Technologies such as machine learning and artificial intelligence are already changing business and manufacturing practices all over the world. ML and AI are still new technologies that are often too expensive for small business owners. However, they will become more affordable over time, allowing small businesses to optimize their operations and increase efficiency.
Switching your business to an AI-powered operation comes with all kinds of challenges. AI solutions are expensive, they require expertise, and data scientists are hard to find. Let's see why digital transformation is worth the effort for small business owners.
Machine Learning in Small Business Operations
Surviving in a digital market requires small business owners to introduce new technologies to keep up with the latest trends. Most businesses have already switched to using various SaaS solutions, cloud-based software, and other digital products designed to help improve efficiency and productivity.
Introducing new technologies always comes with various challenges, and the same can be said for implementing machine learning. The effort needed to create an in-house ML system is a challenge even for highly-skilled developers. Even so, most business owners are willing to invest in AI because of the many benefits it provides.
The good news is that ML integration isn't that expensive anymore. Machine learning found an application in many different industries and business practices, leading to a massive drop in prices. It turns out that ML has a sky-high ROI because it's able to increase production and efficiency through automation. Moreover, ML also helps improve customer satisfaction and leads to informed decision-making. After all, machine learning has been around for decades, so developing a new system doesn't start from scratch.
Data scientists use existing ML systems and re-train them for specific applications. There are dozens of open-source algorithms available online for free. Businesses can train their models, but most simply hire data scientists to do all the hard work. The bottom line is that it's not that hard to create an ML system able to improve small business processes.
Machine Learning Use Cases
As we already said, many industries have been using ML to analyze data and find patterns. The practice has been around for decades, but the difference is that new technologies and increasing computing power make it available to more businesses. Here are some use cases where ML is already making a huge difference.
Machine learning is an essential part of most successful marketing campaigns today. Its ability to analyze customer behavior to identify new opportunities. As many as 43% of marketers already used ML to improve their models and increase revenue back in 2018. The percentage keeps growing annually, and AI will soon take overall marketing efforts.
Automation is a process possible only because of machine learning. ML systems use data to learn how a process works. It looks for patterns and bottlenecks that have a negative impact on production and efficiency.
Once it understands the system, it takes control over processes and completes repetitive tasks automatically. The same approach is used for chatbots, paperwork, and many other everyday business processes.
Machine learning systems require mountains of data to find insights that can help improve a business operation. The more data it gets, the more accurate solutions it can offer. Normally, the approach can quickly identify suspicious behavior and detect fake information. That makes ML ideal for financial monitoring and system security. Every new piece of information is identified immediately, allowing you to remediate the problem before the damage is done.
4. Product Customization
Mass production has been around for a long time now. The rules have evolved since then, and most customers today prefer unique products rather than identical units. For example, Nike allows customers to "create" their sneakers by choosing colors, details, logo placement, unique text, etc.
Machine learning is the ideal solution for personalizing customer suggestions. Just think of Netflix. It remembers the shows you've watched in the past and predicts the shows you might like in the future. The ML model predicts user behavior based on historical data to improve the user experience. The same method is used in advertisements for ad targeting.
Google, Facebook, and other major IT companies use machine learning to improve data quality. That's why websites have to keep optimizing their SEO to stay relevant. Other tools such as Ahrefs use AI to improve pattern tracking and provide accurate feedback to customers.
6. Smart Assistants
Voice-activated smart assistants such as Google Mini and Amazon Alexa use AI to find information and interact with users. For example, they can learn what type of content the user prefers and present only that type of content in the future.
Imagine how hard it would be to track the credit scores and retirements of hundreds of millions of Americans without machine learning. It would take an entire army of people to review data only from one city, let alone the entire country. ML, on the other hand, uses algorithms to predict credit scores and other financial information accurately.
Finding the right person for the job at hand can sometimes feel like an impossible task. Some positions require specific skills and experience, which can take your HR team months to find. AI and ML work together to identify ideal candidates immediately based on their data. Moreover, ML can also help improve employee training and implementation in existing processes.
Challenges of Implementing AI In Small Business Operations
Of course, rearranging existing systems and adopting new technologies such as ML and AI come with certain challenges. Companies such as Amazon, Facebook, and Google offer extensive training and detailed tutorials for business-related practices, allowing small businesses to adopt new techniques and technologies.
However, introducing ML models into business operations isn't that simple. Every implementation has to overcome the two main challenges - data analytics, and the user interface.
Since ML models use data to improve operations, the first challenge is to organize data pipelines. Every ML model learns from data, so you want to ensure that it gets top-quality information. Otherwise, the results might be wrong or inaccurate.
As a small business, you're likely not generating enough data for an ML model to do its job. Depending on the process you want to improve, you can either use data from embedded devices and physical assets with IoT sensors or streamline data from your website. The first approach will lead to improvements in machine manufacturing and product development, while the latter will improve the business aspect of the operation.
You'll also have to find an experienced data scientist to help guide the implementation process. That's much easier said than done in a time where millions of businesses are trying to do the same. Not to mention that you'll also have to keep up with the changing trends in algorithms and data analytics.
Even if all data analytics are in place and all data pipelines are well-organized, that doesn't mean anything without a simple user interface. Most small business owners are not skilled at reading data, so they have to hire a data scientist to help guide the implementation process. In a time when millions of businesses are trying to do the same, it's easier said than done.
It's either that or you need an AI software solution with an easy-to-use interface that is still years from becoming a reality. The user interface is still quite clunky and requires advanced coding skills and years of experience to set up correctly.
If everything you've learned so far seems too complicated, that's because it is. Small business AI and ML solutions are still to reach the point where they become smart enough to do most work. They can help, but nothing can replace human intuition and logic.
Still, ML models can uncover insights people never could. The right implementation leads to drastic improvements in productivity, customer satisfaction, logistics, employee efficiency, and many other key areas.