[The Institute for Innovation Development interview series invites innovation experts, innovative business leaders and emerging fintech companies to talk to our readers about their latest innovation activities. The series seeks to learn from innovative business creators, uncover innovation best practices and discover how to apply these insights into a financial services business model.

At our Atlanta Financial Services Innovation Forum earlier this year, we asked Thomas Mark Keith, managing director of ai Innovation Technology LLC, to share with us his role in leading a major new application for machine learning – digitizing expertise. This “right-data” vs. “big-data” technology has been developed by decision scientists in South Africa, and they are beginning to bring this technology to the United States. We asked Mark to share with us their different perspective on artificial intelligence technology and the applications and implications of this new approach for business leaders in the financial services industry.]


Keith: Our artificial intelligence technology is very different than today’s big data approaches to making decisions.  Where big data is focused on generating new insight by crunching huge data sets, our technology does something completely different.  Our technology focuses on decision makers directly – decoding, digitizing and replicating top decision makers' thinking processes. We are not looking for the insight, we are looking at the best decision makers in a company, at the insights they already possess, decoding and digitizing them, and distributing their expertise at scale across an organization.

Value of subjective versus objective decisions

While many important decisions companies and their employees must make are objective, understand that objective decisions by nature do not grant competitive advantage over time as they can equally be arrived at by competitors using similar tools of modeling, with statistics, and by creating algorithms, for example.  Ultimately, any advantage that you create in the objective decision making space will be lost.

Contrast that with a firm having an expert whose “expertise” is applied against the most consequential decisions inside your organization.  For most firm’s these decisions are going to be the ones that are subjective, otherwise checklists would already be in place, decision trees would be operational, and there would be no need for an expert.

What we know is that, bottom-line, every firm’s success is most dependent on their employees who must make subjective decisions.  The goal is to make the best decisions possible. That is why those who consistently make better subjective decisions are considered firm “experts”.  Lifting all decision makers in a firm to the quality of an expert is a constant challenge, and this is what our artificial intelligence technology can do quickly.

Our firm is squarely focused on the science of subjective decision making. Subjective decisions are the ones that are uniquely human, and they rely upon the experiences decision makers have over a long period of time. Experience improves the quality, the speed, and the consistency of subjective decisioning. The results that suffer most inside an organization are those impacted by decision error.  Subjective decisions are the ones that have proven to be the most difficult to improve, replicate, and scale, for a number of reasons.

Replicating expertise