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JUL-AUG 2019

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16 INTECH JULY/AUGUST 2019 WWW.ISA.ORG COVER STORY Xiaojun Huang, PhD, is senior advisor for Upstream Digital Transformation for ExxonMobil. She is also one of 40 women driving the adoption of artificial intelligence in business and industry profiled in IBM's List of Women Leaders in AI. Xiaojun and her team are using AI to create a CT-like scan of the inner earth. This will help ExxonMobil target drilling investments on the most promising opportunities, adding speed and precision, and minimizing human biases. The current project focuses on deep-water drilling off the coast of Guyana. What do you want to achieve with AI? We are at the early stages of transforming our work processes. Our goal is to allow our domain experts to focus on what they are good at, augmented with AI. The project allows for much more efficient and collaborative planning for Guyana deep water development wells. Ultimately, that will lead to an ever-safer operation and steep improvements in efficiency and profitability. What are some key things that you have learned? We need to change the way we approach our business processes and partnership. Transformation is not about moving every piece of data to the cloud. It is rather about reimagining work processes inside out through the lenses of the art of pos- sibility with all digital technologies, with a focus on business objectives. Digital transformation requires agility and speed. We established our collaboration agree- ment with IBM, put together the team in a month, and delivered the minimum viable product to the Guyana team in 10 months. What do you wish you knew when you first started with your work with AI? We need to have empowered digital champions to help transform a large orga- nization. Understanding both the business and AI, these champions can connect business with solutions, advocate principles and value for change, and act as a conduit between the organization and external innovations. I strongly advise key business champions to get their feet wet on AI. n 3. Focus on worker augmentation, not worker replacement. AI's potential to reduce staff head count attracts the attention of senior business execu - tives as a potential cost-saving initia- tive. A more informed expectation, however, is for applications that help and improve human endeavors, as AI promises benefits far beyond auto - mation. Organizations that embrace this perspective are more likely to find workers eager to embrace AI. 4. Plan for the transfer of knowledge from external service providers and vendors to enterprise information technology and business workers. External service providers can play a key role in planning and deliver- ing AI-powered software, and knowl- edge transfer is crucial. AI requires new skills and a new way of thinking about problems. These include tech- nical knowledge in specific AI tech- nologies, data science, maintaining quality data, problem domain exper- tise, and skills to monitor, maintain, and govern the environment. 5. Choose AI solutions that offer track- ing and revealing AI decisions, ide- ally using action audit trails and fea- tures that visualize or explain results. To that end, Gartner predicts that by 2022, enterprise AI projects with built-in transparency will be twice as likely to receive funding from CIOs. 6. Start small; do not worry about im- mediate return on investment. Digi- tal transformation should begin with small experiments that are purely for learning, says Gartner. Use the time to pilot projects that employ a variety of technologies to assess which make the most sense for the business. n ABOUT THE AUTHOR Renee Bassett (rbassett@isa.org) is chief editor for InTech magazine and Automa- tion.com, and publications contributing editor for ISA. Bassett is an experienced writer, editor, and consultant for industrial automation, engineering, information tech- nology, and infrastructure topics. She has a bachelor's degree in journalism and English from Indiana University, Bloomington, and is based in Nashville. View the online version at www.isa.org/intech/20190801. quickly select, launch, transform, and operationalize IoT data, he says. Jason Mann, vice president of IoT at SAS, says companies can no longer afford to ignore the hidden signals in their IoT data. "To thrive, organizations need a solution that addresses data complexity and automates timely and accurate decision making," he adds. Tips for AI pilot projects According to a recent Gartner survey, 37 percent of organizations are still looking to define their AI strategies, while 35 percent are struggling to iden - tify suitable use cases. Once you have developed a solid understanding of AI and its potential applications, it is time to make a case for a pilot. Here are some tips from Gartner for making the pilot project a success. 1. Be realistic about a timeline. Once you have approval from executives, it can be tempting to think a pilot project will follow quickly. In fact, according to results from Gartner's 2017 Annual Enterprise Survey, 58 percent of respondents in compa- nies currently piloting AI projects say it took two or more years to reach the piloting phase, and only 28 percent of respondents reported getting past the planning stage in the first year. 2. Aim for fairly soft outcomes, such as improvements to processes, cus- tomer satisfaction, products, or finan- cial benchmarking. Gartner Research Circle respondents urged others not to fall into the trap of seeking only imme- diate monetary gains. Aim initially for less-quantifiable benefits from which financial gains would eventually arise. ExxonMobil: Get your feet wet in AI

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