Any way you look at it, AI is complex. There’s so much that’s involved in developing smart solutions, and as they continue to get more and more sophisticated, it’s only going to get harder. So, what’s a company to do? It’s clear that you can’t avoid the AI revolution, nor should you try to.
Smart apps are cropping up everywhere. From personal assistants like Amazon’s Alexa and Google Home, to the Roomba vacuuming your rug or self-driving cars, AI applications seem to be everywhere we turn.
They’re not just making our lives easier, but businesses are benefiting from AI too, with greater productivity and customer service, as well as new insights that lead to better decisions — in areas ranging from manufacturing, financial services, and insurance to retail and healthcare.
Now, organizations are developing AI apps that can predict future behaviors, such as the expected value of a customer, the probability of customer churn, the likelihood of a customer getting behind in payments, or even defects in medical products before they go to market.
On a grander scale, hospitals are using AI to address major issues such as identifying the latest cancer treatments and research to best meet a patient’s needs based on individual markers and other patterns, tracking the potential spread of infectious diseases, identifying potential security risks during the airport security screening process using facial recognition and AI software, and much more.
The companies that don’t focus on AI will be left behind. Forbes noted that companies with strong infrastructure investment, senior-level buy-in and clear goals, are gaining profit margins ranging from 3-15 percent based on the McKinsey Global Institute Study, and discussion paper, Artificial Intelligence, The Next Digital Frontier.
So, what does that mean for businesses really of all sizes? It means the time to act is now. AI requires constant refining and attention to produce quality results. It requires lots and lots of data – the more the merrier – in order to train the AI software to learn, and companies need dedicated data engineers to collect, prepare and maintain this data. Then specialized data scientists are needed to develop algorithms based on the data, and continually test and refine them to improve outcomes.
If that sounds like a lot, that’s because it is. It’s tough for companies, especially mid-sized ones, to dedicate these types of resources to develop a strong AI program. And even if you wanted to and had the capital, time and focus to commit to an in-house AI program, there are very practical obstacles in your way. In addition to recommended investment in a GPU-based server to efficiently process the complex algorithms, there is a real shortage of data scientists.
And the challenges don’t stop there. AI development typically involves more than hiring data engineers and scientists. Many AI programs today rely on crowds to identify patterns in images to help train the software for image recognition. While most use public data sets, such as Amazon Mechanical Turk, it can take time to get results and the algorithms don’t always perform as well as those from private crowds, which rely on trained individuals to identify the specific data-sets you need.
For all those reasons it doesn’t make sense to do it alone; not when there are many service options to consider.
Take a long-term view
How do you decide where to turn for help? It’s best to find a service provider that has capabilities across the AI development spectrum, from helping you identify the problem, collect and organize the training data, build algorithms and experiment with them, to designing and deploying applications, and providing ongoing maintenance and continuous improvement.
The last one – ongoing maintenance and continuous improvement – is just as important as all the others, since AI isn’t the type of application that you can develop and walk away from, or update once or twice a year – rather it requires ongoing vigilance to continue to perform effectively and learn.
AI is driving renewed interest in nearshoring
Unless you are a huge firm with lots of disposable income to build your own AI infrastructure – the GPU servers, the dedicated data scientists and private crowds – most firms understand that it makes sense to deploy AI as a service – paying as you go for the development of specific algorithms.
While service firms are in high demand to meet this new AI need, many firms are turning to nearshoring for the cost-savings and efficiency it enables. Nearshore providers offer the same capabilities that mainland U.S. firms offer, but at a more reasonable price point. For example, billing rates of software developers based in Puerto Rico are typically 30-50 percent lower than their mainland U.S. counterparts, with the same – or even higher – quality.
With proximity to the mainland, nearshorers can provide the same convenience, communication and fast turnaround that companies are used to. By nearshoring to Puerto Rico, companies gain the added benefits of a highly, educated skilled and trained workforce who are U.S. citizens, speak the same language, and have a shared culture and high standards, as well as a deep understanding of their customers’ business environment. Puerto Rican firms follow industry best practices and U.S standards in all of their software development, including ISO 27001 guidelines for an information security management system to ensure security of their customers’ data and intellectual property.
AI software is a critical investment, but fast becoming a necessity for companies who must remain competitive, improve customer relationships and elevate their decision making and profitability. In an age when “build-your-own,” is being replaced with “as a service,” nearshoring is gaining renewed traction as a speedy vehicle to get on the AI fast track.