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How AI is Solving Complex Data Challenges to Enable Smart Business Decisions

October 18, 2019 - Mamta Giriyappa Hunasikatti Senior Analyst – Product Management

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For people in the technology industry, Artificial Intelligence(AI) is the new buzz word, though its true potential or value is yet to be understood and realized.

Facebook shutting down its robot chat program because the robots devised a language of their own, while learning from humans to negotiate, was a huge buzz. For all the noise it is making, the application of AI in everyday life is still limited. The term AI itself can be expanded across different levels of maturity

Domain-wise examples of AI usage to solve complex business challenges

The application of AI in various domains makes for very interesting reading.

Human Resources Management

Using its tools internally, IBM has cut its HR department by almost a third and can now better identify employee skillsets and skill gaps. This has helped the tech firm become more transparent about career paths and opportunities for employees. Another example is of algorithms helping in filtering job applications. The machine is trained to look for specific keywords and skillsets in the applications to shortlist candidates.

Healthcare

The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, Massachusetts General Hospital, and National Health Service have developed AI algorithms for their departments.

Large technology companies such as IBM and Google and startups such as Welltok and Ayasdi have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI to support operational initiatives that increase cost savings, improve patient satisfaction, and satisfy their staffing and workforce needs.  Companies like Hospital IQ are developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.

Retail

In the retail space, AI can help multiple stakeholders. It can help shoppers sift through tons of merchandise by recommending the most suitable product based on their purchase/ browsing histories, it can help CXOs plan more effective promotions and events. It can help HR managers reduce the headcount in customer service by automating the frequently asked questions, it can help business to predict the CFR (Cost and Freight) for orders.

Edgeverve’s TradeEdge suite has a few retail-specific AI-ML offerings –Trade Promotions Optimization, Perfect Order Measurement, Suggested Ordering to name a few.

Legal

Legal analysts are being gradually replaced by algorithms that scan contracts and documents and interpret them as programmed. So, in a way, the case outcome could be partly dependent on a machine.

Financial Markets: Sentiment analysis is an interesting field where machines scan the web to understand the perception of companies based on online content. Trading decisions are based on these sentiments.

The Not-So-Visible applications

Large organizations invest a good percentage of their funds towards promotions planning and execution. However, considering the vast number of internal and external factors that influence buying behavior, it is very hard to justify or apportion the promotion costs to sales. AI is being used nowadays to track promotion effectiveness so that more pointed promotions can be planned and executed.

Another application closer home is the use of AI/ ML-based data harmonization tools in master data management to de-duplicate data, enrich attributes and align master data from multiple sources with a global master.

Other use cases where AI/ ML is being explored are recommendations engine, suggested orders, and perfect order measurement.

In Conclusion

These are just samples of how AI is helping in complex decision-making by unlocking the potential of the huge data volumes that businesses have been storing over the years.

There are quite a few naysayers who warn about the pitfalls in over-reliance on machines for everyday operations as well as critical tasks. However, the reality is that AI is here to stay. If leveraged well and with human supervision, it can ensure great strides towards the future.

Mamta Giriyappa Hunasikatti

Senior Analyst – Product Management

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