With all due respect to William Shakespeare, when it comes to Artificial Intelligence, it is not a matter of “if” you should be utilizing this technology, it’s a matter of “how”. How quickly and how broadly your organization should be experimenting and driving the use of AI and broader machine learning capabilities in your respective industries. Given the pace of change that technology now allows in creating new opportunities and expectations for consumers and businesses alike, the need to disrupt traditional processes and efforts has never been greater.
Similar to any new technology, there is a great deal of hype related to AI. Everything from AI solving world hunger to robots taking over the world have been discussed at length in the past several months, but we need to get beyond the press releases and expert warnings to truly understand the power of this new technology. At its most basic core, AI has the capability to make humans better at decision making, better at solving higher level problems, better at becoming more efficient or effective in their daily jobs and life. The key is to determine the appropriate “use cases” for your specific organization and industry. These opportunities can range from both basic process efficiencies that may save costs to driving marketing technology that generates new sources of revenue and consumer engagement.
Let’s look at a couple examples where AI is working today and driving real and measurable benefits. In our first example, we have a multi-category insurance company with a customer engagement problem. They are looking to provide more personalized service on the existing products each customer currently enjoys which the current contact centers support. Additionally, they would like to utilize these customer touchpoints to anticipate additional insurance products and create new revenue streams within their existing customer base.
The current model employs a combination of humans and “bots” that can answer questions and suggest typical add-on products based upon simple algorithms that basically becomes a multiple-choice Q&A session.
AI has the capability to make humans better at decision making, solving higher level problems, becoming more efficient in their daily jobs and life
While the company is striving for personalization, the experience is quite the opposite and feels very generic. They decide to trial an AI engine that will collect and learn from all products, consumers, and combinations that exist across the company. The AI then “learns” that if a certain consumer with certain attributes, past history and engagement patterns connects with the organization, it will suggest a specific set of messages and interactions to drive the “next best action”– potentially additional products that drive future revenue streams. In certain cases, these interactions will occur without human intervention and has allowed the company to reduce some of its costs, but in many cases, the AI allows contact center associates to become better at their jobs.
Our second example comes from Retail and is focused on utilizing AI to improve the operations within their stores. The amount of data and analysis that is now placed on the shoulders of a typical store manager is immense. Imagine worrying about inventory, hiring/firing, labor scheduling, merchandising, promotional activities, theft, etc., and still have time to speak to consumers inside their store. It’s too much and often drives the manager to focus on the wrong things. This retailer decided to utilize AI to become the “manager assistant” within their stores. Utilizing all the data related to store operations, consumer traffic patterns, merchandising and promotional activity and even the weather, the AI tool became a mobile assistant to help the manager run their store as efficiently as possible. The AI learned the specific traits of each store in the chain and allowed the manager to see how and when to make key decisions each day for an optimally run store. It even monitored transactions and would alert the manager if it believed fraud was taking place in real time on their mobile devices. The AI didn’t supplant the manager; it allowed them to make smarter, faster, and more informed decisions and created more time for them to be on the sales floor interacting with consumers. A win for every party in this transaction.
A few key lessons learned from my own experiences. First, don’t try and do this on your own. This technology is very new and there are many smart people across multiple firms that can help you get started. There are many initial pitfalls that you want to avoid to save both cost and time that these external third parties can help you navigate as you begin your journey. Secondly, metrics are your friend. Ensure that every effort you contemplate for AI has a specific metric for success. Understand clearly the purpose of using AI and what you hope to achieve. It will keep the team focused and ensure you have a clear story to hopefully build upon for future AI efforts. Finally, set reasonable expectations. AI will not solve world hunger (at least for a while!), but it will solve some of your organizational issues. Start small and give it time for both the AI and your organization to learn the impact this will have on your people, processes, and technology moving forward. The change management effort is just as important in this case as the technology and all sides need time to adapt.
AI is here to stay and will quickly permeate a great deal of our existing business processes. Organizations need to quickly understand how this new technology can be used before their competition as it will clearly drive a differentiating advantage to those that can successfully harness its power.