Generative AI Use Case Library for the Passenger Transportation Industry
Identify value-driven generative AI use cases to transform your organization.
- Adoption has been slow. The benefits of Gen AI technologies for passenger transportation are understood but adoption has been slow despite rapid growth in investment and development.
- Passenger Transportation leaders are unaware of relevant use cases.
- Passenger Transportation leaders lack insights for developing a business-aligned Gen AI strategy as part of a transformation effort, nor do they understand how AI and ML can impact the business and provide significant value.
Our Advice
Critical Insight
Passenger Transportation should approach Gen AI adoption strategically and methodically within a larger digital strategy context, with a clear understanding of the specific use cases and benefits.
Impact and Result
Info-Tech’s use case library provides practical guidance to help Passenger Transportation accelerate value-driven Gen AI use case adoption.
Generative AI Use Case Library for the Passenger Transportation Industry
Identify value-driven generative AI use cases to transform your organization.
Analyst Perspective
Artificial intelligence is disrupting every industry, but you can get ahead of the shift.
This is beginning to feel like another business revolution. Every communication channel has something to say about artificial intelligence (AI), and "ChatGPT" has become the de facto standard term used for generative AI.
However, ChatGPT is only one tool for generative AI, and nobody knows whether it is, in fact, the best tool. This should scare anyone who is thinking about how they can leverage AI as a holistic solution for their business.
AI has come a long way to where it is today. The first recorded AI program was written by Christopher Strachey in 1951 as a checkers program, but there is some debate as to whether Alan Turing conducted the first real theoretical work as early as 1935. Geoffrey Hinton (the godfather of AI) has more recently raised concerns that AI could be weaponized to cause serious damage to society as it takes over from being used for chatbots to owning all conversations.
Hinton's 2012 research paper on deep neural networks – an approach to teaching machines to think like humans, only faster than all of humanity put together – set off a race. OpenAI took the first mover advantage to scale rapidly, while Google worked slowly with Bard. Google recently opened Bard to the developer community to counteract ChatGPT by accelerating product development.
Kevin Tucker
Principal Research Director, Manufacturing Research
Info-Tech Research Group
Executive Summary
| Your Challenge | Common Obstacles | Info-Tech's Approach |
| The production operation lacks predictability, and those involved can't see what is happening throughout the process. The CIO must work closely with operations leaders to identify ways to enable better technology support. It's important to implement a program that avoids run-to-failure and the ripple effects of catastrophic outages. Downtime is unpredictable and longer than necessary because service people are often unavailable when equipment or assets go down. |
The organization is not capturing enough information for decision making or is capturing all the information manually. The organization typically does reactive maintenance, without the skills or knowledge of other programs. Shop floor equipment or assets have not been connected by the internet of things (IoT). Equipment has proprietary sensors that are not easy to connect into a central data lake for planning purposes. |
Improve your maintenance plans by using this paper to understand the needs of your business and how predictive maintenance can help. Use Info-Tech's tools to structure and evaluate your current state of maturity, identify software tools to help, and assist with the implementation. Adopt a sensing-based approach that considers a holistically interconnected environment for end-to-end capture of information to provide usage lifecycle visibility. Info-Tech advisors work closely with our customers to provide both strategic and tactical decision-making advisory services. |
Info-Tech Insight
Predictive maintenance is used in conjunction with the industrial internet of things (IIoT) and maintenance services to help organizations make the most of their data in order to increase uptime, improve safety, reduce maintenance costs, and identify hazardous situations before they occur.
Gen AI is an innovation in machine learning
Generative AI (Gen AI)
Gen AI is a form of machine learning. In response to prompts, a Gen AI platform can generate new outputs based on the data it has been trained on. Depending on its foundational model, a Gen AI platform will provide different modalities and thereby different use case applications.
Machine Learning (ML)
ML is an approach to implementing AI, whereby the AI system is instructed to search for patterns in a dataset and then make predictions based on that set. In this way, the system "learns" to provide accurate content over time. (Think of Google's search recommendations.)
Artificial Intelligence (AI)
AI is a field of computer science that focuses on building systems to imitate human behavior. Not all AI systems have learning behavior; many systems, such as customer service chatbots, operate on pre-set rules.
Info-Tech Insight
Many vendors have jumped on "Gen AI" as the latest marketing buzzword. When vendors proclaim to offer Gen AI functionality, you need to pin down exactly what is "generative" about it. The solution must be able to induce new outputs from inputted data via self-supervision - not trained to produce certain outputs based on certain inputs.
Other industries are already using AI, whether or not transportation is prepared
"Traffic congestion is a persistent challenge in urban areas and a significant concern for travelers.
Generative AI can optimize traffic management systems by analyzing real-time traffic data, predicting congestion patterns, and suggesting alternative routes.
By improving the flow of traffic, generative AI can help reduce travel times, enhance transportation efficiency, and provide a more enjoyable experience for commuters."
- Muhammad Usman, Better Call SEO, on LinkedIn, 2023
Info-Tech's approach and team can help, irrespective of where you are in your digital journey
| Where are you in the journey? | Starting | Benefiting | |||
| Establish your digital North Star | Quantify the value of digital use cases | Create your digital roadmap | Deliver digital use cases and realize impact | Create the infrastructure to drive and sustain change | |
| Set aspiration: Vision setting with key business unit (BU) stakeholders to discuss and align on digital aspiration (e.g. big T vs. mini T, self-funded and slow burn vs. investments) | Assess opportunity: Comprehensive end-to-end (E2E) understanding of the digital opportunity across BU/functions (e.g. data analysis, process walks, and interviews) | Design and plan: Bottom-up initiative design and planning (e.g. opportunity to initiatives, financials, phasing, design principles) | Execute: Detailed initiative builds and implementation, execution with rigor and transparency (e.g. process optimization then automation, test, measure, and scale) | Enable: Transformation infrastructure, operating model, and culture to drive value capture and sustain change | |
| Examples of how Info-Tech can help | Digital North Star placemat (e.g. industry trends, top-down opportunity, high-level planning) |
Digital maturity assessments (e.g. current state digital adoption and transformation readiness) |
Initiative bottom-up design (e.g. initiative ideation and business case creation, workplan, investments) |
Initiative build (e.g. zero-based process redesign with technology) |
Transformation infrastructure (e.g. transformation program design, transformation office) |
| Opportunity assessments* (e.g. BUs/function value creation diagnostics, opportunity levers) |
Holistic initiative planning (e.g. phasing, interdependencies, investments) |
Initiative implementation (e.g. testing and pilot, scale-up roadmap, performance tracking) |
IT modernization (e.g. technology infrastructure required to execute digital levers) |
||
| The following content will provide an overview of AI/ML use cases in passenger transportation. This will support opportunity assessments across your organization's value chain. Note: This does not provide the value/ROI specific to your organization. To do that, detailed current state assessments and opportunity assessments need to be executed. | Value assurance assessment (e.g. course correcting and accelerating initiatives underway) |
Change management (e.g. org-wide change program and stories, comms., governance) |
|||
| Performance management (e.g. KPIs - leading and lagging, people mgmt. for continuous imp.) |
|||||
| Workforce management (e.g. upskilling, right people, right place, right time) |
|||||
*Applicable framework element(s) for this document
Measure the value of this document
Document Objective
Highlight best-in-class use cases to spur the initiative planning and ideation process.
Measuring Your Success Against This Objective
There are multiple qualitative and quantitative direct and indirect metrics you can use to measure the progress of your initiative pipeline's development. Some examples are:
- Increased initiative pipeline value
- Number of capabilities impacted by initiative pipeline
- Enhanced understanding of impacts of the initiatives, aligned to your organization's capability map
- Better understanding of which sources of value are being addressed or under-addressed in your organization's initiative pipeline
See Establish Your Transformation Infrastructure in the Digital Transformation Center.