SAP AI
Practical AI Inside Your SAP Landscape
There is a great deal of noise about AI in the enterprise and comparatively little of it survives contact with a real SAP landscape. Konarrion helps organisations work out which SAP AI capabilities genuinely apply to their processes, and then implement them without compromising the core.
Where AI Actually Fits in SAP
The advantage SAP holds in AI is not model quality. It is context. An assistant that can see your master data, your process configuration and your authorisation model can answer questions that a general purpose tool cannot, and it can act inside a governed transaction rather than beside it.
That advantage only materialises when the underlying data and process design are sound. AI applied to inconsistent master data produces confident, wrong answers faster than a human could. We treat readiness as part of the work rather than an afterthought.
SAP AI Capabilities We Work With
SAP Joule
SAP’s generative AI copilot, embedded across SAP applications. We help you understand what it can and cannot do in your landscape, and prepare your teams to work with it.
SAP AI Core and AI Foundation
The BTP services for training, deploying and operating AI models against your business data, with lifecycle management rather than ad hoc scripts.
Document Information Extraction
Extracting structured data from invoices, purchase orders and other business documents, reducing manual keying in finance and procurement.
Embedded AI in S/4HANA
The predictive and intelligent scenarios shipped inside S/4HANA, managed through Intelligent Scenario Lifecycle Management rather than left unmonitored.
Predictive Analytics in SAC
Forecasting and predictive scenarios in SAP Analytics Cloud, built on a governed semantic model so the outputs are defensible.
Custom AI Extensions on BTP
Where standard capability does not reach, side-by-side AI extensions built on BTP and connected through released APIs, keeping the core clean.
Governance Comes With It
AI in an ERP raises questions that a pilot rarely answers: which data leaves the system, who is accountable for an automated decision, how you audit an output months later, and what happens when a model drifts.
We work these through as part of implementation rather than leaving them for a later compliance review. For organisations with EU exposure this is not optional, and for anyone running regulated finance processes it is simply good practice.
How We Approach AI Adoption
1. Use Case Qualification
We start from process pain, not from capability. A use case has to have a measurable cost today, or it does not proceed.
2. Data and Process Readiness
We assess whether the master data, process consistency and authorisation design can support the use case. Frequently this is where the real work is.
3. Pilot with Success Criteria
A narrow pilot with agreed measures defined before it starts, so the decision to scale is evidence based rather than enthusiasm based.
4. Governed Rollout
Deployment with monitoring, human oversight where decisions carry consequence, and an owner named for the outcome.
5. Measure and Iterate
Ongoing measurement against the original criteria, with a defined route to retire anything that stops earning its place.
Where We Usually Start
- Finance document processing – invoice and document extraction where manual keying is measurable and repetitive
- Reporting and analysis – natural language access to governed data, reducing the queue of ad hoc report requests
- Master data quality – detecting duplicates and inconsistencies that quietly undermine every downstream process
- Support and ticket triage – classifying and routing incidents so specialists spend time on the ones that need them
- Team enablement – helping SAP teams understand what these tools change about their own work
Exploring AI for Your SAP Landscape?
We will give you a candid view of which capabilities apply to your processes today and which are not yet worth your budget.