Machine learning (ML) is quickly becoming a mainstay of the enterprise business world, yet entrepreneurs and small-business owners may shy away from investing in it. While you may not fully understand the ins and outs of ML or how it can benefit your small business, you can still make effective use of the technology without being an expert in it.
1. Use Pre-Trained Artificial Intelligence
Most ML models will require tons of data (the majority of them require supervised learning), which translates into a large effort that most entrepreneurs and small-business owners can’t sustain. One approach is to leverage SaaS/PaaS services, such as the AWS portfolio of pre-trained artificial intelligence (AI) services: Comprehend, Rekognition, Lex, Personalize, Translate, Polly and others, each tailored to a specific domain.
2. Streamline High-Touch Functions
Machine learning only works if there is a large enough dataset. Otherwise, you are going to be arriving at insights that are premature. However, small businesses that are strapped for resources can leverage ML to take care of high-touch functions, such as customer care and chat, by looking at past resolutions.
3. Find Analytical Solutions In Known Environments
Machine learning can be used in robotics, business strategy planning, telecommunications—in other words, wherever the environment is known but the analytical solution is not. Entrepreneurs and small-business owners can take advantage of ML to see what actions yield a higher reward over the longest period. Financial companies use reinforcement learning for stock trading, for example.
4. Facilitate Data-Driven Decision-Making
Machine learning can facilitate data discipline, allowing for stronger decision-making. Machine learning is often viewed as an outcome for small businesses that allows for forecasting future growth. Inherent in getting there, however, is establishing a culture of data discipline and demystifying data-driven decision-making across departments. This provides companies with the groundwork that will see them thrive in the coming years.
5. Automate Content Creation
A really exciting emerging use of ML is for content creation, whether marketing or instructional material. There are now ML tools that can automatically create presentations, transcribe videos into documents, or generate the framework for a blog post or white paper based on a bit of input. With a bit of tweaking, these tools can help create content faster and require no knowledge of ML.
6. Develop Go-To-Market Strategies
The central advantage of ML systems is their ability to manage large datasets and extract actionable insights without much human intervention. Smaller and younger companies don’t typically have stocks of big data, but as they build their databases from digital marketing, customer relationship management (CRM) platforms and customer files, they can put ML to work to help develop go-to-market strategies and targeted marketing.
7. Expedite Administrative Work
Use ML to expedite administrative work. Machine learning is increasingly effective at pattern recognition and classification tasks. Therefore, consider using ML to work on your invoices and other administrative activities. Machine learning tools are useful at processing invoices that are acceptable and generating red flags for items that require further investigation.
8. Categorize And Solve Business Problems
Machine learning is about solving business problems through data. Data can help you understand what happened, why it happened, what’s going to happen and what needs to happen. First, learn how to categorize your problems into these four categories. Second, realize that the project life cycle of ML is different from that of most tech. If you understand both of these points, you can get a lot of value out of your initiatives.
9. Synthesize Game-Changing Insights
Machine learning is a great equalizer for entrepreneurs and small-business owners. It gives them the capability to synthesize game-changing insights from massive amounts of data. In turn, business leaders can greatly improve their decision-making abilities. This means they can work smarter, not harder, when it comes to understanding customer needs, improving workflows and even re-engaging leads.
10. Simplify Reporting And Forecasting Processes
Machine learning can help power through mountains of data to find the insights small-business owners and entrepreneurs need. For example, aggregating data from a few tools in their tech stack to produce regular reports to forecast sales and marketing. Tech today produces massive amounts of data, so it’s a challenge to sort through and make sense of it. Machine learning can simplify the process to make it more efficient and useful.
11. Identify Security Dangers
Security is a vital part of a business of any size, and cybersecurity threats are among the most dangerous things that a modern business can come across. Machine learning can help us see and identify the dangers faster, which can result in a higher level of protection and a lower level of spending, which is a game-changer, especially for smaller businesses.
12. Improve Back-Office Operations
Machine learning is everywhere these days, from software to recognize objects and faces to financial analysis, and much more. Business owners can use it for various purposes—to automate and streamline their back-office operations, for example.
13. Deliver Personalization And Convenience
Personalization and convenience are hallmarks of a winning customer experience. Leading cloud platforms have commoditized common ML services that help small businesses deliver on these promises. Now, business owners can focus their efforts on managing potential trust and privacy challenges that arise from the use of the data driving these ML opportunities.
14. Predict Customer Deal Results
Machine learning works if the small business uses a technology solution and has a process for its employees to capture data in that system. So, if a business owner has a CRM to capture sales activities, such as logging calls and emails to leads and customers, then AI can help in predicting which deals can be won or lost quickly, allowing for action to be taken on them.