The last several years have been a testament to the automotive industry's resilience. Throughout the pandemic and its aftermath, the most successful automakers have pivoted successfully to new technologies, aligned with consumer demands in a continuously evolving macro environment comprising of a number of changes.

The first is the inevitable transformation of drivetrains. The significant push towards electrification, alongside developments in alternate fuel technologies versus market demand, has given pause to the significant investments that were made.

Second, the transition from a hardware-centric to a software-driven approach in vehicle development which has not only accelerated, but also fostered a wave of strategic investments and partnerships, promising a enriched future of automotive excellence.

Third, there is a broader re-evaluation of mobility concepts. While the march towards complete autonomy has slowed, impacting some of the newer business models that were being launched, the progress towards post-Level 3 AD/ADAS capabilities continues to grow, creating a key differentiator for the OEMS and improving safety and the overall product experience for the customers.

Fourth, owning the entire customer experience across Shop–Buy–Own has become a top priority. The ability to control, collaborate, and orchestrate the whole experience in and out of the vehicle in order to generate significant customer stickiness.

All of the above has had a confounded effect on operations and processes across the organizations, as well as the need to reduce the complexity in product design, sales, service operations, and manufacturing, supply chain structures and processes. Traditional complexity across the value chain that impacted agility and the COGS is slowly diminishing.

With the automotive industry on the journey of significant changes, it's crucial for every automotive leader to start preparing for the oncoming transformations. These shifts should be at the forefront of every decision-maker's mind as we approach 2025, guiding strategic planning and investment decisions.

1. A paradigm shift in vehicle design and development is underway

With significant investments being made with deferred or low returns, there is a big focus on optimizing the vehicle design and development process. The use of digital models to design, analyze, verify, and validate complex vehicle systems throughout their lifecycle is replacing document-centric engineering with a more integrated and interactive approach through simulation and analysis based on digital models. This shift presents both challenges and opportunities for the industry.

2. Simulation enabled by virtualization and digital twins

Software-defined vehicles allow automakers to address the fundamental challenge of dependency on hardware to develop software. Theoretically, they can create the vehicle software before the hardware is finalized or ready by embracing virtualization. Practically speaking, however, a significant amount of in-vehicle verification and validation is required to ensure that the software and hardware are working as desired as an integrated system. Cloud-deployed digital twins of vehicles will allow automakers to conduct much of this testing in a simulated environment rather than on actual hardware, speeding up the vehicle development cycle and reducing costs by up to 30%. These digital twins can then continue to support the vehicle over its lifetime, providing an environment to efficiently improve operational performance and test new software-defined features and vehicle updates.

3. AI-driven disruption

AI will create significant disruption across the functional value stream, ecosystem, and product bundle (vehicle with coupled services). Effectiveness and efficiency are at the forefront, and AI will also contribute to delivering an enriched consumer experience. The ability of OEMs to harness the power of AI will differ and depend on their readiness on three fronts: (i) readiness of the data landscape to be exploited, (ii) well-defined purpose and approach to applying AI, and most importantly, (iii) regulations in data usage across the world or different markets and maneuvering them to own advantage.  We are already seeing how GenAI co-pilots and other GenAI/AI-powered agents with humans in the loop bring in hyper-automation that enables OEMs to unlock new levels of productivity and innovation. Automakers must be open-minded and ask: “Where can the use of AI make the most significant impact?” It may be in the R&D process transformation, supply chain and operations, or sales and customer service.

4. Reimagination of the customer experience

Redefine and realize omnichannel customer experience supported by differentiated experiences across other domains such as consumer, retail, and non-auto manufacturing coupled with integrated solutions backing the experience with effective fulfillment planning and execution solutions. Reimagining the consumer experience (to provide end-to-end engagement through D2C and hybrid – aided by dealerships and other channels): Consumer expectations are shifting from a technology-driven UX to a branded UX, demanding an expertly choreographed interactive experience across touchpoints. This includes the experience of buying, servicing, and using the vehicle, both within and beyond the car, reflecting the brand’s unique value proposition. To support this shift, the redesign of every touchpoint should be immersive (using metaverse technology), uniform (across different channels), and hyper-personalized.

5. Embracing the new forms of collaboration

Some vehicle innovations will be more cost-effective for the entire industry if built on cooperative rather than competitive models. We are witnessing some unique partnerships between OEMs on SDV and autonomous development. Non-differentiating components of the vehicle platform, such as base software, can be reused by any player in the value chain to realize vehicle functions (e.g., Parking Assist, Auto Pilot) that are differentiated through the user experience. For example, should automakers deliver features in the same way as Apple’s App Store, creating a closed model that is proprietary to a particular hardware ecosystem? Or should all automakers aim to create a shared digital software marketplace for components supporting different vehicle features? With cost pressures, most automakers will likely adopt a reusable open-source model in the short term. Standard-setting bodies like SOAFEE will further accentuate this. The key is integrating the work products of such collaborations seamlessly.

6. Reduction of complexity

Complexity shifts, and differentiated pace of change/adoption of the vehicles are poised to reduce complexity, thereby decomplicating the design, operations, supply chains, and aftermarket. Eventual complexity may be low, but the (potentially long) transition period would require management of the differentiated complexity of the value stream, business processes, and supporting IT solutions.

7. Data-driven software development and control

Is set to play a pivotal role in shaping the future of the automotive industry. Today, most driver assistance features and hyper-personalized vehicle applications are developed using pre-collected data for development and validation. The new approach utilizes real-world data collected by vehicles in operation and development. It requires a practical, intelligent, efficient data acquisition, processing, and AI/ML development infrastructure. A cloud-hosted data platform with intelligent edge infrastructure will be the backbone of this approach.

The automotive industry has undergone a period of evolution and re-invention over the last few years and will continue to do so over the next few years. While this evolution is underway, automakers must identify the right priorities and key imperatives for themselves. The automakers who plan their technology adoption in alignment with some of these trends will be better equipped to scale and grow through 2025 and beyond.

About the Authors

Swarup Mandal, PhD
Global Head – Automotive, Wipro Engineering Edge

Ritesh Kulkarni
Consulting Lead – Automotive

Gautam Sardar, PhD    
Consulting Practice Head – Automotive and Manufacturing 

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