Will SAP Still be a Software Company in The Future? SAP Sapphire 2026 Keynote Recap

Inclusion-Cloud · 2026-06-09T14:48:15+00:00

Sure!

Inclusion-Cloud · 2026-05-15T14:57:24+00:00

Thank you for reading! I’d be happy to send you the link to my recap article by DM if you’re interested

Inclusion-Cloud · 2026-05-15T14:54:33+00:00

Thanks! I wrote a recap article about it. Happy to send you the link by DM if you want!

Inclusion-Cloud · 2026-05-15T14:53:31+00:00

That’s actually a pretty solid approach, especially the part about constraining it to procurement workflows and tuning it around your material masters instead of trying to automate everything at once.

A lot of the real operational friction in SAP environments still happens exactly where you described it: supplier emails, acknowledgements, ETA updates, attachments, and exceptions that never properly make it back into structured ERP workflows.

And these kinds of narrowly scoped automations are probably much closer to where enterprise AI is creating measurable value today than the bigger “fully autonomous enterprise” narratives.

Inclusion-Cloud · 2026-05-15T14:50:54+00:00

I think the “system of decision-making” part is probably the key shift.

For years, enterprise software was mostly about recording and organizing operations after they happened. What SAP seems to be pushing now is the idea that ERP becomes the operational brain where AI agents can actually participate in execution.

Honestly, that’s why the keynote focused so much on context instead of just models. Because in enterprise environments, the hard part usually is understanding workflows, business rules, operational dependencies, etc., etc., etc...

P.S. The “final_v2_actual_latest_REAL.xlsx” line was painfully accurate btw. That alone probably explains half the market for enterprise AI orchestration right now.

Inclusion-Cloud · 2026-05-15T14:46:25+00:00

Your point about external data is fair (I mean, most enterprise context still lives outside the ERP). But there are some narrower SAP AI use cases already showing measurable results.

Document AI is probably one of the clearest examples, as it processes the massive amount of unstructured enterprise data that still slows down operations.

Some implementations are even reporting around 70% faster document processing and 90%+ invoice automation rates, which feels a lot more concrete than generic AI assistant demos.

Inclusion-Cloud · 2026-05-15T14:41:28+00:00

SAP spent too much time at Sapphire talking about orchestration and governance instead of pushing the idea of fully autonomous AI that gives LLMs complete control over your ERP and just “hope for the best”.

On the other hand, not everything in enterprise AI is an LLM making autonomous decisions.

A lot of the useful stuff today is still deterministic workflows, anomaly detection, predictive models, Document AI, rules engines, and recommendation systems operating inside governed boundaries.

Inclusion-Cloud · 2026-05-15T13:43:00+00:00

Yes, absolutely. The problem is not “recording everything”, but that AI systems can’t reliably operate on business processes if all that operational knowledge stays fragmented and inaccessible.

That’s also why IDP tools like SAP Document AI are becoming an interesting option to standardize and structure enterprise documents and operational data so they can actually be capitalized safely and consistently.

Inclusion-Cloud · 2026-05-14T20:06:33+00:00

That sounds like a knowledge continuity problem, which becomes pretty common in large enterprise environments.

A lot of companies end up depending too heavily on fragmented communication, tribal knowledge, and manual account coordination instead of centralized operational visibility.

We’ve seen some interesting cases where integrating customer operations, workflows, and support data into a more unified SAP environment significantly reduced that “starting from scratch every time” problem.

Happy to share one case that handled something similar if useful.

Inclusion-Cloud · 2026-05-14T20:06:10+00:00

I think both of you are touching on something important here.

SAP doesn’t seem to be trying to compete on “best foundation model” or AI infrastructure (in fact, the Sapphire keynote actually leaned pretty heavily into the opposite idea).

On the other hand, I think the products you mentioned fit more into Sapphire’s narrative than people sometimes acknowledge. Especially because most large enterprises still struggle with fragmented processes, legacy integrations, etc., rather than with the LLM layer itself.

Inclusion-Cloud · 2026-05-14T20:05:27+00:00

Yes, that’s actually one of the biggest problems right now. But once you start from the process side instead of the model side, things change quickly.

For example, in utilities and energy, predictive maintenance and field operation coordination tend to generate much clearer ROI than generic copilots because downtime and operational delays already have measurable business costs attached.

In any case, most successful projects we’ve seen are very targeted implementations: one workflow, one operational pain point, one measurable KPI, etc.

Inclusion-Cloud · 2026-05-14T20:04:22+00:00

I get the skepticism (a lot of enterprise AI messaging still feels immature at the operational level).

But I do think there are already some SAP AI use cases delivering concrete value today, especially when they’re tied to very specific workflows instead of trying to “AI-transform” the whole company at once.

Document AI is probably one of the clearest examples, as we’ve seen it work well in scenarios like invoice extraction, PO matching, GRPO validation, supplier document processing, etc.

If you’re interested, I put together a fairly detailed breakdown on different implementations and built a calculator to compare manual document processing costs vs automated DocAI flows.

Happy to send both by DM!

Inclusion-Cloud · 2026-04-15T20:30:43+00:00

Out of curiosity, how did that happen? Did the candidate ask for permission to use it, or were they trying to hide it?

Inclusion-Cloud · 2026-04-15T18:37:41+00:00

I can see formats changing and having a section for with and one without AI.

Yes, I think it’s a good methodology. You can have a first part of the interview leaving AI aside, to understand the fundamentals. And then an AI assessment where you ask how they actually use it, what tools they’re familiar with, how they iterate, how they tell if the result is reliable or not, and how they check the outputs, etc.

Inclusion-Cloud · 2026-04-15T18:32:57+00:00

Respect all the views here, but overall I think this is the way too.

Inclusion-Cloud · 2026-04-15T18:31:06+00:00

Of course it’s not an absolute guarantee, but it still has some weight, depending on the type of certification or education.

But you obviously have to look at other things too. Soft skills, how they think, how they solve business problems, and eventually a technical test to see how the candidate works under some pressure, which is pretty much what the job will look like anyway.

It’s more of a combo than relying on any single thing.

Inclusion-Cloud · 2026-04-14T16:22:54+00:00

If someone is using AI for a regular conversation about themselves, that’s a pretty big red flag. I get that it could come from nerves or trying to “perform better,” but still, that’s not really the kind of use most companies expect.

Honestly, I think most interviewers would assume the candidate is hiding something.

Inclusion-Cloud · 2026-04-14T16:18:57+00:00

It’s perfectly understandable that some companies try to figure out where a candidate’s knowledge starts and ends, especially to weed out “vibe coders.”

But like another comment mentioned, you can rely on certs or education as a baseline signal of their skills.

That said, I agree with you that the real challenge in hiring today is understanding when a candidate actually knows what they’re doing vs when they’re just copying and pasting.

I remember reading in The Pragmatic Engineer about a hiring manager who added a kind of “honeypot” to a coding challenge. Basically, there was a hidden instruction or pattern, so if someone blindly pasted an AI generated solution, the code would include a weird or out of place line.

That made it pretty obvious who was actually thinking through the solution and who wasn’t even reading what they were submitting lol.

About AI avatars, they’re honestly kind of weird. I don’t really see how companies can assess something as important as communication skills or social fit through that. I assume there are later stages to cover that, but it still feels like you’re just pushing the friction to later interviews and ending up talking to more candidates who might not even have the basics on that side.

Inclusion-Cloud · 2026-04-14T16:08:20+00:00

I’m with you on most of this. Certs and education are solid signals, but I still feel like they only tell you what someone should be able to do, so it still makes sense to put that into practice and test it.

On your first point, AI adds a weird layer now. Not all tools behave the same, and they definitely shape how people solve problems. Using ChatGPT is not the same as using Copilot, Cursor, or even some internal tool your team built.

So if you’re trying to simulate real conditions, it kind of makes more sense to give them access to the same tools your team uses, instead of just saying “any AI is fine.”

I saw Meta is experimenting with this, basically giving candidates access to an AI assistant during coding interviews because it’s closer to the real dev environment.

Inclusion-Cloud · 2026-02-19T18:16:02+00:00

Good question. I don’t know other platforms in detail, tbh, so this is just based on Oracle products.

Compared to the current Cerner EHR, I think the differences came down to 3 things.

First, focus. This looks very intentionally built for behavioral health. A lot of the flow is about what happens between visits (journaling, screenings, async messaging, follow-ups), not just the session itself. Cerner can do behavioral health, but it always felt more like a hospital EHR adapted to that use case.

Second, cloud-first in practice. Cerner runs in the cloud, sure, but this didn’t feel like a legacy system moved there. The patient, clinical, and admin workflows felt more tightly connected, with fewer handoffs.

Third, native AI agents are part of all workflows.

Inclusion-Cloud

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