Building AI-Ready Health Systems: Gabriela Rodríguez (Taqua) with Arkangel AI

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Driving Change: Arkangel AI and Taqua Advancing Artificial Intelligence in Health

Explore how Arkangel AI and Taqua are redefining artificial intelligence in health by tackling digital transformation, data culture, and practical AI deployment in the healthcare sector.

Episode Title: Cross-Industry Strategies for Artificial Intelligence in Health with Taqua’s Gabriela Rodríguez

Host Laura Velasquez (Arkangel AI) interviews Gabriela Rodríguez (Head of Innovation, Taqua) to reveal actionable frameworks and proven strategies for building data-driven, AI-ready healthcare organizations in Latin America.

Summary

This episode brings together Laura Velasquez from Arkangel AI and Gabriela Rodríguez, Innovation Lead at Taqua, to discuss artificial intelligence in health. They provide real-world answers on aligning industry culture, data readiness, and digital upskilling to speed transformation across healthcare organizations.

Episode at a Glance

• Guests: Gabriela Rodríguez — Head of Innovation, Taqua
• Host: Laura Velasquez — Co-founder, Arkangel AI
• Topics: Data-driven culture, digital upskilling, AI project frameworks, Latin America market barriers, data readiness
• Why it matters for artificial intelligence in health: Healthcare leaders need precise methods to assess data maturity, align teams, and apply AI responsibly to drive clinical and operational value.

Overview

Artificial intelligence in health is transforming how healthcare organizations tackle operational and clinical challenges, but successful adoption requires more than new algorithms. At Arkangel AI, Laura Velasquez sits down with Gabriela Rodríguez of Taqua, whose cross-industry digital transformation expertise sheds light on aligning organizational culture and building sustainable data pipelines. Taqua’s global innovation center in Mexico is pioneering practical approaches tailored for the unique barriers of the Latin American health sector.

Drawing from firsthand experience, they unpack why readiness matters—demonstrating that lasting impact depends on upskilling teams, mapping data quality, and prioritizing projects where both data and technical skills align. With regulatory complexity and fragmented data common across the region, this episode shares immediately usable frameworks to move from isolated pilots to scalable AI programs in health.

Key Takeaways

• Assess and categorize organizational challenges against available data before launching AI projects.
• Invest in upskilling both data producers and consumers to accelerate AI adoption in healthcare.
• Combine technologies, such as AI and cloud infrastructure, to amplify processing power and workflow flexibility.
• Prioritize projects where sufficient data maturity exists, rather than forcing AI tools into unready domains.

Chapter Markers

• [00:00] Why AI readiness is healthcare’s missing link
• [05:12] The role of culture and skills in digital transformation
• [14:37] Data maturity mapping and the “quadrant” framework
• [22:09] Overcoming barriers: Regulatory, cultural, and multi-source data

Notable Ideas

• “The true value isn’t in a single AI tool, but in the exponential power of combining technologies”—Gabriela Rodríguez, Taqua
• “Upskilling both producers and consumers of AI unlocks long-term transformation”—Gabriela Rodríguez, Taqua
• “Start internally: map your biggest challenges against your data’s maturity, then prioritize accordingly”—Laura Velasquez, Arkangel AI

Why This Matters

Effective deployment of artificial intelligence in health directly impacts patient access, early diagnosis, and operational efficiency across the clinical and supply chain spectrum. Taqua’s quadrant framework equips clinicians, medical affairs, and health systems teams in Latin America with a method to prioritize and implement scalable AI projects—overcoming traditional silos and fragmented data environments.

For health sector leaders lagging in digital readiness, these actionable insights from Arkangel AI and Taqua illustrate how collaborative upskilling, careful data preparation, and strategic project selection can spark measurable improvements and narrow the region’s AI adoption gap.

About Arkangel AI

Arkangel AI works at the intersection of technology and healthcare, advancing artificial intelligence in health for clinicians, medical affairs, and health systems. By developing and sharing practical tools for digital transformation, Arkangel AI empowers organizations—particularly across Latin America—to unlock the full value of data-driven innovation. Learn more at arkangel.ai.

FAQ

• Q: How can a healthcare organization decide where to begin with artificial intelligence in health?

  A: Start by mapping the urgency of key challenges against internal data readiness, as recommended by Arkangel AI and Taqua. Prioritize projects where robust, accessible data meets a significant need, following the “quadrant” methodology discussed in this episode.

• Q: What skills should healthcare teams prioritize to implement AI projects successfully?

  A: Both AI model development and data literacy are crucial. Upskilling teams in data management and digital experimentation accelerates adoption, as Gabriela Rodríguez of Taqua and Laura Velasquez from Arkangel AI emphasized throughout the episode.

• Q: What makes AI adoption particularly challenging in the Latin American health sector?

  A: Regulatory hurdles, fragmented data, and workforce readiness slow progress. However, following frameworks like Taqua’s data maturity quadrant and fostering cross-functional upskilling can help organizations make practical advances.

• Q: Where can I learn more about Taqua’s digital innovation work?

  A: Visit taqua.com for updates on their health innovation initiatives and digital transformation strategies.

• Q: What evidence supports the importance of combined technology strategies in health AI projects?

  A: This episode highlights real-world experiences from Taqua and Arkangel AI, demonstrating that synergistic use of cloud, data platforms, and AI multiplies outcomes compared to standalone solutions. External resources like the World Health Organization’s digital health guidelines provide further context.