AiThority Interview with Rik Chomko, Co-Founder and CEO at InRule

Please tell us about your role and the team / technology you handle at InRule? How did you come to start InRule?

I’m the CEO and co-founder of InRule Technology. Loren Goodman, our CTO, and I started the company in 2002 with the idea that non-technical people can modify the behavior of an application without coding. Effectively giving them the power of computing without the complexity of programming—regardless of their level of technical expertise. At the time, we were consulting with Aon on an insurance rating project and noticed a large amount of back and forth that went on between the programming group and the underwriters to get the rating logic correct. We felt there was a better way to solve the problem and remove the “lost in translation” issue that often surfaced. Based on the project’s success, we decided to productize the offering and start a software company. Nineteen years later, our business rules management system (BRMS) has become a decision platform and is deployed with more than 400 customers in 40 countries.  

What is InRule and what does your product / service offering look like?

InRule is a decision platform that provides enterprises with automated decisions using human-driven and machine-driven algorithms to deliver personalized, highly complex, contextually rich experiences. The platform has evolved from its early days as a BRMS. 

Over the years we’ve added integrations (including Microsoft Dynamics 365 and Salesforce), deployment options (on-prem and in the cloud) and execution models (such as Java, JavaScript, .NET, and .NET Core). Most recently, we’ve added analytics and explainable AI. Our author-first approach enables both technical and business authors to automate decisions and integrate with predictive models – without the need for code modifications. We allow organizations to execute business decisions anywhere—on-premises, in the cloud, offline, or at the edge.

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Can you tell me more about simMachines?

simMachines, Inc. is a Chicago-based company that offers an explainable AI (XAI)/machine learning technology. simMachines de-risks traditional “black box” machine learning models by explaining “the why” behind every prediction made. Understanding the reasoning behind a prediction provides organizations, especially those within highly regulated industries, with greater auditability, transparency, and confidence in the decisions they make.

Tell us how you prepared for your recent acquisition? How do you plan to pass on the benefits of simMachines to your customers and employees?

Human-driven and machine-driven algorithms are stronger when paired together. When you look at the spectrum of problems we solve, often you are using a machine-driven algorithm to make a prediction and then using a human-driven algorithm (aka rules) to refine and act on the prediction. For example, what good does it do to find out that it’s supposed to rain if you don’t act on the prediction by deciding to take an umbrella with you when you leave the house?

With this in mind, we wanted our customers to have the ability to move beyond rule-based decisioning and to realize the benefits of automated decisioning based on predictions from historical data. So we set our sights on expanding the InRule Decision Platform through the acquisition of a machine learning company.

By adding simMachines’ explainable AI technology to the InRule Decision Platform, we’re pioneering the democratization of AI within enterprises. Our decision platform will allow data scientists, developers, and citizen developers to not only automate decisions, but to also understand what predictions and decisions were made, and the “what if” around those predictions and decisions. 

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What is the most contemporary definition of “explainable AI” and what kind of computing prowess does a company showcase to meet customer demands?

Explainable AI transcends the “black box” problem of traditional machine learning models by explaining the “why” behind every prediction. It helps detect bias in real-time by allowing people, regardless of their level of technical and/or data science expertise, to understand a prediction, as well as the factors that affected that prediction. 

Understanding the “why” behind a prediction is key for organizations in highly regulated industries, such as financial services, insurance, government and healthcare. To comply with regulations and maintain customer (and constituent) trust, these organizations must be able to demonstrate precisely why and how decisions and predictions were made, and what factors contributed to the outcomes.

By combining InRule and simMachines, our customers will have access to the most comprehensive explainable AI solution on the market, providing them with unprecedented decision automation capabilities to keep pace with the speed of business. 

Could you tell us more about the current trends in the AI-powered Business Intelligence and XAI industry? How does InRule influence the AI market with its services?

I see two key trends shaping demand for XAI and AI-powered BI – the first is growing demand for compliance. The second is the continued influence of citizen developers. 

Increasingly organizations need to demonstrate they understand how AI is influencing decisions within the enterprise. Without a reasonable understanding, they are at risk for running afoul of regulations that govern how certain demographics can be used in the decision-making process. There needs to be transparency. And beyond compliance, there is also the issue of being ethical. Companies are concerned that AI decisions may go against their values and XAI helps remove that concern.

XAI solves the “black box” problem of traditional machine learning models. What’s nice and works well for us is that the transparency was already “baked-in” for human-driven algorithms we already supported out-of-the-box in InRule. So again, the combination of the two technologies makes a lot of sense.

In addition to the proliferation of automation, the influence of the citizen developer continues to have an impact on enterprises. These “non-developer developers” are seeking – and deploying – solutions that empower them to expedite change cycles, often without involving IT. XAI and decision platforms allow citizen developers, and any non-technical user, to understand and automate complex business decisions and predictions for faster change cycles and greater visibility into processes that were typically opaque. 

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How has your role evolved through the pandemic crisis? How did you stay on top of your game?

Like many other professionals, the pandemic forced me, and our entire team, to get more creative with how we interacted with each other and the broader team. Since video calls became the norm, we had to think about new ways to get to know each other personally and professionally. One way we did that was by having anyone who was willing submit a short 10-30 second video about what their perfect day looked like. We then pulled them together in a video montage, which was really cool. So many people with awesome talent and family interactions that we never really shared before. The other way we connected was through a regular cadence of “Chomko Chats,” quick video updates where I share the latest company news with our team. These, coupled with our monthly town halls, have helped keep the cadence of communication up to ensure that our teams are informed. Recurring company virtual happy hours also helped us catch up socially. Finally, to combat “Zoom fatigue” and other adverse side effects from the pandemic, we offered employees periodic, company-wide mental health days to step away from their computers and focus on priorities outside of work.

One lesson you learned by working with technology and people during the pandemic?

This might seem cliché but honestly there is no substitute for face-to-face interactions. I really like that the pandemic allowed us to see that the entire company can work remotely more often with greater flexibility. But at some point, you need that human interaction. “Collision conversations” just don’t happen on a Zoom call. They happen when interacting in an unplanned way and a fair amount of innovation, brainstorming and critical thinking can occur at these times. I witnessed this firsthand at a small offsite we had in January 2021. Over meals and other non-meeting activities, we covered a range of topics that normally would take weeks to schedule a formal meeting, discus, and act. Instead, it more or less happened naturally. Along the same lines, I learned that we can reach a point of diminishing returns when it comes to people and technology. Our teams had a variety of resources to connect and collaborate and maintain their productivity. However, by removing the commute and the possibility of face-to-face interactions, it quickly became clear that having more ways to connect isn’t necessarily the best way for maintaining energy and morale. We’ve done our best to counter this by offering periodic company-wide mental health days to get people away from their work commitments and the constant dinging of notifications and meeting notifications. Having the whole company step away at the same it alleviates the cadence of inbound, internal messages and allows everyone to take a well-deserved break. 

I was continually reminded that we need to put people first. When that happens, and we do a good job, the rest takes care of itself, and I think my job is to empower our employees and give them the resources they need to be their best. Whether it be setting up the technology to allow employees to remotely work, communicate, and socially interact, creating flexible work schedules, or allowing employees time to reset and step away from work.

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Tell us more about your recent collaborations with B2B companies and how these help you expand your capabilities beyond conventional AI user base?

We work with a large group of technology partners and global system integrators, which allows us to expand our customer base and bring our technology to several different verticals. By working with technology partners such as Microsoft or Salesforce, or global system integrators such as Avanade, Deloitte or DXC, we integrate with numerous technologies to help customers solve problems such as insurance rating and underwriting, government program eligibility, loan origination, and healthcare population and case management. 

How would you define ‘responsible AI’ in the pandemic era through the lens of a general customer?

The pandemic shined a spotlight on many of the inequities in our society, and with them, the biases that perpetuate them. From the varying infection rates among certain populations, to the groups who felt the greatest financial impact and the businesses that were able to obtain l****, the outcome of long-term imbalances and inequities has never been clearer.

Looking at responsible AI through that lens, all enterprises should feel a responsibility to mitigate bias in AI. Responsible AI provides transparency that enables us to correct bias in real time to promote accountability and fairness, building and reinforcing trust in the customers we serve. 

Traditional AI and ML are built to recognize patterns; however these patterns can unintentionally create system biases. In the loan origination process, the AI/ML algorithm looks at a business loan application of a pandemic-affected business and notices a sharp decline in monthly revenue and says this is not like the thousands of other l**** we have improved, so decline this loan application. However, Responsible (ethical) AI can look at that application and identify the “why” of the denied loan application, and highlight the declined revenue of the last few pandemic months. We can use these findings to understand the business better and be a more empathetic business and, in this example, approve more pandemic l****.

Thank you, Rik! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]