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pontesud.com > Blog > Co-Op & Diplomacy > Sinag Solutions Turns 13, Challenge of Conventional Business Thinking
Co-Op & DiplomacyTechnology

Sinag Solutions Turns 13, Challenge of Conventional Business Thinking

Anita Bosman
Last updated: July 4, 2025 5:35 pm
Anita Bosman
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Makati: PonteSud – News Desk

Local digital transformation leader Sinag Solutions marked its 13th anniversary with a challenge of conventional thinking regarding both numerology and technology.

The company hosted an exclusive gathering of digital transformation professionals to explore practical approaches to the adoption of artificial intelligence in the business landscape.

Francis Uy, Chief Executive Officer of Sinag Solutions, framed the discussion by drawing parallels between society’s unfounded fears about the number 13 and common misconceptions surrounding AI.

He shared his early experiences with AI at the University of the Philippines, where he worked with Prolog programming to create chess algorithms, demonstrating that the technology has been evolving for decades.

Uy emphasized that today’s AI revolution, driven by large language models, presents both unprecedented opportunities and new challenges that require thoughtful navigation.

The discussion highlighted the urgent need for businesses to understand emerging regulations, particularly the EU AI Act, which imposes penalties of up to €35 million for non-compliance.

These regulations classify AI systems by risk level and establish corresponding requirements, making governance a critical first step for any organization beginning its AI journey. Uy stressed that companies serving European customers must prioritize compliance to avoid costly consequences.

Real-world applications of AI took center stage during the panel discussions. In healthcare, Uy shared what AI can do in analyzing medical test results, but cautioned against blind reliance on the technology.

Financial sector panelists warned about the risks of algorithmic bias, noting that systems trained primarily on Western data might not adequately serve Asian customers.

Retail examples demonstrated AI’s potential to drive engagement, with one panelist citing Spotify’s AI DJ feature as an example of successful personalization. Uy emphasized that successful AI implementation requires balancing innovation with responsibility.

“Many companies rush into enterprise-wide transformations,” he noted, “when they should be starting with focused pilots that allow for learning and adjustment.” This measured approach helps organizations maintain control while exploring AI’s potential.

The event featured compelling perspectives from leaders at the forefront of AI adoption: Omar Dapul, CEO of Deepfaic, highlighted emerging security threats: “Deepfakes aren’t just videos-audio scams are exploding. Imagine a voice clone stealing your identity over a call. Our tools give enterprises a fighting chance.”

His warning underscored the growing sophistication of AI-powered fraud. David Rosario of Katapult Digital shared a success story about practical AI implementation: “Adding plain-text explanations to dashboards boosted an airline’s ad returns by 13 percent. Sometimes, the simplest AI fix unlocks millions.”

This example demonstrated how small, thoughtful AI applications can yield significant returns. Arup Maity, President of Xamun, addressed implementation challenges: “Enterprises don’t need more code-they need clarity. Our AI co-pilot ensures every requirement translates to working software, one step at a time.” His approach emphasized reducing complexity in AI adoption.

Dr. Ezekiel Bernardo, a Human-AI Interaction Researcher from DLSU, focused on user experience: “People adopt AI when it feels transparent, not just accurate. A well-designed explanation can increase trust by 70% – emotions drive adoption.”

His research highlighted the human element often overlooked in technical implementations. As the event concluded, several key themes emerged that define successful AI adoption. First, regulatory compliance must form the foundation of any AI strategy.

Second, high-quality, locally relevant data is essential for effective AI systems. Third, focused pilot projects provide valuable learning opportunities before scaling. Fourth, human oversight remains critical even as automation increases. Finally, partnering with experienced enterprise architects can accelerate safe adoption.

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