Data Lineage and Governance Key to AI Returns for PH Companies
Philippine enterprises get AI ROI by cleaning data, tracking lineage, and training teams. Use right-time data and RAG to cut risk, reduce hallucinations, and scale safely.
AI ROI for Philippine Enterprises Starts With Clean, Trackable Data
AI is on every agenda, but returns stall without disciplined data management. For Philippine companies, the fastest way to improve AI outcomes is to build clear data lineage, tighten data quality, and prepare the workforce to use these systems responsibly.
Leaders at Kyndryl emphasized that you can't trust or scale AI unless you can track where data comes from, how it changes, and who uses it. That traceability is what lets you integrate data into AI tools, fix issues fast, and reduce risk.
Why data lineage matters to managers
- Trust: Know the source, transformations, and usage of data so model outputs aren't a black box.
- Speed: Troubleshoot data issues in hours, not weeks, by following the trail.
- Security: Spot sensitive data flowing into models before it becomes a compliance incident.
- Scale: Standardized lineage and metadata make it easier to reuse data across multiple AI use cases.
Set the right cadence: real-time vs. overnight
Not all data needs to stream. Classify data by decision need:
- Real-time: fraud checks, network alerts, order promises.
- Daily/overnight: demand forecasts, pricing updates, workforce schedules.
Right-sizing latency reduces cost and simplifies your stack without sacrificing value.
Reduce hallucinations by controlling the data your models see
Hallucinations spike when models lack verified context. In areas like healthcare diagnosis, feeding models with vetted, well-labeled data and clear provenance is essential. Use retrieval-augmented generation (RAG) so models cite approved sources instead of guessing.
The people gap: your biggest AI risk
Kyndryl's People Readiness Report shows only 29% of leaders say their workforce is ready for AI, while 71% say they are not. Expect resistance as teams adjust to new workflows and accountability. Tackle this with role-based training, hands-on pilots, and clear success metrics.
What Kyndryl is seeing in the Philippines
- Demand is growing across banking, financial services, healthcare, manufacturing, and telecom.
- Conversations increasingly focus on secure, responsible, at-scale deployment of generative AI.
- The country has strong potential as firms modernize core systems to support AI use cases.
Management checklist: build data foundations that pay off
- Define 3-5 AI use cases tied to hard metrics (cost per ticket, days sales outstanding, claim cycle time).
- Stand up a data catalog and lineage tool; document owners, transformations, and usage policies.
- Create data quality SLAs (freshness, completeness, accuracy) and monitor them.
- Segment data by latency: real-time vs. batch, and cut scope where real-time isn't needed.
- Centralize approved data in a reliable platform (data warehouse or lakehouse) with role-based access.
- Adopt RAG for GenAI; restrict sources to audited, compliant datasets.
- Evaluate models with test sets that check for bias, hallucination, and safety violations.
- Train teams by job function; add incentives for adoption and data issue reporting.
- Align with the Data Privacy Act and internal governance; log prompts, retrievals, and outputs for audit.
Industry plays you can deploy now
- Banking and financial services: real-time fraud scoring; agent assist with approved knowledge bases; KYC data lineage audits.
- Healthcare: intake summarization from EHR with strict source control; coding assistance; claims triage using verified clinical guidelines.
- Manufacturing: predictive maintenance using sensor data classified by criticality; quality inspection with traceable image datasets.
- Telecom: network anomaly detection with streaming telemetry; customer ops copilots grounded in policy docs and product catalogs.
90-day plan to show progress
- Weeks 1-2: pick two use cases; define KPIs; map data sources and owners.
- Weeks 3-6: deploy a data catalog; document lineage; set quality SLAs; restrict model inputs to approved data.
- Weeks 7-10: run a pilot with 50-100 users; track adoption and KPI lift; fix top data quality issues.
- Weeks 11-13: decide go/no-go; budget for scale; publish governance and training paths.
Why this matters for the economy
Independent analysis with Google Philippines projects AI could add around P1.8 trillion to the economy. Companies that invest in clean, well-governed data and team readiness will be first to capture that value.
Useful resources
The bottom line: AI ROI follows data discipline. Get lineage and quality right, gate what models can see, and upskill your people. Do that, and your pilots will turn into durable wins at scale.
