A successful digital transformation starts with a well-defined strategy aligned to business objectives. This activity evaluates the existence of a digital roadmap, executive sponsorship, allocated budget, and clear KPIs such as adoption, efficiency, customer impact, or digital revenue. Strong governance ensures clear decision-making, accountability, and regular progress reviews at leadership level.

Technology alone does not drive transformation—people do. This activity evaluates digital skills availability, upskilling and reskilling programs, leadership support, and change-management practices. It also considers team structures (agile, cross-functional), incentives for innovation, and the organization’s openness to experimentation and continuous learning

This activity assesses the maturity of your technology architecture, including cloud adoption, system integration, automation, security, and data governance. It also measures how effectively data is collected, managed, and used for decision-making, analytics, and digital products. A mature foundation enables scalability, resilience, and faster innovation.

Purpose: Align digital initiatives with business strategy, secure executive commitment and create decision forums that speed execution.
Key milestones:
Executive workshop to set vision and objectives.
Draft and approve a 12–24 month digital roadmap.
Create a Digital Steering Committee and assign owners.
Define KPIs, budget lines and review cadence.

Purpose: Replace brittle legacy constraints with a flexible, integrated technology and data layer that enables rapid product delivery and analytics.
Key milestones:
Assessment of current architecture and data maturity.
Target architecture design (cloud, APIs, CI/CD, security).
Implement core integrations and first data pipelines (ETL/streaming).
Launch a canonical data model and data governance policies.
Success indicators / KPIs: Reduced system integration time, data quality metrics, number of APIs available, time to deploy.
Suggested owner: CTO / Head of Engineering & Head of Data.
Suggested timeframe: 3–9 months for initial platform & pipelines; scale thereafter.

Purpose: Ensure technology and strategy deliver value by building skills, changing processes, and encouraging experimentation across the organization.
Key milestones:
Skills gap analysis and role mapping (product owners, data roles, SREs).
Launch upskilling programs, coaching and agile pilots.
Implement internal change campaigns and success showcases.
Embed outcomes into performance and budgeting cycles.
Success indicators / KPIs: Training completion rates, number of cross-functional product teams, adoption metrics for new tools, survey of change readiness.
Suggested owner: Chief People Officer in partnership with Digital/Transformation lead.
Suggested timeframe: 3–12 months for first wave; continuous learning ongoing.

Purpose: Quickly align leadership on the highest-impact digital opportunities, validate business cases and produce an executable roadmap.
Key deliverables: Executive workshop output, maturity assessment, prioritized value cases (ROI/effort), a 12–24 month roadmap with milestones and owners.
Success indicators / KPIs: Approved roadmap, prioritized backlog, executive sign-off on at least 2 value cases, estimated ROI and timelines.
Suggested owner: Transformation Lead / CDO with PMO support.
Suggested timeframe: 4–8 weeks.

Purpose: Reframe key customer journeys, prototype product concepts and validate with users to accelerate revenue or retention improvements.
Key deliverables: Journey maps, persona & pain-point analysis, prioritized product backlog, clickable prototypes, MVP roadmap, user test reports.
Success indicators / KPIs: Reduced customer friction (NPS/CSAT lift), conversion / activation improvements, validated MVP metrics from pilots.
Suggested owner: Head of Product / UX Lead in partnership with Marketing.
Suggested timeframe: 6–12 weeks for discovery + MVP sprint cycles (4–8 weeks each).

Purpose: Create a practical data foundation and operationalize analytics/ML so insights become production-ready and repeatable across the business.
Key deliverables: Data maturity assessment, target data architecture (data lake/warehouse + pipelines), prioritized use-cases, proof-of-value models, governance playbook, production deployment plan.
Success indicators / KPIs: Data quality metrics, time-to-insight reduction, number of models in production, measurable business impact from top use-cases.
Suggested owner: Head of Data / CTO with Data Engineering team.
Suggested timeframe: 8–16 weeks for foundation + ongoing model lifecycle support.