Machine Learning Strategy

Machine Learning

The artificial intelligence market is predicted to triple in 2017, becoming a $100 billion industry by 2025. In order to stay competitive, companies are turning to new AI software and solutions in their business operations. For example, Google recently launched an AI-powered job search feature right on its search results page.

As a small business owner who has personally benefitted from AI tools without spending thousands of dollars, I believe it’s critical for you to get educated and start reaping the benefits of AI. Doing so will not only mean a competitive advantage for your business but will also add a strong value driver to your company.

To support a corporate transition to AI, here are five ways to boost your machine learning and AI strategy:

Personalize your customer service.

Prompt and efficient customer support strengthens brand loyalty and client satisfaction. Therefore, what could be better than an opportunity to improve customer service while decreasing costs? Chatbot technology, based on recent advances in natural language processing and continuous learning algorithms, can perform better than many human operators. Advancements in chatbot technology have already shifted preferences of 44% of U.S. consumers to non-human consultants, according to data from the 2016 Aspect Consumer Experience Index.

Companies like iWire365 augment the existing customer support communication with an intelligence layer of proposed answers that humans can either approve or edit before sending. These types of augmented intelligence applications allow humans to mitigate risks of error. They are also gaining adoption across enterprise companies due to their sensitivity to the cost of making mistakes when dealing with customer support.

Speed up your data and predictive analytics.

Business leaders are pressed to make important marketing, sales and investment decisions on a daily basis. However, getting actionable insights from data using traditional data modeling methods may take weeks. Contemporary AI-powered predictive and business analytics solutions solve this problem via automatic interfaces that allow even non-technical users to get instant models, patterns and meaningful insights from data. IBM Watson Analytics, based on Watson’s question answering machine, is one of the most advanced solutions that allows users to receive data-driven responses to questions regarding any aspect of the business, such as sales, finance, human resources and marketing. Integrating predictive analytics can help managers leverage the full power of big data in business operations.

Refine your human resources management.

Hiring the right people in the right positions is a hard task, especially if HR managers are bombarded with thousands of CVs and cover letters. Sometimes, implicit biases can creep into job opening descriptions and the job interviewing process, and qualified candidates might be overlooked.

Automate your marketing.

AI solutions promise to automate many marketing tasks, such as email marketing and lead management. Instead of performing simple and repetitive tasks, marketers can focus on producing creative ideas. AI companies like Marketo are already offering AI-enabled systems to build marketing campaigns, attract and retain customers more efficiently by using predictive analytics, provide sales forecasting, identify potential clients and predict user behavior.

Quickly detect fraudulent activity.

Fraudulent transactions can lead to huge losses for all major businesses. Traditional security methods are good in protecting networks and communication channels, but what about fraud prevention andunmasking it in the making? Modern anomaly detection algorithms leverage the power of machine learning to learn patterns of fraudulent transactions and behavior. Algorithmic security solutions can help companies discover suspicious transfers between individuals and intercorporate connections to prevent corporate espionage and insider trading.

AI’s rapid rise is inevitable, and the question now is not whether to adopt AI or not, but how fast managers can do so.

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