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Machine Learning: Industry Applicability and Adaptability
Machine learning is the most annotated topic on the World Wide Web. It is a diversified area of technology where a vast proportion of companies are deploying smart tools based on Artificial Intelligence. A complete transformation of technology from the outset of AI is yet to be achieved, but the foundation for such a revolution is already established. In the year 2017, threefold jump in investment is predicted in the field of artificial intelligence (AI). The enormous influx of funds is likely to turn AI into a $100 Billion market by the end of 2025. The tech fraternity firmly believes that AI will radically change aging business models in the next five years. Meanwhile, it is likely to free up the time taken by Project Managers to reach consensus.

Machine learning is a growth enabler where companies are seeing fast-paced development through optimization of processes. It is a comprehensive system that takes care of all business aspects including team interaction and customer satisfaction. Here are few instances how companies are tapping the right values through AI and machine learning.

• Delivering Customer oriented services

AI enables businesses to provide rich customer service while keeping costs in check. By design, it favors industries that distil heaps of data using the gold standard of marketing. By collating data from historical transactions with smart algorithms, consumers begin seeing the value with no further ado. Perhaps, 44% of consumers from the USA are more likely to engage with chatbots than a human representative. The machine learning algorithms are modeled to consistently recognize patterns from every interaction so that they can predict and respond to similar situations in the future without human interference.

• Keeping loyal customers closer

With the help of AI, companies can recognize clients who are on the verge of ebbing away. By understanding consumer sentiments along with distinct social behavior, it is easier to anticipate risks ahead of their occurrence. Working with rich dataset allows optimization of existing operation strategies while enhancing the quality of engagement. For example, telecom customers port out from one carrier to another if they are unhappy with the values. If carrier-A employs machine learning model, the system could sweepingly track the call usage pattern to provide right-fit offers before the client is lost.

• Automating commerce

Machine learning models can seamlessly detect anomalous conditions that arise during the financial computing. For example accounting firms manually sort-out anomalies in the financial transactions. It limits the overall speed and efficiency of the workflow. However, machine learning algorithm scans all running processes and learns patterns from specific datasets within the challenge. Subsequent results enable fluidic pipeline movement and spare the finance teams from the burden of manual analysis.

• Forging strong brand value

Machine learning algorithm captures an array of datasets such as insignia, demography, products, and more that are seen on media to decide the optimum method of gaining visibility. This feature is highly influential in obtaining a niche data that will help Corporate sponsors see a profitable return on investment (ROI) for every investment with an in-depth insight of their market. For instance, Machine learning can assist in determining the ideal position for branding agencies to place their insignia to gain better visibility in the televised sports event. The system tracks the elements such a duration of coverage, visible area, and much more.

Identifying fraudulent practice

Annually organizations bear losses of up to 5% of revenue by being victim to false propositions. Having business models that mesh historical transaction data with social information helps engineering machine learning algorithm that adeptly foresees irregularities and red flag scenarios. It helps uncover dubious transactions within a nexus of people. Machine learning based risk detection copes with a wide range of threats that manifest within cyberspace.

Prognosticative service alerts

Machine learning analyses the failure threshold within systems to become aware of possible anomalies in advance. The disruptive alerts are meant to prevent prohibitive repair expenses that may otherwise have to be borne by manufacturers in the aftermath of system failure.

Machine learning is finding its place in many other areas such as:

Planning career trajectory

Data-driven insights could help prospective candidates to plan their career with precision. It enhances the scope of knowledge at the outset so that career decisions can be made based on interests and area of expertise for achieving a better level of satisfaction and growth.

Shelf management of store brands

In the retail shelves, Machine learning keeps track of stocks for a better customer experience. It also helps the brands to cater customer-centric experience based on vivid datasets such as the time spent by users on aisles, product queries, and more. Strategic placement of store display is enabled through a comprehensive data provided by AI.


Machine learning is a way of envisioning propensities of business elements through enhanced marketing intelligence. The tremendous potential of extensive datasets has prompted many existing and emerging companies to assimilate AI into their current technology stack.

However, machine learning comes with certain constraints in the present times especially because the system deals with volumetric data for training intuitive algorithms. In many cases, access to high-quality data is limited and are mostly unusable when required. Using unstructured data can come at a cost as companies can be prone to bad decision-making.

To mitigate risks, companies must leverage the existing pool of information to determine areas where process automation is readily applicable. It is important to align opportunities in the pipeline with long-term digital strategies through prioritization. A chunk of organizations is incorporating AI within their solutions that are tightly knit with various business departments.

Although machine learning comes with the lion's share of benefits for organizations, the ability to touch customer data on regular basis mandates thoughtful governance. AI is primarily a resource to mitigate risks and boost opportunity. Organizations must frequently review the areas of privacy and capture data after obtaining prior consent from people.

The growth of AI can’t be upset by any measures. It is profoundly entangled with rich data that is exponentially growing alongside. It is no longer a debate if companies should embrace AI because it is a proven profit driver for organizations that are taking advantage of its features.

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