Sol Rashidi's Insights on AI Adoption and Team Development: Shaping the Future with Practical Experience
Sol Rashidi in Discussion with Joe Reis
What does it take to lead a team so large it's comparable to managing a small town?
For Sol Rashidi, this was her reality by the time of the interview, overseeing 800+ team members. Holding prominent positions like Chief Data, Analytics, and AI Officer, as well as Executive Vice President (EVP), her perspectives on AI are both profound and practical.
When
asked, “I’m curious to see what happens with AI adoption in the next few years” Sol offered three key predictions that organisations must prepare for:Cost Consciousness
Many organisations have yet to fully recognise the financial impact of running AI workloads. As the costs of cloud infrastructure and AI operations, including model training and deployment, continue to rise, this will soon become a critical consideration. Companies will need to adopt cost-efficient strategies, placing greater pressure on infrastructure providers to offer scalable and agile solutions.Sustainability Challenges
The growing energy consumption of AI poses a challenge to organisations’ sustainability goals. Meeting Environmental, Social, and Governance (ESG) targets while running energy-intensive AI workloads is becoming a delicate balancing act. This tension between AI's resource demands and the need for greener operations will force companies to make difficult decisions in the future.Shift Away from LLMs
While Large Language Models (LLMs) currently dominate AI development, Sol foresees a shift towards managed services and pre-built applications. These solutions allow organisations to integrate AI without the need to develop or train models from scratch, providing a faster and more cost-effective path to implementation.
Beyond these strategic insights, Sol emphasises that the most effective way to develop high-performing teams is through practical experience and learning from failures. She highlights three essential approaches:
Self-Application and Problem-Solving
A great way to build skills is to start small, by identifying areas in your own life that can be improved through AI or automation. Applying these tools to personal challenges helps develop a deeper understanding of how they function and how to use them effectively.Experimenting with Existing Tools
Integrating tools like Chrome extensions or APIs into everyday tasks is another way to build expertise. Exploring how these tools are developed provides insight into the mechanisms behind AI applications and helps bridge the gap between theory and practice.Balancing Theory with Practice
While online courses and theoretical learning are valuable, practical application is essential for meaningful learning. Whether in personal projects or professional settings, using AI to solve real-world problems reinforces concepts and reveals new opportunities for innovation.
Conclusion
Sol Rashidi's ideas offer a helpful guide for organizations looking to adopt AI. By thinking ahead about challenges like costs, sustainability, and changes in AI tools, companies can better prepare for the future. She emphasizes that practical, hands-on experience is important, showing that learning by doing is key in this fast-moving field.
If you're interested in further insights from Sol Rashidi, especially on how data engineers can effectively communicate with stakeholders, be sure to check out our post on her discussion with Joe Reis during his Data Engineering course on Coursera/DeepLearning.AI.
Catch the full conversation between Sol Rashidi & Joe Reis here: [link]
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What are your thoughts on the future of AI as outlined by Sol Rashidi? How is your organization preparing for these upcoming challenges? We'd love to hear your experiences and insights. Share your thoughts in the comments below and join the discussion!