Have you ever wondered how global enterprises turn complex digital systems into tools for strategic growth?
Organizations with divided data, departments, and disparate decision-making are often faced with challenges, but the appropriate form of leadership can make it an opportunity.
Anh-Phuong (Félix) TA has more than 25 years of experience in working through these complexities, bringing profound skills in systems integration, product strategy, and innovation, grounded in AI, to assist organizations to leverage data, streamline operations, and deliver measurable results. The most notable point of his career was when he worked as Chief Data Scientist at Le Figaro CCM-Benchmark, a major French Internet and media group, where he faced the enduring issues related to data and business fragmentation.
The available tools were domain specific, rule based, and siloed and they did not have the intelligence to tie the data together, coordinate the departments and facilitate co-ordinated decision-making. Organizations were struggling with data that was fragmented and workflows that were disconnected, which meant they could not make informed and strategic decisions. That realization led him to leave his role and, together with two partners, found Seinetime.
Based on his extensive experience in AI studies and large-scale systems, he understood the necessity of a domain-independent, intelligent platform. He envisioned and developed an agentic enterprise brain that integrates data, coordinates teams, and makes smarter and coordinated decisions to help organizations to break silos, grow in a sustainable way, and access long-term value.
He further says, “I took on the CEO role to build an agentic based ‘enterprise brain’ that could unify data, break business silos, and help organizations make better decisions and grow sustainably.”
The Human Firewall
He defines the CEO role as a constant balancing act. On a typical day, 60 to 70% of his time is spent on networking, management, hiring, and building strategic partnerships, activities that are largely non-technical. He stays connected to advanced AI research and system design through weekly technical reviews with the team, by reviewing key architectural decisions, and by occasionally reading research papers, though less frequently than before.
He highlights, “What’s important is that I know enough to ask the right questions, challenge assumptions, and understand trade-offs – not code every day.” As a startup CEO, he accepts knowing less technical detail than his engineers and trusting their expertise.
The Science of Leading Intelligent Systems
Anh-Phuong is guided by three core leadership principles when building and scaling teams focused on intelligent and agent-driven systems.
First is humility and attention to detail. In complex AI systems, overconfidence can be dangerous. He believes in remaining intellectually humble, never assuming the best solution is already known, and paying close attention to details in both technology and execution. Many critical failures, as well as major breakthroughs, emerge from small details that are easy to overlook.
Second is trust and true delegation. He places deep trust in the people he works with and empowers them to take real ownership. He does not believe in centralized control or reliance on heroic individual contributors. Instead, his focus is on developing strong leaders, giving them autonomy, and supporting them as they make decisions and learn from outcomes.
Third is culture and organizational design. He emphasizes creating a culture that enables natural collaboration without excessive meetings, heavy processes, or rigid management layers. The goal is a flat, transparent organization where AI research, engineering, product, and business teams work together seamlessly with shared context, fast alignment, and minimal bureaucracy.
“For me, strong leadership in a company is not about command and control, but about creating an environment of trust, clarity, and intellectual honesty, where people can think deeply, move fast, and build complex systems together,” says Anh-Phuong
The Hidden Work Behind Reliable AI
Anh-Phuong outlines the key challenges in building and scaling Seinetime, beginning with data unification, a complex problem the team is solving incrementally. While difficult, this effort has led to several small but meaningful breakthroughs in identity resolution compared with existing solutions. At the same time, scale has been a priority: the platform now handles millions of requests per second during peak usage, which meets current enterprise demand, supported by a scalability-first design with AI embedded at the core of orchestration.
Closely related is AI reliability. While hallucinations may be acceptable in demos, they are unacceptable in mission-critical environments such as healthcare. As a result, products like DocPilot rely on architectures combining reasoning, verification, and control, with human-in-the-loop validation for complex tasks. Cost optimization remains an ongoing challenge, though the team has reduced cost per query by 60% over six months through caching, prompt optimization, and in-house models, and continues to iterate.
Pragmatic Tech Choices
Anh-Phuong explains that in an AI landscape changing daily, he does not claim that Seinetime is absolutely “staying ahead.” Instead, the company focuses on several core principles that deliver real value for customers.
Domain expertise: Rather than competing with OpenAI or Anthropic on foundation models, Seinetime focuses on a deep understanding of enterprise workflows in specific verticals and develops local models to address these challenges. The team understands the pain points because they have worked in these organizations for years.
Execution speed: A small team allows Seinetime to ship quickly, iterate closely with customers, and adapt faster than large enterprise software companies. Solutions can be customized in weeks rather than months.
Pragmatic tech choices: Golang is used for orchestration for its performance, but the true advantage comes from how existing technologies, such as open-source LLMs, vector databases, and orchestration tools, are integrated. Success comes from the integration, not the stack itself.
Expert Team: The founding team brings complementary strengths, combining deep academic and applied AI experience with large-scale engineering leadership in complex AdTech platforms such as DSPs and ad networks, enabling reliable, large-scale orchestration.
The team reviews research papers weekly, not to adopt everything new, but to identify what is production-ready and relevant for customers today.
The Secret Weapon of Scalable AI Teams
Throughout his career, Anh-Phuong has led a team of PhDs in building large-scale, personalized applications aimed at empowering managerial leadership. One standout achievement was guiding the development of an Agentic Intelligence Platform that brings together disparate data sources to deliver real-time, actionable insights for decision-makers. The platform helps leadership teams anticipate trends and anomalies, run campaigns, generate reports, and optimize resources efficiently.
Another important milestone was implementing workflow automation with agentic AI, further boosting leaders’ ability to drive growth and innovation. These accomplishments reflect Anh-Phuong’s vision of shaping enterprise leadership through intelligent systems that make complex data actionable and support better decision-making in real time.
Work, Life, AI: The Three-Lane Highway of Modern Leadership
Anh-Phuong says, “maintaining a perfect work–life balance is challenging, especially when leading complex, high-impact technology initiatives like a startup.” He acknowledges that these roles demand significant dedication and focus, and that sacrifices are sometimes necessary. At the same time, he actively works to improve this balance by prioritizing key tasks, delegating effectively, and setting aside intentional time for personal well-being. He views it as an ongoing journey, understanding that even a thoughtful balance enhances leadership effectiveness. By being deliberate about how time and energy are allocated, he ensures that both professional responsibilities and personal health are sustained over the long term.
The Triple Code of Leadership
Anh-Phuong’s background in computer vision, machine learning, and applied AI shapes his leadership with both technical rigor and strategic insight. It equips him to critically evaluate innovation, understand feasibility constraints, and communicate effectively with engineering teams, all while maintaining a clear business-focused perspective. He believes the most effective leaders combine technical depth with strategic vision and strong human connection.
He asserts, “Years of experience as Chief Data Scientist in a large organization have given me practical management experience to apply these principles at the executive level.”
Mistakes, Humility, and Strategy: The Secret Ingredients of Leadership
Anh-Phuong shares practical advice for aspiring leaders who want to combine deep technical expertise with strategic business insight:
Technical expertise is necessary but not sufficient: In a startup, you will face challenges like hiring the wrong people, running out of cash, or misjudging market timing. These lessons are learned through experience and mistakes, not textbooks.
Listen more than you speak: Coming from a technical background, it’s easy to want to “solve” everything with logic. Business decisions involve humans, emotions, and politics; factors that aren’t purely logical.
Be honest about what you don’t know: With your team, investors, and customers, credibility comes from acknowledging gaps and showing how you will address them, rather than pretending expertise.
Prepare to accept being wrong: Technical training emphasizes finding the “right answer,” but business leadership means making decisions with incomplete information, being wrong 40–50% of the time and learning quickly to course-correct.
Automating Judgment, Not Just Work
Anh-Phuong sees agentic AI playing a key role in enterprise transformation over the next 5–10 years, while acknowledging that the timeline for AGI remains uncertain. Today, most agentic platforms are limited to coding or simple general-purpose tasks. At Seinetime, the team is developing what he calls the “second brain” of the enterprise, a system designed to perceive signals, observe, reason, and act autonomously with minimal human intervention.
Unlike most agentic systems that focus on narrow tasks, the goal is to build a fully capable enterprise brain that supports operations, decision-making, HR, workflow management, and automates repetitive work, ultimately augmenting both leadership and workforce capabilities at scale.
