Wojciech explores how a solid data strategy and governance framework, supported by cloud technologies, enables AI to deliver tangible business value through scalable, secure, and intelligent data platforms.
Problem statement
Innovation without impact is noise. Too many organizations chase AI use cases without the data strategy, governance, and cloud architecture needed to scale safely. They pilot features in isolation, ignore metadata and lineage, skip costs guardrails, and assume quality will appear later. The results are predictable:
Promising proofs of concept that don’t survive production demands or budget scrutiny.
Inconsistent outputs that erode stakeholder trust due to weak data quality and unclear ownership.
Operational fragility when models run on infrastructure that lacks observability, security, and compliance-by-design.
Author’s perspective and expertise
Wojciech Paździerkiewicz, Senior Enterprise Architect, Cloud & Digital, PwC Poland
Wojciech Paździerkiewicz has spent over 20 years across Security, Identity & Access Management, and IT Infrastructure. For the last decade, he has focused on building Digital Cloud Data Platforms—the modern foundations that power real-world AI and machine learning. His consistent lesson: without the right cloud infrastructure and governance, AI becomes a challenge, not a transformative force.
Observations and learnings from recent projects
AI delivers sustained business value only when three elements align—data strategy, governance, and cloud platforms engineered for scale, security, and experimentation. Models are not the bottleneck; fragmented data and ad hoc operations are.
Proposed solution: Pair a value-led AI roadmap with governed data and production-grade cloud platforms.
1. Start with business outcomes and risks:
- What decisions should AI improve within two quarters—reducing downtime, speeding claims, increasing conversion, cutting fraud?
- Which KPIs will move—mean time to resolve incidents, order cycle time, defect escape rate, revenue per customer, false-positive rate in fraud detection?
2. Build governed cloud data platforms:
- Azure Machine Learning for end-to-end ML lifecycle—data prep, training, deployment, MLOps
- Azure AI Foundry for building agentic AI applications on unified tooling
- Vertex AI for unified model development, training, evaluation, and MLOps
- BigQuery ML to build models directly in SQL, tightly coupled to analytics at scale
- SageMaker for comprehensive build–train–deploy workflows.
- Bedrock for managed access to leading foundation models (Anthropic, Meta, Cohere) with enterprise controls.
3. Embed data strategy and governance:
Define domains and ownership: appoint data owners and stewards for customer, product, order, and telemetry.
Codify standards: common terms, master data, and quality rules; document lineage and access policies.
- Instrument observability: monitor cost, performance, drift, freshness, completeness, and security events end-to-end.
4. Treat R&D as a mindset with production in sight:
- Explore emerging techniques where they matter. Example: deep learning models for time series anomaly detection can expose synthetic patterns and manipulated signals.
- In system monitoring, this helps identify artificially generated failures.
- In fraud, it helps spot synthetic or tampered data.
- In data integrity, it helps build trust in AI-generated outputs.Prove value fast using real data and constraints, then scale in waves—by process, product line, or geography.
- Prove value fast using real data and constraints, then scale in waves—by process, product line, or geography.
5. Accelerate software delivery with agentic solutions ((with guardrails):
- SDLC Canvas: translate business goals into features, user stories, and test cases—automating planning.
RapidCraft: convert text into UI components and production-grade code—speeding design and build.
Code Intelligence: analyze large codebases—automating documentation, unit tests, and legacy refactoring.
Keep humans in the loop: architects and engineers validate and approve; AI drafts, humans decide.
A word of caution
- Don’t lead with algorithms alone: If your data is fragmented or undocumented, AI will amplify confusion—faster.
Beware shadow AI and cost creep: Track unit economics (inference cost per outcome) and enforce FinOps practices across teams.
Govern beyond slide decks: Policies must live in pipelines and platforms—role-based access, audit trails, encryption, retention, and model evaluation are non-negotiable.
Avoid lock-in without a plan: Use portable patterns (containers, APIs, IaC) so your architecture evolves with business needs.
Security and compliance are not afterthoughts: Identity, least privilege, encryption in transit and at rest, and regional data residency must be designed in from day one.
Concluding point
AI doesn’t start with algorithms—it starts with data. When strategy, governance, and cloud platforms move in lockstep, data becomes a strategic asset that fuels innovation, drives efficiency, and unlocks new opportunities. Align on outcomes, govern your data, instrument your platforms, and treat R&D as a disciplined path to production. Do that, and you’ll move beyond proof-of-concept to trusted, repeatable impact.
Supporting perspectives
To see how strong foundations translate into business value across the enterprise:
Applying GenAI to Strategic Initiatives like Revenue Optimization by Adam Rogalewicz shows how solid data, pricing, and trade terms come together to drive growth. Read Adam’s perspective.
- Automating and accelerating software development with AI agents by Wiktor Witkowski explains how engineering teams can compress design, testing, and delivery cycles—without sacrificing quality. Read Wiktor’s perspective.
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