Digital transformation is bringing the world closer and is immensely responsible for driving all activities not only in enterprises but also in the healthcare industry, government sectors and much more. With the rise in competitive pressure, companies are being forced to reduce their overall costs while implementing diverse innovative technologies to be more responsive to customers and competitors. Hence, companies are increasingly taking advantage of technologies like cloud technology, artificial intelligence and predictive analytics to generate better customer value through connected applications, data, and services that optimize for agility and economics. With the cloud as a platform and APIs as building blocks for intelligent enterprise applications, AI is available for more people and organizations than ever before. This opens up more possibilities for AI technologies that can give companies a contentious advantage. Also, AI applications have become more efficient than they used to be, thanks to the better availability of training data.
What is Artificial Intelligence (AI)
Artificial Intelligence (AI), refers to a diverse field, incorporating not only computer science but also psychology, philosophy, linguistics and other areas. AI is involved with getting computers to do tasks that would otherwise demand human intelligence. Artificial intelligence is one of the always developing technologies and it is helping the other technologies in the market and society to achieve good levels of technology. This efficiency has changed the perspective of the entire world towards artificial intelligence and its related areas of working and support.
Understanding Predictive Analytics
Predictive analytics deals with information retrieval that is used to predict an unknown event of interest, basically an upcoming event. Using technology that learns from previously accumulated data to predict future events could help enterprises make better decisions. Data can be classified as both structured as well as unstructured. Readily available data like age, marital status and income are considered as structured, whereas textual data from call center, social media content, or other open-text types comes under unstructured. Using previous data, predictive models can determine patterns, which allow organizations to anticipate outcomes based upon more concrete information. Thus, the intention of predictive analytics is to improve human decision-making behavior, rather than relying on human expertise, personal knowledge, or subjective intuition alone.
How Has Cloud Technology Enhanced AI Applications?
A modern enterprise system architecture should be able to seamlessly connect core enterprise applications such as Customer-Relationship Management (CRM) and Enterprise Resource Planning (ERP) with one another. It should also be able to connect it with other platforms to establish an open, collaborative and automated environment.
The result is a scalable ecosystem of enterprise applications and end-users sharing data among each other as well as with Internet of Things (IoT) devices and Cloud models. This could further help an enterprise in better traceability for assets, accurate predictive planning, visibility into mechanisms and better task processing along the application lifecycle. Taking all this into consideration businesses require security, scalability, flexibility, and much more, which makes stakeholders ask whether an organization is indeed ready for innovation and the knowledge required for effective planning and execution. Cloud services provide all this and much more to developers who can seamlessly leverage the benefits of AI from the cloud. Developers can gain unlimited and pay on demand access to AI services from cloud, which can in turn enhance the application performance. Sophisticated AI based add ons can be efficiently implemented into the application or used for application development from the cloud as well.
Technologies That Have Brought Artificial Intelligence To The Forefront Of Application Development
Below, we elaborate on a few broader technology developments that are leap-frogging the adoption of AI. These are few of the trends or factors that have brought AI to more people and organizations, for the purpose of enterprise application development.
One of the first and foremost trends that empower the rapid growth of Artificial Intelligence in enterprise application development is cloud computing. The AI strategies are based on complicated analytical models and require substantial amounts of training data to acquire their intelligent capabilities. Therefore developing, updating and managing AI applications demands extensive computing power. The three Cloud service models (PaaS, SaaS, IaaS) propose that in a scalable and flexible environment at comparatively lower-cost and without huge investments.
Nevertheless, the IT infrastructure of multinational companies are often too large and inflexible to experiment with AI applications across and within the business platforms. AI cloud services such as Amazon AWS AI and IBM Watson, allow developers to quickly build and run enterprise applications.
Next among the accelerators for AI in enterprise app development is big data. AI techniques make it feasible to analyze and process data, allowing businesses to gather relevant insights from the information and enhance their decision making. It can potentially find patterns and complex relevant data by browsing through billions of observations in a matter of seconds.
Possibly the simplest way to begin developing intelligent applications is by utilizing Application Programming Interfaces (APIs). An API is a piece of out-of-the-box functionality that can be requested from a different program or application. For example, your application requires speech recognition, you can call an API rather than program it yourself. Several large-scale technology companies provide APIs for computer vision, image recognition, and natural language processing (NLP) or other cognitive domains on their cloud platforms. Intelligent APIs are pre-configured and pre-trained models for a particular task and serve as a passage to artificial intelligence-based enterprise applications.
When you require to complete a machine learning task that isn’t available through an API, Open Source provides you with the opportunity to build one yourself. Nowadays, more artificial intelligence-based frameworks and algorithms are accessible as Open Source, suggesting they are publicly obtainable and often require no license cost. Therefore, developers of enterprise applications can simply depend on the knowledge and previous achievement of a large user base.
In conclusion, adopting artificial intelligence for enterprise-scale application development can help a company identify what is essential before embarking on large scale journeys into the technical unknown. It provides a myriad of solutions that can make the complicated task of enterprise app development much easier by improving their problem-solving skills and providing them with required assistance.
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