September 4, 2018
By Leanne Sardiga, Partner and Deal Analytics Leader, PwC Deals
Driverless cars. Facial recognition. Workplace automation. Computer vision. The once-imagined applications of artificial intelligence (AI) are becoming a reality for businesses in different industries. Along with its potential to overhaul business models, AI is also beginning to have a significant impact on dealmaking.
Recently l led a panel discussion on technology in deals at PwC’s Deals Exchange. Executive dealmakers from some of the world’s top companies came together to talk about the biggest issues they’re grappling with as they consider transactions to move their businesses forward. It was no surprise that advanced analytics and the emergence of AI were hot topics.
In a real-time survey, 76% of attendees said AI is the technology most likely to disrupt deal strategy in the near future. Even though companies might not be taking full advantage of more basic analytic technologies, such as predictive and prescriptive analytics, we agree AI is going to play an increasingly important role in deal strategy and execution. Companies that understand the applications of this technology will have an advantage over their competition.
Many deals require a review of enormous amounts of contractual and financial data to determine a company’s worth. According to Gartner analysts*, more than 80% of enterprise data today is unstructured, made up of non-financial data such as contracts, social media comments and email conversations. It’s difficult to digest and extract value from this data, so it often goes underused.
My Deal Analytics team has worked with companies to rapidly analyze such data, using machine learning and natural language processing to help review large amounts of text and identify outliers. The technology learns as it goes, increasing confidence in the results. Visualization technology is often used to then develop deeper, more agile insights in real time.
Advanced use of AI also has enabled our deals professionals to extract deeper insights that could have a big impact on M&A, divestitures and other deals. As the number of structured data sets for a particular industry grows, AI can enhance insights on increasingly complex questions and be more predictive in nature. At PwC, we assist clients with thousands of deals a year, which provides the critical foundation for these types of analytics that enable more confidence in forecasts and synergy estimates ahead of a deal decision.
Laying a foundation for disruption
For companies to truly benefit from AI, they need to make the proper preparations today. Similar to deal investments, much of the success is determined by how you plan and execute strategies early in the process. In deal terms, this means four things: Invest in people, build a technology foundation, standardize your approach and enable innovation.
- Invest in people. For many organizations, successfully building an AI capability will require reassessing the existing talent pool. This means looking for more data scientists to help understand the business and how to think about processes, technology and approaches. These skills may sit outside of more traditional job requirements and entail time and effort to integrate different skills and different ways of working into your operation. At PwC, rethinking our people strategy for advising clients in deals includes using advanced machine learning techniques to answer key questions in diligence and implementing advanced analytics for newly acquired businesses, as well as building a team of AI specialists to help navigate issues in deals. It’s critical to have “translators” who can diagnose a given business problem, and also access data scientists with the right technology and analytical skill sets.
- Build a technology foundation. The pace of technological change means many companies will need to invest more in technology to provide a path for future innovation. For most organizations, the traditional IT role will need to be modernized and expected to do more than just provide support. Technology should fit more seamlessly in an active role for creating new business solutions and enabling the workforce. Through such actions as building modern tech platforms on top of older ones, PwC’s Deal Analytics team has invested heavily in understanding emerging technologies and tech infrastructure to ensure that we’re flexible for our clients.
- Standardize the approach. Improving the efficiency of your existing processes will make future AI implementations more effective. For many, AI won’t arrive with a bang but rather through a set of improvements that build on each other. Making your inputs, processes and outputs more standard will leave you in better shape for future process improvements. Along the way, organizations should be challenging how things are done to better “future proof” their business.
- Enable innovation. Because AI will mean different things for different phases of deal work, development of solutions will inherently include experimentation. In many cases, this means rethinking how we view experimentation and failure. Taking risks and being unafraid to fail provides a safe place to innovate around areas of interest. For many organizations, this can be hard to achieve under traditional models. The innovation strategy for PwC Deals includes creating an incubator environment to get ahead of emerging technologies – from deep learning to blockchain – and exploring the deals applications.
Navigating deals in this new world
AI won’t just magically change deal decision-making and investment returns overnight. However, deals applications of AI technology will continue to broaden across industries and the deal process over time – and then reach more of a tipping point. With the right guidance, businesses can use AI to find data-driven insights to help them make better decisions faster – and position them for success in a fast-evolving landscape of technology and advanced analytics techniques. Companies and private equity firms who establish a clear road map early on will gain an advantage in dealmaking of the future.
*Next-generation Data Strategies, 02 February 2018 (https://www.gartner.com/doc/3885882)