Build next-generation SAP AMS.
Make AI useful in real operations.
Operational continuity, practical AI, better change.
Built on experience across SAP migration, SAP logistics, MDM, IT AMS, and operational analysis. Currently working at EPAM Systems as Senior SAP Consultant in SAP transformations projects.
Dzmitryi Kharlanau
Phase 01 - Analysis
The Problem
AMS should create business value, not just resolve incidents.
In many SAP environments, AMS is still delivered through an old model: fragmented knowledge, repeated analysis, weak handover, blurred ownership, and growing dependence on vendor-defined tooling and delivery structures.
Systems stay running, but the organization keeps paying for avoidable friction, slow change, and low reuse of what it already knows.
Avoidable friction
Legacy AMS structures often optimize for ticket flow, not resolution efficiency. The result is that the same structural weaknesses return as recurring incidents instead of becoming root-cause fixes.
The knowledge trap
When support knowledge stays inside individual consultants or vendor tooling, the organization loses institutional memory of its own processes, dependencies, and prior decisions.
Delivery inertia
Standard support structures are rarely designed for fast adaptation. That slows down change, reduces responsiveness, and makes modernization harder than it should be.
Is your AMS a cost center or a catalyst?
Moving from reactive maintenance to proactive value generation requires a different support architecture, not just more tickets, more dashboards, or more tooling.
Strategic Context
Why this matters now
This is no longer only an IT efficiency issue. It is a business capability issue.
Only 48% of digital initiatives meet or exceed business outcome targets.
Foundation dictates function. Infrastructure is the silent partner of innovation.
Infrastructure and operations: only 28% of AI use cases fully succeed.
Organizations with successful AI invest 4x more in foundations.
The shift from experimental AI to industrial-scale business capability requires a radical rethink of the underlying architecture.
Costs rise. Outcomes do not.
Enterprises are spending more on AI and digital change, but results are still weak.
The problem gets worse when organizations keep adding products without redesigning how operations work. This creates overlapping AI copilots, multiple RAG stacks, and AI sprawl: more tools, more overlap, and less control.
SAP is moving AI into a broader commercial model through Joule and AI Units. Clients now need to think harder about architecture, TCO, and dependency before buying more features. The current trajectory leads to diminishing returns unless architectural foundation work comes before procurement.
Architectural Mandate
Success in the SAP ecosystem now requires a pivot from feature adoption to foundation engineering. Integration and data quality are no longer support functions. They are primary determinants of AI ROI.
The Sprawl Audit
Identify overlapping copilots and disparate RAG stacks across the SAP and BTP environment to consolidate cost and reduce duplication.
TCO Recalibration
Assess AI Units consumption against real-world outcome delivery and shift from fixed feature cost to value-based compute.
Governance First
Implement data-quality gates and architectural controls before deploying Joule across critical business processes.
Professional Background
Enterprise Hub
First SAP certification in SAP SD, followed by a broader certification path and 12+ years across support, logistics, migration, master data, and operational analysis.
"Senior SAP consultant at EPAM, working where support pressure, integration complexity, and practical modernization meet."
12+ years
Hands-on work across SAP support, logistics, migration, master data, and operational analysis.
EPAM Systems
Working in SAP transformation environments where support quality and change reliability both matter.
SAP + operations
Connecting process reality, support improvement, and practical technology choices.
SAP and process
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SAP S/4HANA
Core SAP process work, AMS stabilization, migration support, and clean-core decisions.
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SAP MDG
Master data governance, ownership clarity, and controls for reliable process execution.
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OpenAI
LLM workflows and retrieval patterns for support knowledge, triage, and diagnostics.
AI and automation
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OpenAI
LLM workflows and retrieval patterns for support knowledge, triage, and diagnostics.
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Google ADK
Agent orchestration for structured support workflows and controlled tool use.
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Anthropic
Reasoning and governed prompting for production work where control matters.
Build and delivery
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AWS
Cloud runtime for sidecar services, integrations, and modular operational tools.
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Google Cloud
AI infrastructure and data services for side-by-side solutions.
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Next.js
Interfaces for internal tools and AI-enabled workflows that need speed and clarity.
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Python / Data
Automation, retrieval, analytics, and glue code for practical delivery.
Profile
SAP AMS + Practical AI
I bridge the gap between complex SAP operations and intelligent automation. My focus is on making enterprise support deterministic, searchable, and eventually autonomous.
With 12+ years in the ecosystem, I stabilize O2C/SD flows, design SAP MDG governance, and implement RAG-based operational memory to reduce incident recurrence.
What I bring
- Global experience in SAP delivery, integration (AIF), and Master Data
- Architecting 'Agentic' support models for high-volume environments
- Deep focus on 'Clean Core' and AI-ready data strategies
Governance & Strategy
Frequently Asked Questions
Clear answers about SAP support, side-by-side architecture, and practical AI in enterprise operations.
Can you help improve SAP support without replacing the current provider?
Yes. The usual starting point is not vendor replacement. It is clearer ownership, stronger handover, better diagnostics, and less repeated effort.
What is side-by-side architecture around SAP?
It keeps the SAP core clean while moving automation, mini apps, and AI-enabled workflows into more flexible side-by-side services.
How do you reduce recurring SAP incidents?
By finding the repeat pattern behind them: weak handover, missing operational memory, poor controls, fragile integration points, or unresolved process design.
How do you use AI around SAP without governance problems?
Use AI side-by-side, ground it in real operational context, log the important inputs and outputs, and keep the business process control points visible.
How long does a typical assessment take?
Usually two to four weeks, depending on landscape complexity, support scope, and how quickly the right people and evidence are available.