Turning AI execution challenge into opportunity

For years, the corporate discourse surrounding artificial intelligence (AI) has followed a predictable, almost cautious script. In boardrooms across organizations, executives ask: Should we invest in AI? Such discourse belonged to an era of exploration, a period where AI was treated as an experimental luxury or a distant line item in a future budget.

But according to Jonathan Cristobal, marketing head of Globe Business, the conversation is no longer about adopting AI, but about scaling it across operations, decision-making, and customer experiences.

Speaking at the recent BusinessWorld Economic Forum, Mr. Cristobal highlighted that the enterprise landscape faces a sharp, pragmatic pivot as the conversation has fundamentally shifted from a question of adoption to a challenge of impact.

“Today the question is, ‘How do we scale AI?’” Mr. Cristobal observed, pointing to a stark reality that while the barrier to entry has collapsed, the barrier to execution has never been higher.

On paper, enthusiasm for digital transformation is at an all-time high, yet the internal machinery of most organizations is stalling. As Mr. Cristobal noted, “While adoption rates have been good, readiness remains uneven.”

This unevenness exposes the illusion of corporate awareness. Knowing what AI can do is no longer a competitive advantage; knowing how to make it work reliably across an enterprise is. To move past this middle ground, organizations are deploying structured enablement programs.

To ensure AI scales effectively, Globe follows a foundation-first approach by establishing a centralized AI environment referred to as the “AI Kitchen,” which provides shared platforms, tools, and governance to keep initiatives aligned with business priorities.

Under this strategy, Globe operationalizes AI through a dual-funnel approach designed to accelerate innovation at every level of the organization.

The first funnel drives bottom-up innovation by empowering business units to identify, develop, and build AI solutions that address their most pressing operational and business challenges. Supported by shared AI platforms and reusable capabilities from the AI Kitchen, teams can rapidly move from ideas to production  while accessing the appropriate level of enablement needed for each initiative.

The second funnel focuses on top-down enterprise transformation, where AI is embedded directly into Globe’s highest-priority transformation programs.  AI capabilities will be woven into strategic initiatives to deliver organization-wide impact across customer experience, operations, and new business opportunities.

“From a private sector perspective, most organizations are not struggling with acquisition. The challenge is no longer who has access. It is about operationalizing,” Mr. Cristobal said.

This distinction is critical. While access to advanced AI is now democratized, operationalizing these tools remains a monumental hurdle, requiring companies to integrate them into legacy workflows, ensuring data pipelines are clean, and training staff to use them safely.

When properly operationalized, this transition from manual workflows to AI-driven automation delivers massive and measurable efficiency gains and backend optimizations.

Backend development has been accelerated by Globe’s shared infrastructure particularly for Field Services Management, enabling technical teams to resolve bugs 80% faster, create tests 3-4 times quicker, and build internal tools 5 times faster.

Furthermore, Globe has improved backend efficiency by using AI-driven automation to accelerate database pattern extraction for its Electronic Creditable Withholding Tax (eCWT) system, reducing the process from 3 days to 4 minutes.

The financial and technical dividends of this operational shift are substantial. Globe switched from manual quality audits to a Generative AI Quality Audit using Build Your Own AI tools, drastically cutting annual costs. Furthermore, Globe achieved 90% accuracy in fault detection while cutting the mean time to restore service by 70%.

Systemic maturity

Mr. Cristobal maps the corporate struggle with regard to AI to a failure in holistic planning. True organizational readiness is not a single metric; it is an interconnected ecosystem of capabilities.

“Infrastructure and workforce capacity remain challenged, together with governance and digital maturity,” Mr. Cristobal warned. “All of these continue to vary from organization to organization.”

When a company attempts to scale an AI initiative without a mature data infrastructure, the project produces unreliable outputs. When attempted without workforce capability, employees either reject the technology out of fear or misuse it due to unfamiliarity. And when attempted without internal governance, companies may struggle to manage risk and maintain stakeholder confidence. Strong governance frameworks provide the foundation needed to innovate responsibility and scale AI with confidence.

An actionable blueprint for this is found in Globe’s AI Governance and Principles, which establishes executive accountability under a Chief Intelligence and Trust Officer to ensure close alignment between AI innovation, data, cybersecurity, and enterprise risk management.

Furthermore, all initiatives must be grounded in core principles centered on transparency, accountability, safety and security and human-centricity. Local enterprises can translate global frameworks into practical impact by participating in international standard-setting bodies.

This operational friction is compounded by the fact that businesses are playing defense against bad actors who are already fully operationalized.

“AI is making cyber threats more sophisticated. This makes it even more important for organizations to modernize capabilities to counter these risks,” Mr. Cristobal said.

The private sector, therefore, finds itself in a high-stakes race, attempting to scale complex, secure AI systems while simultaneously relying on outdated architecture to protect itself from AI-driven threats.

With execution deeply tied to safety and public trust, the private sector’s ability to scale depends heavily on the regulatory environment. Mr. Cristobal argued that if the government implements rigid and prescriptive laws, it risks paralyzing the exact operational progress businesses are trying to make. Instead, he calls for an agile approach to oversight.

“We need to focus on outcome-based regulations rather than rigid ones,” Mr. Cristobal said. “We must focus on transparency, security, and fairness.”

An outcome-based framework defines the boundaries of acceptable risk, such as preventing algorithmic discrimination or ensuring data privacy, but leaves the specific technical pathways open. This allows businesses to iterate, adapt, and scale their infrastructure as rapidly as technology evolves.

Yet, even as companies automate and build these autonomous workflows, Mr. Cristobal maintains that the final anchor must remain human: “Human oversight should still be at the center.”

For the private sector, the directive is clear: to close the execution gap, corporate leaders must match their technological ambition with the systemic maturity required to scale safely.

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