Like most industries, the current state of the global automotive and manufacturing sectors is characterized by rapid technological advancements (e.g., Artificial Intelligence), macroeconomic shifts and business pressures that are redefining how value is delivered to customers.
Looking Glass is an annual report designed to help business leaders navigate industry shifts and identify opportunities for competitive edge. The 2024 edition identifies 100+ trends through five lenses that we see as defining the future of technology in business.
For business leaders in the Automotive and Manufacturing spaces, this article serves as a “cheat sheet” to the Looking Glass report. We’ve compiled a quick, industry-specific digest, highlighting the tech trends that hold transformative potential for automotive and manufacturing enterprises.
AI everywhere
AI-enabled software delivery:
With vehicles becoming increasingly software defined, there is an opportunity to leverage AI to revolutionize software development and significantly improve its delivery processes. This offers an opportunity to transcend the limitations of the past, by taking an AI-first approach to build the software that drives these vehicles. Using GenAI for the analysis and conversion of legacy code to modernize platforms is a great use case.
>Learn more about our views on GenAI-assisted software development
AI-enabled business operations:
Current advancements in AI (including GenAI) offer an exceptional opportunity to review and optimize business processes through an AI-first design. Customer support, training, D2C channels and financial operations are great candidates for AI adoption and near-term value creation for enterprises.
>Read here about decision-making approaches to evolve your AI Strategy.
AI in IT/OT:
Most manufacturers have adopted Industry 4.0 concepts to drive a digital-first mindset throughout the organization. AI on the edge, sensor data capture and MLOps enable new opportunities for real-time, data-driven decision making. A data strategy and roadmap, coupled with foundational platforms built around domain and product-thinking, will allow companies to build data products in IT/OT and enable a faster path to value.
>Listen to this podcast with Bosch on how they are using GenAI
Data strategy and platform foundation
Focus on data and AI strategy:
Manufacturing enterprises create terabytes of enterprise data, much of which is embedded in COTS applications across multiple domains and ownerships.Creating a holistic data strategy that leverages cross-domain data for analytics requires aproduct-thinking approach, which in turn, provides a framework to define an investment strategy that enables the best short- and long-term ROI for the organization.
>Get more insights into AI strategy here
Data platforms supporting Data as a Product (including AI products):
A strong foundational architecture is absolutely necessary for sustaining any long-term change initiative. For your AI Strategy to effectively deliver value, data platforms are a necessary foundation to support future high value AI-based initiatives. Automotive sensor data, factory data, usage data, machine data and systems data are sources of latent value. Once ingested into a scalable data platform enabling near-/real-time analytics, traditional analytics and cutting-edge AI models can discover hidden value yielding significant impacts to business models and customer experience.
>Read how BMW built a data platform for AI-based connected services and products
Accelerating physical-digital convergence
Multi-agent orchestration platforms:
As robots in the workforce increase, orchestration platforms provide the ability to integrate and communicate across the landscape. The work for systems integration is expected to increase across these domains. Organizations that can effectively scale to meet this demand will have a competitive advantage akin to the advantage enjoyed by the digital platforms. Continue to think about building relevant capabilities in product, platform and data to prepare for this shift.
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Predictive/proactive maintenance:
The ability to analyze historical and real time sensor data, combined with AI decision support systems, can significantly increase asset life, reduce downtime and boost overall yield; contributing significantly to the bottom-line. This applies to individual machines as well as the total factory system. It also offers the ability for manufacturers to create new business/commercial models, such as selling uptime through a more outcome-centric relationship.Combine the IoT data with AR/VR capabilities to unlock and/or alleviate skilled labor availability.
>An example of using data and AI to predict problems with drone flights
Digital Twins:
Digital representations of physical objects in their real-world context are being used in manufacturing to innovate faster and experiment without the excess time and investment on developing new products and processes. To be effective, organizations will need to re-imagine their team structures to drive the convergence of skills, departments, people and technologies to create holistic digital experiences that deliver transformative business value.Read about our work on combining digital twins and XR technology to create a testing platform for autonomous vehicles.
Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of .