Machine Learning (ML) and Artificial Intelligence (AI) are redefining the business landscape. Businesses face constant change and are also quickly becoming inundated with data. For many companies, it has become necessary to bolster resiliency efforts and operations with AI/ML-driven decisions. In fact, the global AI market will surpass the $500 billion mark by 2024, the ML market at $30 billion.
AI and ML are often used interchangeably, but ML is a subset of AI. AI can be thought of as the ability of a computer to solve problems in a human-like way. ML enables computers to learn from data without explicit programming.
ML requires large sets of data. Most AI business systems today use ML, reflected by the multitude of pre-packaged ML cloud services available. For example, businesses can leverage ML services from cloud providers such as Amazon Web Services (AWS) to add intelligence to applications through:
- Text analytics
- Demand forecasting
- Document analysis
- Fraud prevention
- Image & video analysis
- Personalization recommendations
- Real-time translation
- Text to speech
Many businesses are innovating entirely new business models around ML. Consider self-driving cars, zero-touch grocery stores, cardless banking, robo stock advising, etc.
With these opportunities comes risk. Businesses must consider and prioritize ethics, bias, out-of-control algorithms, data preparation costs, and a lack of regulatory oversight. Overall, though, there is more to AI and ML than hype. There is a real opportunity not only for efficiency and optimization gains but for measurable transformation, resilience, and innovation.
With the advent of cloud computing, machine learning is now accessible for small businesses. In fact, in a 2020 IBM survey, 29% of small businesses surveyed reported that they had employed AI or machine learning to some degree in their operations. By 2030, a PwC study estimates that Al will contribute roughly $16 trillion to the global economy.
With all of this growth, there is a lot of hype about an AI revolution displacing millions of workers in the next 10-20 years. However, a 2021 study by McKinsey that takes into account the cost of AI automation estimates that only around 9% of low-skilled jobs will be fully displaced by AI. While that equates to about 14.1 million jobs that entail mostly physical and basic cognitive tasks, McKinsey’s study and the history of automation from the industrial revolution onward tell us that while automation eliminates low-skilled work, it also creates new kinds of work and more opportunities for business growth.
So, how does machine learning help SMBs evolve and grow?
Let’s start with staffing talent and keeping overhead down. Hiring an employee is an expensive prospect — one of the most expensive aspects of business operations. With the ongoing costs of job postings, career events, recruitment fees, background checks, plus onboarding and training, hiring a new team member can add up. According to a recent report, the average total cost for a small business to hire a new employee in 2021 is just shy of $8,000 — before the employee even steps foot in the door.
Machine learning doesn’t just lower the cost of hiring a new employee; it also offers another attribute to augment staff resources: machines don’t clock out. That means when your employees go home for the night, machine learning systems are still at work analyzing data sets and developing new solutions to further give your business an advantage.
Put simply, machine learning can do many of the tasks that require human intelligence — especially when it comes to recognizing patterns, understanding language, picking up on cues, solving problems, and learning from data. That means businesses can rely on machine learning for some highly specialized tasks, giving business owners the choice to utilize human staff in other areas and ultimately clear the way for innovation.
Staff can be reallocated from performing basic cognitive tasks to more productive tasks within your company. Machine learning helps you refocus your human resources on doing work that improves your bottom line and increases competitiveness. According to the same McKinsey study, the majority of business leaders no longer leverage technology primarily as a means to reduce costs; instead, they now “see technology as a way to build a competitive advantage, expand new products and services, and enable new customer channels and ways of working.”
An area where machine learning can help reduce costs is with training. With its inherent capability to learn and execute, there’s less need to train entire teams adept in strategic data analysis — machines can take care of much of the heavy lifting. But it can also do so much more. With cloud computing services offering subscription models for pre-packaged machine learning tools, taking advantage of the human resources who have advanced data analytics skills is more affordable for small businesses than ever before.
Some small companies lack full-time HR departments. However, new developments in HR software make it possible for small businesses to track, manage, search, and engage potential hires without the need for a fully resourced human resource management team.
By 2022, 70% of all customer interactions will be powered by technologies such as machine learning, chatbots, and mobile messaging, up from 15% in 2018, according to Gartner. This savvy technology is no longer reserved for Wall Street. Main Street has embraced the power of machine learning in a big way. SMBs can keep customer service teams small but maintain a high level of service by leveraging smart assistants and ML-driven knowledge centers that anticipate a customer’s need and direct them to the information they need.
And if you’ve ever used the chat box that pops up on many service or retail websites, you’ve engaged with AI and machine learning. Chatbots are an increasingly popular way for companies to offer customer support solutions around the clock. They provide faster service, targeted answers to specific questions, and a database of answers to frequently asked questions. AI-powered chat services build their own complex web of decision trees through programming and learning via human interaction and only get smarter and more helpful with time.
Have you ever noticed how Spotify’s suggested playlists are exactly what you want to hear, even if you’ve never heard of the artist before? That’s due to the savvy insights of the company’s machine learning algorithms, which analyze your music preferences and build playlists around your preferred artists, genres, moods, and more.
Small businesses can employ this same technology in their digital storefronts. Machine learning makes it possible to leverage data insights from tracking website visitors’ interests and purchased products to suggest similar products. They can also improve their site search functions to deliver smarter, data-informed results that promote conversions instead of sending their customers down a rabbit hole.
AI and ML have become a marketer’s dream, especially for a stretched-thin marketing team within a small business. So much data and so little time is often the complaint we hear. With ML, not only can these teams understand their data more easily, they can even automate the use of it. And this is no secret. 84% of marketing professionals used AI-driven technology in 2020, a significant jump from 29% in 2018.
As Machine Learning becomes more accessible for small businesses, their teams can better understand their customers, build brand awareness, engage new customers, develop thought leadership, and ultimately gain a competitive edge in their marketing efforts. For example, building trust through personalized content has become a growing focus for marketers, and while simple enough in small efforts, when scaled, this initiative becomes impossible. Machine learning can take specific data points into account — content and pages viewed paired with heatmaps to determine which specific parts of the content kept their attention longest, for example — to determine how close a customer is to making their next purchase. Then, marketers can automatically distribute targeted content to usher customers to the next step of the buyer’s journey. A/B testing tools make uncovering the best messages even easier, streamlining and expediting customer acquisition.
Predictive analytics paired with machine learning also empowers small business marketers to personalize advertising for a unique promotional offer for each customer. They can leverage historical purchases, customer behavior (both purchase and digital storefront navigation paths), frequently paired products, and more to put a unique offer at an attractive price for each customer at the point of sale online. By further combining these data points with both inventory and demand data, businesses can provide even better customer offers. They can even send out automated and highly personalized promotional emails when inventory gets low or gets restocked.
Delivering a high ROI on online advertising is also made possible by ML. Specifically for pay-per-click advertising, data-driven decisions are crucial. Providers leverage ML to give businesses insights into where they place their ads, how to structure their ad content, and when to put which message in front of which prospect. ML plays an even bigger role in real-time bidding, ensuring businesses spend the money they need to outbid competitors without ratcheting up their budgets to an unmanageable level.
Business leaders make critical decisions every single day. Using data provides a clearer path to the right answers, but it still requires a significant time commitment without the right technology. Machine Learning helps automate analysis and make the decision-making process easier, faster, and smarter. Streamlining operations across departments, so often unattainable in the past, is much more within reach with the aid of Machine Learning. For example, you can simplify inventory management by getting better insights on how data from e-commerce sales, warehouse operations, shipping logistics, customer data, and marketing trends all work together and affect one another, and pinpoint efficiency lags in your workflows
As data grows more important and small businesses feel the triple pressures of staffing, streamlining, and competitive innovation, machine learning is the smart, cost-effective solution. Fortunately, it’s easier than ever for small businesses to add these capabilities within their business applications and strategies using pre-packaged, pay-as-you-go cloud services. We work with small- to medium-sized businesses to enhance their critical business systems with AI and ML capabilities. By leveraging advanced capabilities with Amazon Web Services (AWS) cloud technologies, data collection, analysis and predictability are employed to build better insights, streamline operations, and increase revenue. Fast. Contact our team to learn more.