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Can AI Be an Asset for Business?

6 ways to improve productivity and profits with machine learning

The next great revolution for business has already started. Under the umbrella of artificial intelligence (AI), machine learning involves “training” computers to augment human actions and ultimately learn, identify and predict. AI can provide businesses with improved processes that not only enhance employee productivity, but can also have a serious positive impact on the bottom line.

The algorithms, fed vast amounts of data, once required a great level of knowledge to interpret. But rapid AI advancement over the past few years, including machine learning outpacing mobile app development, means these techniques have become easier and even more accessible for businesses to adopt.

Let’s take a deep dive into how AI, and specifically machine learning, can improve employee productivity, business efficiency and profitability.

Artificial Intelligence Coming From Head

1. Improve Sales and Marketing
Redefining sales and marketing efforts using AI is the most commonly used current application of machine learning for business, with chatbots leading the charge.

Chatbots simulate realistic conversations with website visitors. Virtual assistant chatbots rely on artificial intelligence to learn and figure out a customer’s needs. There are also messaging chatbots that prompt for the right keyword and return information based on scripts. Going beyond product information, chatbots can do things such as schedule meetings and help process purchases.

As the top digital trend businesses will implement in 2019, chatbots have evolved to be more intelligent and more human than ever before. The virtual assistant chatbot market is set to experience a hike from $1.6 billion in 2015 to $15.8 billion by 2021. In addition, an estimated 1.7 billion people will use messaging chatbots in 2019. Chatbots are expected to cut business costs by $8 billion by 2022 according to Juniper Research. That does not include billions in savings by eliminating front line customer service salaries, while freeing up employee time and budgets for higher level projects.

2. Boost Customer Satisfaction and Loyalty
AI has grown in consumer adoption because machines can find information quickly and more accurately than human counterparts. One machine learning application is computerized prescreening in contact centers, quickly routing incoming calls to the right department, helping to reduce call durations and increase first-call resolutions. Using customer’s time efficiently increases repeat business. This holds especially true for the short attention span of millennials, who want instant service at the moment it is needed or they will look elsewhere.

Another example that impacts customer service is AI that learns and responds to your preferences. When you watch a movie on Netflix, browse or purchase products on Amazon, machine learning algorithms record and interpret your preferences then offer suggestions for your next interaction. When a customer revisits a site such as Amazon, preferences from earlier sessions will be remembered because of machine learning.

A variety of industries are adopting these predictive algorithms to deliver content or recommend products based on specific customer preferences, helping to boost customer satisfaction and loyalty. The newest trend to watch is AI applications that prompt for even more consumer input, helping companies provide highly personalized and guided shopping experiences.

3. Increase Employee Health and Safety
AI is enhancing a series of workplace innovations for occupational health and safety. Integrated workforce monitoring via the cloud, trackers and digital platforms capture data while engaging with employees. The benefit to companies is reduction in accidents and sick time, while employees benefit from increased productivity and wellness. Here are a few specific applications of machine learning (ML):

Cognitive insights: ML can process large amounts of employee data and apply predictive analysis to improve employee performance and recognize unhealthy or dangerous modes of work. Cognitive engagement tools can range from intelligent resources for targeted training to assisting with confidential health treatment recommendations.

Workplace relationships: ML can provide insights into team dynamics, track progress and projects, and report how well employees collaborate during the day.

Office design: ML can improve physical and mental health by helping architects design buildings that create spaces that feel open and limitless for better productivity, transparency and safety.

Equipment predictive analytics: ML can be applied to monitor equipment health in real-time and predict malfunctions or failures that might put employees at risk. Machine learning algorithms can quickly scan through historical data to understand which factors may have led to catastrophic safety events, preventing future events or identifying areas of highest risk.

4. Streamline Operational Processes
Where machine learning really shines is in streamlining operational processes, creating greater efficiency and enhanced productivity. Data from different business sectors can be seamlessly accessed, breaking down silos and automating entire processes and workflows. Intelligent automation takes this a step further by incorporating both historical datasets and real-time data from things such as sensors.

Accessing and interpreting big data sets provides valuable insight into customers’ behaviors, demographics and buying patterns.

5. Automate Processes
Automating mundane or repetitive work saves employee time and costs. For example, fields that access and apply large data sets, such as attorneys and architects, can cut time and effort when building a case or project. This doesn’t mean that machine learning is used as a replacement for human thinking, design or problem solving. But ML does accelerate processes by letting computers handle data tasks.

6. Improve Security
Cybercrime is an unfortunate hallmark of the times we live in, with bad actors continually finding new ways to compromise corporate computers and systems. A once hugely labor-intensive process to search through security and system logs can now be handled efficiently by AI.

Machine learning and analytics identifies and fixes threats while optimizing performance. The result is safer data handling and less work for security and IT professionals.


The Takeaway
If you know little more about AI than what the initials stand for, you are not alone. According to a 2017 McKinsey Global Institute survey, only one in five executives are using any AI-related technology. But in this rapidly growing industry, machine learning applications are becoming more accessible at the enterprise level. The best way to jump in to the game is to start with a problem you want to solve, such as increasing employee productivity, and look at available AI solutions. Being aware of what is available to enhance your business bottom line through machine learning is using your human brain wisely.

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