Frequently Asked Questions
AI Consulting Practice: Frequently Asked Questions
Everything you need to know about AI Consulting Practice engagements with MYGO Consulting.
Where should we start with AI in SAP?
We recommend starting with use cases that have clear business value, accessible data, and manageable complexity. Common high-ROI starting points include intelligent document processing (invoice automation), demand sensing for supply chain planning, and predictive quality management. These use cases typically have well-defined success metrics, sufficient historical data in SAP, and clear process integration points that reduce time to production deployment.
What is SAP AI Foundation?
SAP AI Foundation is a set of AI services and infrastructure on SAP BTP that provides the tools to build, deploy, and manage AI models within the SAP ecosystem. It includes SAP AI Core (model training and serving), SAP AI Launchpad (model management and monitoring), and pre-built AI services like Document Information Extraction and Conversational AI. Using SAP AI Foundation ensures that your AI models are integrated natively with SAP applications and use the SAP data model.
How do you handle data privacy and compliance in AI projects?
Our responsible AI framework addresses data privacy, model bias, and transparency requirements from the project outset. We implement data anonymization and pseudonymization for model training, conduct bias detection analysis across protected categories, and provide model explainability documentation for regulated industries. For GDPR compliance, we ensure that AI models processing personal data have appropriate legal basis, data retention policies, and data subject rights procedures.
Can AI models integrate directly with SAP transactions?
Yes. AI models deployed on SAP AI Core can be consumed by SAP applications through standard APIs. For example, a predictive quality model can provide risk scores within the QM inspection process, or an intelligent document processing model can auto-populate invoice fields in the AP posting transaction. We design the integration so that AI insights appear in the user's normal workflow rather than requiring separate tools or dashboards.
How long does it take to deploy an AI use case?
A focused AI use case from assessment through production deployment typically takes 3 to 5 months. The first 4-6 weeks cover data preparation and exploration. Model development and validation take another 4-6 weeks. Integration, testing, and production deployment require the remaining 4-6 weeks. Simple use cases like document extraction can be deployed faster (6-8 weeks) because pre-trained models reduce the development effort.
Do we need a data lake or data warehouse before starting with AI?
Not necessarily. Many AI use cases in SAP can be built directly on SAP data using CDS views, BW/4HANA extractions, or SAP Datasphere as the data source. A separate data lake is only required if the use case needs non-SAP data sources (IoT sensor data, external market data, social media signals) or if the training data volumes exceed what SAP systems can efficiently serve. We assess data architecture requirements during the opportunity assessment and recommend the simplest viable approach.
How do you ensure AI models remain accurate over time?
We implement MLOps practices using SAP AI Core that include automated model performance monitoring, data drift detection, and scheduled retraining pipelines. Performance dashboards track model accuracy, precision, and recall against established thresholds. When performance degrades below thresholds, automated retraining is triggered with the latest data. Human review gates are included for high-stakes use cases where model decisions have significant business impact.
What ROI can we expect from AI implementation?
ROI varies significantly by use case. Document processing automation typically delivers 60-80% reduction in manual processing time with payback within 6-12 months. Demand sensing improvements of 20-30% forecast accuracy translate into inventory carrying cost reductions and improved service levels. Predictive maintenance can reduce unplanned downtime by 30-50%. We build detailed business cases with baseline measurements and target KPIs for each use case during the opportunity assessment.
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