When you think about technologies that really take off, it’s usually due to a combination of factors – technology advancements, market need and often being in the right place at the right time.
For example, before the iPhone could be developed there needed to be advancements in cameras, touch screens, miniaturized chip technology, and Wi-Fi to name a few. It also needed a receptive market. With the advent of the Internet, consumers enjoyed the power of being in the driver’s seat and in charge of their online experience. And as road warriors became more commonplace, they needed to conduct business and connect with everyone at any time, no matter where they were.
The growth of artificial intelligence (AI) has had a similar trajectory. The technology has become more sophisticated, for example, enabling chatbots to understand and respond to human language and text in a human-like manner. And similar to the iPhone explosion, the market need is great. Consumers are demanding real-time information and support from call centers, which can no longer feasibly or economically staff them with employees 24/7. Similarly, medical device manufacturers need to be able to predict when their devices might fail, and oncologists need to match clinical trials and research that might be available for their clients, just to name a few examples. The ability to sort through huge amounts of data quickly and see patterns in it is becoming more and more valuable for all types of industries.
Challenges to AI Growth
But there are a couple of challenges that can slow down the growth of AI. One issue is having the right people to do the job – data engineers and data scientists are needed to develop algorithms that train the smart software to learn. Unfortunately, there is a shortage of these professionals. According to an IBM report, annual demand for these professionals will reach 2,720,000 openings by 2020. This makes it difficult for every company to hire their own data scientists and data engineers.
In addition, AI projects often require additional manpower to prepare the training data needed by data professionals to do their tasks. Typically, image recognition AI programs use crowds to identify patterns in images and label them accordingly to produce the datasets needed to train the software. Public crowds, such as Amazon Mechanical Turk, can be used to create these datasets but it is a more time-consuming process and not as accurate as those developed with private crowds.
Since the accuracy of the algorithms depend on crunching a huge amount of data, it is becoming increasingly important to invest in hardware – GPU servers that can process information quickly. All of this points to the need to turn to AI-as-a-Service, where you can outsource the development of algorithms to professional firms that have the expertise, capabilities and manpower.
Nearshoring is an Ideal Solution
So, we have the “how” new AI technology gets accelerated, the “why” of the market need that is propelling AI growth, and we also have the “where,” which is fast becoming nearshore regions.
Nearshoring, in which a company outsources its software development to a region in fairly close proximity, enables companies to more easily and cost effectively initiate complex AI projects in collaboration with their service provider, who most likely speaks the same language, has the same currency, time zones and has similar – if not the same – regulations.
Consider a work project that needs to be delivered on a certain date. If a company needs to change the requirements, they would need to wait until the work day starts in another location around the world. The back-and-forth communication would not only be impacted by the time differences but also by nuances in the language that would be best understood by a native speaker. The ability to communicate effectively and collaborate is particularly critical when developing complex technologies such as AI, deep learning and other smart apps, which need to be trained to understand all of the subtle nuances of language.
Nearshoring enables companies to tap into the expertise of an outside firm, but typically offers the same shared language, culture, work values, and proximity that you could find in-house.
Puerto Rico, and other U.S. territories, play a unique role in nearshoring, providing added benefits. In addition to the convenience and fast turnaround of being close to mainland U.S., Puerto Rico offers a highly educated and skilled workforce of U.S. citizens, trained at U.S. universities. And, as importantly, Puerto Rico adheres to the same best practices and U.S. standards in software development and security management to protect data and intellectual property.
There is also a financial benefit to working with nearshoring firms in Puerto Rico, which follow the same high standards and produce the same quality as mainland U.S. firms, but typically at fees that are 30-50 percent lower.
It is becoming imperative for companies across industries to offer AI solutions to advance their customer service, quality goods and services and capabilities. However, the staffing requirements and technology infrastructure required to develop these solutions are beyond the reach of many organizations. By turning to nearshoring, these organizations can get the high-quality AI solutions they need cost-effectively. And by leveraging new technology advances in this time of growing market need, and filling the much-needed AI development gap with highly skilled custom software engineering services, nearshoring can provide the missing ingredient to faster AI deployment and lower costs.