Artificial intelligence is no longer just a tool of the future but an application for the present. Many firms in the dealmaking community have already begun to implement the technology in order to transform how investments are sourced and data is processed. But as the technology becomes more engrained in dealmaking, how ready are M&A practitioners to understand and apply such evolving capabilities?
In order to find out how AI is being used in the deal process and how proficient dealmakers need to be in order to successfully implement and evaluate the technology, Toppan Vintage commissioned Mergermarket to interview five experts in the industry.
Toppan Vintage question: Do you think dealmakers and business executives will need to be familiar with the details of how artificial intelligence works in order to be effective in their jobs going forward? Or can they leave it to the technical folks? Leading industry experts weigh in...
Dean Harvey, Partner, Perkins Coie says: With the most successful AI today, the user does not need to understand what is happening beneath the surface. A good example is digital assistants, in which you interact with them as you would with a human. You need to understand what their capacities and limitations are, but you don't need to know what particular mathematical algorithms are going on underneath the covers.
On the other hand, if you are a dealmaker looking at a strategic acquisition, you really do need to understand the AI capabilities of the company that you're buying. Do they really add value, or are they purely combining some open-source resources and adding a few engineers? If all you really want is the engineering capabilities, that may be enough, but you still need to understand what you're buying.
Troy Ungerman, Partner, Norton Rose Fulbright adds: I agree with Dean – I do not think dealmakers will need to be familiar with the express details of how AI works in the future. I do think they will need to appreciate the utility of AI and the efficiency it can engender. AI tools are becoming much more user-friendly and are making knowledge of the intricacies of data science less important to end users of AI products. Although some tools do require a coding or programming background, as AI technology continues to grow, most tools will not require any computer science background. In fact, self-taught coders are sufficient at this stage. I see this trend only continuing.
Adam Robert Pah, Professor, Kellogg School of Management weighs in: I would also concur with Dean and Troy – I don't think it's necessary for managers to have a deep technical understanding of AI, as long as they know that every tool has some essential data input and then a prediction output. Typically these things are set up to try and make someone's life easier.
But what every user must understand is where the tool gets its data from. You also need to understand how it operates, because there's no such thing as having perfect and complete information. There's always going to be some kind of blind spot, and if the user isn't aware of that during usage, it will affect their interpretations of results or answers. It’s interesting that people don't completely understand the bounds and limitations of the tools they're using, because there aren't purely deterministic tools anymore. In six months, if the AI gets retrained, it's not going to give the same answer as before.
Natalie Pierce, Shareholder, Littler Mendelson says: At the end of the day, careers are no longer static. We're in a time when constant learning and evolution of skillsets are necessary for everyone. Industries are being disrupted by transformative technologies, and big data and AI applications dramatically increase the speed of that disruption. So it's important for dealmakers, executives, and everyone else to stay as current as possible with AI applications, how they can be used in transactions, and how they add value to the deal.
I'm not saying that everyone needs to become data analysts or machine learning experts, but some level of understanding – for instance, in order to be able to ask the right questions – will enhance dealmakers' and executives' effectiveness and success. Today, AI is still at a pretty granular level of managing, sorting, and tagging data in an efficient manner, so for dealmakers, it is probably more important to know if a target company is making appropriate use of AI in its operations and a bit less important to understand the AI your deal team is using.
Noah Waisberg, CEO, Kira Systems adds: With our Kira software, users don’t really need to understand the details – it's much more plug-and-play. From my experience as a lawyer, I know it can be very difficult to convince attorneys to undergo training, so we built our system to be simple enough to use without any training. There are more complex features available within the system that require some training to use, but people have had good results by picking up the software and starting to use it. That being said, we recommend training, we provide it, and we have a very experienced director of training streamlining the process for our users.
There is also the potential for roles at law firms coordinated around using our tool in more complex ways, and helping people to use it. If you want to teach the system to find something new, you just highlight examples in your contracts you would like Kira to learn and the software learns from them. In these instances where you're trying to teach the software a new concept, it pays to have a little bit of training.
But what distinguishes Kira from other examples of machine learning technology is that you don't need a person with a computer science PhD to get it to work. You can literally put in a contract, or a thousand contracts, and say "I would like to find these 10 things” by just checking some boxes on the menu. It can find the title, party, date, change of control, confidentiality, exclusivity, and so on.
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Artificial Intelligence Enters the Mainstream
Find out how AI is being used in the deal process and how proficient dealmakers