Adopting AI in engineering: Accelerating time to value
Lessons learned from hundreds of AI project implementations.
AI is reshaping the way engineers work. With AI and machine learning, engineering teams can harness their test data to uncover new design insights, streamline testing efforts, create superior products, and speed up time-to-market.
Integrating new technologies into the engineering workflow is a complex challenge that goes beyond technical aspects like modeling and programming., and experimenting with ChatGPT. Achieving real impact with AI requires strategy alignment, team coordination, robust data management, infrastructure readiness, and team upskilling.
In this webinar, Monolith AI’s Customer Success team will share key insights, lessons learned, and best practices from hundreds of AI projects with leading engineering teams across many industries.
Register today to discover common challenges, best practices, and ideas for approaching AI for engineering.
Webinar highlights
- Overcome common challenges: Learn about typical obstacles companies face and how we help clients overcome them.
- Drive project delivery and impact: Monolith has developed a structured engagement process and project discipline to ensure business and strategy alignment in finding quick wins and long-term success with AI in engineering. We’ll review some of our tools and best practices learned from many customer engagements.
- Develop a data strategy: Models are only as good as your data. Hear about best practices to align across teams and processes to make your data is a strategic advantage.
- Accelerate AI adoption: Success and long-term value generation with AI requires an intentional plan for organizational maturity across many aspects. We’ll discuss how we help customers build a plan for more than just projects, but long-term business impact as well.
- Real-world success stories: Hear about real cases where Monolith AI’s solutions have driven significant business and technical value.
If you are unable to attend the live session, we would still encourage you to register to receive the webinar recording.
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Who should watch:
- Engineering leaders responsible for product development or validation who are looking to integrate new AI tools to their engineering processes.
- Engineers facing challenges getting their leadership to move aggressively into AI to boost project efficiency and effectiveness.
- Organisational leaders who are driving efforts to incorporate AI and want to understand how different teams need to cooperate and align to make the entire process work.
- Engineering teams struggling to leverage internal data science experts for scalable AI solutions.
