Executive Point of View

AI-Assisted
Platform Engineering

Five transformation imperatives. One operating model to deliver them all.

Infrastructure modernization, application re-architecture, developer experience, AI-assisted delivery, and governance at scale — your technology organization faces all five simultaneously. Kyndryl provides the platform, the agents, and the delivery model to execute without trade-offs.

One platform. One operating model. One team.
01
Infrastructure Modernization Cloud-native landing zones, IaC, and AI-validated provisioning at enterprise scale.
02
Application & Mainframe Modernization COBOL-to-cloud, microservices, and AI-accelerated refactoring of legacy portfolios.
03
Developer Experience Platform Self-service IDP with golden paths, AI-guided onboarding, and a living service catalog.
04
AI-Assisted Delivery Agents that generate, test, and continuously improve code and runbooks from production signals.
05
Governance & Change Velocity Absorb AI-driven change at speed — EU AI Act compliance, guardrails, and audit trails built in from day one.

Kyndryl AI Platform Engineering · Executive POV 2025

2025 Findings

DORA 2025 Key Findings

Near-universal AI adoption with measurable impact and new risk signals.

90%
AI Adoption
Developers using AI in software workflows
65%
Heavy Reliance
Professionals relying heavily on AI
>80%
Productivity Impact
Respondents reporting higher productivity
59%
Code Quality
Reporting improved code quality
90% AI Adoption
90% using AI
65% heavy reliance
⊕

AI is a mirror and multiplier

Returns depend on the underlying system, not just the tools.

◎

Trust paradox

24% report high trust while 30% report little or no trust in AI outputs.

Team Model

Delivery Performance Baseline

Elite performance targets remain the operational bar for speed and stability.

Lead Time
<1 day 1–6 months
Deploy Frequency
On-demand Monthly–Biannual
Change Failure Rate
5% 40%
MTTR
<1 hour 1 week–1 month
Elite
  • Golden paths with measurable adoption.
  • AI guardrails embedded in pipelines and runbooks.
  • Quality gates tuned to release risk.
Non-Elite
  • Ticket-driven provisioning and approvals.
  • Late-stage QA/security bottlenecks.
  • Reactive operations without SLO guardrails.
Operating Model

AI-Driven Operating Model

Any team demands assets. Agents generate, personalize, and continuously improve them from production metrics.

Business Teams Requirements & OKRs
IT Teams Tech specs & standards
Product Teams Stories & roadmaps
AI Agent Layer
✦
Generate
Assets from any spec
◎
Personalize
By team, stack & context
↑
Improve
From production metrics
Human-in-the-loop approvals
Platform Assets
Golden Paths IaC Modules Pipelines Runbooks API Specs
↩ Observability Loop
Deploy freq
↑
MTTR
↓
Fail rate
↓
Capabilities

Capability Domains

Standard domains with clear contracts and service levels.

INF

Infrastructure Provisioning

Compute, network, and data with policy validation.

AI role
  • Policy-aware plan generation with cost and risk scoring.
  • Automated compliance evidence pack before apply.
  • Drift detection with remediation recommendations.
CI/CD

Software Delivery Pipelines

Build, test, deploy with standards by artifact type.

AI role
  • Pipeline synthesis and drift control.
  • Risk-based quality gate tuning.
  • Failure triage with auto-remediation.
OBS

Observability & Reliability

Dashboards, SLOs, and runbooks as code.

AI role
  • SLO modeling and burn-rate alert tuning.
  • Telemetry coverage checks.
  • Incident correlation and root-cause summarization.
DXP

Developer Experience Platform

Self-service portal with golden paths, service catalog, and AI-guided onboarding.

AI role
  • AI-guided scaffolding — new services in minutes, not days.
  • Auto-generated catalog docs, dependency maps, and ownership.
  • Onboarding cut from weeks to days. (Spotify Backstage · 3,000+ adopters)
SEC

Security & Compliance

Shift-left controls with automated evidence.

AI role
  • Vulnerability prioritization with exploit context.
  • Automated remediation PRs with evidence capture.
  • Policy enforcement with audit trails.
Impact Programs

High-Impact Programs

Use cases where AI-assisted platforms change economics and risk at scale.

01

Large-Scale Cloud Migrations

AI-generated landing zones and factory migration waves cut delivery time by up to 40% while reducing misconfiguration risk at scale.

  • Automated compliance evidence packs per migration wave.
  • Drift detection and self-healing for post-migration deviations.
40% faster IDC 2024
02

SDLC Platform Transformation

Consolidate fragmented toolchains into a unified, policy-driven engineering platform — faster delivery with guardrails built in from day one.

  • Golden path templates enforce security and standards by default.
  • AI-assisted migration scripts from legacy SCM and CI/CD stacks.
50% faster to prod Gartner 2024
03

Application & Mainframe Modernization

AI accelerates COBOL-to-cloud translation and iterative refactoring — cutting modernization cycles by 40–60% compared to manual approaches.

  • AI code analysis ranks modernization candidates by business risk.
  • Automated test generation preserves business logic during migration.
40–60% time ↓ IDC 2024
04

Internal Developer Platform Build

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.

  • New service scaffolding from days to minutes. (3,000+ CNCF adopters)
  • Engineer onboarding cut from 2–4 weeks to 3–5 days. (Spotify 2024)
37% more deploys CNCF / Spotify
05

FinOps & Cloud Cost Engineering

AI continuously right-sizes resources, detects cost anomalies before bills land, and automates per-team showback and chargeback reporting.

  • 15–30% average cloud spend reduction within 90 days of deployment.
  • Savings recommendations and anomaly alerts delivered daily by AI.
15–30% cost ↓ IDC 2024
06

Hyper-Automated Operations

AIOps reduces alert noise by 85%, accelerates root-cause detection by 50–70%, and automates routine runbooks end-to-end with full audit trail.

  • AI triage routes incidents to expert runbooks in seconds, not hours.
  • Predictive anomaly detection cuts MTTD before user impact occurs.
50–70% MTTD ↓ Forrester 2024
Governance

AI Governance & Guardrails

Enterprise controls for responsible AI — aligned with EU AI Act, ISO 42001, and leading analyst governance frameworks.

Knowledge Grounding

  • Enterprise knowledge base with curated sources, lineage, and quality controls.
  • Hybrid retrieval with reranking and confidence thresholds (Anthropic MCP, 2024).
  • Grounded responses with cited evidence — reduces hallucination risk.
  • Continuous index refresh with stale-data detection and version history.

AI Governance Framework

  • Model risk register aligned with EU AI Act risk classification tiers.
  • Data access control, provenance tracking, and full auditability.
  • Policy ownership, change management, and multi-stakeholder approvals.
  • Model lifecycle controls, release governance gates, and periodic reviews.

Hallucination Reduction

  • RAG grounding with retrieval quality gates and confidence scoring.
  • Automated evaluation pipelines and factuality checks per release.
  • Human-in-the-loop escalation for low-confidence outputs.
  • Abstention logic and safe fallback paths to prevent bad output propagation.

Operational Guardrails

  • Policy-as-code enforcement embedded in every pipeline stage.
  • Approval workflows with immutable audit logs for regulators.
  • Automated rollback, kill switches, and circuit breakers.
  • Rate limits, blast-radius controls, and quota management per agent.

Regulatory Compliance

  • EU AI Act readiness: risk classification, conformity assessment, and technical documentation.
  • SOC 2 Type II and ISO 27001 controls automated as policy-as-code.
  • GDPR and data residency controls enforced at the platform layer.
  • Continuous compliance monitoring with audit trail for external regulators.

ModelOps & Evaluation

  • Offline evaluation suites for accuracy, safety, and bias detection.
  • Regression tests for prompts, tools, and data drift between versions.
  • Red-teaming exercises and adversarial testing before production release.
  • Observability for latency, cost, and failure modes across all AI workflows.
Executive Point of View

Strategy and execution,
delivered by one team.

Kyndryl turns AI adoption into durable operating advantage with platform engineering, governance, and execution.

Infrastructure CI/CD Observability APIs Security
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Kyndryl | AI Platform Engineering
Executive POV and delivery model for technology leaders.
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Kyndryl AI Platform Engineering