Phase 1: Platform Modernization – Data Platform
Establish trusted, scalable, governed data foundation.
Client Initiatives:
- Cloud Data Migration → Migrate legacy EDWs/databases to Snowflake, BigQuery, Redshift, Synapse, Oracle Cloud.
- Data Lakehouse Adoption → Unify structured + unstructured data (Databricks, Microsoft Fabric, BigLake).
- Governance & Compliance Setup → Deploy catalog + lineage (Purview, Collibra, Alation).
- Self-Service BI → Expand Power BI, Tableau, Looker across business units.
Engagement Model:
- Cloud migration accelerators.
- Governance frameworks.
- Self-service BI enablement.
- Training for business analysts.
Client Value Outcomes:
- Elimination of data silos → single source of truth across business units.
- Lower infrastructure costs by moving from legacy on-prem to cloud.
- Faster reporting cycles (days → hours) with centralised BI dashboards.
- Stronger compliance & audit readiness (CDR, GDPR, ESG).
- Empowered business teams with self-service analytics.
Phase 2: AI Experimentation
Move from dashboards to predictive + generative AI pilots.
Client Initiatives:
- Data Quality & Trust – Deploy AI-assisted DataOps (duplicate detection, anomaly detection)
- Natural Language Analytics – Enable Copilots (Power BI Copilot, AWS Q, Google Duet AI, Snowflake Cortex)
- Targeted AI Pilots (Top 2–3 Use Cases) – Customer churn prediction (retail, telco, banking)
- Cashflow forecasting (finance, SMBs) – AI knowledge assistant (HR, legal, operations)
- Embed AI Insights into Workflows – Integrate into CRM, ERP, email, Teams/Slack.
Engagement Model:
- Use case discovery workshops.
- Proof-of-concept builds (2–3 weeks each).
- ROI measurement frameworks.
- Governance + AI ethics guardrails..
Client Value Outcomes:
- Faster decision-making with AI copilots (plain English → instant insights).
- Early proof of ROI from targeted AI pilots (e.g., churn reduction, cashflow forecasting).
- Reduced dependency on IT teams for analytics (democratization of insights).
- Predictive capabilities replace reactive reporting (anticipate issues before they occur).
- Integration of insights into workflows (CRM, ERP, Teams/Slack) → better adoption.
Phase 3: AI at Scale
AI becomes embedded across enterprise functions.
Client Initiatives:
- Operationalise AI (MLOps + AI Ops) – Automated pipelines for training, deployment, monitoring.
- Cost + performance tracking – Enterprise-Wide AI Use Cases
- Personalization engines (retail, finance) – Predictive maintenance (manufacturing, mining).
- ESG reporting automation (cross-industry) – AI copilots for knowledge workers (legal, marketing, HR).
- Data Marketplace / Sharing – Internal + external secure data sharing (Snowflake Marketplace, AWS Data Exchange).
Engagement Model:
- Build AI accelerators per industry vertical.
- Offer managed AI Ops/FinOps services.
- Partner with hyperscalers (AWS, MS, GCP, Snowflake, Oracle).
- Deliver outcome-based AI programs.
Client Value Outcomes:
- Measurable revenue uplift via personalised customer engagement.
- Reduced operational costs through predictive maintenance & AI Ops automation.
- Increased agility → faster time-to-market for new products/services.
- ESG compliance automated → reduced manual overhead and risk.
- Data & AI monetisation via secure marketplaces and ecosystem sharing.
- Competitive differentiation as AI is embedded into every business workflow.