Artificial intelligence is no longer strictly a research project for
larger businesses. Artificial intelligence is becoming an embedded
characteristic of enterprise systems and SAP is embedding AI into core
workflows and organizational usage. SAP S/4HANA has provided the structure for
core business processes and SAP Business Technology
Platform (BTP) for customization and extensions. AI is transitioning from
experimental work to a real opportunity for organizational tool that supports
real decision-making.
SAP has already acknowledged hundreds of embedded AI scenarios and has also
committed to considerable investment in copilots and automation and agent-based
intelligence. These inventions are directed at providing efficiencies but also
allow companies to become agile, reduce risks, and create new value.
Top AI Use Cases Gaining Traction
1. AI Copilots and Agentic Workflows
SAP Joule, the AI assistant of the
company, is becoming a true copilot. It is able to understand natural language
requests, collect data across multiple S/4HANA data sources, and trigger
actions. A new example shows a supply chain
manager can ask Joule a question like a summary of late shipments and the
system can respond with either suggested actions or drafted communications to
clients as a result.
Why it matters
Employees
no longer have to go in and out of multiple transactions just to gather
information. The copilot is meant to speed up responses and less errors,
allowing teams to focus on fixing a problem rather than going through a system.
Example: Sales reps receive an immediate check on margins by chatting and get an approved quote recommendation earlier, speeding up generating quotes.
2. Finance and Record-to-Report
AI is enabling significant changes within finance processes, using automation
to perform processes such as cash application, account reconciliation, and
journal entry recommendations. These features are found in S/4HANA finance use cases, leading to
faster month-end close with more focus on accuracy.
Why this matters
Reducing repetitive manual processes allows finance professionals to spend more
time on interpreting results, advising on strategic decisions, and enhancing
forecasting accuracy. Timely close cycles give leadership a better
understanding of the organization’s current financial state.
Example: A global retail company is
using SAP S/4HANA Finance with AI cash application to automatically match
thousands of customer payments received against open invoices.
3. Supply Chain Planning and Execution
Artificial Intelligence models in S/4HANA and in BTP are being deployed for
various purposes such as demand forecasting, inventory optimization, and
disruption management. When a shipment is delayed, the AI models will suggest
rerouting alternatives, identify other sources, or modify production schedules.
Why it matters
Organizations provide greater resilience and are able to act quickly when
demand changes or supplier performance falters. This reduces stockout risks,
eliminates excess inventory costs, and smooths order fulfilment.
Example: Shorter lead times, fewer stockouts, and better service levels.
4. Procurement and Contract Intelligence
Procurement teams will utilize AI for contract reviews, extracting relevant
clauses, risk assessing for compliance concerns, and recommending options for
suppliers. AI services available in
BTP
allow organizations to evaluate a multitude of purchasing documents and
contracts in a fraction of the time it took to do it manually.
Why it matters
This will deliver quicker procurement cycles, better compliance consistency and
stronger supplier relationships. Teams are enabled to pivot from hours spent
reviewing legal documents to focusing on strategic negotiations.
Example: Quicker onboarding, fewer assessed risky contracts, and reduced maverick spend.
5. Health, Safety, and Environment (EHS)
AI functionalities in S/4HANA will aid in automating EHS processes. Employees
can report incidents using conversational bots. AI can produce permits, or
create safe work instructions, or documents; by analyzing risk data, or
incidents from the past.
Why it matters
It improves safety in the workplace, because issues that get reported can be
acted upon quicker. Compliance is better established, because companies can
make use of documentation and tracking for safety processes; not just
individuals writing reports.
Example: Improved retention by allowing targeted interventions and
faster role matching then better EHS compliance.
6. Developer Productivity on SAP BTP
For IT teams, SAP BTP provides AI services and development capabilities that
enable faster application development. Developers can harness generative AI to
develop code snippets, shorten testing time, and build industry specific AI
applications that can augment core SAP systems.
Why it matters
This allows for shorter development cycles and less burden for IT Teams. For
business stakeholders, it enables them to receive tailored solutions faster,
with greater alignment to operational needs.
Example: Faster delivery of customer specific extensions to S4/HANA.
7. Integration with Cloud and Productivity Tools
SAP is positioning to ensure that its AI capabilities are integrated to
work with all leading cloud vendors and productivity applications. Users will
be able to interject SAP data into Microsoft CoPilot or productivity workplace
applications, which means employees won’t need to switch applications to access
insights from SAP.
Why this matters
This way employees will be able to work more efficiently, in their preferred
application, and have better access to insights from SAP. It lessens friction,
saves time, and enables teams to take action based on real-time data.
Example: A finance team working in
Microsoft Teams with Microsoft CoPilot can have real-time insights from SAP
S/4HANA and obtain that data right from their meeting without having to log
into SAP.
8. Responsible AI and Data Quality
SAP is deeply investing in responsible AI with a focus on governance, data
quality, and transparency. BTP services have features that promote model
management, monitoring, and compliance, all of which ensure AI meets
enterprise-grade definitions of acceptable practices.
Why this matters
Overall, enterprises can more fully adopt AI, without fear, because they are
convinced that outputs are trustworthy, valid, and audit-proof. This limits
compliance risks, it reduces bias, and it guarantees that the actions are
aligned with trustable business outcomes.
Example: A manufacturing company
leverages AI Core and AI Launchpad on BTP to deploy a demand
forecasting model.
How Enterprises Can Start
Organizations that have started using AI in SAP
usually start with focused, very high-value use cases like cash application,
contract intelligence, demand forecasting, etc. To achieve AI adoption,
organizations will have to start with clean master data, have a governance
model in place, and work closely with business users and IT teams. Starting
with small-scale pilots provides teams with a quick learning opportunity and
gives them the confidence to deploy AI across more complex processes.
Conclusion
AI in SAP S/4HANA and BTP is no longer an experimental add-on, it is now
becoming a critical productivity, resilience, and growth enabler. AI is being
used as a solution to real business problems across finance, supply chain,
procurement, and safety in organizations and being shown to create results and
measurable value. Organizations with a strong data foundation, sound
governance, and practical pilots will be best placed to tangible value from
these new capabilities come 2025 and beyond.[KG1]