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Meet Perficient’s Chief Strategists: Christine Livingston

Thrilling our clients with innovation and impact – it’s not just rhetoric. This belief is instrumental for our clients’ success. In 2018, we announced the first class of Chief Strategists, who provide vision and leadership to help our clients remain competitive. Get to know each of our strategists as they share their unique insights on their areas of expertise.

Artificial intelligence (AI) is among the fastest growing technologies today, both in capability and implementation. It is an exciting, fast-paced area, with advancements occurring by the minute and adoption rates steadily increasing. AI is essential to any company’s future strategy.

Christine Livingston, AI Chief Strategist, leads our artificial intelligence practice with 10 years of advanced technology experience. We recently spoke to Christine and learned more about her role as a chief strategist, her perspective on the future of artificial intelligence, and her life beyond the role of chief strategist.

What does your role as a Chief Strategist entail?

My role at Perficient is to lead our artificial intelligence team. Our team focuses on creating solutions leveraging AI capabilities, optimizing and automating processes, and helping customers develop and implement strategies to adopt artificial intelligence. I help many clients develop AI roadmaps, including the human elements of the strategy, such as governance and human capital requirements.

Best practices for AI are still in the early stages of development. I think the role of Chief Strategist is especially important because AI is still in an early adoption cycle; having the personal experience of been-there-done-that and proven successful deployments in this space is quite valuable to our customers.

Strategically Speaking

What do you see happening with artificial intelligence in the future?

Artificial intelligence is a hot topic. It’s everywhere, permeating our culture outside of work. Watson even appeared in a Super Bowl ad! With that context, there’s a general perception that AI is widely deployed today, but it’s actually not. Gartner published a survey of CIOs in 2018 that showed only four percent had actually invested or deployed artificial intelligence to date and 46 percent were planning to deploy.

I think [AI] will continue to develop rapidly, and we will see wider adoption both in corporate and individual settings. AI’s greatest strength is also its greatest challenge. The technology is evolving so quickly and prescriptive progress is hard to predict. Today’s strategy will result in a roadmap for 6 to 12 months, and the technology will be vastly advanced by then. One of the things we’ve learned, and we typically recommend to our clients, is that they leave room for iteration and be willing to adapt and evolve with the technology.

It will also be interesting to see what happens from a build versus buy perspective. Meaning, there are many platforms doing some really interesting things right now. While at the same time, a lot of companies are also trying to influence the same concepts and build their own solutions. [Companies] want to own their data and own their models. It will be interesting to see what the open source, “build-your-own” community does in relation to some of the larger players in the space.

Why does strategy matter for deploying artificial intelligence solutions?

AI is not widely adopted enough yet for people to say, “Here’s the typical path or roadmap.” It’s really important [for companies] to understand the training process and the value they seek to drive, rather than just attempting to implement use cases prior to that fundamental understanding. There’s typically a natural progression when you look at deploying these solutions so you can start [achieving] economies of scale.

As an example, clients have come to us and said, “We have four or five different proofs of concept and technologies running on different platforms (because everyone went out and they did their own proof of concept.) Now we need to actually do something with it.” Realizing that they need to put it in production and industrialize it, they ask, “What do we do with all of these platforms?”

The challenge is everyone’s deployment of AI will be different because it’s so specific to your business. We are helping clients look at their particular pain points and goals, where artificial intelligence is driving value, and helping them come up with a strategic roadmap for implementation. [Strategy] is going to be critically important over the next year or two, as we continue to see early adopters [deploy] their first solutions.

Think Like a Chief Strategist

How does your team help clients on their digital transformation journey?

Among the major concepts regarding digital transformation is this notion of omnichannel, 24/7 support, and continual connectivity. We’re seeing many companies starting to deploy artificial intelligence within customer service functions or on the customer experience side to drive true omnichannel, consistent experiences across all those entry points. If you train and deploy a central AI platform, then you can expose it across different channels to fuel that experience and, ultimately, you’re using the same intelligence and decision making process on the back end to drive objectivity and consistency. We’ve done a lot of work in the virtual agent space to reduce costs in call centers and create an omnichannel customer experience.

Another way we’ve helped our clients digitally transform is with text analytics, which is essentially deriving meaningful information out of unstructured text. For example, in a manufacturing environment, we analyzed customer feedback and survey data to identify product level defects and influence its engineering change lifecycle based on that information.

From the analytics perspective, digital transformation is about optimization. Companies should ask, “What’s the best decision I can make given all the information I have?

In healthcare, we’ve worked with clients on challenges such as patient population identification, optimizing patient outcomes, and readmission indication. Unstructured data, which is effectively invisible to traditional analytics systems, contains a wealth of information.  For example, in the readmission indication use case, it’s important not only to understand if [ patients] will likely be readmitted but also to identify and address the underlying factors to ultimately improve their outcomes. Healthcare providers can now implement care plans based on what they know about the patient holistically to prevent future readmissions.

Automation is another explosive area where we are working in concert with technologies such as robotic process automation (RPA) to minimize required human capital and elevate the tasks on which employees are working.

How is your team helping clients with their AI strategy?

Creating a strategic approach [for AI] can minimize or eliminate some of the previously mentioned pain points. We’re seeing successful implementations with companies that take the time to identify and prioritize the right go-first use case. These use cases are big enough to drive value, but not so small that you can’t train for it and realize value in a reasonable timeframe. Clients that are willing to take more of a strategic, slightly slower approach have differentiated themselves in terms of AI success. It may take a bit more time, but in the long run, they end up with a better outcome.

In addition to strategy, we help on the platform side with guiding the selection process for the best AI platform for your company. If our team is engaged early on, we can also help clients avoid losing time, money, and effort to optimize their training cycles. This is really important because companies typically need to involve highly-paid subject matter experts (SMEs) in the training of these platforms.

Healthcare SMEs include physicians and nurses. In the legal space, you need your lawyers. For financial services, your financial advisors are the SMEs. All of these examples are typically very educated, highly paid, and high-demand roles. If we need leverage their time to train technology, then we want to use it wisely.

What else is important for organizations to understand for achieving success with AI?

Realize that the technology is never going to be 100 percent accurate. Achieving accuracy or confidence in the 90 percent range is equal to or above the output you’re getting from people. I’ve heard clients say they must be at 100 percent or they won’t deploy AI, but it is not realistic.

People are not perfect, nor 100 percent accurate. I think that’s a misconception [with AI] that you have to address. You have to understand the cost-benefit analysis of continuing to train the platform for accuracy gains. You need data to be “accessible,” clean, and accurate before you’re ready to deploy a full AI solution.

Beyond the World of Strategy

Tell us about yourself and your interests when you’re not wearing the Chief Strategist hat.

My family is a huge part of my life outside of work. My three-year-old started playing soccer for the first time. I played soccer all the way through college, so that’s something I’ve always enjoyed and loved. I’m excited to be joining the ranks of the soccer moms.

I also love to travel, which we haven’t done as much since having small children, but we try to take an international trip annually. I even flew my oldest, who was just one-year-old at the time, to Australia, just the two of us, to meet my husband!


Follow along on this series to learn more about each of our Chief Strategists. And, take a look at recent blog posts they’ve written on trending topics for their industries.

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