For years, business discourse around artificial intelligence (AI) has followed a predictable, almost watchful script. On the boards of every organization, executives are asking: Should we invest in AI? Such talk belonged to an experimental era, a time when AI was considered an experimental luxury or a distant line item in future budgets.
But according to Jonathan Cristobal, head of marketing for Globe Business, the conversation is no longer about adopting AI, but about extending it to all operations, decision-making, and customer experience.
Speaking at the recent BusinessWorld Economic Forum, Mr. Cristobal emphasized that the business environment is facing a sharp, rational pivot as the conversation has moved from the question of acquisition to the challenge of impact.
“Today the question is, 'How do we do AI?'” Mr. Cristobal commented, pointing to the fact that although the barrier to entry has fallen, the barrier to killing has never been greater.
On paper, enthusiasm for digital transformation is high, yet the internal machinery of many organizations is stagnant. As Mr. Cristobal noted, “Although adoption rates have been good, readiness remains uneven.”
This imbalance creates an illusion of corporate awareness. Knowing what AI can do is no longer a competitive advantage; knowing how to make it work reliably across the company. To move past this middle ground, organizations use structured empowerment programs.
To ensure AI metrics are successful, Globe is following a ground-up approach by establishing a central AI center called the “AI Kitchen,” which provides shared platforms, tools, and governance to keep efforts aligned with business priorities.
Under this strategy, Globe uses AI through a dual-funnel approach designed to accelerate innovation at all levels 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 AI Kitchen, teams can quickly move from ideas to production while achieving the right level of enablement needed at each step.
The second funnel focuses on bottom-up business transformation, where AI is embedded directly into Globe's most important transformation initiatives. AI capabilities will be woven into strategic initiatives to deliver organization-wide impact across customer experience, operations, and new business opportunities.
“From the point of view of the private sector, many organizations do not struggle with procurement. The challenge is no longer who has access. It is about performance,” said Mr. Cristobal.
This distinction is important. Although access to advanced AI is now democratized, using these tools remains a major hurdle, requiring companies to integrate them into die workflows, ensure clean data pipelines, and train employees to use them safely.
When done right, this transition from manual workflows to AI-driven automation brings significant and measurable benefits and back-end efficiencies.
Backend development is accelerated by Globe's shared infrastructure specifically for Field Service Management, allowing technical teams to troubleshoot 80% faster, create tests 3-4 times faster, and build internal tools 5 times faster.
In addition, Globe has improved backend efficiency by using AI-driven automation to speed up database pattern generation for its Electronic Creditable Withholding Tax (eCWT) system, reducing the process from 3 days to 4 minutes.
The financial and technical benefits of this operational change are huge. The world has shifted from manual quality audits to Generative AI Quality Audit using Build Your Own AI tools, which has significantly reduced annual costs. In addition, Globe achieved 90% accuracy in fault detection while reducing average resource recovery time by 70%.
System maturity
Mr. Cristobal attributes the business struggle with AI to a failure in overall planning. True organizational readiness is not a single metric; it is an interconnected skill ecosystem.
“Infrastructure and operational capacity are still challenging, as well as management and digital growth,” warns Mr. Cristobal. “All of this continues to vary from organization to organization.”
When a company tries to scale an AI initiative without a mature data infrastructure, the project produces unreliable results. If it is tried outside the workforce, employees may reject the technology out of fear or misuse it due to unfamiliarity. And if attempted without internal governance, companies may struggle to manage risk and maintain stakeholder trust. Strong governance structures provide the foundation needed to establish accountability and scale AI with confidence.
A practical blueprint for this is found in Globe's AI Governance and Principles, which establish accountability under the Chief Intelligence Officer and Trust to ensure close alignment between AI innovation, data, cybersecurity, and business risk management.
In addition, all efforts must be based on core principles that focus on transparency, accountability, safety and security and human-centeredness. Local businesses can translate global frameworks into practical impact by participating in international standard setting bodies.
This operational conflict is compounded by the fact that businesses they play defense against bad players who are already in full force.
“AI is making cyber threats more complex. This makes it even more important for organizations to develop capabilities to deal with these risks,” said Mr. Cristobal.
The private sector, therefore, finds itself in a race to the top, trying to scale complex, secure AI systems while at the same time relying on outdated architecture to protect itself from AI-driven threats.
With operations closely linked to security and public trust, the private sector's ability to scale is highly dependent on the regulatory environment. Mr. Cristobal asserted that if the government implements strict and fixed rules, it runs the risk of crippling the operational progress that businesses are trying to make. Instead, you're looking for a way to quickly supervise.
“We need to focus on results-based laws rather than strict ones,” said Mr. Cristobal. “We must focus on openness, security and fairness.”
The outcome-based framework defines acceptable risk parameters, such as preventing algorithmic discrimination or ensuring data privacy, but leaves certain technical methods open. This allows businesses to iterate, adapt, and scale their infrastructure quickly as technology evolves.
However, as companies evolve and develop these autonomous applications, Mr. Cristobal insists that the last anchor must always be human: “Human supervision must still be the priority.”
In the private sector, the order is clear: to close the emissions gap, company leaders must match their technological ambitions with the system maturity needed to grow safely.
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