Artificial intelligence and cognitive technologies may seem as if they are light years away, but the reality is that they are very much part of today’s business marketplace – and something tax and accounting professionals ought to know about. In fact, we can give you definitive examples that are happening right now, but first, a bit of background.
Artificial intelligence (AI) is an area of computer science that deals with giving machines the ability to seem like they have human intelligence, or said another way, the power of a machine to copy intelligent human behavior. Cognitive technologies (CT) are products of the field of AI that are able to perform tasks that only humans used to be able to do. Examples of cognitive technologies include computer vision, machine learning, natural language processing, speech recognition and robotics. They learn as they go and become smarter over time.
Several big names in technology have done quite a bit of work in these two areas. For example, IBM uses Watson to act as if it’s a small- to medium-size enterprise, and then tasks it with problem solving. Other examples include Google Assistant, which leverages broad databases and search engines, Amazon’s Echo with its Alexa assistant, Apple and Siri, and Microsoft’s Cortana.
Google and Amazon, among others, have made massive AI investments in recent years, fueling excitement about commercial applications. However, it’s not yet clear which profession or industry will be the next to embrace this technology. Many believe that machine learning will fuel innovation in every industry, and will likely be adopted first by companies that are responsible for dealing with large data sets. In order to properly adopt AI and fully reap the benefits, companies have to turn to good old-fashioned coding.
That’s right: software development will be crucial for achieving success with AI, and who knows … at some point, Intuit® ProConnect™ Tax Online, Lacerte® and ProSeries® may very well include AI and CT. Here are three major areas of software development that will be key to ensuring the success of AI integration:
- Data integration: Integrating key data from multiple sources will play an important role in preparing for the integration of AI into applications and systems within an enterprise. Having the necessary resources in one central location will significantly eliminate the risk of errors, and establish a clear path for machine learning. As a result, software developers will probably be as important as bookkeepers to finance departments.
- Application modernization: Regardless how current a company’s software programs may be, they will require some type of update in order to enable the integration of machine learning functionality into existing products. Rather than starting a gut renovation and slowing down existing operations – or bringing them to a complete halt – companies will have to look for less intensive updates that modernize existing software. The best way to tackle this challenge is to make tweaks and updates on a regular basis to prevent more work needed down the road. Think of it as routine maintenance and the occasional replacement of parts that take wear and tear in a vehicle. Small changes over time add up to a much healthier machine over time. For example, microservices architecture has showed us that we do not need to create massive software systems to deploy our business solutions; we can create various microservices that together provide the business with the needed functionality. Are your current systems ready to interact with microservices? Which current functionality can be modernized into a microservice?
- Employee education: Even the most talented and future-forward technology staff needs to understand machine learning and its implications on every aspect of the technology stack. This is particularly true for software developers and project managers who oversee sprints and other development cycles that can be interrupted by a transition to a new technology, or even enhanced by the addition of new systems. Tackling questions early, and providing resources often, will be key to helping teams get on board effectively. Online courses by Coursera, Udemy or Stanford Online can provide the needed education and training.
Across the board, AI and CT can have far-reaching benefits for companies in virtually any profession, but the key to harnessing the potential of artificial intelligence is to ensure that your systems, and people, are adequately prepared.