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Beyond Chatbots: The Power of AI to Transform Your Business
Josh Sutton, Global Head, Data & Artificial Intelligence, Publicis.Sapient
Predictive modeling is one of the most important capabilities that Artificial Intelligence enables. Nearly every commercial business today relies on forward-looking predictions to some degree. The ways that this occurs range from the obvious, such as forecasting how much product to build to the subtle such as predicting which message to show to a customer to generate the best outcome. For instance, a major electronics retail firm increased their online sales conversion by approximately 10 percent within weeks of incorporating machine learning into their recommendation engine. Predictive modeling can also be used to identify ways to optimize your current spending. Google used their own machine learning technologies internally and managed to reduce the amount of energy that they use for cooling their data centers by up to 40 percent.
Most companies are in the early days of exploring the role of machine learning in their enterprise, but those that have begun to invest are more often than not accelerating their efforts. Predictive modeling is a use case that is one of the best to start with for an enterprise IT division since the benefits, both in terms of cost reduction and increased revenue, are easy to quantify and are typically realized in the span of weeks or months versus years.
The questions that keep a company’s leadership team up at night often revolve around what their customers think about them and what drives their behavior
The questions that keep a company’s leadership team up at night often revolve around what their customers think about them and what drives their behavior. The good news is that there is more data available than ever before to answer these questions. The bad news is that the traditional means of reviewing that data via armies of analysts is no longer cost effective – especially with the rate of data creation roughly doubling every year. Artificial Intelligence is providing an answer to this dilemma and the combination of natural language understanding tools and machine learning is providing a means for firms to quickly focus on what topics actually move the needle on the KPI’s that a company cares most about.
The largest AI firms in the world including Microsoft, Google, and IBM all have invested in developing tools that you can use to extract real insights from your structured and unstructured data. They can process volumes of information that would never have been possible by a purely human workforce, or even with the aid of some of the big data platforms that have been developed over the past decade. There is also a brand new set of companies, such as Quantifind and Luminoso that focus exclusively on this problem and provide you with the capability to understand what specific concepts are influencing your KPIs. Some examples include your Net Promoter Score (NPS), a customer’s propensity to purchase, and what drives your churn rate.
Automation of Knowledge Work
This is the most forward-looking of the three categories discussed in this article, but will likely have one of the most profound long-term impacts on business. Just as robotics has forever changed manufacturing, AI is likely to have the same impact on traditional 'white collar' businesses. Many tasks today are performed by people that require what we think of as some combination of domain expertise and common sense. These jobs are all subject to massive change moving forward. As Andrew Ng, former head of Google Brain and former Chief Scientist at Baidu tweeted, “Pretty much anything that a normal person can do in less than one second, we can now automate that with AI.”
The implications of this are already being felt in a resurgence of Robotics Process Automation (RPA). This is a concept that has been around for a number of years but is becoming relevant once again as AI technologies are making the automation solutions less brittle than they once were. Everything from the processing of financial transactions through to the optimization of supply chain decisions is now being reviewed with an eye towards what can be automated. JP Morgan Chase was able to leverage machine learning to save roughly 360,000 hours of time that had previously been spent by lawyers and loan officers interpreting commercial loan agreements.
Putting it all Together
In summary, AI isn’t one single tool or technology. Nor is it going to be applied to any single use case or problem, but is much more likely to impact a wide range of the activities that are performed in most companies today. Tomorrow’s leaders will be those firms that assess what opportunities are best for them to invest in across this range of possibilities and proactively drive the transformation of their company.
A part of Publicis Groupe, Publicis.Sapient is a leading digitally-centered platform focused exclusively on digital transformation and the dynamics of an always-on world.