Is the Mid-Market Ready for AI?

October 17, 2017

Artificial Intelligence (AI) is finally allowing organizations to do something with all that data, and transforming the way they do business, regardless of their size or industry. From companies using virtual assistants in the boardroom to answer specific questions, to ecommerce firms using chatbots to act as personal shoppers, AI-based tools are augmenting the role of humans, with the ability to absorb huge amounts of data that would be impossible in a human.

But until now, many businesses have thought of AI as something straight out of Star Wars. The reality is, it’s alive and well today and being implemented to solve specific business challenges. Mid-market companies are embracing AI solutions and it’s easier to achieve than you would imagine.

AI is Not Just for the Big Guys

Not only is AI not something confined to huge businesses with the resources, cash and expertise to implement it, but it actually has benefits that are more suited to mid-market companies.

AI’s ability to take over the key functions of humans allows companies with limited staff to seem much larger and improve customer service – even providing 24/7 call center support when it would be impossible to hire enough people to do this. Additionally, at approximately $25K to build a single-purpose chatbot, the cost is within reach for most mid-market firms, who would pay much more to hire a customer service representative. These chatbots, for example, can quickly and accurately answer customers’ questions or interact with them as they shop.

If you’re just starting your AI journey, don’t worry, you’re not too late to the game, but you should start now, to reap the benefits in the next few years. So what are the key considerations for mid-market companies looking to take the cognitive plunge?

It’s all about the data. While it’s not necessary to modernize legacy equipment to run AI programs, it is critical that you have a way to store data which is the lifeblood to good AI apps. If you don’t currently have the needed data to train an algorithm, this information can be collected in about 3-6 months.

Ask the right questions. Before you even think about building a predictive model, you need to know what you are trying to solve and work backwards, making sure you have the right data to find it. For example, in insurance it may be to determine where fraud may be occurring; or in banking it may be to determine the customers who may be good candidates for new loans, or who may actually default on a loan. You may also want to determine churn rates, the amount of customers coming into your company versus going out. Gathering the correct data helps to ensure accurate predictive models.

Don’t go it alone. While implementing AI doesn’t require huge modernization requirements, it does require experienced data scientists – working alongside software engineers. Since these positions are not typically filled at mid-market companies, work with a service provider that can provide these capabilities – that know what data to collect, how to collect it and that have the high-performance hardware and infrastructure to support your project.

Continuously maintain the app. Once you build a predictive analytics model, a chatbot or another form of AI, it doesn’t end there. The algorithm you develop is only as good as the data it receives. It’s important to understand that an AI project is not a one-time thing, but must continuously be maintained, trained and receiving updated information to be effective.

Mid-market companies are embracing AI as a way to increase customer satisfaction, better understand customer behavior and automate tasks so that humans are available to take on more strategic roles. It’s clearly the future of computing and companies who don’t embrace it could be at a clear disadvantage.

 

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