How to let teams move fast on data and AI—without losing control

Mariusz shows how ‘freedom in the box’ enables secure, efficient cloud adoption—balancing compliance with speed through Landing Zones, approved services, and automation.

Problem statement

Modern Data and AI solutions rely heavily on the cloud. On paper, that sounds quick and easy. In practice, organizations often swing between extremes:

• Move fast, skip controls: Projects launch quickly, but weak guardrails lead to policy violations, data exposure, or costly rework.

• Over-index on control: Manual approvals, duplicative reviews, and bespoke patterns turn every initiative into a months-long slog.

The outcomes are predictable: frustrated teams, shadow IT, surprise costs, and slow time-to-value. Meanwhile, the market pressure is rising: 

• Gartner predicts 30% of generative AI projects will be abandoned after proof-of-concept by 2025—even as 79% of executives say AI is critical.

• Forrester reports most enterprises are experimenting with GenAI, yet fewer than one in five have pushed solutions into production.

• IDC estimates AI-centric spending will surpass $300 billion by 2026, while data quality and integration remain top barriers to ROI.

• PwC’s UK CIO survey (2024) found 47% of CIOs are struggling to meet ROI expectations from technology investments. 

With investment and expectations increasing, the question becomes: how do you give teams speed and autonomy while protecting data, reputation, and cost?

Author’s perspective and expertise

Observations and learnings from recent projects

Sustainable speed comes from guardrails, not gates. A well-designed cloud foundation—landing zones, approved service catalogs, reusable templates, and automated onboarding—creates “freedom in the box”: clear boundaries with easy-to-use building blocks that let teams move fast, safely.

Proposed solution: Build a paved road for Data and AI with strong foundations and simple, automated controls.

• Start with a Cloud Landing Zone
  • Standardize identity and access (SSO, least privilege), network baselines (segmentation, private endpoints), encryption (at rest and in transit), and logging by default.
  • Use cloud-native patterns:

• Publish an approved, enabled service catalog
  • Offer a curated list of secured, preconfigured services (data platforms, AI services, storage, compute) with policy-as-code baked in.
  • Provide reference architectures and implementation templates so teams don’t re-certify the same patterns for every project.

• Automate onboarding and lifecycle
  • Self-service portals provision compliant environments in minutes (not weeks), including network, IAM, secrets management, data zones, and monitoring.
  • Embed FinOps from day one: budgets, alerts, showback/chargeback, and unit economics (e.g., inference cost per outcome).

• Bake in data strategy and governance
  • Define data domains, owners, and stewards. Enforce standards for master data, metadata, lineage, and quality rules.
  • Implement privacy-by-design (masking, tokenization, retention) and audit-by-design (immutable logs, evidence packs).

• Enable AI safely with golden paths
  • Provide paved-road patterns for common AI workloads (RAG, fine-tuning, batch inference) with evaluation harnesses, model/version registries, human-in-the-loop, and content/abuse filters.
  • Support both exploration and scale: sandboxes for experiments, plus production pipelines with approvals, testing, and monitoring.

What “freedom in the box” looks like in practice:

• Example 1: A data science team needs a RAG prototype. They request a workspace and get policy-compliant storage, vector DB, network rules, and a baseline evaluation suite—provisioned automatically. They deliver results in days, not months, without bypassing controls.

• Example 2: A product team wants to ship a new service. Using a reference template, the environment includes CI/CD, secrets, logging, and security scans by default. Architecture review focuses on edge cases, not basics already handled by the template.

• Example 3: A GenAI assistant moves from pilot to production. The landing zone enforces data residency, PII masking, model usage limits, cost alerts, and audit trails—so scaling safely is the default, not a special project.

A word of caution

• Avoid manual gatekeeping: If every exception needs a meeting, teams will route around the platform. Automate approvals and embed policies as code.

• Don’t let catalogues go stale: Regularly update approved services and patterns, or developers will turn to shadow tools.

• Beware lock-in without a plan: Use portable patterns (APIs, containers, IaC) so you can evolve architectures and providers as needs change.

• Controls must be visible: Make policy decisions, cost alerts, and security findings transparent to product teams so they can self-correct quickly. 

• Training matters: Even the best paved road fails without enablement. Offer short, practical “how to ship on the platform” paths for engineers and data scientists.

Concluding point

You don’t have to choose between speed and safety. With strong foundations—landing zones, a secured service catalogue, reusable templates, and automated onboarding—you create freedom in the box: clear boundaries with simple, powerful building blocks. Balance control with trust, embed standards and automation, and your teams will innovate faster without compromising data, reputation, or motivation.

Supporting perspectives

To see how these foundations translate into business outcomes:

• 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.