AI Strategy & Digital Transformation FAQ

Months 1–2: Discover and assess (strategy, data, tech, use cases, risks). Months 3–4: Design and pilot (solution designs, PoCs, initial governance). Months 5–6: Implement and scale (productionise 1–2 use cases, operating model, KPIs).

Months 1–2 include strategic alignment workshops, mapping AI opportunities, readiness and risk assessments, and establishing a data and technology baseline. Months 3–4 focus on selecting 2–3 pilot use cases, designing solutions with architecture and vendor selection, running proof-of-concepts, and establishing initial governance frameworks. Months 5–6 involve moving 1–2 validated use cases into production, setting up the operating model with clear roles and responsibilities, defining success metrics and KPIs, and creating a roadmap for continued scaling and optimization.

Use Red/Amber/Green scoring across six domains:

1. Strategy and leadership: Clear AI vision linked to business strategy, executive sponsor with budget/time commitments, defined success metrics and risk appetite.

2. Data and content: Inventory of key data sources, basic governance (definitions, quality rules, access controls), secure mechanisms to provide data safely to AI systems.

3. Technology and architecture: Modern infrastructure capable of supporting AI workloads, cloud-readiness assessment, integration capabilities with existing systems.

4. Talent and skills: Assessment of current AI/ML capabilities, training needs identification, consideration of build vs. buy vs. partner strategies.

5. Process and governance: Change management readiness, ethical AI guidelines, regulatory compliance framework (privacy, fairness, transparency).

6. Culture and adoption: Stakeholder buy-in assessment, communication plan, clear success stories and quick wins to build momentum.

Step 1: Map and measure the current process (steps, actors, systems, time/cost, pain points).

Step 2: Identify AI augmentation opportunities (repetitive, rules-based, pattern-recognition tasks). Decide if AI assists, partly automates, or fully automates with oversight.

Step 3: Define target state and guardrails (AI steps, human approvals, constraints).

Step 4: Select tools and integration points (built-in platform AI, custom models, LLM assistants; CRM/ERP embedded capabilities, API integrations, or standalone solutions).

Step 5: Pilot with controlled rollout (shadow mode testing, A/B comparison with manual process, collect feedback from users).

Step 6: Iterate and refine based on real-world performance, user feedback, and business outcomes.

Step 7: Scale and monitor (expand to full deployment, establish KPIs, continuous improvement loops, regular model retraining schedules).

Financial and productivity KPIs: Revenue growth attributable to digital channels or AI offers; cost per transaction/case; FTE hours saved; time-to-market.

Customer and experience KPIs: NPS, CSAT, CES; digital adoption and self-service rates; response/resolution times.

Process and quality KPIs: Cycle time, throughput, defect rate, rework, compliance violations, first-contact or straight-through processing.

AI-specific and technology KPIs: Model accuracy, precision, recall; prediction latency; data pipeline reliability; model drift indicators.

Strategic KPIs: Speed to market for new capabilities; innovation pipeline health; employee digital literacy scores; sustainability and carbon impact metrics.

Best practice: Link each KPI to a specific business outcome and review quarterly to ensure continued relevance.

1. Principles and scope: Fairness, transparency, accountability, privacy, security, social benefit; define which systems are considered AI and which domains are in scope.

2. Roles, committees, accountability: AI Governance Committee; assign business owner, technical owner, and risk/compliance liaison for each AI system.

3. Policies, standards, processes: Policies for acceptable use, oversight, data sources, testing, monitoring, and decommissioning; lifecycle processes for intake, assessment, approval, and retirement.

4. Risk assessment and controls: Bias testing, explainability requirements, privacy impact assessments, security reviews, and ongoing monitoring thresholds.

5. Training and awareness: Role-specific training for developers, business users, and executives; clear escalation paths and incident response procedures.

6. Audit and continuous improvement: Regular internal audits, external assessments where required, metrics tracking, and feedback loops to update governance as AI capabilities evolve.