Artificial Intelligence: A Guide for Small and Medium Businesses

As we enter into the new decade of the 2020s, it is becoming abundantly clear that we are witnessing a revolution.
Josh Levine
Josh Levine // Photo courtesy of Josh Levine

As we enter into the new decade of the 2020s, it is becoming abundantly clear that we are witnessing a revolution. Our current environment is reminiscent of the late 18th Century’s Industrial Revolution, a time when steam power and machinery fundamentally changed our modes of production, transportation, and communication. Our current revolution can be similarly impactful, and it is centered on a transformational technology: Artificial Intelligence.

With companies such as Boston Dynamics, Uptake, and Lemonade leading the way, Industry 4.0 has already commenced. In conversations I have with small- and medium-sized businesses, I’ve found that many companies see AI as a luxury only fit for large enterprises with vast budgets and robust IT staffs. In this article, I will be dispelling this notion and outlining the key elements of AI your business should be leveraging to maximize your competitive advantage, minimize your risk, and capitalize on the changing paradigms of our increasingly intelligent world.

Better Insights

In 1597, Sir Francis Bacon famously wrote in his Mediationes Sacrae, “Knowledge is power.” Though this statement is often dismissed as cliché, in fact, its sentiment proves timeless. A benefit we now have that Sir Francis Bacon was not privy to is the ability to track and store data. At our current pace, we produce 2.5 quintillion bytes of data every day, with 90 percent of all data ever created being in the last two years alone. Data is even said to have overtaken oil as the world’s most valuable resource. Just like oil, however, data is meaningless if it is not properly cultivated and exploited. In the case of data, this involves the utilization of analytical tools that give the information value and context.

Machine learning is one such technology that is transforming the way businesses can extract and interpret information. This technology can take into account complex data and determine patterns and cycles free of human intervention. An issue related to machine learning, though, is the high barrier to entry from a business perspective. In order to successfully implement enterprise-wide machine learning, a team of data scientists must accurately develop and test algorithms. To combat this obstacle, a technology has emerged known as AutoML, or automated machine learning. This development is allowing companies to implement a prebuilt model without the need for specialized technical expertise. Upon implementation, AutoML allows companies to analyze both their current and past states, and most importantly, accurately predict future demands. The current leader in the AutoML space is the Boston-based DataRobot, valued at $1 billion. Other companies of note include ZestFinance and Zylotech.

A related cutting-edge technology of interest is known as natural language processing. NLP is a branch of artificial intelligence where computers can be programmed to understand, interpret, and manipulate human language. Though NLP is still in its nascent stages of business application, innovative-use cases have already been found. Companies have leveraged the technology to analyze their purchase orders and contracts to determine common themes and anticipate possible issues. Furthermore, banks in developing countries have begun implementing NLP in assessing client creditworthiness in situations where clients have little or no credit history. The third, and most exciting, development stemming from NLP is the proliferation of chatbots. Chatbots are software that conduct a conversation through auditory or textual methods. Think of the text boxes that pop up on your favorite retail websites asking, “Is there anything in particular you are looking for today?” This, once niche technology, is expanding rapidly, with chatbots expected to account for 85 percent of customer interactions by 2020, according to Gartner. In general, by 2020, 30 percent of all B2B companies are projected to employ AI to augment at least one of their primary sales processes.

A practical use case where artificial intelligence has already proven to be instrumental is in regard to the supply chain. A supply chain is a network between a company and its suppliers to produce and distribute a specific product to the final buyer. Supply chains are complicated mechanisms with extemporaneous factors that can prove the difference between profitability and loss. Consumer demand, politics, and inclement weather are some examples negatively impacting to a company’s bottom lines. The ultimate cause of these obstacles are in economic terms known as asymmetric information, where suppliers are uncertain of critical values that effect their bottom line. AI is now allowing business to acquire more complete information and create both a granular and a holistic view of the flow of goods. In brief, artificial intelligence is allowing companies to augment their sources of knowledge and leverage these insights into productivity and profitability gains. Just as Francis Bacon predicted, as enterprises become more knowledgeable, they rise in power as well.


When working in collaboration with IT executives, I always implore them to think about how their IT staff is currently being utilized. Are their employees being used as a source of innovation? Or, is a significant portion of employee time occupied by mundane tasks such as patching, backup and recovery, installation and configuration, performance monitoring, and troubleshooting? I’ve found that the latter typically rings true. This is not to say that these tasks are unimportant, because they are essential. What is no longer essential, however, is the human labor requirement in accomplishing these goals. Such tasks that can often take up a majority of your employees’ time can now be accomplished through the use of artificial intelligence. The result, in economic terms, is output gains, with 54 percent of executives saying AI solutions have already increased productivity.

A complementary trend that has emerged from the utilization of automation has been the reduction of downtime. In technical terms, this is defined as increased availability, a term used to describe the probability a system works as required during the period of a mission. In the absence of availability, a system goes into downtime. A system in downtime can be immensely costly for businesses, with unplanned downtime costing manufactures $50 billion annually. Automation is working to prevent these losses by actively analyzing system performance and designating appropriate times for predictive maintenance, update, and backup. On a more granular level, 42 percent of downtime is a result of asset failure. With a data-centric approach though, manufacturing companies can now even predict pending part failures in order to resolve them before they prove costly. Such procedures are now no longer serving as a deterrent to productivity and innovation, as they can essentially work in the background free of human input.

Where automation truly excels, however, is in regard to disaster recovery. In simple terms, disaster recovery can be viewed as risk mitigation efforts to ensure that if a disaster does strike, its effects are minimized. In the IT space, a significant threat posed by disasters is the loss or corruption of valuable data. Gone are the days when employees must come in at inopportune times to perform disaster recovery procedures. Employees can now rest easy by taking a proactive, as opposed to a reactive, approach to disaster recovery. To use a Football analogy, AI has expanded the playbook allowing companies to be both better prepared and more organized. In simple terms, automated software is now readily available to replicate data center workloads. These added safeguards of automation have substantially reduced potentially massive losses to a company’s bottom line. Companies can now shift their focuses to what they do best: innovate and deliver superior customer experiences.

Manual tasks outside of IT can now also be fully automated through the use of AI. Use cases are apparent across industry verticals and lines of business, including but not limited to HR, education, Health Care, and Retail. One such industry of relevance in Michigan is the manufacturing/OEM space, a market that makes up 19.05 percent of the total state output. Ironically, the industry is the birthplace of the Ford Factory System, where Ford ingeniously optimized his labor force by eliminating unnecessary human redundancies and leveraging technological efficiencies. AI is similarly allowing manufacturing companies to augment their factors of production. Industrial companies have recognized this fact as the global robot density in the manufacturing industry has rapidly expanded to 74 robot units per 10,000 employees, according to the International Federation of Robotics.

Robotic Process Automation, or RPA, is another AI related development that has greatly augmented enterprise productivity. It allows businesses to automate mundane rules-based business processes with uses including, but not limited to, transaction processing, data manipulation, and communication triggering. The RPA software market grew by 63 percent in 2018 and was forecasted to reach revenues of $1.3 billion in 2019. A company to familiarize yourself with in the field of RPA is UiPath. UiPath has taken the industry by storm, earning several coveted rankings including first place in the Deloitte Fast 500 and third place in the Forbes Cloud 100. UiPath was actually recently valued at $7 billion, showing the massive market for RPA. Competitors of UiPath also of note include Automation Anywhere, Blue Prism, and NICE. AI should not be viewed, though, as a substitution for human labor. Instead, it is acting as a complement to workers. A collaborative approach capitalizing on both human workers and artificial intelligence is proving to be the next step in the evolution of business operations.

What’s Next?

A famous concept in the IT world is known as Moore’s Law. In essence, the law hypothesizes that the speed and capability of our computers will increase overtime, while the cost of these higher-productivity computers simultaneously diminishes. This law is of relevance in the conversation of artificial intelligence because it provides a glimpse into the exponential rate of change characteristic of technology. While this incredible pace of innovation is certainly inspiring, it is also humbling. If technology continues to develop at an ever-increasing pace, how are companies to remain up-to-date and modern?

My answer to this question is simple, and it harkens back to the aforementioned quote of Francis Bacon, “Knowledge is power.” Companies and executives must remain aware of emerging trends in order to properly take advantage of their capabilities. Fortunately, the internet has simplified the knowledge acquisition process with many sites serving as barometers of sorts for the current state of technology and the greater market.

In closing, it is important to remember that the conversation of technology is incredibly complex. The journey toward a truly optimized enterprise is not one that has to be taken alone. As Confucius once said, “If you are the smartest person in the room, then you are in the wrong room.” I always recommend leveraging all the resources and brain power possible to ensure adequate consideration is given. This can include both internally, as well as third-party consultants and businesses that are experts in the field. The modern Industrial Revolution is underway. Are you prepared?

Josh Levine is an account executive at Oracle (Michigan), where he leverages the company’s market-leading database, middleware, and business intelligence platforms to help companies turn their high growth into sustained excellence.

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