Artificial intelligence (AI), expert system, machine learning. (It doesn’t really matter what we refer to it as now because it eventually turns into Skynet and leads to the rise of the machines. But, that’s another story.) As we continue to see big names in technology speak to the business nirvana that AI will deliver us, it’s important to sift through the buzzwords and hype to truly determine the value of these new tools.
For example, at Dreamforce 2016, intelligence was declared to be the “electricity of our generation.” That is a huge claim! How do we get to a point where that notion is a reality?
When you deconstruct AI to its simplest form it has three main areas: science and math, data, and application. Let’s break it down:
AI Component No. 1: Science and Math
Data science has been a growing field for the past 30-plus years. In fact, Forbes stated that data science was the top job to pursue in 2016. While there are some standard mathematical equations that can be applied in many different areas (some of the basic machine learning algorithms developed for the first chess- and checker-playing machines are still valid), there are nuances for specific business uses. As we see the continued growth of AI, specialized data science that speaks to the unique needs of B2B industrial manufacturers and distributors will be in high demand. Your math must give you a competitive advantage. In most industrial companies, this means finding a smarter way to enhance the value of each and every customer relationship.
AI Component No. 2: Data
When looking at the relevance of data in the world of artificial intelligence, two factors must be taken into consideration, quantity and quality. This is one of the main reasons that AI has had a much faster impact on the consumer world than it has on the business world. The amount and organization of data that B2C companies have captured over the past decade are astronomical. But as businesses leverage better ways of capturing, sorting, segmenting, and prioritizing data, that distinction between B2C and B2B is decreasing. For example, in working with industrial manufacturers and distributors, data can sometimes be difficult to categorize. One of the truest and most relevant pieces of data are historical transactional data. While that data can sometimes be in disparate systems, the data can tell us what customers are likely to purchase, when they are likely to defect to competition, and what their price sensitivity is for each unique deal circumstance. As organizations in this space begin or continue their digital transformation these data points provide a solid foundation.
AI Component No. 3: Application
Just like electricity, AI doesn’t have an impact on our lives until there is a relevant application. (Unless, of course, you are struck by lightning.) Dale Carnegie said, “knowledge isn’t power until it is applied.” This is where many of the business intelligence (BI) tools and rear-facing analytics fall short. An analysis is good, but unless you are providing actionable insights your analysis is for naught. Especially when working with sales people. The data and insights you gathered must not skip the last step of “do this.”
Part of what makes AI so appealing and ground-breaking are the vastly different areas of our life that it can impact. It goes way past suggesting what songs you might like. As more and more applications for AI find their way into the business world, the way in which we conduct business will fundamentally change
One thing strikes me as strange: Much application of the data and science in the sales arena has presented itself in the front-end of the pipeline funnel (demand gen, lead scoring and the like). However, as most sales and marketing professionals have heard before, it costs five to seven times as much to sell to a new customer as it does to sell to existing one. With that being the case, it’s surprising how heavily most AI applications focus on new customer acquisition. Why not focus on utilizing AI to optimize the relationship you have with your existing customers? The power to understand your current customers buying behavior is a game-changer in the hands of a salesperson.
In order for AI to truly shift from buzzword to business impact, ensure your math is specialized to the area you are trying to impact, the data you use is from a solid foundation (such as historical transaction data), and the application is in an area of your business that will show returns like maximizing the revenue from your existing customer base. Find out more.