November 5, 2020

How Industries Are Using Artificial Intelligence To Scale Operations

From inventing wheels to inventing intelligence, the history of humankind has been phenomenal, and the biggest motivation behind all these inventions is the comfort we love to possess and celebrate as a collective society. The common denominator of our behaviour as an individual is to find out how we can do something productive without exhausting ourselves.

Who doesn’t want to enjoy the luxury of life without working tirelessly?

Ask yourself and you will find a positive answer. Here, I am going to tell you exactly about one of the biggest achievements we have given ourselves and how it works.

Artificial Intelligence (Not a Rocket Science)

We often run away from exposing ourselves to science because of its complexity and make it more unreachable for common people presuming our hardship to understand coding or data or algorithms. Here, I would simplify it by connecting our daily life to the process of science related to intelligence (both human and artificial).

Artificial intelligence (AI) has been moving forward, and somehow we are becoming part of it whether knowingly or unknowingly. However, the question is not about recognizing the very existence of AI or Machine Learning but becoming aware of its rate of adoption.

According to Amara’s law, “the impact of a new technology is typically overestimated in the short term, but underestimated in the long term.” So, if you think we are still very far from exploiting the full capacity of AI, bear with me. Let me introduce how different industries deploy AI to scale their operations up by using machine learning to optimize their work processes - a.k.a. the democratization of AI.

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Five Possible Ways for Scaling Operations

Aligning AI Strategy with Your Business

The first thing that comes to mind when we talk about involving AI into any field of industry is its cost-cutting ability and making the whole process more efficient. But we tend to misunderstand the cost-cutting tool by eliminating staff instead of seeing it as a tool that reduces repetitive and dull parts of our jobs. Companies are expanding their businesses through collecting data from consumers and forming creative strategies by leveraging the data with the help of AI i.e. data lakes.

AI is where the most important profit and development can be made; building machine learning models that are about your venture, its products/services, and your specific customer offer. It requires you to retrieve the right data, and use it, rather than simply employing or procuring generic AI-based tools. Different e-commerce and social media companies are taking advantage of AI to create more efficient strategies for their businesses.

Cross-Functional Collaboration Platform

Cross-functional AI platform is a process of collecting intelligence from real-time enterprise data and available information while continually learning and creating knowledge for future tasks. Different sectors largely use cross-functional patterns to make a solid single application that targets the demand of the market. Data extraction comes from various business functions like marketing, IT, operation, customer success.

Cross-functional collaboration generally involves different employees for a certain period of time including data scientists, data architects, data managers, translators, user interface designers, and business analysts. This is a cost that brings benefits such as avoiding duplication of efforts, automating low-value tasks, and improving the reusability of work. Companies like Grammarly, DeepMind, CloudMinds, and Nauto are using this method to meet the demand of their consumers.

Algorithm as Microservice

Peter Sondergaard from Gartner once said, “data is inherently dumb. Algorithms are where the real value lies.” Data might be the new oil for the world community, but algorithms are what makes it valuable. Many big companies like Google, Netflix, and Facebook are using algorithms to create value out of it and making a big change to society. Algorithms as microservice helps data flow from one place to another, which means code can be written anywhere and stored in any cloud storage that is very affordable to keep huge chunks of data safe and protected. Companies would be able to work efficiently by manipulating data and extracting key insights at a later time.

Software companies use algorithms as microservices to reinvent their applications and better perform to meet the expected result. Continuous flow of data gives better judgment capability to AI models through which companies scale up the supply to the market. For example, YouTube offers better recommendations after analysing the data stored from its consumers, which dramatically improves the engagement with their websites.

Applying Strong Data Practices

Data is the base for Artificial Intelligence and Machine Learning; the more you invest in data, the better the results it will provide. Many companies are investing to get strong valuable data to make their AI mature, which will eventually improve service efficiency and performance. Companies hire data engineers, data scientists, data analysts, and data designers to make data more usable for machine learning. Purifying and designing data is as equally necessary as transferring it via algorithms.

Strong data practices include which data can be used; how and where different data sets should be stored; how data quality is monitored and maintained; how data is used and tracked; and how metadata, including data definitions and data lineage, is documented.

Cloud Components for Agile Development

Software companies use Agile software development methodology to boost and update their product from time to time. The testing and development continue to grow together without wasting time by using multiple data collected from different sources. Agile is a term used to describe software development approaches that employ continual planning, learning, improvement, team collaboration, evolutionary development, and early delivery. For this entire process, software corporations use cloud computing to cater different data from various sources at one time. Customers get filtered data where they can choose their favourable product and make changes to it according to their needs.

Conclusion

Industries are very welcoming to artificial intelligence not only because of the surplus and the cost-cutting abilities but also because it makes their product closer to the demand arising from the market. We are already witnessing cars racing through traffic without drivers and drones supplying food to the doorstep. Denying the power of artificial intelligence would be a big miss. The era of artificial intelligence has just started, and this is the right time to match human intelligence to the AI revolution for our future development.