Foundation Sprint
Landing zones, policy baseline, and first production MVP with measured business value.
How to deliver AI platform engineering capabilities with agentic workflows and governance by design.
Most teams have pilots. Few have a capability model that scales delivery, enforces controls, and improves reliability at the same time. Kyndryl combines platform engineering, agentic delivery, and governance to move from isolated experiments to repeatable production operations.
Kyndryl AI Platform Engineering · Executive POV 2026
Scaling AI is an execution challenge: enterprise provisioning must be industrialized across infrastructure and application domains with controls from day one.
Grounded in enterprise transformation programs delivered across 2024-2026.
Standardized landing zones, reusable pipelines, and policy packs reduce setup overhead per domain.
Security, risk, and compliance controls are embedded in every provisioning workflow.
Runtime signals continuously improve templates, policies, and modernization decisions.
Joint Kyndryl-client operating cadence keeps AI outcomes tied to live service reliability and SLOs.
Kyndryl turns fragmented AI pilots into a repeatable platform delivery capability model with measurable engineering outcomes.
Differentiator: We deliver platform architecture, implementation, and operating cadence as one team, not separate workstreams.
Kyndryl co-delivers and co-operates critical services until agreed KPIs are sustained in production.
Accurate, enterprise-specific outcomes for the AI use cases that matter - with control, traceability and operating discipline from day one.
Parallelized delivery model to move from first MVP to enterprise scale in six months with governance and reliability built in.
Landing zones, policy baseline, and first production MVP with measured business value.
Replicate golden paths across domains, onboard product teams, and enforce runtime controls.
Scale modernization waves, optimize cost and resilience, and lock governance as BAU.
From many opportunities to an executable Wave 1: three priorities now, one expansion stream next.
Standardize landing zones, policy gates, and runtime controls to de-risk scale.
Consolidate fragmented tooling into golden paths with governed self-service.
Accelerate app and mainframe modernization with governed AI-assisted delivery.
Scale savings and reliability after Wave 1 controls and delivery foundations are stable.
From priorities to execution portfolio: top three streams start now; three expansion streams follow once controls are stable.
AI-generated landing zones and factory migration waves cut delivery time by up to 40% while reducing misconfiguration risk at scale.
Consolidate fragmented toolchains into a unified, policy-driven engineering platform - faster delivery with guardrails built in from day one.
AI accelerates COBOL-to-cloud translation and iterative refactoring - cutting modernization cycles by 40-60% compared to manual approaches.
Build and operate a Backstage-based IDP with golden paths and AI-guided self-service - reducing cognitive load and accelerating every team on the platform.
AI continuously right-sizes resources, detects cost anomalies before bills land, and automates per-team showback and chargeback reporting.
AIOps reduces alert noise by 85%, accelerates root-cause detection by 50-70%, and automates routine runbooks end-to-end with full audit trail.
To protect delivery speed at scale, controls are embedded by design and audited continuously across all domains.
Curated enterprise knowledge base, hybrid RAG with confidence thresholds, and continuous index refresh with stale-data detection.
Policy-as-code in every pipeline stage, automated evaluation suites, ModelOps lifecycle gates, and adversarial red-teaming before every release.
EU AI Act risk classification, SOC 2 Type II, ISO 27001, and GDPR enforced as policy-as-code with continuous compliance monitoring.
Current control posture across critical AI governance obligations for enterprise operations.
Source tagging, confidence thresholds, and stale-data controls are active on critical assistants.
Policy-as-code, escalation paths, and high-risk action approvals are embedded in delivery pipelines.
Vendor due diligence, usage boundaries, and fallback policies are being expanded to all high-risk domains.
EU AI Act mapping, ISO-aligned controls, and immutable audit records are generated as part of normal operations.
Board-level view: expected enterprise value, risk posture improvements, and the decisions required in this meeting.
Industrialized provisioning and modernization waves reduce execution cycle time across infra and app domains.
2-3x faster delivery cycleStandardization and continuous FinOps controls reduce waste from duplicated tooling and manual intervention.
15-30% cloud cost reductionPolicy-as-code, evaluation gates, and auditable operations reduce operational and regulatory exposure.
90%+ control coverage targetKyndryl commitment: transformation value is recognized only when it is operating in production with sustained reliability.
We propose a steering session with CIO, CISO, Head of Engineering, and Platform Lead to approve scope, owners, and 90-day commitments.
Transformation brief, approved priority roadmap, governance decision log, and Wave 1 mobilization plan.
Schedule steering session