Canada is a huge and diversified market. Its merchandise imports were approximately C$789 billion in 2025. At the same time, Canada is deliberately diversifying its trade relationships beyond the US and strengthening its economic ties with the Indo-Pacific.

This creates an opportunity for the Philippines.

We should identify products where the Philippines has a genuine advantage and develop long-term relationships with Canadian buyers. These include tuna and seafood, coconut products, bananas, pineapple, mangoes, sugar, processed food, electronics and electrical components, garments, furniture, medical products and selected automotive components.

Canada, in turn, can supply products that are strategically important to the Philippines. The most obvious example is potash and fertilizer. Canada is one of the world’s leading suppliers of potash, while the Philippines is heavily dependent on imported fertilizer.

There is an obvious opportunity to connect these two realities.

From Barter to Strategic Trade

The idea of exchanging Philippine products for Canadian products is not new. But rather than thinking of it as simple one-for-one barter, we should develop a more sophisticated countertrade or commodity-clearing arrangement.

Philippine exporters could supply tuna, coconut products, sugar, bananas and other agricultural products to Canadian buyers. Canadian companies could supply potash, fertilizer, wheat, pulses, lumber and agricultural machinery to Philippine buyers.

The transactions would still be commercial transactions, priced against international benchmarks and properly documented. The difference is that the two governments would create a framework encouraging reciprocal trade and long-term supply commitments.

Physical barter can become complicated because different commodities have different prices, grades, shipping costs and delivery schedules. A better approach is to establish reciprocal commercial commitments over a defined period.

In simple terms: Philippine exports help pay for strategic imports.

Why Fertilizer Should Be at the Center

If we want to improve Philippine agriculture, we have to address productivity. Our farmers cannot become competitive if their production costs remain too high and their yields remain too low.

Fertilizer is one of the critical inputs. Canada’s enormous potash industry provides an opportunity to establish a more predictable and potentially diversified source of fertilizer for Philippine agriculture.

Imagine a long-term arrangement in which Canadian suppliers provide agreed quantities of potash or other fertilizer products to the Philippines while Philippine exporters develop corresponding long-term markets in Canada for agricultural products.

The objective should not be to obtain fertilizer at any price. It should be to obtain it competitively, reliably and in quantities that support measurable improvement in agricultural productivity.

Better fertilizer availability can mean higher yields. Higher yields can mean lower unit production costs. Lower production costs can make Filipino farmers more competitive. Higher productivity can increase farm income. And higher farm income can create demand for businesses and services in rural communities.

Tuna for Timber? It Makes Economic Sense.

The Philippines has a well-established seafood-processing industry and is capable of producing high-value tuna and other seafood products. Canada has a globally significant forest-products industry, while the Philippines has enormous requirements for housing and construction materials.

Why shouldn’t we explore a long-term commercial arrangement in which Philippine seafood exports help support the purchase of Canadian lumber and engineered wood products?

The same principle can apply to Canadian wheat and pulses. The Philippines needs wheat for bread, noodles, biscuits and other food products. Canada is a major producer of wheat and pulses.

The Philippines can supply tropical products that Canada cannot efficiently produce domestically. Canada can supply temperate agricultural products that the Philippines cannot produce competitively.

This is not a zero-sum relationship. It is economic complementarity.

Coconut, Banana, Pineapple and Mango

The Philippines should look beyond raw agricultural commodities. Our objective should be to export more value-added products.

Instead of simply exporting coconuts, we should increasingly export coconut water, coconut milk, coconut cream, coconut oil, desiccated coconut, coconut flour and coconut sugar.

Instead of simply exporting bananas, we can expand banana chips, dried bananas, banana flour, banana puree and other processed products.

The same principle applies to pineapple and mango.

Processing creates jobs. Processing increases the value captured in the Philippines. And processing creates opportunities for rural enterprises.

Where PITC Could Make a Difference

This is where the Philippine International Trading Corporation (PITC) could potentially play an important role.

PITC is a government-owned trading corporation under the Department of Trade and Industry. Its government profile includes exports, trade services and special trading arrangements among its functions, while its charter provides authority relating to international trading activities. The Philippines also has historical experience using PITC in countertrade arrangements.

Rather than creating another government bureaucracy, the government should examine whether PITC’s existing mandate can be strengthened and specifically utilized for this purpose.

If legally and institutionally validated, PITC could be tasked to develop and manage a Philippines–Canada Strategic Trade Mission. Its role could include identifying Canadian buyers and suppliers, matching them with Philippine producers and importers, structuring reciprocal commercial arrangements, monitoring commitments and reporting results.

DA and DTI could establish the agricultural and trade priorities. PITC could develop the transactions. Private companies could actually buy and sell the products. Financial institutions could provide trade finance. Appropriate government agencies could provide oversight and audit.

This would keep the government in its proper role: facilitating and organizing the market, not replacing private business.

Saving Foreign Exchange—But More Importantly, Creating More

The Philippines needs foreign currency to pay for fertilizer, fuel, food, machinery and other essential imports. At the same time, we have exportable products that can generate foreign exchange.

The objective should therefore be to use our export capacity more strategically.

If Philippine tuna, coconut products, fruits, sugar and manufactured goods generate export earnings that help finance the purchase of Canadian fertilizer, wheat, lumber and machinery, we create a productive cycle.

We are not simply spending dollars. We are using trade to obtain the inputs that can help us produce more and eventually import less.

That distinction is important.

The Bigger Objective: A Rural Middle Class

For me, however, the most important reason to pursue this proposal is not the trade statistics. It is the Filipino farmer.

For decades, many rural Filipinos have remained trapped in low-productivity agriculture. They often face high input costs, uncertain markets, inadequate post-harvest facilities, weak logistics and limited access to technology and financing.

We should change that.

A farmer who has access to reliable fertilizer, better technology and a dependable market can become more productive. A productive farmer can earn more. A farmer who earns more can invest.

That investment creates demand for tractors, transport, storage, machinery repair, retail stores, banking, education, housing and other services.

In other words, agricultural productivity creates a rural economy. And a stronger rural economy creates a rural middle class.

A growing rural middle class can provide greater economic independence and opportunity for Filipino families. It can also strengthen communities and create citizens with greater expectations for competent, transparent and accountable government.

Economic development cannot eliminate corruption by itself. But a society with a broader and more economically independent middle class is better positioned to demand good governance.

That is why improving rural livelihoods should be considered not merely an agricultural policy, but a national development strategy.

What Should Government Do?

The government should establish a small inter-agency working group involving DA, DTI, PITC and other relevant agencies to identify the most promising opportunities.

The group should determine:

1. What products can the Philippines competitively export to Canada?
2. Which Canadian products do we strategically need?
3. What are the current Canadian import volumes and prices?
4. Who are the major Canadian buyers?
5. What tariffs and standards apply?
6. What Philippine producers can meet the required volume and quality?
7. What fertilizer and other agricultural inputs could be secured through long-term contracts?
8. What transactions can be launched within the first 12 months?

The first objective should not be a massive government program. It should be five to ten successful commercial transactions that demonstrate that the concept works. Once the model is proven, it can be expanded.

A Practical Five-Year Vision

By 2030, the Philippines could have a much deeper economic relationship with Canada built around agricultural exports, strategic agricultural inputs, food security, manufacturing and electronics, forest products and construction materials, agricultural machinery and technology, and investment and industrial partnerships.

The opportunity could eventually involve billions of Canadian dollars in additional two-way trade, but the numbers should follow actual product-level competitiveness rather than political targets.

The program should be judged by measurable outcomes: increased Philippine exports; lower or more predictable input costs; higher agricultural productivity; higher farmer incomes; more rural jobs; increased food processing; greater private investment; improved supply-chain resilience; and measurable foreign-exchange benefits.

From Trade to Transformation

The Philippines has often treated international trade primarily as an issue of exports and imports. We should think bigger.

Trade can be an instrument for national productivity.

Canada has resources, technology, capital and agricultural products that the Philippines needs. The Philippines has people, agricultural resources, tropical products, manufacturing capabilities and a strategic location that Canada can benefit from.

The two countries can build on these complementary strengths.

The proposal is not about asking Canada for favors. It is not about government replacing private business. And it is not about barter for barter’s sake.

It is about creating a mutually beneficial commercial relationship that uses each country’s strengths to address the other’s needs.

The guiding principle should be simple:

Export what we can produce competitively. Secure what we need to become more productive. Invest the resulting gains in our people.

If done properly, the ultimate result should not simply be more containers moving through Philippine ports.

It should be more productive farms, higher rural incomes, more jobs, stronger communities and a larger Philippine middle class.

That is the real value of expanding trade with Canada.

And that is why the Philippines should seriously pursue a strategic Philippines–Canada trade and countertrade initiative now.

[The author is member of the Agribusiness Committee of the Management Association of the Philippines or �����Թ�.�� He is the Chair and President of BNL Management Corporation. Feedback at <map@map.org.ph> and <romeogdavid@gmail.com>].

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In the modern economic landscape, mental health is no longer merely a public health concern or an employee welfare benefit; it is an imperative driver of national productivity and macro-economic resilience. For the Philippines, a country whose growth trajectory is fueled heavily by a young, tech-savvy workforce, the Business Process Outsourcing (BPO) industry, and service sectors, human capital is the primary engine of development.

To maximize this economic potential, mental well-being must be positioned at the core of labor policies, workplace design, and national legislative frameworks. A mentally healthy workforce translates directly to higher innovation, reduced absenteeism, lower turnover, and maximized output across all economic sectors.

This will be a 3-part series to delve a little into different areas where mental health is an imperative picking up from the Management Association of the Philippines (�����Թ�) General Membership Meeting Panel last October 16 featuring Sen. Risa Hontiveros as speaker and Ms. Jeannie Goulbourn and Doc Edgardo Juan Tolentino as panelists with Agnes Gervacio as Moderator.

PART 1: The Workplace Imperative — Operationalizing RA 11036 and Inclusive Employment

In my nearly four decades across the telecommunications and healthcare sectors, I have spent countless hours in executive boardrooms analyzing operational efficiency, capital allocation, and bottom-line growth. Yet across every balance sheet and strategic plan, one truth remains undeniable: human capital is the ultimate driver of national productivity. Parallel to my corporate career, my time as a frontline volunteer for the Natasha Goulbourn Foundation’s Hopeline exposed me to the deeply human side of this equation. Sitting with individuals in their moments of acute crisis taught me that psychological well-being is not an abstract personal matter—it is the foundational infrastructure of human capability.

When Republic Act (RA) No. 11036, the Philippine Mental Health Act, was enacted eight years ago, it marked a historic shift by recognizing mental healthcare as a fundamental right. However, as business leaders, we know that policy declarations on paper do not automatically yield economic productivity. Execution does. Today, as a member of �����Թ� and its Health Committee, my personal mission extends beyond promoting basic awareness of the law. We must urgently operationalize RA 11036 across our organizations so that millions of working Filipinos can experience its tangible benefits in their daily lives.

Translating Policy into Daily Workplace Practice

To bridge the gap between statutory intent and workplace reality, corporate compliance must move past passive measures, like annual wellness webinars or printed employee handbooks. Implementing the Mental Health Act alongside our Occupational Safety and Health Standards (OSHS) requires embedding actionable mental wellness protocols into the operational bloodstream of our companies.

First, businesses must establish clear, confidential, and stigma-free pathways for professional psychological support. This means integrating robust Employee Assistance Programs (EAPs) and tele-mental health services into standard health benefit packages. Second, managers and middle supervisors must be trained in basic psychological first aid and empathetic leadership. Frontline leaders are the first line of defense; equipping them to recognize early indicators of burnout, anxiety, and severe stress prevents preventable medical leave and voluntary attrition. Third, operationalizing the law demands structured workplace accommodations. Flexible work arrangements, temporary duty reassignments, and manageable workload adjustments should be viewed not as operational disruptions, but as strategic retention tools that safeguard valuable talent.

Unlocking the Potential of Mentally Challenged and Neurodivergent Workers

A critical, yet underutilized, dimension of RA 11036 is its explicit alignment with RA 7277, the Magna Carta for Disabled Persons. The Mental Health Act affirms that individuals with psychosocial, neurological, and mental health conditions retain full legal capacity and the fundamental right to meaningful employment.

Integrating mentally challenged and neurodivergent individuals—such as those living with clinical depression, bipolar disorder, adult ADHD, or autism spectrum conditions—into the formal labor market is not merely an exercise in Corporate Social Responsibility. It is an economic growth strategy. Neurodivergent individuals frequently bring exceptional attention to detail, innovative problem-solving abilities, and unique analytical perspectives that enrich organizational culture and foster creative problem-solving.

When employers provide reasonable accommodations—such as structured daily routines, sensory-friendly workspaces, and clear performance frameworks—these workers thrive. Integrating this population directly increases household incomes, reduces reliance on state support, and expands our national tax base.

A Call to Action as Business Leaders:�� Build a Resilient Economic Future

As routine tasks become increasingly automated, the true competitive advantage of the Philippine economy lies in complex human capability. Mental health issues like presenteeism—where employees are physically present but cognitively disengaged—cost the global economy billions annually in lost output. By actively implementing RA 11036, breaking down social stigma, and opening our doors to individuals facing mental health challenges, we build a resilient, highly productive workforce. Investing in workplace mental wellness is not a corporate expense; it is a high-return investment in our nation’s economic future.

(The author is Co-Vice Chair of the �����Թ� Sports, Fellowship and Wellness Committee and Member of the �����Թ� Health Committee.�� She is the Group Chief for Strategic Accounts and Patient Experience of The Medical City. Feedback at <map@map.org.ph> and <rrcagurangan@themedicalcity.com>.)

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Layer 2: Governing Predictive Machine Learning

Objective: ensure that probabilistic outputs remain accurate, stable, and ethically sound over time.

Predictive systems are used to score credit applicants, price insurance contracts, detect financial fraud, forecast demand, and screen job candidates. Unlike BI, they generate probabilities rather than facts and degrade over time as customer behavior and real-world conditions change.

Core controls

• Drift monitoring. Two types of drift require attention. Data drift occurs when the statistical properties of input data change, such as when a new customer segment is added. Concept drift occurs when the relationship between inputs and outcomes shifts, for example, due to changing economic conditions. Both can gradually reduce model effectiveness.
• Explainability (XAI). Techniques, such as SHAP and LIME, help identify which factors drove a particular outcome. For high-impact decisions, such as those in credit, underwriting, and hiring, the organization must be able to justify the outcome to a customer, a regulator, or a court.
• Fairness and bias audits. Test models across population segments before deployment to detect disparate impact, defined as a disproportionately adverse outcome for a protected group, even if unintentional. Ongoing testing is required after deployment.

Typical failures include silent performance decay, in which model accuracy declines without detection; feedback loops that reinforce historical human bias; overfitting; and excessive false positives, which raise operational and customer costs.

Board and Risk/Audit focus: The central issues are algorithmic bias liability, consumer protection compliance, and model risk management. In the Philippines, the BSP’s credit risk management guidelines under Circular No. 855 address credit scoring models and risk-rating systems. Beyond the circular’s specific requirements, BSP supervisory expectations in recent years have pointed banks toward full life cycle model risk management: an inventory of models, independent validation, ongoing monitoring, and clear accountability. To support effective oversight, Boards should require regular management reporting on the complete model inventory, the status of independent validation, and the results of ongoing model monitoring and bias audits. This keeps Directors informed about how risks are managed in practice and enables timely, risk-based oversight. (The BSP has also issued guidance on the ethical and responsible use of AI. Boards should confirm whether it is binding or an expectation.)

Layer 3: Governing Generative AI

Objective: reduce hallucinations, safeguard proprietary intellectual property and personal data, and prevent confidential information from leaving the enterprise.

Generative AI is easy to adopt because any employee with a browser (or by downloading the free AI mobile apps) can enter sensitive information into external tools. The primary governance challenge is preventing data from leaving the organization, not model effectiveness.

Core controls

• Model gateway. A model gateway sits between users or applications and foundation models. It screens prompts and files to ensure that confidential information and customer personally identifiable information (PII) do not reach external models or become part of vendor training data. It also provides centralized logging and access control.
• Guardrails. Real-time guardrails screen inputs and outputs for toxicity, jailbreak attempts, prompt injection attacks, and likely hallucinations. While these controls reduce risk, they do not eliminate it. Generative models may still produce convincing but fabricated content, so critical use cases require human review and validation against verified enterprise sources.

Typical failures include hallucinated or fabricated data, such as fabricated citations and figures; data exfiltration and loss through unauthorized tools; and copyright infringement, either from generated content or from employee-submitted material.

Board and Risk/Audit focus: Boards should align governance with strategy and confirm that AI investments support business objectives. Oversight of AI vendors is critical. Boards must require robust due diligence on any external AI vendor, focusing on three key contract terms: (1) clear data-use restrictions to prevent vendors from retaining or reusing enterprise data, (2) enforceable commitments to zero data retention, and (3) strong indemnification and liability provisions for intellectual property infringement. Boards should also expect management to regularly review vendors’ security practices and audit rights as part of ongoing oversight.

Enterprise risk policies should address “Shadow AI,” defined as employee use of unapproved AI tools, and management should demonstrate its ability to detect and manage such use. The Board should also expect a plan to comply with emerging AI regulations in the Philippines and in other jurisdictions where the organization operates. The 2024 EU AI Act is binding only where the organization operates in or serves the EU; elsewhere, it serves as a reference point.

Layer 4: Governing AI Agents

Objective: enforce boundaries on autonomous decisions and actions.

Agents differ from earlier layers by taking action, as systems that combine models, tools, permissions, and instructions, not just models. They can query systems, execute code, modify records, and send communications, often chaining multiple steps without human intervention. The risk shifts from incorrect answers to incorrect actions executed rapidly across systems.

Core controls

• Agent registry – Records which agents are live, who owns each agent, the authority each agent holds, and which data and systems each agent may access. An agent not in the registry should not be running.
• Human oversight – Scaled to risk, two common models are Human-in-the-Loop (HITL), where a person approves actions before execution, and Human-on-the-Loop (HOTL), where a person supervises and can intervene or stop the agent. Governance should set verification thresholds so that high-risk actions, such as executing transactional code or altering databases, require approval, while lower-risk activities are monitored.
• Observability – Maintain complete logs of what each agent did, why, and with what data, so the enterprise can reconstruct and audit actions.
• Failover and shutdown process – If an agent must be stopped, the supported business process must continue via manual fallback, rollback procedures, or a degraded mode. A “kill switch” is effective only if it has been tested and the process remains operational.

Typical failures include unconstrained agent behavior and processes that fail when agents are unavailable. The latter is often overlooked; organizations that automate processes and lose manual capabilities trade resilience for efficiency.

Board and Risk/Audit focus: Boards should ask about agent observability, guardrails, “kill switches”, and process resilience. Internal audit should verify that these controls exist and have been tested.

Cross-Cutting Priorities for the Board

Several themes cut across all four layers:

Maintain an inventory. Organizations cannot govern what they cannot see. Register reports, models, generative AI use cases, and agents, and assign owners.

Tier controls by materiality. Not every dashboard requires the rigor of a credit model, and not every generative AI use case requires the oversight of an autonomous agent. Classify use cases by potential impact and apply controls proportionally. This aligns with risk-based approaches, such as ISO 31000.

Assign clear accountability. Governance fails when responsibility is diffused. Specify who owns data quality, validates models, approves AI use cases, and has the authority to halt an agent’s operation.

Plan for the entire life cycle. Failures in Layers 2 and 4 often surface after deployment. One-time approval is insufficient; ongoing monitoring, periodic revalidation, and a defined retirement process are essential.

Treat vendors as part of the control environment. Many of the AI capabilities an enterprise uses are delivered by third parties. Contract terms, vendor assurance reports, and exit options serve as governance controls.

Know what binds you. Distinguish three tiers: binding law and regulation (for example, the Data Privacy Act of 2012, SEC reporting rules, and BSP circulars for banks); supervisory expectations and guidance; and voluntary frameworks (for example, ISO 31000 and the NIST AI Risk Management Framework, or AI RMF 1.0). Management should identify which layer each obligation comes from and who owns compliance.

Protect personal data by design. AI governance also requires lawful processing of personal information. Before personal data enters a model, tool, or vendor, management should confirm the lawful basis, purpose limitation, data minimization, and retention in accordance with the 2012 Data Privacy Act and National Privacy Commission guidance. This applies to training data, prompts, and outputs, not just to actual leaks.

Divide oversight without diluting accountability. Board Committees enable deeper review but do not replace the full Board’s accountability. Subject to each company’s charters and Board Committee workloads, the Risk Committee can lead on the AI risk profile, appetite, and scenarios; the Audit Committee on reporting reliability, controls, and independent assurance; and the Corporate Governance Committee on policies, decision rights, ethics, and Director capability. Mandates will likely overlap, so coordination is needed. Some Boards may designate one Director to coordinate AI oversight.

Questions for Directors and Executives to Ask

• Can we trace the figures in our financial and regulatory reports to their source, and who attests to their accuracy?
• Do we have a complete inventory of the models in use, and which ones influence customer or employment decisions?
• How would we know if a model’s performance was silently deteriorating or if it was producing disparate outcomes?
• What happens to confidential data when an employee uses a generative AI tool, and what does our contract with the vendor allow them to do with it?
• How many AI agents are operating today, who owns each one, and what are they authorized to do?
• If we had to turn off an agent tomorrow, would the process it supported continue to run?
• What evidence shows that our controls are working on an ongoing basis, and who has independently tested them?
• What material AI risks remain after controls, and what is management doing about them?
• Are the business benefits we expected from our AI investments materializing?

If management cannot promptly answer these questions, it indicates a governance gap or an unclear owner.

Conclusion

AI governance should not be separated from data governance. As systems become more autonomous, AI governance is an extension of the same discipline. Each layer introduces new risks while retaining previous ones. Effective organizations build data quality, add model reliability and fairness controls, protect data in generative AI, and set clear boundaries for agent autonomy.

Boards do not need to understand the mechanics of neural networks or the state management and orchestration for AI agents. But they must ensure that management can demonstrate accountability, define controls, test them, and show how the organization would respond to failures.

Ultimately, Boards should:

• Demand clear accountability across all layers of data and AI governance.
• Require evidence that strong controls are in place and functioning as intended across all layers.
• Insist that management regularly test response plans for failures or incidents and report the results to the Board.
• Track both sides of the mandate: that AI delivers the expected business value and that residual risks remain within an organization’s risk appetite.

By following this checklist, Directors can proactively safeguard the organization and foster responsible innovation.

Thanks to Anton Bonifacio, Pam Cabudoy, Pete Maniego, and Dan Natindim for their valuable comments.

(The author is Governor and Secretary of the Management Association of the Philippines or �����Թ�.�� He is a retired banker and Globe and Ayala executive, and a member of the Analytics and AI Association of the Philippines and the Institute of Corporate Directors. He serves as an Independent Director at GT Capital Holdings, Puregold Price Club and Megawide Construction. Feedback at <map@map.org.ph> and <iamgilgenio@gmail.com>.)

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What becomes valuable when technology is everywhere

Stavros Lee, co-founder of the US gaming platform Oncade, put the principle simply: value moves toward what is scarce. Speed, access and scale used to be hard to come by. Artificial intelligence (AI) is making all three abundant.

What it does not supply is judgment: deciding which problem is worth solving, which risk is worth taking and which trade-off an organization can live with. An AI model can tell a bank which loan applicants look risky. It cannot decide how much risk the bank should carry for a farming town after a typhoon, or what the bank owes a community it has served for decades. So the useful question about AI is no longer whether to adopt it. The real question is what AI should scale for us, and what we should keep human by design.

In my own field, where the work has always been about how institutions earn and keep the trust of the people they serve, this is a practical matter. People may accept that a machine drafted a statement or flagged a risk. They will still hold a person accountable for the decision. Judgment, and the accountability that comes with it, stays with leaders.

When the fear is getting it wrong

Many leaders are less worried about missing AI than about buying the wrong thing. Dallas Dolen of PwC US answered that fear with a story. As a teenager playing piano beside Miles Davis, Herbie Hancock hit a wrong note, and Davis answered with a chord that made it right. Plenty of leaders have already made bad calls on AI, Dolen said; what matters is that the next decision is the right one. Some spending is safe in any future: protecting data, training people, keeping systems flexible. Start with the outcome you want and redesign the work around it, rather than buying the technology first. Measure the value created, not the activity. Use the tools yourself before asking your people to. And accept that you will sometimes be wrong. He is wrong often, he said, and sleeps fine.

The second question

Judgment also shapes whom we bring along. Dr. Kunihiko Hirabayashi, secretary-general of the ASEAN-Japan Centre, spoke about the Regional Comprehensive Economic Partnership (RCEP), the trade pact that links ASEAN with China, Japan, South Korea, Australia and New Zealand. Agreements like RCEP lower barriers, but his larger point was about agency: we cannot wait for certainty before we act, and the better answer to anxiety is collective action.

He also questioned our assumptions about who drives growth. Young people, women entrepreneurs, startup founders and small businesses are often treated as beneficiaries of growth; he argued that they can be the channels through which growth is created and scaled. In a country where micro, small and medium enterprises make up more than 99 percent of businesses and about two-thirds of jobs, according to the Department of Trade and Industry, the point is hard to dispute. So every leader weighing a new opportunity should ask a second question. After “What does this create?” comes “Who do we enable so that it can multiply?”

Innovation that starts with ordinary problems

Two Filipino entrepreneurs showed what that looks like in practice. AgriDom, the Davao-based company led by Dom Subang, started from a plain problem: drones make farming faster and more precise, but many smallholders cannot afford one. Its answer is a service model. Farmers book drone work when they need it and pay only for that work. Subang was candid that wider adoption still has a long way to go, and that educating farmers is critical to getting there.

Anna Magalona started But First, Coffee as a one-person venture during the pandemic and grew it into a national chain by keeping prices within reach without giving up on quality, using branding, new products and technology to do it.

Neither business began with a breakthrough invention. Each began with a familiar problem looked at differently: a gap in access, a price that kept people out, a customer the market was not serving well. Technology creates impact only when people can reach it and use it.

Fix the roof when the weather is good

Catharine Kho, senior economist at the ASEAN+3 Macroeconomic Research Office (AMRO), offered the day’s most practical line: “Fix the roof when the weather is good.” Our region has tremendous potential, but it also faces fragmentation, ageing populations and climate risk. Resilience built in the middle of a crisis costs more and comes too late. The time to diversify suppliers, train people and strengthen partnerships is now, while there is still room to choose.

What leaders should ask next

So here is what I would ask every leader to bring to the next management meeting. What will we scale with technology? What will we keep human? Who will we enable along the way?

The answers will differ from one company to the next. What should not differ is the habit of asking them early, before certainty arrives and before the advantage has gone to someone else. Machines will keep getting better at producing answers. Deciding which questions matter, and for whom, remains our work.

(The author is Co-Vice Chair of the �����Թ� CEO Conference Committee, Chair of the �����Թ� Trade, Investments and Tourism Committee, and founder and CEO of The EON Group. Feedback at <map@map.org.ph> and <junie.delmundo@eon.com.ph>.)

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Most organizations didn’t adopt AI with a single decision. Business Intelligence (BI) dashboards evolved into predictive systems, then generative AI tools, and now autonomous agents acting on the enterprise’s behalf. Each new capability introduces distinct risks and requires tailored controls. Governance designed for one stage will not protect the organization at the next. These layers often coexist and mature unevenly across the business; they are not a strict ladder. Boards should regularly assess which layers are in use, how they connect, and whether governance has kept pace. By staying proactive, Directors help the organization anticipate and manage emerging risks.

Boards govern and challenge; management implements. That role has two sides. On performance, directors ask whether data and AI are advancing strategy, productivity, customer outcomes, and resilience. On conformance, they ask whether risk appetite, accountability, controls, and assurance are in place. Governance demands both performance and conformance.

This article presents a framework for data and AI governance, organized into four layers: BI, Predictive Machine Learning, Generative AI, and AI Agents. For each layer, it outlines objectives, core controls, common failure modes, and key questions for Boards and Risk or Audit Committees.

The Governing Principle: Risk Moves from the Data to the Decision

As systems progress from reporting to predicting, generating, and acting, the primary risk shifts from data accuracy to the soundness and limitations of system decisions. In BI, risks center on data quality and access. For predictive models, reliability and fairness are paramount. Generative AI introduces risks of data loss, privacy breaches, and inaccurate content. For agents, the main concerns are the extent of their autonomy and our ability to monitor and intervene.

The layers are cumulative. An agent using a generative AI model that relies on predictive scores and enterprise data inherits the vulnerabilities of all underlying layers. Governance that focuses only on the newest layer leaves foundational risks unaddressed. Boards should require evidence of strong controls and oversight at every layer. This comprehensive approach enhances organizational resilience and accountability.

Layer 1: Governing BI

Objective: a single source of truth with high accuracy and auditability.

BI is the oldest and most overlooked layer, yet it underpins financial reporting, regulatory submissions, and most management decisions. Errors at this stage are carried forward into all downstream models and agents.

Core controls

• Role-based access so that people see only the data their roles require.
• Data governance as an enterprise program with defined data owners, stewards, and quality standards.
• Data dictionaries and metadata management so that terms, such as “active customer” or “net revenue”, have the same meaning everywhere.
• Master data management to maintain authoritative records for customers, products, suppliers, and counterparties.
• An enterprise data catalog that documents what data exists, where it comes from, and how it moves (data lineage).

Common failures include inaccurate source records and broken extract-transform-load (ETL) data pipelines. Outdated logic is especially problematic because calculations that were once correct may continue to produce inaccurate results after changes to products, policies, or accounting standards.

Board and Risk/Audit focus: Boards should ensure compliance with applicable financial reporting standards (such as IFRS/PFRS) and with the reporting and internal control requirements of the relevant securities regulator, such as the SEC. Data retention mandates, access controls, and the maturity of the data governance program should also be on the agenda. Audit Committees should ask whether the data used to prepare financial and regulatory reports is traceable from source to disclosure.

Layer 2: Governing Predictive Machine Learning

Objective: ensure that probabilistic outputs remain accurate, stable, and ethically sound over time.

Predictive systems are used to score credit applicants, price insurance contracts, detect financial fraud, forecast demand, and screen job candidates. Unlike BI, they generate probabilities rather than facts and degrade over time as customer behavior and real-world conditions change.

Core controls

• Drift monitoring. Two types of drift require attention. Data drift occurs when the statistical properties of input data change, such as when a new customer segment is added. Concept drift occurs when the relationship between inputs and outcomes shifts, for example, due to changing economic conditions. Both can gradually reduce model effectiveness.
• Explainability (XAI). Techniques, such as SHAP and LIME, help identify which factors drove a particular outcome. For high-impact decisions, such as those in credit, underwriting, and hiring, the organization must be able to justify the outcome to a customer, a regulator, or a court.
• Fairness and bias audits. Test models across population segments before deployment to detect disparate impact, defined as a disproportionately adverse outcome for a protected group, even if unintentional. Ongoing testing is required after deployment.

Typical failures include silent performance decay, in which model accuracy declines without detection; feedback loops that reinforce historical human bias; overfitting; and excessive false positives, which raise operational and customer costs.

Board and Risk/Audit focus: The central issues are algorithmic bias liability, consumer protection compliance, and model risk management. In the Philippines, the BSP’s credit risk management guidelines under Circular No. 855 address credit scoring models and risk-rating systems. Beyond the circular’s specific requirements, BSP supervisory expectations in recent years have pointed banks toward full life cycle model risk management: an inventory of models, independent validation, ongoing monitoring, and clear accountability. To support effective oversight, Boards should require regular management reporting on the complete model inventory, the status of independent validation, and the results of ongoing model monitoring and bias audits. This keeps Directors informed about how risks are managed in practice and enables timely, risk-based oversight. (The BSP has also issued guidance on the ethical and responsible use of AI. Boards should confirm whether it is binding or an expectation.)

Layer 3: Governing Generative AI

Objective: reduce hallucinations, safeguard proprietary intellectual property and personal data, and prevent confidential information from leaving the enterprise.

Generative AI is easy to adopt because any employee with a browser (or by downloading the free AI mobile apps) can enter sensitive information into external tools. The primary governance challenge is preventing data from leaving the organization, not model effectiveness.

Core controls

• Model gateway. A model gateway sits between users or applications and foundation models. It screens prompts and files to ensure that confidential information and customer personally identifiable information (PII) do not reach external models or become part of vendor training data. It also provides centralized logging and access control.
• Guardrails. Real-time guardrails screen inputs and outputs for toxicity, jailbreak attempts, prompt injection attacks, and likely hallucinations. While these controls reduce risk, they do not eliminate it. Generative models may still produce convincing but fabricated content, so critical use cases require human review and validation against verified enterprise sources.

Typical failures include hallucinated or fabricated data, such as fabricated citations and figures; data exfiltration and loss through unauthorized tools; and copyright infringement, either from generated content or from employee-submitted material.

Board and Risk/Audit focus: Boards should align governance with strategy and confirm that AI investments support business objectives. Oversight of AI vendors is critical. Boards must require robust due diligence on any external AI vendor, focusing on three key contract terms: (1) clear data-use restrictions to prevent vendors from retaining or reusing enterprise data, (2) enforceable commitments to zero data retention, and (3) strong indemnification and liability provisions for intellectual property infringement. Boards should also expect management to regularly review vendors’ security practices and audit rights as part of ongoing oversight.

Enterprise risk policies should address “Shadow AI,” defined as employee use of unapproved AI tools, and management should demonstrate its ability to detect and manage such use. The Board should also expect a plan to comply with emerging AI regulations in the Philippines and in other jurisdictions where the organization operates. The 2024 EU AI Act is binding only where the organization operates in or serves the EU; elsewhere, it serves as a reference point.

Layer 4: Governing AI Agents

Objective: enforce boundaries on autonomous decisions and actions.

Agents differ from earlier layers by taking action, as systems that combine models, tools, permissions, and instructions, not just models. They can query systems, execute code, modify records, and send communications, often chaining multiple steps without human intervention. The risk shifts from incorrect answers to incorrect actions executed rapidly across systems.

Core controls

• Agent registry – Records which agents are live, who owns each agent, the authority each agent holds, and which data and systems each agent may access. An agent not in the registry should not be running.
• Human oversight – Scaled to risk, two common models are Human-in-the-Loop (HITL), where a person approves actions before execution, and Human-on-the-Loop (HOTL), where a person supervises and can intervene or stop the agent. Governance should set verification thresholds so that high-risk actions, such as executing transactional code or altering databases, require approval, while lower-risk activities are monitored.
• Observability – Maintain complete logs of what each agent did, why, and with what data, so the enterprise can reconstruct and audit actions.
• Failover and shutdown process – If an agent must be stopped, the supported business process must continue via manual fallback, rollback procedures, or a degraded mode. A “kill switch” is effective only if it has been tested and the process remains operational.

Typical failures include unconstrained agent behavior and processes that fail when agents are unavailable. The latter is often overlooked; organizations that automate processes and lose manual capabilities trade resilience for efficiency.

Board and Risk/Audit focus: Boards should ask about agent observability, guardrails, “kill switches”, and process resilience. Internal audit should verify that these controls exist and have been tested.

(The author is Governor and Secretary of the Management Association of the Philippines or �����Թ�.�� He is a retired banker and Globe and Ayala executive, and a member of the Analytics and AI Association of the Philippines and the Institute of Corporate Director. He serves as an Independent Director at GT Capital Holdings, Puregold Price Club and Megawide Construction. Feedback at <map@map.org.ph> and <iamgilgenio@gmail.com>).

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This gap between innovation and execution is becoming one of the defining business challenges of our time. In a rapidly changing environment characterized by technological disruption, organizations can no longer afford to treat innovation as a sporadic activity or an isolated function. Instead, innovation must become a replicable and scalable capability embedded across the enterprise.

This is the essence of Innovation@Scale, one of the three pillars of the Transformation Realized framework alongside Humans@Center and Technology@Speed. These drivers help organizations create and sustain dynamic competitive advantage in an increasingly uncertain world. The framework recognizes that value creation today requires a shift from predictability to agility, from rigid planning to continuous innovation, and from static operating models to adaptable systems thinking.

Understanding Innovation@Scale

When the concepts of innovation and scale come together, Innovation@Scale emerges as the ability to continuously generate, test, and implement new ideas while rapidly scaling successful innovations across the organization. It involves moving beyond isolated innovation efforts, such as pilot programs or innovation labs, and embedding innovation into the core business model. Organizations practicing Innovation@Scale build adaptive capabilities that allow them to respond to their needs, disruptions, and market shifts through emerging trends and technologies while creating long-term value. Importantly, Innovation@Scale is an ongoing commitment requiring adaptability, resilience, and a long-term vision.

Many organizations recognize the importance of innovation, yet previous innovation efforts have often failed to deliver meaningful results. Large corporations frequently struggle with organizational complexity that slows decision-making and experimentation. Some organizations are not bold enough to pursue transformative ideas, preferring incremental improvements that feel safer but generate limited competitive advantage. Others become trapped by traditional methodologies that are no longer suited to today’s pace of change. In some cases, innovation becomes a compliance exercise where activities are performed merely to demonstrate progress rather than achieve meaningful outcomes.

Innovation@Scale addresses these shortcomings by focusing on a set of core principles that enable innovation to become a sustainable organizational capability. The first principle is fostering creativity. Innovation flourishes in environments where experimentation is encouraged and where failure is viewed as an opportunity for learning rather than punishment. Organizations that create psychological safety enable employees to test ideas, share perspectives, and challenge assumptions with confidence. In such environments, learning becomes a more important measure of success than short-term return on investment.

The second principle involves scaling innovation across the enterprise. Successful innovation requires more than great ideas; it requires structural support, funding mechanisms, incentives, and flexible organizational models. Internal venture-funding approaches help prioritize promising ideas, while shared digital infrastructure reduces duplication of effort and accelerates execution. By removing organizational barriers and connecting resources more effectively, organizations can move innovations beyond isolated teams and into everyday operations.

Another critical principle is leveraging ecosystems. Organizations rarely innovate successfully in isolation. Partnerships with startups, academic institutions, technology providers, industry platforms, and government agencies can significantly accelerate innovation by providing new capabilities, perspectives, and resources. Innovation ecosystems enable organizations to access ideas and expertise that would be difficult or costly to develop independently.

Continuous reimagining of value creation is equally important. Organizations must continuously monitor emerging trends, evolving customer behaviors, competitive developments, regulatory changes, and geopolitical shifts. Market intelligence and scenario planning can help leaders anticipate disruption and identify new opportunities before they become mainstream. Rather than reacting to change, organizations practicing Innovation@Scale proactively prepare for it.

Applying Innovation@Scale

One of the most important insights from Innovation@Scale is that successful innovation requires deliberate organizational mechanisms. Effective organizations establish clear leadership sponsorship and align innovation efforts with strategic priorities. Small, autonomous teams with defined ownership can move faster and make decisions more effectively than large, heavily coordinated groups. Structured governance helps balance experimentation with accountability by maintaining portfolios that include short-term improvements, growth opportunities, and long-term transformational bets.

Organizations also benefit from systematic experimentation, rapid learning loops, capability-building programs, innovation funding models, and repeatable scaling mechanisms that allow successful ideas to be adopted more broadly.

However, scaling innovation remains difficult for many organizations. Findings from EY’s Reimagining Industry Futures Study 2025 indicate that while organizations continue to invest heavily in emerging technologies, most deployments remain concentrated in proof-of-concept phases and pilot projects. Scalability, integration challenges, and data governance concerns have emerged as significant barriers to broader adoption. Organizations are often successful at generating innovative ideas and launching trials but encounter difficulties when attempting to integrate those innovations across the enterprise.

The research also highlights several barriers that prevent innovations from progressing beyond initial trials. Complex integration with legacy systems, budget limitations, technology immaturity, insufficient skills, organizational culture readiness, lack of demonstrable return on investment, and regulatory concerns frequently slow adoption. These findings reinforce an important reality: scaling innovation is not simply a technical challenge. It is equally an organizational, operational, and cultural challenge. Organizations must modernize infrastructure, develop talent, strengthen governance, and align incentives to unlock the full value of innovation.

To address these challenges, organizations must adopt disciplined approaches to innovation funding and portfolio management. Successful companies allocate resources across different types of innovation initiatives, balancing investments in core improvements, emerging opportunities, and transformative innovations. They establish governance structures that enable leaders to monitor progress, make funding decisions, manage risk, and ensure alignment with business priorities.

Equally important is innovation accounting, which measures not only financial outcomes but also indicators, such as experiment velocity, pipeline conversion, employee participation, and time-to-market. These metrics help organizations assess whether innovation capabilities are becoming stronger over time.

Organizations can complement innovation accounting with an innovation audit, a structured assessment of whether the capabilities needed to innovate and scale are working effectively across the enterprise. Such an assessment can examine the alignment of innovation with strategy, the strength of leadership and governance, the maturity of processes, the availability of talent and funding, and the ability of data and technology to support execution.

Finally, organizations must avoid common pitfalls that undermine innovation efforts. Innovation theater, where innovation activities are disconnected from strategy or funding decisions, can create the appearance of progress without delivering results. Siloed innovation labs often struggle to transfer successful experiments into core business operations, while celebrating failure without extracting meaningful lessons can create complacency rather than learning.

Transforming ideas into scalable value

In an increasingly disruptive business environment, competitive advantage will belong not to organizations that generate the most ideas, but to those that can consistently transform ideas into scalable value.

Innovation@Scale is ultimately about creating an organizational capability that repeatedly identifies opportunities, tests solutions, scales successful innovations, and adapts continuously to change. When innovation becomes embedded in the way an organization thinks, operates, and creates value, it ceases to be a project and becomes a lasting source of competitive advantage.

[This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the authors and do not necessarily represent the views of SGV & Co. and the �����Թ�. The author is a member of the NextGen Committee of the Management Association of the Philippines (�����Թ�). He is the Ecosystems and Knowledge Head and Financial Services Leader of SGV & Co. This article was co-written with April Anne M. Corpuz, Business Consulting Partner of SGV & Co. Feedback at <map@map.org.ph> and <christian.g.lauron@ph.ey.com>).]

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Imagine walking into a room wearing a Rolex Daytona. From across the table, everything looks right: the proportions, the ceramic bezel, the weight of the bracelet. Someone notices it. Someone compliments your taste and quietly revises upward what they assume about your career or success.

But one person in the room knows with certainty that it is not a Rolex: you.

That private discomfort resembles the anxiety surrounding irresponsible use of generative AI. The presentation is polished. The report sounds intelligent. The strategy deck contains the right frameworks. Your colleagues are impressed. Yet somewhere inside, you know you could not have produced it yourself, and you may not fully understand what you have presented.

As AI improves, that gap between appearance and substance will become harder to see and more important to confront.

When output stops proving competence

Modern counterfeit watches can be convincing because they reproduce many of the signals visible to an outside observer. The philosophical problem is not simply that an imitation exists. It is that the distance between the genuine article and its appearance has become small enough to create information asymmetry.

AI is creating a similar problem in knowledge work. The spreadsheet works. The essay reads beautifully. The code runs. The proposal uses the language of an experienced consultant. Nobody necessarily knows how much of the intellectual machinery came from an AI system. Perhaps nobody cares until the questions begin.

Why did you choose this model? What assumption drives the forecast? What evidence contradicts the thesis? What happens when the client’s constraint changes? Eventually, someone opens the caseback.

We have long treated a finished artifact as evidence of the process behind it. A well-reasoned article implied that someone had read, discarded weak arguments and struggled toward a conclusion. A financial model suggested that its author understood the assumptions underneath it. Working software implied some knowledge of programming.

Generative AI weakens that inference. The output may still be excellent, but output alone no longer proves competence. We can now acquire many outward symbols of intellectual accomplishment without building the internal models that once produced them. The applause can remain real even when the capability is borrowed.

AI can build real capability

An easy anti-AI argument fails here because AI can create genuine capability. In a randomized experiment published in Science, Shakked Noy and Whitney Zhang found that professionals using ChatGPT completed certain writing tasks 40 percent faster while their output quality rose by 18 percent. A Harvard Business School field experiment with Boston Consulting Group found that consultants using GPT-4 finished appropriate tasks about 25 percent faster and produced markedly better work.

The qualification matters: appropriate tasks. The Harvard researchers described a jagged technological frontier. AI performed extraordinarily well in some areas and proved unreliable just beyond them. Competence therefore includes knowing when the machine has crossed that frontier.

Watchmaking offers a useful comparison. A modern Rolex is not produced through romantic, preindustrial craftsmanship. Skilled watchmakers work alongside automation, advanced metallurgy, proprietary machinery, laboratories and rigorous testing. Technology did not eliminate the craft; it became part of the craft.

The relevant question is not whether a machine helped. It is what the machine allowed the person to become capable of doing.

Two people might ask AI why the Daytona matters. One copies ten reasons into a social media post. The other keeps asking: Why does a column-wheel chronograph matter? What are the trade-offs between finishing, robustness and serviceability? Why can provenance make one reference more valuable than a technically superior watch? Then that person reads independent sources, challenges the answers and develops an informed view.

Both used AI. Only one outsourced curiosity. The other accelerated it. AI becomes corrosive when it ends inquiry and powerful when it generates the next question.

Protect the friction that teaches

A 2025 MIT Media Lab preprint offers an early warning. In a small study of essay writing, participants using a large language model showed weaker brain connectivity than participants who wrote without tools. They also recalled less of their own work and reported less ownership of it. The paper has attracted methodological criticism and should not be treated as a final verdict, but it asks the right question: What happens when producing an answer repeatedly replaces constructing one?

The answer is not to preserve inefficient work for its own sake. Accountants need not abandon spreadsheets. Architects need not reject computer-aided design. Writers need not surrender spellcheck. AI should remove transcription, formatting, routine correspondence, first-pass summaries and other cognitive drudgery.

The harder task is distinguishing wasteful friction from formative friction. Reading 30 papers to find three useful insights sounds inefficient, yet the seventeenth paper may give a researcher an intuitive command of the literature that no summary can supply. Writing a difficult first draft may reveal that an argument is not yet understood. Debugging software by hand can build the mental models that later help an engineer recognize bad generated code.

Some inefficiencies are apprenticeships in disguise. We should automate the friction that consumes human capacity while protecting the friction that creates human capability.

Open the caseback

AI also changes where intellectual advantage lives. When almost anyone can generate a plausible answer, the scarce skill becomes judgment: knowing what to ask, which assumption is missing, what would falsify a claim and when the machine is confidently wrong.

For leaders, the ethical boundary is not whether someone used assistance. Companies employ researchers. Writers work with editors. Executives rely on teams. The boundary is whether we create a false impression of capability where that capability materially matters.

Using AI to format a proposal compromises little. Selling a cybersecurity architecture to a bank without understanding its threat model compromises a great deal. Improving the grammar of an academic paper is different from outsourcing the intellectual work that the degree is meant to certify.

A practical discipline follows. Use AI to remove drudgery, but retain responsibility. Interrogate any claim important enough to repeat. Never outsource the final judgment. Occasionally work without the tool, not as punishment but as calibration. Can you still explain the concept, defend the decision and recognize when the answer is wrong?

Rolex famously linked its Oyster watch with swimmer Mercedes Gleitze’s 1927 English Channel feat. The story worked because the object pointed back to an achievement. The watch was a signal, but there was something underneath the signal: engineering, testing and human endurance.

Our use of AI should meet the same test. Let the machine make us faster, expose us to unfamiliar fields, challenge a thesis and clear away boilerplate. Then open the caseback. Do I understand this? Could I defend it? Did the tool sharpen my judgment or merely improve its appearance?

In an age when almost anyone can produce an answer, we may learn to value something deeper: how carefully a person thinks, how intelligently they question and whether, after the machines have done their work, there is still a capable human being underneath.

Even if nobody else opens the caseback, you still know what you are wearing.

[The author is Member of the �����Թ� NextGen Committee, Managing Partner and CEO of Springboard Philippines, a professor at De La Salle University, and author of Careless Machines: 24 Rules to Keep Your Judgment in the Age of AI. Feedback at <map@map.org.ph> and <jake@springboardph.com>].

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Many organizations continue to invest heavily in technology, recognizing its potential to improve performance and create new sources of growth. However, simply having access to technology is no longer a competitive advantage in itself. Cloud platforms, artificial intelligence (AI), automation, analytics, and other digital capabilities are increasingly accessible to businesses of all sizes. If competitors can access similar technology stacks, what distinguishes the market leaders from the rest?

The answer is speed. Organizations that can adopt, integrate, scale, and extract value from technology faster than their competitors will create a sustainable advantage.

This concept lies at the heart of Technology@Speed, one of the three pillars of EY’s Transformation Realized framework. Together with Humans@Center and Innovation@Scale, Technology@Speed serves as a key driver of organizational value creation. The framework emphasizes that successful transformation focuses on delivering long-term value across four dimensions: customer value, people value, societal value, and financial value. True transformation occurs when all three drivers work together to accelerate business outcomes and create lasting impact.

The need for speed

Technology@Speed is often misunderstood as a race to implement the newest technology, when in truth, it is about adopting technology at a pace that enables meaningful competitive advantage and value realization. Organizations practicing Technology@Speed can launch products faster, gain access to organized and actionable data sooner, proactively serve customers, increase operational capacity more quickly, and respond to market disruptions with greater agility.

The need for speed has never been more urgent. Industry boundaries are rapidly disappearing: Digital banks are expanding beyond payments into lending, insurance, and investments, while retailers are embedding financial services into their customer experiences. Meanwhile, startups can launch sophisticated products using relatively small teams empowered by AI-enabled tools for coding, marketing, and content creation. As competitors emerge from unexpected industries and business models converge, organizations must continuously adapt to remain relevant.

While technology can enable transformation, technology constraints are not always the primary obstacle to progress. Many organizations face the constraint of complexity, with legacy systems, manual processes, fragmented technology investments, inconsistent data sources, cybersecurity concerns, and cumbersome reporting requirements often creating friction. Heavy reliance on spreadsheets and email, disconnected systems that fail to communicate, and the duplication of technology capabilities across business units contribute to inefficiencies that delay value realization.

EY’s Megatrends 2026 and beyond report highlights that advances in AI, robotics and other emerging technologies are driving a new human-machine hybrid era, making adaptive decision-making, governance and business model transformation critical priorities for organizations. The study suggests that competitive advantage will increasingly come not from access to technology, but from an organization’s ability to scale technologies faster and more effectively than others.

The critical role of leadership

Leadership plays a critical role in overcoming these barriers. Research conducted by EY in collaboration with Oxford Saïd Business School found that 96% of transformation programs encounter challenges significant enough to create a turning point, while 76% of leaders consider these turning points unavoidable. However, organizations that successfully navigate these moments can achieve 2.1 times faster transformation speed and 1.9 times better KPI performance.

The difference lies in how quickly leaders identify issues, make decisions, remove obstacles, and adjust execution when circumstances change. Technology may facilitate transformation, but leadership determines how rapidly value is realized.

This insight highlights an important reality for today’s executives. Speed comes from responsive leadership and disciplined execution. Organizations that continually look ahead, instead of relying solely on past successes, are better equipped to anticipate disruption and capitalize on emerging opportunities.

To accelerate Technology@Speed, organizations can focus on five key leadership levers. The first is adopting cloud and software-as-a-service solutions that provide faster deployment, lower infrastructure complexity, continuous innovation, and greater scalability. Rather than building everything internally, organizations can devote resources to activities that create unique business value.

The second is building a unified and trusted data foundation that serves as a single source of truth, enabling faster reporting, better decision-making, and more effective AI adoption.

The third lever is harnessing AI and automation to enhance productivity, improve customer experiences, eliminate repetitive work, and scale operations more efficiently.

The fourth is leveraging ecosystems by collaborating with technology providers, regulators, suppliers, startups, and other partners to accelerate access to innovation and reduce implementation risk.

Finally, organizations should strengthen governance and leadership by establishing clear ownership, accountability, and decision rights for technology investments. Effective governance does not hinder innovation; it enables organizations to scale innovation with greater confidence and consistency.

Utilizing speed as a competitive advantage

In the years ahead, virtually every organization will have access to AI, cloud platforms, advanced analytics, and automation technologies. As these capabilities become increasingly commoditized, competitive advantage will come from the ability to adopt technology quickly, integrate it effectively, scale it across the enterprise, and realize business value faster than competitors.

Technology@Speed is a leadership mindset and an organizational capability. It requires focusing on reducing complexity, strengthening execution, scaling innovation, and aligning technology investments with long-term value creation. In a world where transformation is continuous, the organizations that move fastest from investment to impact will be best positioned to lead, compete, and thrive.

(This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the authors and do not necessarily represent the views of SGV & Co. and the �����Թ�. The author is a member of the NextGen Committee of the Management Association of the Philippines (�����Թ�). She is the Business Consulting Leader and the Consumer Products and Retail Sector Leader of SGV & Co. This article was co-written with Lee Carlo B. Abadia, Technology Consulting Principal of SGV & Co. Feedback at <map@map.org.ph> and <maria.kathrina.s.macaisa@ph.ey.com>).

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The discussions centered on infrastructure, trade facilitation, digitalization, and regional economic integration. These are important priorities that I fully support.

However, I noticed something missing. No one had raised the issue of corruption.

When I finally brought it up, the room erupted in applause. That reaction was revealing. It suggested that many business leaders recognize the problem, yet we remain reluctant to discuss it openly.

We can build more roads, ports, airports, and digital platforms. We can simplify trade procedures and attract billions in investments. But if corruption, unethical practices, and weak governance remain embedded in our systems, how much of that investment will translate into sustainable economic progress?

Investments follow confidence. Confidence comes from integrity, strong institutions, and capable supply chains.

This is why ethics and supply chain leadership must become central to our national and regional competitiveness agenda.

Supply Chains Determine Competitiveness

Supply chain management (SCM) is often misunderstood as simply purchasing goods, managing warehouses, or transporting products.

In reality, supply chains connect the entire business ecosystem, from planning demand and sourcing materials to managing suppliers, manufacturing, distribution, and delivering products and services to customers.

They determine how efficiently companies operate, control costs, respond to disruptions, and serve their markets. A company may have excellent products, strong financial resources, and outstanding marketing. But without a capable supply chain, it will struggle to compete.

The same applies to countries. Nations compete not only through natural resources, labor costs, infrastructure, and investment incentives, but also through the reliability, transparency, efficiency, and resilience of their supply chains.

Global investors need dependable ecosystems that allow them to source materials, manufacture products, move goods across borders, and serve customers with confidence.

With geopolitical tensions, economic uncertainty, and accelerating technological changes, supply chain leadership has become a boardroom priority. Companies and countries will increasingly compete through their supply chains.

Making the Philippines the Supply Chain Services Capital of the World

For nearly 25 years, our advocacy through the Procurement and Supply Institute of Asia (PASIA) has focused on developing professionals, strengthening organizational capabilities, and advancing best practices in SCM, procurement, and logistics.

Through PASIA Institute and our business solutions arm, PASIA Shared Services, we have supported more than 500 companies in improving their capabilities and operations. Today, we have established a leading regional presence in supply chain, procurement, and logistics capability development and services.

Our vision, however, goes beyond supporting individual businesses. We believe the Philippines can become the Supply Chain Services Capital of the World—not simply by becoming a manufacturing or logistics hub, but by becoming a global center for supply chain expertise, services, and solutions.

SCM is a specialized, knowledge-based profession. It requires expertise in planning, procurement, strategic sourcing, supplier management, logistics, technology, and business transformation.

The Philippines has the people, professional talent, and service capabilities to compete globally in this field. By combining our people, proven processes, and technological capabilities with artificial intelligence, we can offer integrated supply chain services and solutions to businesses worldwide.

This includes large multinational corporations as well as small and medium enterprises (SMEs), which often lack the resources to establish sophisticated supply chain operations independently.

Through AI-powered shared services, SMEs can gain access to professional expertise, advanced technology, strategic procurement, supply chain planning, and logistics capabilities previously available mainly to larger organizations.

This democratization of supply chain expertise can help smaller businesses become more efficient, competitive, and capable of participating in global markets. It also presents an opportunity to create high-value employment, develop Filipino professionals, expand our services exports, and contribute to economic growth.

Just as the Philippines established itself as a global leader in business process outsourcing, we can build a new generation of knowledge-intensive, AI-enabled supply chain services for the world.

We need not wait until every domestic supply chain challenge has been resolved before pursuing this opportunity. By serving global markets, we can simultaneously strengthen our own capabilities, develop new industries, and create expertise that benefits Philippine businesses and institutions.

However, technology and expertise alone will not be enough. Our global competitiveness must be built on trust.

Ethics: The Foundation of Sustainable Competitiveness

Corruption and unethical business practices weaken supply chains, distort supplier selection, increase procurement costs, compromise quality, and undermine confidence in institutions.

In the public sector, these failures can affect essential infrastructure, healthcare, education, and public services. In the private sector, unethical sourcing and supplier relationships expose companies to financial losses, operational disruptions, reputational damage, and legal risks.

Conversely, ethical supply chain practices strengthen transparency, accountability, fair competition, and responsible decision-making. They build confidence among investors, customers, business partners, employees, and the public.

Ethics is not merely a compliance requirement. It is a competitive advantage and a foundation for sustainable investments and economic growth.

If we aspire to serve global businesses as their trusted supply chain partner, ethical leadership must become part of our professional identity and organizational culture.

Artificial intelligence can help us improve transparency, detect irregularities, evaluate suppliers, and strengthen decision-making. But technology must operate within ethical frameworks and remain subject to human accountability. Ethics must extend beyond declarations into measurable actions and results.

PASIA’s Higher Drive for Ethics and Supply Chain Leadership

Recognizing this need, PASIA is intensifying its advocacy through a comprehensive Ethics and Supply Chain Leadership initiative for private and public organizations. We will formally launch this drive during PASIAWorld 2026 on October 8–9.

Our objective is to help organizations institutionalize ethical practices, strengthen leadership capabilities, and demonstrate measurable improvements.

The initiative will encompass ethical standards, organizational and professional assessments, structured training, examinations, independent validations, and maturity index reporting. It will also facilitate industry collaboration, benchmarking, best-practice sharing, targeted interventions, and recognition programs.

These mechanisms will help organizations understand their current capabilities, identify improvement opportunities, and measure progress toward ethical and effective SCM.

We invite companies, industry associations, government institutions, global development partners, foundations, donors, and individuals to support this advocacy through partnerships, funding, expertise, and participation. This is a shared responsibility requiring collective action.

Dare to Lead����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������

PASIA Institute & Shared Services

The Philippines has the opportunity to become the Supply Chain Services Capital of the World by serving businesses globally with Filipino expertise, AI-powered solutions, and ethical leadership.

We have the people, processes, and potential. What we need is the collective determination to lead.

As PASIA approaches its 25th anniversary in 2027, we are strengthening our commitment to this vision.

I invite business leaders, policymakers, supply chain professionals, and development partners to join us at PASIAWorld 2026 on October 8–9 at the SMX Convention Center MOA, as we advance our theme:

“ASEAN Supply Chains in Flux: Synergy, AI & the New Competitive Edge — Building Intelligent, Resilient and Ethical Supply Chains.”

Together, we can build trusted businesses, attract sustainable investments, create high-value opportunities, and develop supply chains that contribute to stronger economies and better lives. Let us dare to lead.

(The author is a member of the Management Association of the Philippines (�����Թ�) Trade, Investments and Tourism Committee, and �����Թ� International Relations Committee. He is Founder and Chair of PASIA and PASIA Shared Services. He has held leadership roles at 3M, Coca-Cola, Chevron/Caltex, Ariba, China Bank, and LBC. He currently serves as Senior Supply Chain Advisor under the U.S. Department of State’s LEAP Program and champions ethical, AI-enabled supply chain services and the development of Filipino expertise for global markets. Feedback at <map@map.org.ph> and <charlie.villasenor@transprocure.com>).

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