The following insight is derived from a recent ‘Insights from the Field’ event featuring Philipp Kandal on Artificial Intelligence for Transforming Business Operations at the Digital Data Design Institute.
Meet Our Guest Contributor:
Philipp Kandal | Chief Product Officer at Grab
Philipp Kandal is the Chief Product Officer at Grab, the leading all-in-one digital platform in Southeast Asia. Philipp leads the Product, Design, Analytics teams as well as the Business Development team and the overall Geo/Maps group. Philipp is also engaged in the start-up world as a Chairman at Techsylvania (a tech conference in Romania) and as an angel investor and mentor for several startups.
For more information, visit Philipp’s LinkedIn profile.
Overview: Why does this matter?
Artificial intelligence has the potential to radically transform the operational aspects of businesses. Delivery businesses are a quintessential example of where such an AI-enabled transformation could occur. For example, companies like Grab (Southeast Asia’s largest all-in-one digital platform provider) employ cutting-edge AI algorithms to enhance overall efficiency of its delivery fleet. Grab leverages complex machine learning algorithms to predict delivery demand and to manage logistics, thereby increasing customer satisfaction. Such applications of AI could help businesses cut down on cost and allocate resources more efficiently, improve the on-the-job experience for their employees, and understand the needs of their customers better to provide the holistic service their customers need when they need it.
What aspects of Grab’s operational infrastructure are enhanced by AI? How does this translate into improved performance across Grab’s delivery fleet and optimization of customer experiences?
Grab uses complex machine learning algorithms to support its delivery personnel and optimize its services across the following business pillars:
| Business pillar | How AI is used to optimize pillar | What it looks like in action |
| Pricing and resource allocation | Employ AI to dynamically adjust pricing levels and match drivers with riders more efficiently | Avoid having to make millions of decisions manually; achieve optimal pricing and minimal waiting time for both customers and drivers |
| Employee and customer safety | Apply AI algorithms to monitor rides | Match drivers with passengers while considering factors such as driver’s end-of-day location to minimize cancelations. Predict optimal routes and delivery timelines to maximize productivity and customer satisfaction |
| Personalization of customer experience | Integrate AI into digital menus to help differentiate food items based on customer preferences | Help customers make informed decisions based on their diet preferences (vegetarian, halal), and taste preferences (spicy, no-spicy) |
| Safeguarding operational delivery | Employ AI-powered algorithms to facilitate safe navigation (GrabMaps) and secure financial transaction | Utilize AI-powered cameras to scan unmapped areas to enhance safety and productivity |
| Seamless business-to-customer communication | Utilize AI for real-time language translation | Break down language barriers and provide a seamless customer experience |
In what specific ways can businesses leverage AI and machine learning technologies to enhance their operations?
- Embed AI across the entire organization: instead of isolating AI in a specialized department, integrate its capabilities across an entire organization. This ensures that AI solutions are developed with a clear understanding of operational needs and challenges of the entire organization (as well as specific departments and teams), which ultimately leads to more practical and meaningful applications.
- Promote and support guided experimentation and testing: businesses need to implement a robust AI experimentation framework where new features and improvements are rigorously tested through A/B testing or similar methodologies to maintain competitive advantage. This affords businesses to evaluate the real impact of AI interventions on user experience and other key metrices before full-scale deployment. Promoting and supporting experimentation extends to investing in scalable AI infrastructure such as cloud computing and data storage resources, as well as the appropriate security measures to handle the heightened data processing needs of AI and machine learning algorithms.
- Prioritize data strategy: developing a centralized data strategy to ensure all parts of the organization have access to necessary data in a secure, efficient, and compliant manner is imperative for organizational success. In return, a unified data repository enhances the ability of AI systems to learn from comprehensive datasets and generate more accurate outputs based on desirable metrices.
- Continuous learning and adaptation: the fields of AI and machine learning are rapidly evolving, and staying up to date about emerging technologies, current trends, and best practices is paramount for remaining viable and competitive. This includes providing training and support for non-technical professionals to help them understand the basic workings of AI systems and to support them to leverage these tools to advance their roles. Another important point under the theme of continuous learning and adaptation in the space of AI is ethics. Businesses that choose to integrate AI have the responsibility to set up a transparent, fair, and secure deployment strategy to ascertain sensitive customer data is protected.
- User-centric AI solutions: in general, the most exciting affordance of AI for businesses is in terms of delivering a personalized and just-in-time user experience. AI’s ability to enhance customer experience is only going to get better, and it will behoove businesses to focus their AI use to aid the customer-facing aspect of their operations. If used well, AI can help, not only improve operational efficiency, but deliver an exceptional value to customers that they cannot refuse.
Supplemental resources
- How AI will accelerate the circular economy (Harvard Business Review)
- Marking workplaces safer through machine learning (The Regulatory Review)
Disclaimer
The “Insights from the Field” initiative is a platform for guest contributors – who are industry leaders, subject-matter experts, and leading academics – to share their expert opinions and valuable perspectives on topics related to the fields of Business, Artificial Intelligence (AI), and Machine Learning (ML). Our guest contributors bring a wealth of knowledge and experiences in their respective fields, and we believe that their insights can significantly enrich our community’s understanding of the dynamic and intertwined spaces of business, technology, and society.
It’s important to note, however, that the Digital, Data, and Design (D^3) Institute does not explicitly endorse opinions expressed by our guest contributors. With this initiative, we hope to facilitate the exchange of diverse perspectives and encourage critical thinking, with an overarching goal of fostering meaningful and informed discussions on topics we consider are important to our community.