/ The Deal with AI

An interview with Sam Riley, CEO of Ansarada by Chris Kerns.

Sam Riley thinks about deals all day. As the CEO of Ansarada, an AI-powered dealmaking platform, he focuses not just on making the deal experience transparent for all parties—including corporate acquisitions, mergers, and partnerships—but also on learning from the 12+ years of data the company has seen fly through its system, to identify patterns in the deal-making process, and opportunities for businesses to reduce risk.

On founding Ansarada and the problem they were solving:

We started about 12 years ago. My partners were involved in a company, getting it ready for a sale, and they needed to facilitate due diligence to allow the potential buyer access to critical information.  The existing technology was so expensive that our CTO ended up just building something himself. We stepped back and figured there could be a business there. We dove into the process of how major transactions happen, and I did research on all the different players in a typical deal—lawyers, accountants, bankers, CFOs, CEOs—and we ended up creating a platform that’s simple for them to use to disclose the right information and to manage, but is also very secure for managing risk.

On extending the platform with AI:

During the first eight years of the business, we focused on being a productivity and risk management tool. Then we realized that most of these deals were being approached and managed like they were the first one ever done and that with the data we had on over 10,000 deals at that time, there must be patterns of behavior we could find. We realized that we could automate certain pieces, reduce risk, and add data-driven insight to the decisions being made.

For example, we looked at behaviors of successful buyers compared to groups that had dropped out early in the process—people we believe were just in there to kick the tires. We built an algorithm looking at 56 different behavioral attributes, such as which roles are involved, how often a group logs in, and what types of questions they ask on particular topics. We ended up with an algorithm that is now 97% accurate at predicting which groups are engaged and which ones aren’t by the seventh day in a deal, which can be a huge piece of knowledge for groups on the other side. We’ve built out our own natural language app, and an automated deal assistant. Plus, lots of new technology in the works.

On other industries that are ripe for disruption via AI:

We use a lot of marketing automation tools, and it astounds me how little people do with their own data, just on their own prospects. In general, you have to look at the most repetitive, mundane work that needs to be done, but for something else of value to occur. For us, it was clear after working in the space for a while—for example, in M&A delivery, everyone wants a due diligence report, but that takes assembling documents, reading through them, finding clauses, and so on—there’s a huge opportunity here for how AI can improve all types of business functions.

“To conduct an orchestra, you have to turn your back to the crowd.”

On finding and retaining data talent:

It’s important to have moon shots in your business. You don’t have to make your whole data strategy that ambitious, but you need one or two big, challenging projects to work on that will attract people to the challenge. It might be something they can chip away on or even do a bit of R&D on. Data folks love to not only solve those kinds of puzzles, but also share that knowledge as well. They hang out in those communities and go to meetups, they share what they’re working on with friends and peers, and that means more of them are more likely to end up wanting to work with you, too.

I remember hiring one guy, and when we told him our plans, he said, “Are you guys really going to do this?” He asked us this a few times throughout the interview. When we asked why he kept pressing on the subject, he said, “Well, all my friends get sold in interviews on working with AI, and then three weeks later we’re working on JavaScript and C#.” So, you have to be serious about these projects. Have one or two initiatives where you can let people think really big and play with some big problems.

On what data he needs to do his job as CEO:

Lots. I need a healthy mixture of leading and lagging indicators—leading indicators around the top of our sales and marketing funnels and lagging indicators like financials—but, I’m increasingly leaning on data that’s more actionable. That often involves less generic data and more specific. So, for example, if I can break out sales data by geography, industry and transaction types, I can see that deals about capital raise in the U.S. tech industry are converting much faster than the normal timeline. We can take action on that type of data which has a real impact on our business to amplify the good and manage the risk on the others.

On how to spot a good area of investment:

Ideas have to be tested for validation. Even with the smallest test, you can save a lot of time and money.

When people come to me with an idea, I first ask them how they’re going to measure its success. If they don’t have a good answer, they need to do more work before we look at the idea. Not that long ago, someone thought we should put chat functionality in our deal room product, but I wasn’t sure that customers wanted to chat in our product due to the secretive nature of deal-making. So, we tested the idea via the cheapest method we could think of—we just added a link in the product to a chat window that didn’t exist. If someone clicked on it, we’d just pop up a window saying “chat is coming soon.” We had over 50,000 users see the link and not one person clicked on it, so it was clear that there wasn’t a need to build chat.

That being said, you also know when to lock down and go with a product vision instead of asking for constant feedback from customers. There’s a quote I love: “To conduct an orchestra, you have to turn your back to the crowd.” As we’ve been in the space for a long time now, we have a feel for what’s next and need to be able to trust that for our direction.

On his favorite business books:

As a company, we read a book every quarter for the past 10 years around something that we’re focused on at the time. Some of those books have been particularly useful— a recent one titled Play Bigger that covers creating a new category with a solution-based mindset—proved great read across the board. From a company perspective, Exponential Organizations has helped us understand the right way to scale and the best way to engage with customers and communities. And then there’s also No Limits: Blow the Cap off Your Capacity, which walks through Maxwell’s five levels of leadership, which is about raising the bar by identifying and growing 17 critical capacities we all have.

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