AI Competitive Edge for Business Solutions and Applications
The growth of Artificial Intelligence in recent years brought forth a major challenge for brands in deploying such AI solutions. Many brands lack the clarity regarding where to start the AI integration process and profitably deploy these solutions in the most effective manner.
A PwC report estimates that AI is capable of contributing almost $15.7 trillion to the world economy by 2030. Today, every business is aware of the AI’s operational benefits in the modern technological territory ruled by data mining. However, such a position also poses a major challenge in front of human operators: which tasks are to be assigned to software and which are to be kept under their watch.
Implementing AI Solutions Systematically to Solve Real Problems
The first step to develop intelligent platforms is the implementation of AI in the application layer. Database operations can assist in enabling AI-driven APIs to process the NLP, image patterns and speech/text. As AI applications become diverse, a systematic way to solve real-time challenges becomes significant to make way for a centralized paradigm shift:
Utilizing Existing Platform or Building Own
It is highly probable that AI will change the technology game by enhancing the efficiencies of business processes. However, businesses are still unsure about AI’s potential and are in constant confusion how it can bring efficiency in their processes. Hence, the biggest question is – whether to implement an existing application or build an own. Utilizing an existing platform/application built by an AI development company is definitely going to save costs, time and maintenance. Moreover, they enable an easy entry to the market with lower barriers. Many app development companies provide AI and Machine Learning services for developing real-time solutions. It also makes an organization free from all the risks associated with data security and validation. Some of the most popular AI applications are TensorFlow by Google, Fast Text by Facebook, Keras which is also written entirely in Python.
On the other hand, businesses can also build an AI application of their own if they are dealing with a very niche product. Since niche products require complex processes, many standard AI platform vendors don’t usually include them. Plus, such organizations might already have invested in data intelligence resources. If they want to build their own application, many cloud platforms also offer ways to build an AI application with all the required tools and modules.
Beginning Small with Lots of Smart Data
Implementing AI in the database and operations, should be a planned process instead of just jumping onto the bandwagon. Whether it’s a small, medium or large scale business, massive and immediate investment can prove to be detrimental. They can begin by first-party app integration to increase employee productivity. Afterwards, they can move towards AI systems that are open-source, and provide more flexibility in the workflow. This way, they can keep concentrating on a smaller goal and yet gain a positive Return on Investment.
Smart data form the backbone consisting of the right quality and type of clean and structured first-party and third-party data leading to smart decisions. Businesses can gain precision and a comprehensive view of the data, along with constant and real-time upgrades. Smart data is a great source of obtaining dynamic signals and classification for forecasting demand and supply operations. Cleaning the input data is another important process that involves entry-point protection, verification, maintaining and refreshing data, flagging irregularities, and then connecting the cleaned data across various systems..
Injecting Transparency and Implementing Analysis
Gartner’s 2018 survey highlights that out of the companies surveyed, only 4% have invested and implemented AI solutions. Implementing AI includes everything from applications, production, investments, an AI-driven culture, work environment and management. It would help in creating an organized two-way network between Machine Learning with human supervision. It would eventually assist in gaining better insights regarding producing the right output from the machine. The supervision can ensure that humans are capable of enhancing or restricting the output algorithms.
AI analysis is another vital process that includes predictive analysis for broadening the scope of a business. It’s excellent for the businesses not looking to invest heavily on ML. Many analytics software offer business intelligence solutions like H20, Microsoft Onboard, and Amazon Machine Learning. AI cloud is a great option for any business to invest. They help in saving costs, maintaining infrastructure, and are easily scalable. Businesses can avail direct sources of AI and ML services without worrying about the right selection of algorithms and models. Plus, they provide exposure to voice and text bot services, enabling businesses to develop digital assistants with IoT.
Strategizing, Experimenting and Integrating
The success of AI implementation in business processes depends highly on how well the applications are aligned with the business goals. Hence, strategies should also be created by defining the purpose and prioritizing them. Investment from all sectors almost tripled two years back in 2016 showing a jump to $39 billion from $26 billion. Since the current AI technologies like neural network ML and NLP are proving their mettle, it’s a good time for brands to experiment. Using AI in scaling businesses and core operations results in increasing revenue, gaining market share and revolutionizing their products.
Another crucial parameter to consider is assigning teams for handling AI initiatives. Both technical and business teams must be assigned to supervise such processes for launching compelling new technologies. A systematic approach to utilize the tools from a spectrum that can either solve business problems or have higher potential; can help in building a growth path that is robust and proactive.
Final Thoughts
AI deployment in production processes has its own risks for both small and large scale businesses. It needs proper planning and research to build an AI application from scratch. Plus, there are further risks to maintain and support it, if the algorithms break. New codes are also full of many bugs and errors when they are implemented and deployed. However, current scenario has many AI platforms and cloud applications that offer access to third-party tools and user-friendly applications to businesses. Companies offering next-generation solutions enable a smoother workflow apart from making businesses capable of meeting an increasing number of data intelligence challenges.