I asked ChatGPT to help me think through the big new AI opportunities to help SAP customers get more value out of their software, with key ingredients like faster knowledge, faster deployments, community, partner ecosystem, and AI for change management.
Here’s the list it generated, and I’ve added my commentary under each — I’d be interested in your thoughts!
1. AI-Native Expert Networks & Knowledge Liquidity
Idea: AI-curated and AI-generated “expert networks” that surface hyper-relevant insights in real-time.
- AI dynamically builds expert graphs from internal SAP knowledge, user interactions, and external sources (like LinkedIn, GitHub, SAP blogs).
- Customers get real-time, context-aware knowledge from both internal data (their own historical implementations) and external experts.
- AI-generated “playbooks” automatically update based on industry trends, compliance changes, and best practices from top-performing implementations.
My commentary: This is a no-brainer, and is already underway, e.g. SAP Consulting Capability for Joule — and will inevitably be extended and improved over time
2. AI-Driven Change Management as a Service
Idea: AI models that predict change resistance and proactively suggest strategies to increase adoption.
- AI scans emails, collaboration tools, and SAP usage logs to detect friction points before they escalate.
- Simulates the impact of different rollout strategies to determine the best way to introduce new SAP features.
- AI-generated personalized training plans based on each user’s role, learning preferences, and historical resistance levels.
My commentary: I think this is a huge underestimated opportunity, and I already covered it as one of my “big ideas for 2025“
3. AI-Powered Pre-Configured Industry Cloud
Idea: AI automatically assembles industry-specific configurations & process flows for SAP implementations.
- AI scans company structure, industry trends, and best practices to pre-build optimal configurations.
- AI works with SAP’s partner ecosystem to identify the most successful third-party add-ons for a given industry.
- No more “blank slate” SAP implementations—AI generates a pre-tested, 80% configured system.
My commentary: I see this as combining with 4 below
4. AI for Partner-Led Growth & Automated Solution Discovery
Idea: AI matches SAP partners to customer needs in real-time.
- AI analyzes customer ERP usage patterns to suggest partner solutions that improve performance.
- AI dynamically bundles partner solutions into “best practice” packages.
- Customers no longer need to search for add-ons—AI curates the best partner ecosystem experience.
My commentary: another no-brainer that is already being put into place. SAP Signavio calls it “Plug and Gain“, and the concepts are clearly part of the RISE and GROW methodologies (although there is still plenty more to do)
5. AI-Powered Autonomous ERP Optimization (2nd-Order AI)
Idea: AI continuously optimizes SAP workflows without human intervention.
- AI observes user behavior and adjusts workflows in real-time for efficiency.
- Identifies bottlenecks, redundant processes, and data inconsistencies before they cause issues.
- Predicts upcoming process failures and suggests fixes proactively.
My commentary: I’ve presented this notion in the past, calling it “automated automation” or an “innovation escalator” that contrasts with today’s painfully-manual innovation staircases. I think it’s an extension of the framework of 4 above. We’re not there yet 🙂
6. Generative AI for Enterprise Digital Twins
Idea: AI creates “digital twins” of entire business processes to simulate different operational scenarios.
- AI automatically maps a company’s SAP configuration into a virtual simulation.
- Companies can test changes (pricing models, supply chain adjustments, workforce shifts) before deploying them in real life.
- AI generates “what-if” analyses at scale.
My commentary: I did cover part of this notion many years ago in relation to “total analytics”: the idea of having a system where you’re so aware of what’s going on that you can use it as a continual experimental test bed, where as you make changes you can immediately see what is working or not working — and some of that technology is already available, eg. the what if scenarios in SAP Analytics Cloud. This would take it a step beyond.
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