it is time to run inference.
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3) Understanding Why Price Prediction is Key
In just about every industry, prices for raw materials have become increasingly challenging to forecast because of unforeseen events related to the pandemic, politics and extreme weather.
As these variables continue to change, AI-powered price prediction can help enterprises overcome challenges to bring stability to operations and help maximize profitability.
AI price prediction models assess a number of data points that vary depending on the application:

A rideshare price prediction model might factor in time of day, weather and geolocation routing area.
A model to predict future wheat prices might include data on seasonal demand, weather and political activities.

Training an AI model to predict prices involves foundational data science work, including preparing the data for processing. In the rideshare example, building a price prediction model would involve evaluating datasets, including the dataset pickup points, drop-off points, fare amount, number of passengers, demand for rides and possibly even the weather.
Again, price prediction models need access to large datasets that must be processed quickly before the information becomes stale and out of date. Accuracy and efficiency require accelerated computing to ensure the prediction hits the mark. If accelerated data science is a new workload for your enterprise, labs can help teams polish their skills with speed.
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Launching Your First AI Project
So where can a company begin its AI journey? Developing the skills to run these and other key AI workloads doesn’t need to be costly or require a return to academia.
Enterprises eager to expand their own AI capabilities can invest in the skills of their existing teams, or hone their abilities, in a variety of virtual testing and either company-sponsored or third-party “learning labs” around the world.
A good, hands-on lab experience will let users see, understand and test-drive the kinds of AI applications that could be most beneficial to its particular industry.  AI can have a tremendous impact on virtually any industry or organization. This is true for the development of a new, time-saving chatbot for an airline reservation system, an image classification application that speeds up warehouse operations, or price prediction models that save the food retailing industry billions of dollars.
While the value of AI in businesses is high, testing out a few AI application ideas can be free. So, take the time now to assess where you want to start and take advantage of one of the many free virtual labs available worldwide to start your journey.
About the Author: 
Justin Boitano is VP of Enterprise and Edge Computing at NVIDIA. 
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