I requested ChatGPT to assist me suppose by means of the large new AI alternatives to assist SAP clients get extra worth out of their software program, with key substances like sooner data, sooner deployments, neighborhood, accomplice ecosystem, and AI for change administration.
Right here’s the listing it generated, and I’ve added my commentary below every — I’d be desirous about your ideas!
1. AI-Native Knowledgeable Networks & Information Liquidity
Concept: AI-curated and AI-generated “knowledgeable networks” that floor hyper-relevant insights in real-time.
- AI dynamically builds knowledgeable graphs from inner SAP data, consumer interactions, and exterior sources (like LinkedIn, GitHub, SAP blogs).
- Clients get real-time, context-aware data from each inner knowledge (their very own historic implementations) and exterior consultants.
- AI-generated “playbooks” robotically replace primarily based on {industry} traits, compliance modifications, and finest practices from top-performing implementations.
My commentary: This can be a no-brainer, and is already underway, e.g. SAP Consulting Functionality for Joule — and can inevitably be prolonged and improved over time
2. AI-Pushed Change Administration as a Service
Concept: AI fashions that predict change resistance and proactively recommend methods to extend adoption.
- AI scans emails, collaboration instruments, and SAP utilization logs to detect friction factors earlier than they escalate.
- Simulates the impression of totally different rollout methods to find out one of the best ways to introduce new SAP options.
- AI-generated personalised coaching plans primarily based on every consumer’s position, studying preferences, and historic resistance ranges.
My commentary: I believe it is a large underestimated alternative, and I already coated it as one among my “massive concepts for 2025“
3. AI-Powered Pre-Configured Trade Cloud
Concept: AI robotically assembles industry-specific configurations & course of flows for SAP implementations.
- AI scans firm construction, {industry} traits, and finest practices to pre-build optimum configurations.
- AI works with SAP’s accomplice ecosystem to determine the most profitable third-party add-ons for a given {industry}.
- No extra “clean slate” SAP implementations—AI generates a pre-tested, 80% configured system.
My commentary: I see this as combining with 4 beneath
4. AI for Associate-Led Progress & Automated Answer Discovery
Concept: AI matches SAP companions to buyer wants in real-time.
- AI analyzes buyer ERP utilization patterns to recommend accomplice options that enhance efficiency.
- AI dynamically bundles accomplice options into “finest follow” packages.
- Clients now not have to seek for add-ons—AI curates the finest accomplice ecosystem expertise.
My commentary: one other no-brainer that’s already being put into place. SAP Signavio calls it “Plug and Acquire“, and the ideas are clearly a part of the RISE and GROW methodologies (though there’s nonetheless loads extra to do)
5. AI-Powered Autonomous ERP Optimization (2nd-Order AI)
Concept: AI repeatedly optimizes SAP workflows with out human intervention.
- AI observes consumer conduct and adjusts workflows in real-time for effectivity.
- Identifies bottlenecks, redundant processes, and knowledge inconsistencies earlier than they trigger points.
- Predicts upcoming course of failures and suggests fixes proactively.
My commentary: I’ve offered this notion previously, calling it “automated automation” or an “innovation escalator” that contrasts with right now’s painfully-manual innovation staircases. I believe it’s an extension of the framework of 4 above. We’re not there but 🙂
6. Generative AI for Enterprise Digital Twins
Concept: AI creates “digital twins” of whole enterprise processes to simulate totally different operational situations.
- AI robotically maps an organization’s SAP configuration right into a digital simulation.
- Corporations can check modifications (pricing fashions, provide chain changes, workforce shifts) earlier than deploying them in actual life.
- AI generates “what-if” analyses at scale.
My commentary: I did cowl a part of this notion a few years in the past in relation to “whole analytics”: the concept of getting a system the place you’re so conscious of what’s happening that you should utilize it as a continuing experimental check mattress, the place as you make modifications you’ll be able to instantly see what’s working or not working — and a few of that know-how is already obtainable, eg. the what if situations in SAP Analytics Cloud. This is able to take it a step past.
