AI's Next Phase in APAC: A Practical Playbook for Marketers
APAC has become a proving ground for AI adoption, driven by a mobile-first, culturally diverse population and a digital economy that rewards speed and precision. Industry estimates put the region's Data & AI market at around $90B this year, with close to 80% of enterprises building AI into their digital strategies.
Amid that momentum, one consistent lesson has emerged: sustainable data foundations beat one-off pilots. That's where Ekimetrics has focused its approach for the past decade-scaling measurable outcomes without creating technical debt.
Who is Ekimetrics and why marketers should care
Founded in Paris in 2006, Ekimetrics is a global AI and data consultancy with nearly 500 experts across three continents. The firm expanded to Asia in 2015 to help organizations deliver sustainable AI impact at scale across diverse markets, from mature to emerging.
As Ekimetrics' APAC leadership notes, AI's value is straightforward for marketers: better customer experience, smarter media investment, and leaner operations. The challenge is making those wins repeatable across countries, channels, and teams.
What changed in the last decade-and what matters now
- From pilots to platforms: AI shifted from isolated experiments to business-wide programs with governance, shared assets, and MLOps.
- From third-party cookies to first-party truth: Identity shifts forced brands to build consented data and durable measurement.
- From vanity metrics to ROI: MMM, incrementality testing, and unified KPIs replaced channel-by-channel reporting.
- From generic content to relevance at scale: Personalization moved from simple rules to dynamic, AI-driven experiences.
The marketer's AI playbook (proven in APAC)
- Data foundation first
- Stand up a clean, consented first-party data layer; enrich with privacy-safe signals.
- Standardize taxonomy for media, creative, and product to enable consistent reporting and optimization.
- Measurement you can trust
- Run Marketing Mix Modeling (MMM) for budget planning and channel allocation.
- Use incrementality tests to validate lifts from retail media, affiliates, and walled gardens.
- Combine MMM with granular experiments for both strategic and day-to-day decisions.
- Personalization that scales
- Build audience strategies around value and consent, not just reach.
- Use AI to adapt offers, content, and timing across markets while preserving brand consistency.
- Ops that keep models alive
- Put models into production with clear owners, SLAs, monitoring, and retraining cycles.
- Create reusable components (features, pipelines, templates) to cut time-to-value across markets.
- Governance and ethics baked in
- Document data lineage, consent, and model usage to meet local rules and internal standards.
- Run bias checks and human oversight for high-impact decisions.
Where Ekimetrics typically drives value
- Media ROI and budget reallocation using MMM plus market-level experimentation.
- Customer analytics for CLV, churn, and upsell across markets with different data maturity.
- Personalization frameworks that standardize audiences, content logic, and testing rhythms.
- Retail media and e-commerce analytics to balance on-site, marketplaces, and last-mile channels.
Operating across APAC: practical guardrails
- Local nuance matters: Build regional blueprints, then localize data, language, and channels.
- Privacy by design: Map consent and data residency up front; align with frameworks like Singapore's Model AI Governance Framework (read more).
- Interoperability: Standardize core metrics and model artifacts so teams can share and improve them over time.
What's next for marketers in APAC
- First-party data becomes the growth engine as cookies fade and identity fragments.
- GenAI scales creative and service workflows-with clear human review and brand controls.
- Retail media demands advanced incrementality and SKU-level optimization to avoid double counting.
- ESG and responsible AI move from policy to practice with measurable reporting.
How to start (or level up) within 90 days
- Audit: Map your data sources, consent, and current models; identify quick wins and gaps.
- Measure: Stand up an MMM baseline and two high-impact experiments (e.g., creative variation, audience switch).
- Build: Create a reusable feature store and orchestration for one priority use case.
- Govern: Define guidelines for data use, model review, and localization; train teams on the process.
If your team needs structured upskilling to make this stick, explore focused learning paths for marketers (see certification).
The bottom line
APAC has proven that AI at scale is possible-and repeatable-when marketing, data teams, and governance move in sync. Ekimetrics' decade in the region shows a clear pattern: start with data you can trust, measure what matters, productionize, and keep improving across markets. Do that, and AI stops being a slide and becomes a consistent growth engine.
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