How much do you understand about Artificial Intelligence? Have you implemented it in your business yet, or are you using it to help your processes?
Simply put, it’s the technology making machines as capable as humans, if not more efficient in terms of speed and objectivity. Images of walking, talking robots may spring to mind, but actually, we’ve been using AI in much simpler ways for many years. Google’s RankBrain provides us with “Did you mean..” when we misspell something in Google search, and Facebook gives us the option to “Show translation” when something is written in a different language. Chatbots are also on the rise and with applications like Siri and Amazon’s Echo, almost everyone has a virtual personal assistant.
With these examples, we can see how AI has been applied to our daily lives and how simple it can be. The question is, does this technology have the ability to be really powerful or is it over-rated? And most importantly, how can it help us in business?
The Technical: We’re “Still Learning”
Machine Learning is a subset of Artificial Intelligence, that is designed to enhance all aspects of our daily lives. Machine learning is at play when Netflix predicts what we’d like to watch next, and when Spree finds the item we’ve been looking for.
Deep Learning, a subset of machine learning, takes inspiration from the human brain in creating algorithms known as artificial neural networks (ANN). The concept of deep learning existed years ago, too, but has recently become popular in the mainstream due to advancements in computing power. The difference, and what sets deep learning apart, is that deep learning algorithms perform better the more data they are fed, while other algorithms reach a plateau in performance.
The Practical: In the Workplace
Today’s leading corporations are implementing machine learning-based tools to automate their design process and they are starting to experiment with more advanced uses of deep learning for digital disruption. Investments in artificial intelligence are predicted to triple this year, becoming a $100 billion industry by 2025. In a recent survey by PwC, 30% of respondents said that they believe AI will be the biggest disruptor to their industry within the next five years. This will no doubt have major consequences on the workplace. Machine learning and deep learning is enabling companies to automate processes, increase their top-line growth, improve employee advocacy and enhance customer satisfaction. Here are a few concrete examples of how AI is creating value in the workplace:
1. Five-Star Customer Service
The potential to improve customer service while lowering expenses makes this one of the most exciting opportunities for companies. 42% of consumers are already use digital assistants, while 72% of business execs and 53% of millennials are using them. By combining natural language processing, historical customer data and deep learning algorithms that continue to learn from interactions, these numbers will keep growing. Customer service reps can jump in to handle issues or exceptions, while the algorithms can look over their shoulder and learn for next time.
2. Improve Customer Retention
Algorithms can now mine customer social sentiment data in order to identify customers who are at a high risk of churning. This allows companies to optimize “next best action” strategies and decide when to focus on personalised customer experiences.
A good example is when young adults come off their parents’ cellphone plans often move to other carriers. Mobile communications companies can use AI to anticipate this behaviour and make customized offers based on the customer’s usage patterns, before they leave and go to a competitor.
3. Hire the Right People
Most recruiters say that finding the right people in a pool of equally qualified candidates is the most difficult part of their job. Today, certain algorithms are able to quickly sift through CVs and shortlist candidates who are most likely to be successful at a company. There is software available that can even combat human biases by automatically flagging biased language in job descriptions and detect highly qualified candidates that may have been overlooked.
4. Measure Brand Exposure
Automated processes can now recognize products, people and logos. Advanced image recognition can now be used to track the position of a brand’s logo that appear, for example, in film footage at a sporting event, such as a rugby match. This is exciting for corporates for want to measure the exposure and return on investment of their sponsorships, with detailed analyses such as duration, size and placement of their logos.
5. Anomaly Detection
On average, companies lose about 5% of revenue per year due to fraud.
Machine learning algorithms can use pattern recognition to spot anomalies, exceptions, and outliers. This helps detect and prevent fraudulent transactions in real-time, even for previously unknown types of fraud. These models are built based on historical transactions, social media information and other external data points.
Banks, for example, can use this historical transaction data to develop algorithms that can detect fraudulent behaviour. These algorithms can also discover suspicious patterns of transfers and payments. This type of “algorithmic security” can be enormously beneficial in the financial industry, and is applicable to a wide range of situations; including cyber-security, corruption and tax evasion.
Invest in the Future
Now is the time to start looking at the different ways that your business will benefit from using AI, specific to your industry. To avoid investing in cutting-edge technologies is to avoid development and growth. Meltwater is the global media intelligence company that uses in-depth machine learning and deep learning to cover a broad range of situations: from measuring media exposure, detecting fraud, improving customer satisfaction and getting air-tight security. If you’re interested in learning more about how AI can improve your business processes, contact Meltwater today.