AI Strategy in Emerging Markets
Published:
Emerging markets face a dual tension: the urgency to adopt AI for competitiveness and the constraints of infrastructure, talent density, and data fragmentation. This essay outlines pragmatic, phased patterns for responsible adoption.
1. Anchor on Business Critical Domains
Prioritise processes with measurable latency, cost, or quality pain. Resist novelty-first pilots.
2. Lightweight Readiness Assessment
Score data quality, integration surface, regulatory posture, and internal capability maturity. Use a 5-point rubric to drive sequencing.
3. Hybrid Knowledge Approaches
Blend retrieval (structured + semi-structured) with curated prompt libraries; avoid premature vector over-engineering.
4. Multi-agent Where Justified
Use cooperative agents only when distinct reasoning roles reduce complexity (extraction vs validation vs enrichment). Keep orchestration explicit.
5. Guardrails & Observability Early
Capture input/output pairs, track drift, and define thresholds for escalation. Instrument before broad rollout.
6. Talent Upskilling Tracks
Parallel tech enablement (architecture, MLOps) and business fluency (capabilities, limitations). Treat as a product, not ad hoc sessions.
7. Ethical & Cultural Considerations
Local language nuance, socio-economic impact, and fairness across demographic slices must be embedded in evaluation design.
8. Cost Discipline
Model total cost: inference, storage, data movement, human validation loops. Optimise unit economics before scale.
Outcome: disciplined sequencing accelerates sustainable AI maturity without over-extending resources. Follow-up essays will expand each pillar.