Conceptualizing products of significant business value is relatively easy. However, designing and prototyping to develop the product requires time and resources. Moreover, ensuring that the product functions as intended within the set parameters and yields favorable results can be a challenge. That’s why engineering teams must also double down on simulation testing. All these often lead to extended development lifecycles and budget overruns. Fortunately, the recent advances in Generative AI (GenAI) can immensely help engineering teams amplify their productivity and speed up time-to-innovation.
What is so special about GenAI? It transcends the main constraints of its previous generations — narrow learning capabilities and limited comprehension skills. In line with the early technology predictions, it shows unparalleled learning and task execution closely matching human ingenuity. That’s why businesses across industries are actively augmenting and enhancing their manual processes with GenAI to boost employee productivity. Engineering services companies are no different. As a matter of fact, one of the innovations that will considerably impact organizations within ten years is GenAI-enabled software engineering (Gartner, 2023).
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Gartner has placed AI-augmented software engineering on the peak of the 2023 GenAI Hype Cycle
Source: 2023 Gartner Hype Cycle for Emerging Technologies
GenAI: A Catalyst for Engineering Productivity
GenAI has the potential to revolutionize engineering productivity by automating mundane, repetitive tasks and augmenting human capabilities. The key to successful product engineering is setting specific objectives, firmly establishing a product’s foundation in those objectives, and retaining the flexibility to adjust course as the market demands. Using simulations is one of the greatest methods to predict future market trends and make changes accordingly. GenAI-powered simulations allow engineers to explore infinite scenarios by manipulating parameters. This can reduce costs and improve user experience.
But that’s just on the surface. A deep dive shows us many possibilities of GenAI and the scope of improvements in engineering productivity and efforts.
● Improved simulation at the design phase
The critical role of GenAI becomes evident from the design phase itself. Engineers can use GenAI-powered tools to simulate product performance under different scenarios and measure their performance. As a result, your product engineers can cross-check new variations or features and implement them before the product hits the development stage.
● Better and deeper testing capabilities
GenAI can also help set up different testing parameters and assess environments and designs with more nuanced algorithms. This unlocks a new era of product innovation that was previously only a wishful thought. Instead of relying on pre-configured actions, GenAI can keep improving on the go and match the human ingenuity needed in the testing phase.
● Enhanced product data analysis
GenAI can harmonize data from diverse sources and offer a more dynamic edge to product development by harnessing multiple perspectives and adding a layer of analytics. Its human-like inference ability can reduce false positives or negative results during analytical processes.
● More cost-effective and user-oriented
Product engineers can use GenAI to analyze user feedback and development costs under different scenarios. This can help them make the products more cost-effective and user-oriented.
Using GenAI to Innovate Existing Products
Refinement of current products can benefit from the same GenAI techniques that enhance the design of new items. It is possible to apply aspects such as user input to existing products at new depths and iterate new versions. For instance, renowned aircraft manufacturer Airbus reduced fuel consumption by 3.5% by using GenAI to create a more effective wingtip for its Airbus model jet. In the same vein, Adidas provides a poignant example of how to set parameters for cost, durability, and structure and use GenAI to develop a lattice-like construction for shoes that adds value and gives wearers additional support and durability.
GenAI can also detect and resolve user problems in real time and iterate different updates to keep the software competitive, freeing developers to concentrate on the more human-centered aspects of the user experience.
Overcoming Challenges and Seizing Opportunities in GenAI Adoption
While GenAI unlocks the benefits, integrating it within the system can still be challenging. Organizations must actively make astute decisions to implement AI successfully within their processes. They should ensure clean and up-to-date data to train GenAI applications for business-related use cases. Further, we can reduce the risk of AI ‘hallucinations’ leading to unfortunate inflictions through timely interventions from product experts.
Besides, to maximize the benefits of the GenAI revolution, an enterprise must ensure sufficient computing resources, thorough testing, and a dependable IT infrastructure. Centralized knowledge is critical to keeping data consistent and allowing AI to learn. Finally, GenAI projects should resonate with clear business objectives. While the pursuit of perfection is tempting, delivering solutions that solve significant slices of the problems often yields quicker and more impactful results. Success in building AI support systems relies mainly on goal clarity, expert teams, and efficient development processes, much like the principles for other data-driven initiatives.
About the Author:
RV Narasimham (RV)
President – Engineering Services, Tech Mahindra
RV has over 26 years of experience in the IT and engineering R&D services industry. Under his leadership, Engineering Services has transformed to deliver a full range of digital product and platform engineering solutions across industry verticals. He has extensive experience helping global clients in communications, manufacturing, retail, and hi-tech verticals with their digital transformation and IT outsourcing journeys. Prior to joining Tech Mahindra in 2018, RV held executive and senior leadership roles at Atos, Accenture, Cognizant, and Wipro, managing over billion-dollar revenues. RV actively participates in key industry forums, such as NASSCOM, where he shares insights and collaborates with peers on technology initiatives as a member of the ER&D Council. He holds an MS degree in Engineering from Missouri University of Science and Technology (USA) and an executive management certificate from IIM Bangalore.
References:
- Oct 11, 2023. “Gartner Says More Than 80% of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026”. Gartner.
https://www.gartner.com/en/newsroom/press-releases/2023-10-11-gartner-says-more-than-80-percent-of-enterprises-will-have-used-generative-ai-apis-or-deployed-generative-ai-enabled-applications-by-2026#:~:text=By%202026%2C%20more%20than%2080,%2C%20according%20to%20Gartner%2C%20Inc. - “Reimagining the future of air travel”. Autodesk.
https://www.autodesk.com/customer-stories/airbus - Craig, W. Nov 12, 2018. “Adidas Futurecraft 4D: One shoe to rule them all?” Harvard Business School. https://d3.harvard.edu/platform-rctom/submission/adidas-futurecraft-4d-one-shoe-to-rule-them-all/
