AI adoption, says Jessica Anderson, principal in Deloitte Transactions and Business Analytics LLP’s Discovery & Data Management practice, “is not about one big bang, but rather a series of deliberate steps that add up over time.” It’s about increasing awareness and thoughtfulness in how data are generated, stored, reviewed, analyzed, and presented. The possibilities are endless, but so is the responsibility to get it right. Read on for her thoughts on how.
Ramin Tabatabai: Please describe your role in your organization and how technology plays a part in it.
Jessica Anderson: I lead our Discovery & Data Management practice in the US for Deloitte. Technology is the foundation for all that we do: leveraging technology to manage, analyze, and gather insights from unstructured data sets to help our clients respond to the legal and regulatory requests they receive and events they face.
What were your interests early on and what drew you to your line of work?
Pretty simple: I loved solving puzzles. Very early on, I wanted to become a doctor so I could solve medical mysteries. However, in college, I was exposed to statistics and data science—which led me to the combination of technology, data, and programming as a way to solve a wide array of business issues. In 2003, I was exposed to the world of forensic investigations and never looked back.
What professional accomplishment are you most proud of?
It is not about one thing, but series of things. Seeing those around me thrive in their career, both in how our teammates grow into leadership roles as well as how our clients have an impact on their respective organizations and grow in their leadership roles.
I am also proud to show that there isn’t one mold to follow to thrive in your career: we can live our lives to the fullest with our family and our work. It’s fulfilling to me to show that challenging the status quo can sometimes help you—as well as your whole team—grow.
Any advice for those who are interested in following your career path?
Be intellectually curious. Ask a ton of questions and seek to learn about all dimensions of a problem, really listen to others’ perspectives, and challenge yourself on why their ideas might be better than or enhance your own.
When you’re not working, how do you like to decompress?
Spending time with my kids and being on the sidelines of their dance, soccer, hockey, or swimming events. When we are not doing that, cooking or working out are my outlets.
What is artificial intelligence?
Artificial intelligence, in its simplest terms, is machines performing work that has historically required human intelligence.
I have a pretty practical view of AI as well: it is a combination of everything from simple rules-based logic to complex data science being applied to achieve the outcomes and answers we seek.
Why does AI matter for what you do?
AI matters because data volumes are growing exponentially. With sources of evidence growing at exponential rates, humans cannot begin to sift through all of the evidence in the same ways as in the past.
What aspects or outcomes of AI are you most excited about?
I am most excited about the speed of advancement and the momentum with which advancements are happening to make life better. In our field, that’s by empowering lawyers with real data to get to the truth; in broader society, it’s improving critical, life-saving elements like health care and safety.
When you think about your business or your industry in five or ten years, how will AI have affected it? How do you feel about that future?
AI is not only changing how we interpret the data, it is changing how data is created, stored, and used. We have to think differently about what data is available, how we access it, and how we use it responsibly.
I think it is pretty thrilling when we think of how it has improved, and will continue to improve, how we live, but it can be scary if leveraged in improper ways.
What are some of the structural barriers that keep your industry from adopting new technologies?
Many companies have done little to document the structure of their data, standardize its interpretation, or make it accessible for analysis. Getting this data in shape for AI systems to use can take great effort. Siloed data, dirty data, and restricted data get in the way.
What’s your advice for organizations hesitant to adopt AI?
It needs to be an iterative approach. It is not about one big bang, but rather a series of deliberate steps that add up over time. Further, AI is highly dependent on good data, so being deliberate about data hygiene and data inputs is critically important.
How can technologies like AI help organizations uncover more value?
A great deal of the focus has been on cost reduction: that includes looking at less data and focusing on the right content during the discovery process. While that continues to be critically important, I see us pushing capabilities into new areas not just around cost reduction, but also that seek to protect individuals and organizations. That could include areas like product safety or environmental or cyber monitoring.
In your opinion, what does the integration of AI mean for the future of the human workforce generally and in your industry?
AI already requires a pivot in the workforce to look at contemporary business issues in a multi-disciplinary way. Experts from all fields should integrate capabilities—data scientists are needed, but so are historians, psychologists, industry experts, and many others to work collaboratively in order to build and train systems, achieve optimal results, and apply those insights.
This will also continue to drive an increased demand for young professionals to pursue education and employment in STEM fields.
Legally trained professionals have a natural intellectual curiosity. This makes many lawyers interested in the exploration of AI, and it also pushes their traditional sense of exploration to really validate what is happening and push boundaries of bias. I think those are healthy inputs to the AI process.
What is one small thing someone could do today to move toward an AI-enabled future?
I think everyone needs to lean in; AI adoption requires that series of iterative steps, and requires perspectives from multiple disciplines to refine its application. Everyone can start by educating themselves on AI, break apart solutions to understand how they work and how they might continue to be improved for your use cases, and speak up on new potential use cases.
What’s a unique barrier to AI adoption that service providers in the legal industry face?
There are a few, but predominantly: legal is built on precedent and these professionals are taught to be skeptics. There is a sense of “trust but verify.” Much of AI can feel like a black box, and a lack of precedent it makes adoption of the technology slower.
Alternatively, is anything easier about leveraging it for your organization than one operating in a different industry?
I think the one area that has made it easier for us is that legally trained professionals have a natural intellectual curiosity. This makes many lawyers interested in the exploration of AI, and it also pushes their traditional sense of exploration to really validate what is happening and push boundaries of bias. I think those are healthy inputs to the AI process.