Navigating AI Leadership: Insights from Cal Al-Dhubaib
Artificial intelligence (AI) continues to transform industries, offering groundbreaking capabilities while presenting unique challenges. In a recent episode of ODSC’s AiX Podcast, Cal Al-Dhubaib, Head of AI and Data Science at Further and a prominent advocate for responsible AI, shared invaluable insights from his journey. Spanning topics from leadership and team building to AI governance, Cal’s reflections serve as a guide for practitioners, executives, and anyone navigating the complexities of AI in high-stakes environments.
You can listen to the full episode on Spotify, Apple, and SoundCloud.
The Reality of AI Systems: Embrace Probabilistic Thinking
Cal Al-Dhubaib begins by addressing a fundamental truth about AI: it will make mistakes. Unlike traditional deterministic systems, AI models operate probabilistically, meaning that errors are inevitable. Cal stresses the importance of setting expectations early with clients and stakeholders.
“The art of building AI systems isn’t about achieving perfect accuracy,” he explains. “It’s about creating guardrails for when to trust the system and when human oversight is required.”
This emphasis on AI literacy is crucial. Teams must educate stakeholders on the inherent uncertainties of AI and outline risk mitigation strategies upfront. Such transparency not only builds trust but also ensures alignment with the organization’s goals and regulatory standards.
From Practitioner to Leader: Shifting Perspectives
Transitioning from an individual contributor to an executive role was a pivotal moment for Cal Al-Dhubaib. For him, the shift required a reevaluation of what brought joy and fulfillment in his career.
“I went from finding joy in solving technical problems to finding joy in rallying teams around those problems,” he reflects.
Central to this transition was mastering storytelling. Whether communicating the value of a technical solution to a CEO or aligning teams around a shared vision, Cal’s ability to distill complex engineering concepts into compelling narratives became a cornerstone of his leadership approach. Storytelling, he notes, isn’t about oversimplifying but about connecting technical achievements to real-world impacts.
Lessons from Building AI-Focused Startups
Cal Al-Dhubaib’s entrepreneurial journey began with Triple Analytics, a startup focused on predictive modeling in healthcare. While the company didn’t succeed, it provided a critical lesson: product-market fit isn’t just about solving technical problems; it’s about addressing the right business needs.
His subsequent venture, Pandata, adopted a different approach. Instead of offering software solutions, the focus shifted to providing data science expertise tailored to heavily regulated industries like healthcare and finance. This pivot led to significant growth and eventual acquisition by Further.
“Understanding the gap between technical possibilities and practical business needs is key,” Cal advises. “It’s about iterating on your value proposition and knowing when to say no to projects that don’t align with your strengths.”
Building and Leading Data Science Teams
Managing a team of data scientists presents unique challenges. These individuals, often highly educated and deeply analytical, require leadership that aligns their expertise with organizational goals.
Cal Al-Dhubaib organizes his teams into three pillars:
- Strategists: Data scientists focused on translating business needs into technical requirements.
- Machine Learning Engineers: Experts in scalability and system observability.
- Modelers: Specialists in developing machine learning models.
“The art is in pairing the right individuals for the right phases of a project,” he explains. This alignment not only optimizes performance but also fosters a culture where team members’ strengths are maximized.
The Power of Saying No
One of the most counterintuitive yet important lessons Cal Al-Dhubaib learned is the value of saying no.
“Not every project is a good fit,” he says. “Saying no to something good allows you to focus on finding something great.”
By creating processes like fixed-fee discovery phases, his team could identify early whether a client’s needs aligned with their expertise. This not only prevented resource drain but also ensured that the team’s efforts were directed toward high-value opportunities.
Governance and Risk Management: The Foundation of Responsible AI
Working in regulated industries has underscored the importance of AI governance for Cal. Early and proactive engagement with legal and risk management teams is critical to avoiding roadblocks during deployment.
His team’s approach includes:
- Comprehensive training in AI governance and compliance.
- Encouraging a culture of raising potential risks early.
- Aligning project objectives with legal and ethical standards from the outset.
“AI is no different from other transformative technologies,” Cal notes. “Governance frameworks are essential for ensuring safety and trust.”
2025 and Beyond: The Growing Importance of AI Evaluation
As AI systems become more complex, especially with the rise of generative AI and multi-agent systems, Cal predicts a heightened focus on evaluation and quality control.
“Evaluation is the next frontier,” he asserts. “Data scientists and machine learning engineers who specialize in quality control will be in high demand.”
This aligns with his broader vision of making AI “boring” by emphasizing stability, reliability, and transparency over flashy but unreliable advancements.
Advice for Aspiring Leaders
Cal’s advice for those transitioning into leadership roles centers on three key skills:
- Storytelling: Inspire and align teams by clearly communicating the “why” behind projects.
- Negotiation: Understand others’ perspectives and create win-win scenarios.
- Letting Go: Empower team members to take ownership, even if it means tolerating occasional mistakes.
Conclusion
Cal Al-Dhubaib’s journey from data scientist to AI leader offers a roadmap for navigating the evolving landscape of artificial intelligence. His emphasis on governance, team alignment, and the human side of AI underscores a critical truth: technology alone isn’t enough. Success lies in building cultures, systems, and teams that balance innovation with responsibility. As we move further into 2025, these lessons will only grow in relevance, shaping how organizations and leaders approach the transformative power of AI.
Be ready for AI in 2025
ODSC East 2025 coming up this May 13th-15th in Boston, MA, in addition to virtually, is the best AI conference for AI builders and data scientists there is. Come learn from experts representing the biggest names in AI like Google, Microsoft, Amazon, and others, network with hundreds of other like-minded individuals, and get hands-on with everything you need to excel in the field.
If you’re antsy and want to start upskilling sooner rather than later, you can also check out the 4-week virtual training summit, AI Builders! Starting January 15th and running every Wednesday and Thursday until February 6th, this event is designed to equip data scientists, ML and AI engineers, and innovators with the latest advancements in large language models (LLMs), AI agents, and Retrieval-Augmented Generation (RAG).
