There’s no arguing the impact that Artificial Intelligence (AI) is having on the world of business—from machine learning to natural language processing to automation, AI is transforming the enterprise for the better.
But despite AI’s relatively lengthy history, we’re still in the nascent years of business application; a great deal of ink is spilled on the potential for AI in enterprise business, but despite the current impact, a lot of that potential is as yet unrealized.
That’s poised to change in 2019, in a big way—thanks to growth in enterprise data and an explosion in supporting technology like connected devices, robotics, and cloud computing. The use cases for AI are about to increase exponentially and your organization is in a prime position to take advantage.
So where is AI headed in 2019, and how can forward-looking organizations capitalize?
- Put the ‘IA’ in ‘AI’
Is your enterprise looking to launch some large-scale AI initiatives in 2019? Better have your data in order. In Forrester Research’s report Predictions 2019: Artificial Intelligence No Pain, No Gain With Enterprise AI, the number one challenge for AI adopters is quality data.
Think about it: you can’t just fling an AI algorithm at a jumble of data and expect to extract any meaningful value from it. In order to derive any value from the coming AI boom, enterprise businesses will have to have their data in order.
What does that mean? For one, a greater investment in business information architecture (IA) will be necessary in order to collect, sort, and store the data necessary to execute on AI initiatives. Secondly, enterprise businesses will need to also invest time and resources in internal data collection guidelines and policies, ensuring that data is accurate, complete, consistent, unique, and timely in order for AI to be properly trained.
- Rise of the Things
In 2019, IoT is set to become the biggest driver of artificial intelligence in the enterprise. As interconnected devices are brought online in the coming year, they’ll stand to generate incredible amounts of data that can be used to optimize machinery, streamline operations, and improve logistics chains.
While Industrial IoT is the top use case for artificial intelligence application, machine learning models based on neural networks will also be capable of being optimized to analyze video, speech, and both time-series and unstructured data generated by devices such as cameras and microphones.
- AI Gets Interoperable
One of the limitations preventing the development of neural network models is the choice of framework (including PyTorch, Apache, Microsoft Cognitive Toolkit, and more). Once a data scientist or developer selects a model and trains it in a specific framework, it’s incredibly complex to move to another framework.
Because of the restrictions around porting models between frameworks, the adoption of AI in the enterprise has been limited. Hope is on the horizon, however: AWS, Facebook and Microsoft have collaborated to build the Open Neural Network Exchange (ONNX), making it possible to apply neural network models across multiple frameworks.
In 2019, ONNX will play an essential role in the enterprise, as businesses will no longer be limited to developing and deploying neural network models on one framework, driving greater AI adoption and new neural network use cases benefitting business.
- Trust the Process (Automation)
Robotic process automation (RPA) software has become ubiquitous in the enterprise in 2018, for good reason. Being able to automate repetitive, time-consuming tasks, and assist employees with supervised automation, is an obvious benefit to businesses of all sizes, freeing up human capital to focus on high-level execution.
Robotic process automation is poised to experience explosive growth as a market separate from general AI. According to a Forrester report, the RPA is estimated to grow from $250 million in 2016 to $2.1 billion by 2021.
“The RPA momentum started way before AI piqued the interest of enterprises,” The Forrester analysts explain. “Firms have been treating these set of technologies distinctly—RPA for automation, AI for intelligence. But to create breakthrough opportunities, we believe that an RPA plus-AI technology innovation chain will turbocharge your innovation efforts. Firms are already combining AI building block technologies such as ML and text analytics with RPA features to drive greater value for digital workers in four use cases: analytics that solves nagging platform issues; chatbots that boss around RPA bots; internet-of-things (IoT) events that trigger digital workers; and text analytics that lifts RPA’s value.”
Traditionally, enterprise companies have been the primary purchasers of RPA solutions, but moving forward into 2019, much of the growth will be driven by small businesses adoption—potentially leveling the playing field with the enterprise in terms of efficiency.
Leverage AI with OnActuate By Your Side
With new use cases emerging for AI as quickly as the technology is developing, enterprise businesses would be remiss to not capitalize on the incredible potential offered. Driven by continued adoption of IoT, cloud computing, and automation solutions, AI stands to be a gamechanger for forward-thinking enterprises that take a long-term view towards the technology in 2019.
Contact our team today to learn more about how you can start using AI in your business operations today and how AI can help attract new customers and keep your current customers happy. At OnActuate, we have the best knowledge and experience to help you optimize your technology, adapt your business model and compete on a global scale.
