Skip to content


Harry Cassin
Publisher and Editor

Andy Spalding
Senior Editor

Jessica Tillipman
Senior Editor

Bill Steinman
Senior Editor

Richard L. Cassin
Editor at Large

Elizabeth K. Spahn
Editor Emeritus

Cody Worthington
Contributing Editor

Julie DiMauro
Contributing Editor

Thomas Fox
Contributing Editor

Marc Alain Bohn
Contributing Editor

Bill Waite
Contributing Editor

Shruti J. Shah
Contributing Editor

Russell A. Stamets
Contributing Editor

Richard Bistrong
Contributing Editor

Eric Carlson
Contributing Editor

Dan Adamson: Top AI Trends for Enterprises in 2017

There’s no doubt that the growing commercialization of Artificial Intelligence will continue in 2017. Much of that commercialization effort will be focused around robotics, cars, and consumer side applications. However, 2017 should also start to see real growth of applications for AI in enterprises as well.

The volume of data is still growing exponentially each year. The regulatory burdens of corporations are still getting more complex to navigate. And fraudsters continue to get more sophisticated. Traditional technological approaches to dealing with these problems don’t work well enough to deal with these changes. These are the types of challenges where cognitive computing solutions can help.

Culling through that data is challenging for any human researcher and time-consuming, and systems that can learn to do this for the researcher are showing enormous ROIs. This doesn’t mean that I’ll be predicting the extinction of workers in 2017 — far from it. It does mean that workers will need to open themselves up to the support and efficiencies that these cognitive computing tools can provide. Efficiency, consistency and better decision making will start to become realities for enterprises that embrace these technologies, giving these enterprises a real competitive advantage.

So how does Artificial Intelligence start getting used as “Enterprise Intelligence” in 2017?

Intelligent Workflows for Complex Tasks. We are starting to see a new breed of capabilities from leading workflow providers such as Pega to new providers such as WorkFusion that manage to “watch” and “learn” about workflows for tasks that are too difficult, nuanced or complex to develop workflows from traditional rule-based approaches. These approaches can also make creation of workflows simpler, because it might not require an expensive rule-building project to produce.

The reality is that a lot of the buzz-worthy use cases in this space have shown great ROIs, but it has been hard to distinguish between the automation a traditional workflow would have obtained vs. an AI-based approach.   Look to 2017 to start answering these questions as these tools become more widely used.

Intelligent Research and Investigations. We’re seeing a wide-scale adoption of investigative cognitive computing solutions by many government entities, financial institutions and corporations seeking to assess and vet at the on-boarding stage or monitor for bad behavior on an on-going basis.

Companies are seeing AI as a tool to reduce their risk while meeting their growing regulatory requirements in a more efficient manner. With ever-increasing data resources, there is a growing challenge on background checks when vetting new employees, vendors, principals behind an investment opportunity or law enforcement clearance, for example. Manual approaches can be replaced with an AI solution that cuts the time and risk involved.

DDIQ, for example, uses the information it learns about an entity to reduce false positives, and then uses that information to decide where to go next to find out more about that entity (or related entity in the case of a broader investigation). These systems don’t get tired like a researcher, and can be tuned for research type applications from investment intelligence to KYC to investigations.

Anomaly Detection and Fraud Protection. The predictive capabilities of a system that’s always learning and assessing false positives has become a requirement in today’s sophisticated world of fraudsters, who learn to adapt faster than human rules-based approaches can be implemented. The problem is that many of these learning or anomaly detection approaches are flagged by false positives. As an example, a trip to Singapore that is outside of your normal travel and purchase patterns may result in a hold being placed on your card. A card company using all available data, including a cognitive computing system, may be able to reconcile this false positive faster than the traditional model does today.

Incumbent vendors, such as IBM, BAE and SAS, as well as new contenders, such as Feedzai, are all upping their game. Expect AI to allow these systems to get smarter and, with more context, further reduce false positives. The holy grail of preventing issues in all cases might not be reached by these systems any time soon, but by constantly monitoring behavior with additional context, these systems should minimize the impact and the costs of the resources needed to clear these false positives each day.

Supply Chain Intelligence. Manufacturers understand that reputational damage is as costly as a recall or product defect. In 2017, AI will become more prevalent in the vetting and monitoring of every supplier in a company’s supply chain so that the executive team knows exactly what is happening all the way down the line.  DDIQ has a monitoring capability, for example, that enables entire supply chains to proactively be managed based on external indicators that would otherwise be lost in a sea of noise. Look for corporate compliance and supply chain risk functions to leverage these capabilities to give companies a large competitive advantage and avoid risks their competitors might miss.

Marketing. As consumers, we are sharing a growing amount of data with companies that are important to us.  They know our buying patterns and whatever personal data that we share with them. Companies will become even more reliant on AI solutions to help them leverage this data and the knowledge they have about their customers for cross-selling, marketing, purchase predictions, and patterns and automated personalization opportunities. For non-profits, this means that possible donors can be targeted with programs that are most important to them at the times they’ve demonstrated they are most likely to give. 

*     *     *

It’s going to be an exciting 2017 as we begin to see the commercial success of cognitive computing solutions adopted across the enterprise.

Ultimately, a new, more competitive breed of AI-enabled enterprises will emerge as leaders in many industries. Exciting times!


Dan Adamson is the Chief Executive Officer of OutsideIQ, a company that develops investigative cognitive computing solutions, including DDIQ, to address today’s growing compliance requirements. He can be contacted here.


Share this post


Comments are closed for this article!