Think about programming a destination into a GPS before the roads to get there are fully built. The route looks clear on screen and the technology is working exactly as designed. But somewhere along the way, the path runs out, and you’re left improvising.
That’s a fair comparison to where many manufacturers are in their efforts to achieve autonomous SAP production planning right now. The destination is well defined: AI-driven production scheduling that anticipates disruptions, adjusts in real time and executes across SAP and connected systems without constant manual intervention. Investments and roadmap conversations are happening. SAP Cloud ERP has the capabilities, and the SAP Production Planning (PP) module continues to evolve.
But according to Redwood Software’s “Manufacturing AI and automation outlook 2026,” roughly 98% of manufacturers are exploring or preparing for AI-driven automation, whereas only about 20% consider themselves fully prepared to execute on it.
The destination is there, but the path hasn’t been cleared. Here’s what’s in the way.
1. Production data is still fragmented across systems
SAP production planning is only as accurate as the inputs feeding it. Demand signals, inventory positions, quality results and MES outputs all need to arrive consistently and on time. In most manufacturing environments, those sources still live in separate systems that weren’t intended to share data automatically.
Around 20% of manufacturers identify a lack of integration across ERP, MES and PLM as a direct bottleneck. That number likely understates the problem, because partial integration — where connections exist but data quality or timing is inconsistent — can be just as limiting as no integration at all. Planning in SAP operates on whatever it can see. When visibility is incomplete, the plan reflects that.
2. Manual exception handling breaks the automation loop
Production rarely runs exactly as planned. Equipment fails, suppliers miss windows and quality deviations surface mid-run. Those disruptions need a response, and right now, for most manufacturers, that response is a person.
Only about 40% of manufacturers have automated exception handling. The other 60% rely on teams to identify, triage and act on disruptions, then manually update the systems involved. That process takes time, creates gaps between what happened and what SAP knows about it and makes closed-loop planning effectively impossible.
If exceptions are the moments that matter most in production, automating around them while leaving the exceptions themselves to manual workflows puts a ceiling on how autonomous your planning can get.
3. Planning cycles are batch-driven, not event-driven
Traditional SAP environments run planning jobs on schedules: nightly MRP runs, periodic capacity updates, batch refreshes of demand data. That made sense when the alternative was manual. It doesn’t make as much sense when production conditions are shifting continuously throughout the day.
A schedule change, a material shortage or a machine coming back online are things that happen in real time. Planning tools that update on a cadence can’t reflect them until the next cycle runs. By then, decisions downstream have already been made on outdated information.
Autonomous planning assumes the system responds to events right when they happen. Getting there requires moving from time-based job scheduling to event-driven orchestration, where a change in one system triggers the right response across all the connected ones, immediately.
4. Forecasting inputs are inconsistent and disconnected
Accurate production planning in SAP starts upstream, with the demand forecasts and signals feeding into it. When those inputs come from disconnected sources, arrive on inconsistent schedules or require manual reconciliation before they’re usable, the planning outputs reflect the same uncertainty.
Roughly 24% of manufacturers cite forecasting accuracy as a major supply chain bottleneck. What’s often behind that number isn’t the forecasting model itself, but the data reaching it. Disconnected demand signals, late updates from commercial systems and cross-functional coordination done via email rather than integrated workflows all degrade forecast reliability before any planning algorithm runs.
You can’t optimize what you can’t trust.
5. Skills and ownership of automation are unclear
Autonomous production planning sits at the intersection of SAP configuration, systems integration, process design and operational knowledge. That’s a lot of ground for any one team to cover, and in practice, it tends to fall awkwardly between IT and operations. It’s not owned by either clearly enough to move fast.
About one-third of manufacturers cite a skills gap in advanced automation technologies as a barrier to progress. This points to organizational structure rather than a lack of talent. When automation initiatives require coordination across multiple teams and knowledge domains, momentum slows. People spend cycles on alignment that could go toward execution. The work that needs to be done is clear; who’s accountable for doing it often isn’t.
This is one of the more underestimated barriers. Technical complexity gets a lot of attention, but organizational complexity doesn’t get enough.
6. Change management feels riskier than the status quo
Production-critical processes carry a particular kind of weight. When something touches the line, the tolerance for disruption is low. That’s a reasonable instinct, and it’s also one of the reasons autonomous planning initiatives stall.
Around 22% of manufacturers cite retraining teams and change management as barriers to adopting new automation approaches. Shifting how planning decisions get made, how exceptions get handled and how workflows are structured touches roles and habits that teams have built over years. Even when the destination is clearly better, the path there feels uncertain.
The organizations making progress have found ways to reduce that perceived risk: starting with contained workflows, building confidence incrementally and showing teams how the changes work before asking them to trust them at scale. Incremental adoption isn’t a compromise. It’s often the only path that actually holds.
7. Perceived integration complexity causes teams to stall
SAP production planning doesn’t operate in isolation. It touches finance, procurement, warehouse management, quality systems and shop floor execution. Making planning more autonomous means those connections need to work reliably, not just for the data going into SAP but for the actions coming out of it.
About 24% of manufacturers cite system integration concerns as a primary barrier to automation progress. That’s not surprising when you consider what the integration surface generally looks like, with multiple SAP modules, third-party platforms, cloud environments and on-premises systems, all of which need to stay in sync as planning decisions cascade through them.
The perceived complexity here often leads to underinvestment. Teams assume the integration work will be costly and disruptive, so they defer it. What they’re deferring is the connective tissue that autonomous planning depends on.
Autonomous planning is closer than it seems
These seven challenges share a common thread. None of them is about SAP being insufficient. And none of them is about AI not being ready. They’re about the data pipelines, production processes and connections — the roads — not yet being ready to support autonomous operations end to end.
Organizations that have worked through these gaps by connecting data sources, automating exception responses, replacing scheduled MRP run cycles with event-driven triggers and clarifying ownership of automation are already seeing measurably better resource utilization and operating at higher levels of automation maturity. When infrastructure catches up to ambition, autonomous production planning stops being a future goal and starts being next quarter’s project.
RunMyJobs by Redwood has been providing this kind of deterministic orchestration infrastructure for manufacturers for years — the event-driven workflows, cross-system coordination and purpose-built SAP integrations that make autonomous planning operationally possible. Think of it as the paving crew that’s already been at work: many Redwood customers are already running production environments where planning responds to real conditions rather than scheduled cycles. The roads to autonomous operations are more built out than most teams realize.
The “Manufacturing AI and automation outlook 2026” examines where manufacturers stand across all of these dimensions: where the gaps are, what separates early adopters from those still in early stages and what the path forward looks like for organizations at different points in the journey.
Download the report to see how other manufacturers are approaching the shift to autonomous, AI-driven operations.