Intelligent Automation

Our Craft
Our Edge
AI Footprint
Micro-Use Cases

Our Craft

The Agentic Ladder

An enterprise AI operating model can be viewed across three maturity levels: assistive, where AI provides suggestions, summaries, and next-best actions; semi-autonomous, where AI proposes plans but humans retain approval and override authority; and autonomous (guardrailed), where agents execute actions within defined policy, budget, and risk limits while escalating exceptions. A reference architecture to enable this typically flows from data and event sources (ERP signals, logs, tickets, documents, IoT) into an AI layer (LLMs, smaller models, vector stores, and retrieval policies), supported by an orchestration layer (workflow and agent frameworks with skills, tools, and guardrails), which then connects to an action plane (SAP BTP or Power Platform automations, APIs, RPA, iPaaS), all governed by controls such as identity management, segregation of duties, audit trails, evaluation frameworks, rate and cost limits, and fallback mechanisms.

Our Edge

Clean-Core AI Architecture with Controlled Extensibility

Enterprise AI Design Guardrails

Clean-core first

Standardize in SAP/Dynamics; extend on BTP/Power Platform so AI remains decoupled from the core.

Guardrails by design

RBAC/SoD, data minimization, prompt security, offline evals, human-in-the-loop.

KPI ownership

Every agent has an owner and a target (cycle time, FCR, plan accuracy, test coverage).

Where AI Can Live in Your Business

(Aligned to QBA’s Digital Foundation + Digital Transformation)

Digital Foundation

Cybersecurity (Threat-aware, policy-safe)

AI uses: identity/transaction anomaly detection, phishing triage, SoD breach prediction, incident summarization.
Outcomes: MTTR ↓, false positives ↓, audit findings ↓.
QBA tie-in: Zero-Trust patterns, IAM hardening, continuous control testing.

Cloud Consulting (Elastic, cost-aware)

AI uses: workload right-sizing, cost anomaly alerts, autoscaling advisors, failure prediction.
Outcomes: cloud spend/GB ↓, SLO compliance ↑, change-failure rate ↓.
QBA tie-in: FinOps guardrails, IaC pipelines, AIOps on Azure/SAP landscapes.

Business Transformation (Process intelligence first)

AI uses: discovery/conformance, value-leak detection, KPI forecasting, playbook recommendation.
Outcomes: cycle time ↓, rework ↓, throughput ↑.
QBA tie-in: Signavio-led fit-to-value, ROI wired to program governance.

Digital Transformation

Enterprise Platforms (Outcome-led)

Finance: close copilots, auto-reconciliations, variance explainers → close days ↓, DSO ↓
Supply Chain: demand-signal enrichment, late-order risk, ATP assistants → forecast error ↓, service level ↑
Procurement & HR: RFQ/PO drafting, catalog normalization, skills matching → maverick spend ↓, cycle time ↓
QBA tie-in: Clean core (S/4, D365) + side-by-side on BTP / Power Platform, governed via Cloud ALM.

Data Engineering (One truth, many uses)

AI uses: semantic model completion, data-quality scoring, entity resolution, policy-aware retrieval (RAG).
Outcomes: time-to-insight ↓, data reliability ↑, self-service adoption ↑.
QBA tie-in: Datasphere + SAC / Fabric + Power BI, governed feature stores for agents.

Application Engineering (Build faster, safer)

AI uses: requirement mining, AC generation, secure code suggestions, PR auto-reviews, runbook agents.
Outcomes: lead time ↓, CFR ↓, MTTR ↓, developer throughput ↑.
QBA tie-in: DevSecOps factory, policy-as-code, RAP/UI5 and Low-code (Power Apps) guardrails.

Intelligent Automation (Agentic orchestration)

AI uses: exception handling (invoices, orders), human-in-the-loop approvals, cross-system task routing.
Outcomes: manual effort ↓, exception rate ↓, SLA adherence ↑.
QBA tie-in: Agent framework + workflow (SAP Build, Power Automate), KPI-linked playbooks.

Workspace Modernisation (Assist where work happens)

AI uses: meeting/action summaries, governed knowledge answers, policy-checked drafting.
Outcomes: time-to-first-draft ↓, search time ↓, adoption ↑.
QBA tie-in: M365 Copilot, Teams/SharePoint with DLP and retention.

Testing Services (Quality at release speed)

AI uses: test generation from requirements/telemetry, self-healing scripts, flaky-test triage, risk-based coverage.
Outcomes: authoring effort ↓, defect leakage ↓, release cadence ↑.
QBA tie-in: Cloud ALM + Tricentis; coverage mapped to O2C/P2P/R2R.

Unified Support Services (Operate, learn, improve)

AI uses: L1→L2 deflection, root-cause clustering, knowledge drafts, SRE-style anomaly explainers.
Outcomes: FCR ↑, ticket volume ↓, MTTR ↓.
QBA tie-in: ITIL-aligned service desk, AIOps overlays, value dashboards.