Incorporating AI in Business
Luis G. Gonzalez, Chief Operating Officer for Power, Aboitiz Data Innovation

Incorporating AI in Business

5 min read

As Aboitiz Data Innovation (ADI) chief operating officer for power and Aboitiz Power Corporation’s (AboitizPower) chief data officer, Luis focuses on expanding the development and implementation of data science & AI solutions for AboitizPower in support of enhancing operational efficiency, revenue generation, risk management, and ESG initiatives.

Luis has 24 years of experience in leading and developing AI solutions, enterprise software and digital transformation of industrial enterprises. He has been involved with over 50 customers in Asia Pacific, North & South America, Europe and the Middle East. He is co-founder of the AI Asia Pacific Institute, a non-profit research institute for ethically deploying learning algorithms in society. He is also a Guest Lecturer at Nanyang Technological University MBA Program and under the Kellogg and Harvard Data Science Programs at Emeritus Platform. Luis sits on the advisory board of different startups that apply machine learning in NFTs, social media, education and neuroscience.

Prior to joining ADI, Luis was the managing director of Element AI Asia, a Canadian deep-learning startup. He was also vice president & chief customer success at GE Digital, responsible for the delivery and execution of all GE Digital projects across power, aviation, O&G and transportation. He was previously the GE power chief digital officer responsible for power digital PNL in Asia.

Luis holds a bachelor’s degree in International Business and Computer Science from the University of Victoria and a postgraduate degree in Software Engineering from the Chinese University of Hong Kong. He has also earned postgraduate diplomas in Machine & Deep Learning, Data Science, Internet of Things, and Smart Manufacturing at the Massachusetts Institute of Technology.

Impact of emerging developments in Artificial Intelligence

In the power business, as well as in all industrial sectors, the primary competitive advantage in the past was efficiency. Continual small-margin improvements were made to eliminate inefficiencies and maximize ROI on fixed assets. The disciplines of Six Sigma and Kaizen originated from this focus on enhancing the efficiency and effectiveness of technology. However, with the drive towards sustainability and the imperative to move toward net zero, our engineering and technology have nearly reached their limits, necessitating an adaptation of established models.

The next competitive edge for power companies will be adaptability. For a large industrial company, becoming flexible and adaptable is a significant challenge. Thus, access to near real-time data is essential to make quick decisions that allow the business to adapt to climate change, volatile market conditions, new emission targets, and dynamic demands from fuel sources and customers. The speed of decision-making, the vast amount of data generated, and the ability to respond to these challenges will not be possible without AI. Only with AI will we be able to dynamically optimize power generation to respond to market demands, intelligently distribute energy where it's needed most, and, importantly, help ensure energy systems' reliability, affordability, and sustainability.

Your experiences from successful data initiatives that have positively impacted decision-making and business outcomes

Aboitiz Data Innovation (ADI) works closely with AboitizPower to transform its commercial plant operations to tie in its assets and commercial operations further. This synthesizes the end-toend process of power generation, asset management, contracting, and trading of energy assets. We are also helping them transform their fleet of generation plants into smart power plants, which will eventually connect with trading operations to realize the strategic vision of a virtual power plant. Finally, we have taken steps toward improving the reliability of AboitizPower’s assets by implementing anomaly detection and asset health identification algorithms for key components of their power plants and grids.

Develop an ecosystem of solution providers around you and cultivate a culture aware of what it takes to be a digital enterprise, along with the competence to collaborate with data science teams.

Challenges in your business that current services are unable to solve

The most significant challenge with AI solutions to date is their development under real-life conditions, with all the critical considerations required by organizations operating in the power business. Even with our partners in the industry, we often find a gap between proof of concept and true market fit, which pure technology companies struggle to bridge.

Recognizing this, we have taken the initiative in the adaptation process, understanding that strategic projects for our AI investments must be in a paradigm where we are central to the development of the solution, not merely consumers of a premade product. This means being involved in the development and maturation of the solution rather than waiting for others. This is the raison d'être for Aboitiz Data Innovation and underscores the importance of developing a digital competence capable of managing product development for the Aboitiz Group and beyond.

Data and AI shaping the future of our industry

Data and AI are set to profoundly reshape industries. In the power sector, we anticipate more adaptable power plants, signaling the end of the era of large-scale generation facilities. The future will welcome hybridization—combining fuel-based generation, renewables, and energy storage systems in a more distributed and integrated fashion. Data will enable better-designed power installations and diversified asset risk management. Moreover, we will become more adept at brokering demand, responding faster to energy consumption, and better anticipating trends and seasonality. Power-sharing schemes and distributed generation will likely enhance grid resilience. Finally, we must complement the traditional energy paradigm with the consumers' perspective. Once customers are more involved and aware of energy conservation, they will make trade-offs to save money and help the environment.

Advice to professionals working in tech industry in terms of do's or don'ts

If you are a tech provider, understand that the adaptability of technology is its most significant strategic asset. It will necessitate the creation of almost ‘Made to Measure’ solutions or productized services. This means evolving your customer relationship into a true partnership, considering both parties' shared ownership, revenue splits, and other market-making growth. Success lies in new business models, not just marginal gains (e.g., efficiency vs. adaptability). If you think you can develop a universally applicable and scalable technology solution with your customer's data, that business model is outdated and will hinder growth. We are entering an era of closer relationships, where the insights from your models should foster a deeper understanding and strengthen partnerships.

If you're in energy asset operations or energy sales, learn to jointly build and operate new solutions with partners. Develop an ecosystem of solution providers around you and cultivate a culture that is aware of what it takes to be a digital enterprise, along with the competence to collaborate with data science teams. Transformation comes from building these partnerships and developing new business models together. Believing that you can simply adopt the next best-in-class AI solution as a fast follower is risky; you may not be able to adapt your business quickly enough to survive. The way we generate, distribute, and consume power and other services is set to fundamentally change in the next 20 years. Incumbent utilities may not survive with the models that have been in place since the early 1900s. Transformation of a large industrial enterprise will take 7 to 10 years at a minimum, and all incumbents are running out of time to adapt.

(The article was originally published on CIOReview APAC.)