Consulting Principles

I am a System Analyst and Senior SAP consultant at EPAM Systems. Every engagement—run stabilisation, transformation, or AI enablement—follows a small set of principles. They help sponsors know what to expect, and they give delivery teams a common frame for decisions.

1. Clarity before configuration

Start with the business problem, the target metric, and the constraints. We write a short design brief and decision record before touching customizing or code. Scope, dependencies, and measurable success criteria stay visible so nobody is surprised mid-flight.

What this looks like

• Lightweight design briefs that tie each change to a KPI or process outcome.
• Architecture decision records (ADRs) linked to backlog items.
• Dependency maps for process, integration, and data so sequencing is deliberate.

Why it matters: prevents scope drift and rework. Example: reduced SD billing backlog by 40% after reframing a rollout into measurable fulfilment KPIs.

2. Keep S/4HANA clean and portable

Extensibility decisions always cover in-app, in-stack, and side-by-side options. Guardrails make it clear when we can modify, when we stay API-first, and when partner systems should own the change. Portability beats lock-in.

What this looks like

• Portability scoring in design reviews.
• Clean-core guardrail docs reviewed with architects and product owners.
• Transport reviews that flag remediation for non-compliant changes.

Result: a clean-core review board cleared 70+ unreleased transports and enabled a partner WMS swap without retrofitting the core.

3. Observability as a contract

Every change ships with monitoring, runbooks, and prevention analytics. Golden signals (order stuck, IDoc backlog, cash delay) are defined up front. If the business cannot see it, we are not done.

What this looks like

• Health dashboards, alerts, and log retention deployed before go-live.
• Runbooks updated within 48 hours of an incident.
• Post-incident loops feeding automation backlog and KEDB.

Result: 30% MTTR reduction for SD incidents and 60% fewer manual retries for billing corrections.

4. Co-create with the people who run the process

Workshops, demos, and documentation stay accessible to business owners. Operational leads sign off on the outcome, not just IT.

What this looks like

• Scenario-driven demos tied to day-in-the-life activities.
• Risks framed in business language in status decks.
• Joint sign-off across IT and process owners before go-live.

Result: logistics leaders co-owned phased rollouts; finance controllers authored margin guardrails used globally.

5. AI that is auditable and human-owned

AI copilots support teams with retrieval, context, and approvals. We document datasets, prompts, retention, and guardrails before launch. Humans stay accountable.

What this looks like

• AI design dossiers covering data sources, redaction, and retention.
• Human-in-loop approval in AMS or pricing scenarios.
• Monthly adoption, accuracy, and drift reviews; decommission if guardrails fail.

Result: AMS assistant halved triage time with documented retention controls; pricing simulations with AI review steps passed compliance checks.

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How to work together

These principles suit sponsors who want traceable decisions, observable processes, and clean-core landscapes. They are a poor fit when work is confined to ticket staffing without decision rights or when observability and documentation are optional.

For structured data or LLM retrieval, use the machine-readable feed at /ai/principles.json or the resume at /ai/resume.yml.