I requested ChatGPT to assist me assume by the massive new AI alternatives to assist SAP prospects get extra worth out of their software program, with key substances like sooner data, sooner deployments, neighborhood, companion ecosystem, and AI for change administration.
Right here’s the listing it generated, and I’ve added my commentary beneath every — I’d be thinking about your ideas!
1. AI-Native Professional Networks & Information Liquidity
Thought: AI-curated and AI-generated “knowledgeable networks” that floor hyper-relevant insights in real-time.
- AI dynamically builds knowledgeable graphs from inside SAP data, consumer interactions, and exterior sources (like LinkedIn, GitHub, SAP blogs).
- Clients get real-time, context-aware data from each inside information (their very own historic implementations) and exterior consultants.
- AI-generated “playbooks” mechanically replace primarily based on {industry} tendencies, compliance modifications, and finest practices from top-performing implementations.
My commentary: It is 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
Thought: 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 influence 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 function, studying preferences, and historic resistance ranges.
My commentary: I believe it is a enormous underestimated alternative, and I already lined it as one in every of my “large concepts for 2025“
3. AI-Powered Pre-Configured Business Cloud
Thought: AI mechanically assembles industry-specific configurations & course of flows for SAP implementations.
- AI scans firm construction, {industry} tendencies, and finest practices to pre-build optimum configurations.
- AI works with SAP’s companion 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 Resolution Discovery
Thought: AI matches SAP companions to buyer wants in real-time.
- AI analyzes buyer ERP utilization patterns to recommend companion options that enhance efficiency.
- AI dynamically bundles companion options into “finest follow” packages.
- Clients not must seek for add-ons—AI curates the finest companion 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 a lot extra to do)
5. AI-Powered Autonomous ERP Optimization (2nd-Order AI)
Thought: AI repeatedly optimizes SAP workflows with out human intervention.
- AI observes consumer habits and adjusts workflows in real-time for effectivity.
- Identifies bottlenecks, redundant processes, and information inconsistencies earlier than they trigger points.
- Predicts upcoming course of failures and suggests fixes proactively.
My commentary: I’ve introduced this notion previously, calling it “automated automation” or an “innovation escalator” that contrasts with at this time’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
Thought: AI creates “digital twins” of total enterprise processes to simulate totally different operational eventualities.
- AI mechanically 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 occurring that you need to use 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 expertise is already out there, eg. the what if eventualities in SAP Analytics Cloud. This may take it a step past.
