by Eric Braun, Head of Innovation, Intralinks

Recently, I was asked to give a keynote presentation at the annual global conference on M&A Tax by Arco Verhulst, Global Head of Deal Advisory at KPMG. He wanted me to discuss technology that is here today and coming soon that will have an impact on the way Mergers and Acquisitions are done. As someone driving innovation in software technology in the M&A space, the topic was near and dear to my heart. This blog post is part of a series, The Impact of Technology on Work in M&A, stemming from that talk and my work at Intralinks and other tech companies.

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The work of the future is no longer about enhancing physical capabilities, instead it’s based on enhancing intellectual work, complex cognitive decision-making and human insights. 

For the first 20 years of the 21^st Century, we have seen the technology revolution blast forward like a rocket ship climbing towards the moon. Although it’s possible for private individuals to go off the grid, if you’re in the business world, there’s no chance of that. As Geoff Tuff of Deloitte says, it’s our economic imperative to innovate – adapt to and adopt new technology or perish.

The techno-ride is fast and often unpredictable, which can lead to confusion, pain and frustration. The panacea for this is better understanding of the technology we have and insight into what’s coming in the near future. Combining this understanding with an innovation program, that’s based on thinking and behaving like a startup, can supplement a healthy and successful business.

Four Areas of NextGen Technology

As I reflect on what my innovation team and my colleagues in the industry are working on, I see four areas that I believe are defining our future. These four key areas of technology and innovation are:

• Artificial Intelligence
• Voice Interaction
• “One-Click”
• Ubiquitous Mobile

For this article, let’s dig in a little deeper into AI.

1. Artificial Intelligence

What is AI? Here’s a definition I compiled from a few leading sources:

“AI is a set of computer programs that mimic human intelligence to identify complex patterns, analyze them, “learn” from them and make conclusions and recommendations.”

But it’s less about what it is and more about what we do with it. Typically, we see three value propositions of AI:

• Insights – to assist in complex decision-making by analyzing and assessing patterns
• Automation – to speed up complex processing based on the patterns
• Predictions – to get ahead of the competition by attempting to predict future patterns

Insights assist in complex decision-making. 

Insights are the analysis of patterns that reveal trends or predictions. From the early days of computers, we’ve wanted to get insights to make our brain’s work easier. Simple calculations and graphing can provide pretty good trend reports, and this has been true ever since computers became advanced enough to display visual representations of data. But, we always want more, and today, with AI, there’s so much more we can do with the data.

The power of AI is that it can dig much deeper and provide much better analysis and representation. Multiple sources of correlated data can be combined and analyzed to see trends that aren’t obvious in a single dataset but come to light in the combined sets. Additionally, AI has the ability to see obscure patterns more quickly and easily than the human brain. The algorithm may not know what it means, but that’s where we humans come in.

Also, we can apply language analysis to provide outcomes that have the potential to be much more accurate and insightful than what we’ve seen before. We have the possibility to go from showing trends to providing insights into the trends and even predicting future events and scenarios.

AI Automation speeds up complex processing.

Once you achieve insights through data, you can begin to see trends and opportunities for automation. For example, what activity is happening in the VDRs and what does it mean? AI insights can help you make sense of subtle activities to speed up your decision-making and help you determine if and how you want to buy or sell an asset. Advisors can use this insight to better serve their clients as well.

Predictions allow us to get ahead of the competition as well as to speed up complex decision-making.

Looking at historical trends are often used to predict the future, but this is a cautionary proposition. Anyone who has read a fund prospectus knows that “past performance is not indicative of future results”. Trends are complex, and sometimes the patterns take much longer to reveal themselves than expected or they need additional information to provide relevance. Stiil, using AI to find patterns that lead to predictions – in conjunction with human intelligence and intuition – can be real, because we often make decisions based on what we know of past trends. AI can help us do this better, even if it’s not fail-proof. 

All in all, AI is enhancing our brain’s ability to make better decisions when the information is not clear or the situation is too complex for fast, easy decision-making. It doesn’t necessarily make us smarter, it makes us faster at being smarter than we would be on our own.

AI is not making decisions for us. It’s enabling us to make better decisions through insights and recommendations.

We already see this stage happening in our everyday lives. Yes, it could be better, but it’s on the verge of evolving more quickly. In entertainment, Netflix uses AI to help us decide how we watch movies and which specific ones we might want to watch. Spotify uses similar concepts with music. Photo apps by Google, Apple and others learn facial patterns and use them to search for photos of people or things the app has learned about. I search Google Photos for “cat”, and it lists most of my cat pictures.

AI asks the question, “Is there a pattern in this set of data and, if so, what could it mean?” It’s up to us to validate the insights and make use of the patterns in the work we do, in our complex decision-making. If we allow an app to learn individual preferences by “Tinderizing” the AI, it can adapt what it recommends to be even more accurate.

As we dig deeper into the uses of AI in our work processes, we need to make ethical decisions on how to use data – when to request opt-ins and when to keep it private.

We come back to the initial question – Where is AI going in the Future? In the near term, it won’t be automating the most value-added intellectual jobs away. But the better it becomes at pattern identification and matching and at finding meaning in those patterns, the more valuable the insights and predictions will be. It will become a supplement to our human intuition, which often times can be the unique identifier in value-added services and behavior.

In the next part, I’ll dig deeper into some specific applications of AI for the M&A deal lifecycle.