Whether your company is large or small, well-established in your industry or a brand new startup looking to make a big splash, you should know that machine learning and artificial intelligence can help you make better decisions, do more with less, and get an edge over your competitors.
The use of AI and ML is growing because companies find that it helps them maintain a healthy bottom line.
McKinsey’s latest global survey about artificial intelligence reports that “a small contingent of respondents coming from a variety of industries attribute 20 percent or more of their organizations’ earnings before interest and taxes (EBIT) to AI.” McKinsey also noted that half of respondents say their companies have begun using AI for at least one business function.
With that in mind, it would be helpful for you and fellow stakeholders to review various ways AI and ML could play a role in your business. This will help stimulate your imagination to use these powerful software tools sooner rather than later.
Find Hidden Value Locked in Your Ever-Increasing Trove of Data
Think about all the data your organization collects and stores on customers, potential employees, vendors and other parties that you engage with or would like to engage with.
While structured data is automatically set up to make it easier to search and use, such as in a spreadsheet or database, you probably also have a large amount of unstructured data, which is difficult to analyze under normal circumstances. But you can apply machine learning to examine this treasure trove of information.
You’ll gain new insights that you can use to support future business decisions. Unstructured data come from various sources, such as responses to customer surveys, emails filled with complaints and praise and technical support reports on a new trend or problems. What kind of insight into your company might you glean from scanning the text of every exit interview? It might keep you from making bad hiring decisions in the future or prompt you to change how you onboard and train recruits.
Other data, such as what you derived from scanning the full texts of myriads of inquiries from vendors, potential investors and members of the news media, can all provide more data clues that you can start putting to good use today. But this will only be possible if machine learning is involved because no human can examine enormous data sets and get helpful insight. There’s simply too much to absorb, examine and synthesize.
Supervised Machine Learning is the Key to Hit Your KPIs
If you know the benefits of machine learning but have never deployed ML before in an enterprise, remember that it will be prudent to use supervised machine learning to get more out of all the information you’ve amassed. Why? Because without supervision, you cannot measure the performance and adjust models to ensure you are hitting your business goals.
As CIOs know, supervised learning isn’t about human beings supervising the machine-learning process. Supervised learning uses training data to teach a machine learning solution to get the output you’re looking for based on models showing inputs and correct outputs. Conversely, “unsupervised learning” is meant to guide humans in understanding data structure, e.g., customer segments, but it needs to be switched to supervised learning to optimize business KPIs.
In this way, ML enables you to do things such as forecast sales demand so you can optimize inventory levels or plan your resources properly (such as workforce or vehicles or your production line), or generate optimal promotions and recommendations. As an example, Google used ML to optimize their data management (resources) based on demand forecasts.
Customer Service With Robots
Depending on the season, your need for customer service employees may expand and contract accordingly. Bots can be a good way to serve your customers in a highly responsive way if used correctly – it is important to note, however, that Bots are never a complete substitute for human interactions but can be a great way to provide an immediate response while gaining valuable insight that can be used to route the right customer to the right specialist with the correct level of urgency.
As the U.S. Chamber of Commerce noted, “Facebook has created a virtual testing ground for chatbot companies with its Messenger app, but these findings signal another impact machine learning will have on business operations. Already, businesses are employing virtual chatbots to filter customer service requests, identify potential customers and streamline the customer service process.”
The key to customer success is ensuring that your highest-priority customers get to the right specialist as quickly as possible. While bots are not currently capable of fulfilling this specialist role, they can be used very effectively to engage with and triage lower-priority customers, as well as to ask good qualifying questions to provide input to ML models aimed at optimizing prioritization, routing and matching with sales or support specialists.
At a minimum, you’d use chatbots to be the front lines of customer service, taking care of the most routine and mundane tasks that computers can easily handle. This leaves the more complicated issues to your human workers to address.
Make the Most of Your Marketing Budget
It would be a more efficient use of your resources if you applied machine learning to your marketing efforts. Using ML to process profiles of customers that you’ve cultivated over the years will allow you to send them customized messages for better engagement.
You’ll be segmenting customers by age, gender, interests, purchase history and other factors, with analytics fueling your engagement program. For example, machine learning can help you optimize promotion planning – investing in promoting the right products or services or customers upselling/cross-selling and recommending products/services to increase profit margin or keep your customers engaged (which could lead to lower churn rate).
Identify Trends Such as Bias in Hiring
Machine learning can potentially streamline a wide range of processes in your enterprise. But it also could play a significant role in finding trends that would be troubling if you don’t intervene early on.
For example, Forbes pointed out “that many companies talk about trying to reduce bias in their hiring processes. Feeding all hiring data — everything from resume review to interview feedback — into an ML algorithm can paint a clear picture of the level of bias in the process.” Machine learning is well suited for examining data in painstaking detail to identify trends that ordinary humans find difficult or impossible to notice.
If your company is mandated to improve your workforce demographics by paying more attention to the hiring process, machine learning in the human resources department could help you meet such a worthy goal more efficiently. It is worth noting that there has been much talk of ML as one of the most dominant factors for unfairness or biased decisions, so it’s critical to implement it with a focus on fairness as a response to the bias problem.
Ready to Harness AI and ML to Transform Your Business?
Machine learning and artificial intelligence are solutions to data problems that companies, large and small, will increasingly be making use of in the coming years to maintain their share of the market as well as drive new business.
The team at F33 has over 15 years of experience helping organizations integrate AI/ML into their processes, transforming them to outperform their competitors. In particular, we have expertise in setting up companies to use machine learning with Snowflake for the Google Cloud platform. To learn more about how we can help you use this disruptive technology, please contact us today.