Achieving Real Business Outcomes from Artificial Intelligence

Foreword

Enterprises are under the impression that they’re on their way to using artificial intelligence. They’ve set up a few machine learning models and have had new algorithms work their way into previously deployed Software as a Service applications. Inside the organization, it feels like they’re checking all the right artificial intelligence (AI) boxes.

But the true end goal of AI in the enterprise is something much more sophisticated. Oliver Ratzesberger and Mohanbir Sawhney expressed it succinctly in their book, The Sentient Enterprise (Wiley), noting, “Our objective is to position the enterprise in such a way that analytic algorithms are navigating circumstances and making the bulk of operational decisions without human help.”

With the exception of a few Bay Area tech giants, the industry hasn’t experienced highly proficient natural-language processing, image-based detection, or other skills that would enable this next generation of AI to drive significant business outcomes instead of just performing basic business tasks.

Imagine if AI platforms could identify and bring together data sources and then explain to their human counterparts the “why” behind the recommendations—something like AI for data engineering and data science. Or, imagine if chatbots could interpret problems and provide solutions using natural language that satisfy buyers more quickly and more effectively than current call centers. Imagine if key business functions were being driven by algorithms with the necessary autonomy to self-learn and change tactics at a level of speed and accuracy that far surpasses any human or team of humans. These scenarios will one day be mainstream, but how are companies going to get there?

One of the biggest challenges for AI in the enterprise is that each company—even within the same industry—has unique problems. So, for the most part, businesses today need custom AI solutions to drive specific value.

However, the reality for most companies is that homegrown, custom AI solutions aren’t feasible for a number of reasons. Not only is it an expensive initiative to take on, but AI development also has a very small talent pool, and it would be difficult to get that kind of brain trust in one organization at an affordable, sustainable rate. The information and opportunity for AI development, however, is out there. To truly accelerate AI, companies should work with partners that have created custom AI solutions before, enabling them to share a vision for how AI will drive business outcomes.

AI is not going to be easy. There is no out-of-the-box AI solution that will transform a company overnight. Instead, building a custom AI solution will take persistent, coordinated effort and deep organizational change. These investments will be necessary not only to develop AI capabilities; they will be necessary for companies to survive.

This book is a thoughtful primer for digital transformation leaders in large enterprises seeking to outpace their competition by embracing the technological and organizational change that comes with AI. In it, the authors review potential enterprise AI use cases and discuss authentic case studies in which companies have realized value from custom AI solutions. For those readers looking for a higher level of engineering detail, the authors include a technical dive into a deep learning solution implemented at Danske Bank.

You will gain insight into the very real challenges that organizations will face as they make this difficult but necessary transition, and various measures that you can implement to approach those challenges. Finally, the book includes a look toward the next several years of AI innovation to give a preview of what organizations can expect to see.

Ultimately, this book provides a practical roadmap for understanding how an enterprise can begin to approach using artificial intelligence to harness its most powerful asset: data.

Diego Klabjan

Professor, Director of Master of Science in Analytics

Northwestern University

Professor Klabjan’s research focuses on developing models and algorithms for machine learning, in particular deep learning, problems with emphasis on the industries of healthcare, marketing, sports, and finance. He has led data science projects with large companies such as Intel, Allstate, HSBC, The Chicago Mercantile Exchange, FedEx, General Motors, and many others.