How to accelerate your SAP S/4HANA migration
Why most migrations overrun, the case for a new transformed approach to migration, and what it takes to keep up
A white paper by Valliance
·
Published May 2026
How to accelerate your SAP S/4HANA migration
Why most migrations overrun, the case for a new transformed approach to migration, and what it takes to keep up
A white paper by Valliance
·
May 2026
How to accelerate your SAP S/4HANA migration
Why most migrations overrun, the case for a new transformed approach to migration, and what it takes to keep up
A white paper by Valliance
·
May '26
Executive Summary
Chapters
1 to 7
_Executive Summary
AI is transforming every aspect of enterprise technology. Isn’t it time your S/4HANA migration evolved, too?
Your migration is still likely being run with the same methods that were used twenty years ago. That means manual assessment of hundreds of thousands of lines of ABAP, with sequential testing of integration landscapes that should run in parallel. Data quality issues that only surface in User Acceptance Testing (UAT) because no tool was profiling the full estate from day one, and point-in-time status reports built by the same advisory firm billing you to keep the programme running.
These methods work. They have delivered every SAP migration that has ever been completed. But they have also produced one of the worst track records of any major enterprise programme category. More than nine in 10 miss their schedule, programmes run 30% over time on average, and most exceed budget.
AI fundamentally changes this. With Palantir Foundry as the data layer, AIP as the AI layer, and the Ontology providing a single semantic model that spans ECC, S/4HANA and the surrounding application estate, discovery, mapping, transformation and validation run continuously and concurrently rather than in sequence. The Ontology built during the migration persists afterwards as the operational layer the business runs on.
This leads to true timeline compression:
Compression on ABAP remediation.
Panaya auto-fixes up to 98% of the routine syntax and table-structure changes needed to make custom code S/4HANA- compatible
70–98%
Compression of data mapping.
The Ontology eliminates the “paper mapping” exercise that has historically consumed the majority of explore-phase efforts.
80–90%
Compression of data mapping.
The Ontology eliminates the “paper mapping” exercise that has historically consumed the majority of explore-phase efforts.
80–90%
Compression in reconciliation and validation.
Continuous error surfacing with full lineage replaces end-gate validation.
60–80%
Compression in reconciliation and validation.
Continuous error surfacing with full lineage replaces end-gate validation.
60–80%
Accuracy of reconciled records within hours of starting a migration.
96%
Accuracy of reconciled records within hours of starting a migration.
96%
A programme is only as fast as its slowest workstream. The technical stages can compress, but the migration only fully compresses when the consultancy delivering is AI-native.
Valliance extends the same model into the human-paced workstreams.
End-users are mapped to functions, processes and data domains in the Ontology. Training audiences are identified automatically.
Communications are targeted by what is actually changing for each group. UAT is segmented by role.
The output of every design workshop flows straight into the technical pipeline.
The combined effect across technical and consulting workstreams is what compresses the whole programme by 70%. What’s more, (see Section 4 for a full breakdown of how this compression compounds across programme waves), the value continues to compound after go-live, in the most-overlooked stages of every SAP programme:
Evolution: the painful backlog of change requests that should make the implementation actually work for the business. This rarely lands on time, and the opportunity cost is bigger than the direct cost. Every quarter that change requests are deferred is a quarter the business runs on a less-capable system than the one it replaced.
Reintegration: the replumbing of analytics, BI, planning and the hundreds of connected applications that depend on the core. This is where most migration tooling is decommissioned, and where the next £50m of post-go-live spend lives. A Foundry-led migration arrives at both stages with the operational layer already in place; AI use cases are deployable in days rather than months, and the audit trail that proves the migration was complete and accurate (the evidence auditors look for on any major systems change) is generated as a by-product. This is where the lifetime compression claim earns its keep.
The 2027 deadline is the same for everyone. What differs is whether you meet it by paying in full for a manual migration, or whether you save 70% of the cost of the technical programme by reducing the timeline using AI, freeing up the rest of your budget to invest in change management.
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The 2027 deadline is not the most important consideration
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The 2027 deadline is not the most important consideration
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The 2027 deadline is not the most important consideration
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The 2027 deadline is not the most important consideration
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The 2027 deadline is not the most important consideration
SAP ends mainstream maintenance for ECC in December 2027. But behind the date, the more pertinent numbers define the overall migration timeline. A technical S/4HANA migration of any complexity requires 18 to 24 months of focused execution, followed by a stabilisation period before the business can operate confidently on the new core. For an enterprise that has not started, the window for a controlled, well-executed programme is already narrow. For an enterprise that is mid-flight and behind schedule, which is most of them, the runway is shorter still.
SAP has extended the maintenance deadline three times already and it may well move again. Extended maintenance is also available at a price. But that price amounts to significant multiples of standard support without any functional improvement, while the underlying system continues to age.
The wider context adds further challenges. With most large enterprises now in active migration, demand for experienced S/4HANA architects, data migration specialists, finance leads, testing teams and cutover engineers is running well ahead of supply.
30–50%
The expected rise in consulting rates for experienced S/4HANA resources across 2026–27, as demand approaches three times the available supply. Source: The Silicon Partners, Why 2026 is the Decision Year, April 2026. Based on 250+ SAP transformation programmes.
While the migration is running, the operational cost of the interim state quietly grows. Phased rollouts (the norm in any large enterprise) create extended periods during which ECC and S/4HANA operate in parallel. Master data, pricing conditions, bills of material and reporting estates have to be maintained in both systems. Reconciliation between them becomes a continuous operational expense, routinely adding millions in annual IT and business costs during the rollout window. This is all money that does not appear in the original business case, because it sits between programmes.
The real question for any board, CIO or CFO evaluating their S/4HANA position is not whether the deadline will move, but whether the chosen approach will compress the timeline enough to land cleanly, reduce dependence on increasingly expensive specialist labour, and contain the interim-state cost while the work is being done. If the answer is no, it’s time to change how the programme is run.
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Why do so many SAP S/4HANA migrations fail?
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Why do so many SAP S/4HANA migrations fail?
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Why do so many SAP S/4HANA migrations fail?
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Why do so many SAP S/4HANA migrations fail?
Too many SAP migrations are still running on legacy migration methods, when AI can drastically improve outcomes. Linear, stage-gated programmes have delivered every SAP migration that has ever completed, yet independent research and industry surveys consistently report that fewer than one in 10 S/4HANA migrations finish on schedule. Plus, overruns of 30% are the norm rather than the exception, and the majority exceed budget. This challenge is structural, rather than being primarily technical.
This section sets out three reasons for failure, showing how AI technology can be used to rectify the problem.
2.1 Most enterprises are exploring at least two approaches
Different parts of an enterprise estate need different approaches for migration. As such, most large organisations will end up exploring at least two, sometimes more. These approaches can be categorised as follows:
Greenfield programmes
Deliberate process re-engineering. The legacy system has been customised and configured to such an extent that any level of technical upgrade is simply impossible.
The answer is to redesign fresh on top of S/4HANA, with a big focus on keeping a clean core. A representative pilot is taken first, usually covering multiple operating entities that are neither the largest or smallest. The clean-core blueprint can then be rolled out, however it’s likely that different versions of that blueprint will still be required. Every new jurisdiction, joint-venture accounting requirement, and regulatory variation, will bend the template a little further.
The largest greenfield programmes in oil and gas, FMCG and industrial manufacturing have spent in excess of $500 million and are still working through their first wave of operating entities. While there’s a standard template for these programmes, it should be considered a starting point, rather than as the end state.
Brownfield programmes
A forced migration or upgrade, which usually occurs in one of two ways.
The first is a manual root-and-branch review of every line of custom code and every variant of standard transaction, with a programme of decustomisation that gets the system to a state where the technical upgrade can run cleanly.
The second is the SLO / SNP-style forced technical upgrade that pushes structural change at the kernel and base configuration level, dragging the system across to the new platform. The business process logic is preserved, but the technical foundation is replaced.
Brownfield is cheaper than greenfield, but the cutover is a big-bang event with very high stakes.
Selective Data Transition (SDT)
This is a brownfield programme with surgical greenfield characteristics.
Certain modules, master data structures, and business areas are redesigned during the technical upgrade. Master data structures for profit centre, account, and functional areas are realigned as part of the move.
The complexity is higher, but the optionality is greater.
RISE with SAP
This is the most controversial option. SAP takes full responsibility for hosting, infrastructure and the upgrade cycle, resulting in a standardised footprint.
It is often the right path for smaller, simpler organisations with less complex needs, or for a divisional unit within a larger group that can be carved out cleanly. The largest, most complex enterprises are unlikely to be on RISE in the near term, but individual divisional units within those enterprises may well be.
In any large organisation, there will be appetite for at least two of these approaches across different parts of the estate.
Enterprises often start with a greenfield approach, moving to brownfield at least in part, following complications, and are now finding the RISE pitch increasingly appealing for its simpler edges. A single-mode acceleration story (one that talks only about greenfield, or only about brownfield, or only about RISE) does not describe the reality, and neither does a single-mode delivery model.
2.2 The eight stage gates of an SAP migration: what really happens
The stereotypical perception of an SAP migration is split into six stages, but the reality is that there are eight. The eighth is hidden, and so too is the cost.
Stage 1, Discovery:
Understanding the legacy estate.
Most ECC systems have accumulated decades of customisation, custom ABAP code, undocumented business logic and master data scattered across modules with inconsistent definitions. Information is held in data dictionaries, integration documentation stored in PDFs, requirements captured in spreadsheets and compliance frameworks held in regulatory documents.
It takes months of SME interviews, manual documentation review and reverse engineering, producing an output with quality that varies depending on the patience of the team that did the work.
Stage 2, Business Case and Architecture:
The blueprint for migration.
The promises made at this stage almost always get compromised. In reality, the blueprint that is approved at month three usually bends by month fifteen, when the first operating entity exposes the customisation that the global template ignored. Everyone privately knows the timeline will slip, and the cost estimate ends up being revised at the first steering committee after go-live.
Stage 3, Design and Build:
Blueprint becomes reality, with challenges frequently ignored.
The clean core target comes up against the customisations that were used for the last 25 or 30 years, which were there for a reason. While some of those reasons will no longer be valid (whereby the customisations can be retired), many still will be, so the design will have to bend to accommodate them.
This is the phase when tricky feature requests are often put on hold, while a deferred requests list quietly grows.
Stage 4, Data Migration:
Data transfer takes place, and previously ignored problems come home to roost.
Everything that was missed in discovery, every customisation that was deferred in design and master data quality issue that the original ECC system was tolerating, surfaces here.
Cross-module relationships break as reference data fails to resolve. Period balances do not reconcile.
Each defect triggers an investigation that necessitates a walk back through the pipeline by a different team to the one that led the original transformation. Upstream data refreshes and the cycle restarts, delaying the process by weeks.
Stage 5, Testing and QA:
Six months of upstream optimism becomes six weeks of unplanned remediation.
User Acceptance Testing (UAT) identifies defects that should have been caught in unit testing; integration tests surface dependencies that should have been documented in design, and performance tests reveal architectural decisions that should have been challenged in the business case.
Each of these would be manageable on its own, but together, they consume the float that the programme plan never really had to begin with.
Stage 6, Cutover and Go-Live:
The highest-stakes moment in the organisation’s year.
or a brownfield programme delivering a major global ERP, the cutover takes the system down for 72 hours or more. During that window, the business grinds to a standstill, with every action that would have been scheduled through the system being put on hold. The team needs the data to be right the first time, or businesses risk massive disruption.
If reconciliation fails, the attempt is scrapped. A second is scheduled two or three months later, with another £20m to £30m on the price tag, confidence reduced, and risk heightened.
Stage 7, Hypercare and Optimisation:
The deferred action list comes back to bite.
The list that was growing through design and build now arrives in production. The deferred customisations and workarounds the business has been promised all arrive with the operations team in the months after go-live. Each one comes with a business case, a design change, a regression test cycle and a cost, and solving all of them together, can cost multiples of the original implementation.
Stage 8, Evolution and Reintegration:
The stage every vendor framework leaves out.
Evolution is the painful backlog of change requests that are needed to make the implementation actually work for the business.
These are the functions that were shelved during design, promised at hypercare, and which are never delivered on time. The direct cost of implementing these often mounts to tens of millions across the post-go-live years, but the opportunity cost is much higher. Every quarter these change requests are deferred is a quarter the business runs on a less-capable system than the one it replaced. As a quick fix, SMEs invent workarounds and manual processes proliferate. The new system becomes a bottleneck, and the benefits case that justified the original business case slips, quarter by quarter.
Reintegration is the replumbing of everything around the core. As underlying ERP structures are changed, ACDOCA tables are moved, and different S/4HANA structures are transferred to... all data warehousing, reporting and analytics platforms need to be replumbed as a result. Everything must be assessed, redesigned, rebuilt, retested and re-deployed.
That means every reporting estate that was built on top of the old data model: every business warehouse, analytics platform, planning system and compliance tool that depends on data flowing out of the ERP, not to mention the 1,000 connected application portfolio data sets. It also means every one of the hundreds, sometimes thousands, of connected applications that fed the old system and were fed by it.
For most large enterprises, this is a second big investment bucket queueing behind the first, which is often discovered halfway through the original programme and quietly costed into a separate budget line so it does not show up against the headline migration number.
Together, evolution and reintegration are where the next £50m to £100m of post-go-live spend lives. Most migration tooling is decommissioned before it gets there and the data context built during the migration is thrown away, with lessons learned scattered across people who are now back in their day jobs.
2.3 The wheel still goes nowhere.
Running faster does not change the destination.
Where AI is applied to migration programmes, it is typically bolted on at individual stages of the linear pipeline, for example AI-assisted ABAP code interpretation; AI-augmented data mapping; AI-generated test cases, or AI-driven documentation.
While each of these examples can be useful at the point applied, delivering measurable acceleration on its specific workstream, the overall structural problem is not addressed, so the track record remains unchanged.
A linear pipeline accumulates errors, with defects at every stage propagated forward and compounded. AI that compresses one stage from twelve weeks to six weeks moves the start of the next stage by six weeks, but it doesn’t eliminate the rework cycle that begins when validation fails at the end of the pipeline and the painstaking work to find the origin of the defect begins. The handoff between teams remains, with the context built by one team failing to transfer to the next, and the data model still differing between tools.
This is the hamster wheel problem. The wheel might run faster – the pipeline is more efficient in its individual stages – but the hamster is the same, i.e. the same cumulative outcome is achieved.
The fix is not faster stages; rather, the whole pipeline must be re-wired, with context preserved across the whole lifecycle. Validation becomes continuous and manageable, rather than being a high-stakes, all-or-nothing event.
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How AI changes the numbers
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How AI changes the numbers
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How AI changes the numbers
03
How AI changes the numbers
03
How AI changes the numbers
An AI-led migration approach with a central brain coordinating six specialised arms.
The solution to these migration challenges requires comprehensive contextual awareness maintained across the entire migration lifecycle, with specialised AI capability deployed where each stage actually needs it. This approach, realised through Palantir’s Octopus model, fundamentally changes the entire programme.
Independent ‘arms’, each capable of autonomous neural processing, are coordinated by a central ‘brain’, that maintains the full picture.
This approach has three integrated components:
The brain takes shape as an AIP Hivemind, an application that holds the migration’s unified context, and coordinates work across each of the six arms.
Each context piece is held by specific platform capabilities:
Legacy structures held as source datasets and virtual tables, with Data Lineage providing the cross-pipeline view;
Target requirements held in the Foundry Ontology, which names entities in business terms (Material, BusinessPartner, JournalEntry) independent of the source SAP tables;
Business rules held in Health Checks and Data Expectations at the pre-Ontology layer, and in AIP Logic and Foundry Functions at the Ontology layer;
Compliance standards held as Ontology objects and in Notepads alongside the data;
Delivery goals held as Ontology objects (Workstream, MigrationGate) so the same context covers both what is being migrated and when each piece must land.
The Ontology is the layer that matters most for an SAP migration.
It is the semantic layer that maps data from underlying systems into business objects with explicit relationships, properties and the actions that can be performed on them.
The Ontology is not a static data model. Instead, it supports both semantic elements (the nouns: object types, properties, link types) and kinetic elements (the verbs: actions, functions, governed permissions). It can be populated from ECC data, from S/4HANA data, or critically, from both at once. During the interim-state period of any phased migration, the Ontology sits across both systems, providing a single canonical view that downstream consumers (reporting, analytics, planning, compliance) read from. The dual-maintenance burden that typically dominates phased rollouts collapses into a design choice.
HyperAuto V2 is the SAP-specific accelerant.
HyperAuto offers out-of-the-box understanding of the standard SAP data model, including table relationships, schema mapping, and pre-canned cleansing transforms (null handling, type fixes, whitespace) for common objects (MARA, LFA1, KNA1, BSEG, EKKO). HyperAuto V2 is optimised for SAP migration specifically, supporting both ECC and S/4HANA with or without an SLT Replication Server. It removes the need for most of the bespoke pipeline work that’s usually required for anything that fits the standard SAP shape.
The Octopus arms map directly to the stages of a migration:
Arm 1 - Data Understanding: connects to sources, interprets schemas, parses legacy documents and data dictionaries.
Arm 2 - Code Interpretation: translates legacy ABAP and custom code to extract business logic, producing a structured Z-inventory that classifies every routine (retire, port, or refactor) with rationale.
Arm 3 - Transformation: maps legacy values to new standards, remapping hierarchies and organisational structures, with the heavy lifting in Pipeline Builder for expert-led work and in PySpark transforms for migration-specific logic.
Arm 4 - Validation: continuous validation metrics and real-time error identification, with failed records carrying a structured reason code surfaced via an Ontology property (reconciliation_status: RECONCILED | DELTA | UNRESOLVED).
Arm 5 - SME Interfaces: custom Workshop applications, conversational analytics through AIP Analyst, SME-authored business rules through AIP Logic.
Arm 6 - Upload and Execution: writes prepared data to S/4HANA via action webhooks for real-time push, scheduled exports for batches, BAPI, OData services, or LTMC staging tables. Foundry maintains live connections to both legacy and target systems through the parallel-run period, so business operations don’t stop on cutover weekend.
The whole thing is operated by AI FDE, Palantir’s AIP-powered agent that does the engineering work across all arms on behalf of a human Forward Deployed Engineer. AI FDE reads Data Lineage, writes transforms, edits the Ontology, drafts rules and validates its own changes against continuous integration checks. It also proposes work on a Global Branch or in a pull request. Decision making is retained by the human; the human FDE reviews the technical approach, and the affected SME reviews the business impact, before the change merges.
Supportability - There are two important things to note in relation to supportability:
First, the Foundry Connector 2.0 for SAP Applications is an SAP-certified add-on, installed via SAINT on the SAP application server. It supports SAP_BASIS 7.4 SP5 and above, covering both ECC and S/4HANA, and accesses data through SAP’s native application logic rather than at the database layer. Authorisations are governed by SAP user permissions, which can’t be bypassed. System load is checked before extraction begins, and SAP Basis teams can install and govern the connector through their existing processes.
Second, SAP and Palantir announced a formal joint engineering partnership in 2025. The partnership commits both companies to maintaining HyperAuto as the primary connector across the ECC to SAP Cloud ERP Private transition, and to deepening interoperability between SAP Business Data Cloud and Palantir’s Ontology and AIP. This will also support customers on the RISE with SAP programme,
These points mean that the model described here is not a workaround that requires customers to take an aggressive position relative to their ERP vendor. Instead, it sits inside the supported envelope of the SAP estate.
The Octopus model
An AI-led migration approach where the central 'brain' coordinates six specialised 'arms' - Valliance / Palantir May 2026
AI FDE
Operates Foundry across all six arms on the human FDE's behalf
Human always keeps control of decisions.
Worked Example
€184k reconciliation gap
€184k reconciliation gap
Traditional - 1-2 weeks
Octopus - 85 minutes
The Octopus model. Central AIP Hivemind brain holding the unified context (legacy structures, target requirements, business rules, compliance, delivery goals). Six radiating arms each labelled with stage of migration and key Foundry / AIP capabilities.
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Acceleration in practice, and where it stops
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Acceleration in practice, and where it stops
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Acceleration in practice, and where it stops
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Acceleration in practice, and where it stops
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Acceleration in practice, and where it stops
ABAP estate, data quality, and integration landscape, can all be assessed simultaneously, dramatically speeding up tech migration and reducing cost by as much as 70%.
The 70% claim listed above is a bold assertion that deserves explanation. This compression has four layers.
| Layer | What it is | Realistic compression |
|---|---|---|
| 1. Per-workstream technical | Specific technical activities where AI is genuinely transformative, including ABAP remediation (Panaya), data mapping (Ontology), validation and reconciliation (Palantir AIP). | 60–98% |
| 2. First-wave whole programme | A whole programme on its first wave, anchored by the activities that remain human-paced, such as operating model decisions, business sign-off, UAT, training, the SAP technical migration window itself. | 30–50% |
| 3. Multi-wave compounding | As the programme rolls forward, gains feed each other. Better discovery in wave one produces a cleaner Ontology for wave two and cleaner data feeds shorter testing, with shorter testing feeding tighter cutover. | ~70% |
| 4. Lifetime, including post go-live | Most enterprises evaluating an approach like this one are not starting from a clean sheet. They are two years into a programme and twelve months from a target go-live they no longer believe in, looking at a cutover window with anxiety. The common reaction to the case set out above is often to think a change would have been useful eighteen months ago, but is too late now. | Better than 70% |
Walking through the same eight stage gates, here is where the compression actually shows up:
| Stage | Per-workstream technical | First-wave whole programme | Notes |
|---|---|---|---|
| 1. Discovery | 60–75% | ~47% | AIP interpretation AI ingests all dictionaries, PDFs, and requirements docs at once. Months of SME interviews become weeks of structured profiling. |
| 2. Business Case & Architecture | 40–55% | ~38% | Scenario modelling, sizing, and impact analysis accelerates. Strategic decisions remain with human team members. |
| 3. Design & Build | 70–98% | ~60% | The biggest absolute saving in the programme, with Panaya on routine code, and Ontology on mapping. |
| 4. Data Migration | 75–90% | ~57% | Continuous validation replaces end-gate validation, with >96% accuracy within hours. |
| 5. Testing & QA | 40–55% | ~27% | UAT doesn’t compress, but defect-to-fix loop does, because impact analysis comes from lineage. |
| 6. Cutover & Go-Live | 30–45% | ~37% | Modest on time, the SAP DMO engine sets the floor, dramatically reducing the level of likely risk. |
| 7. Hypercare & Optimisation | 60–70% | ~47% | Full lineage means root cause analysis is instant. Smaller team, shorter period. |
| 8. Evolution & Reintegration | Structural | Structural | Value multiplication rather than compression. The Ontology stays constant, with change requests prioritised by simulated business impact and replumb starting from a working model. |
It’s worth exploring some of the above rows in more detail:
Stage 3, Design and Build. The biggest absolute time saving sits here.
ABAP remediation compresses 70%-98%. Panaya deployed for the routine syntax and table-structure updates needed to make custom code S/4HANA-compatible, CeleRITE and smartShift at 70%-80%. SAP Joule covers 40%-60% of standard ATC issues.
Data mapping compresses 80%-90% through the Ontology, which eliminates the paper-mapping exercise. Mock migration cycles compress because continuous validation surfaces errors at once with full lineage, rather than after each end-to-end run. For a complex brownfield programme, what was 28 weeks halves, becoming 10-14.
Stage 6, Cutover. Modest on time reduction but transformative in terms of managing risk.
Palantir’s automated reconciliation runs continuously with full lineage, surfacing discrepancies in real time rather than discovering them in days of manual spreadsheet comparison. The Go / No-Go decision is based on a programme health score derived from data, rather than on the opinion of whoever is most confident in the room. Cutover compression on the headline number is 30%-45%, but reduction in the risk of a failed go-live is the case that matters most, potentially eliminating £20m-£30m in re-attempt costs.
Stage 8, Evolution and Reintegration. The evolution backlog is prioritised based on simulated business impact rather than political weight.
A proposed change request can be modelled against the Ontology, the affected entities and processes identified, with the cost-benefit articulated against the actual estate rather than being based on estimates. Change requests are therefore delivered with AI assistance and enabled by the persistent Ontology. The opportunity-cost gap (the period during which the business runs on a less-capable system than before) closes in weeks rather than years, and the reintegration scope (analytics, BI, planning, the connected-application ecosystem) reuses the existing Ontology rather than rebuilding context from scratch.
This is where the lifetime compression claim earns its keep, and where the case for not decommissioning the migration tooling at go-live becomes obvious.
A worked example: 85 minutes versus two weeks
The capability is easier to see in an example than in a heatmap.
In the following instance, at week 8 of the S/4HANA migration, reconciliation surfaces a €184,000 gap on one cost centre between the source ECC balance and its transformed equivalent.
| Time | Step |
|---|---|
| 00:00 | SME opens the failing cost centre in the reconciliation app. 412 JournalEntry objects are displayed with their reconciliation status. |
| 00:05 | Filtering unreconciled entries shows 4 of the 412 failing. All four share the same posting key. |
| 00:30 | The human FDE asks AI FDE why these rows are excluded from the ACDOCA aggregation. AI FDE runs the transform code and the data lineage, and identifies an outdated filter clause introduced two weeks earlier. |
| 01:00 | AI FDE recommends a fix, which the human FDE approves. AI FDE applies the change on a Global Branch, runs the transform on branch data only, and opens a pull request. |
| 01:15 | Impact analysis reports show 4 JournalEntry objects changed, 1 cost centre affected, and the period balance shifts by exactly €184,000. |
| 01:20 | SME reviews and approves the pull request and branch merges to main. Audit trail records both approvals. |
| 01:25 | The reconciliation app shows the cost centre as reconciled. The workstream’s rolled-up reconciliation rate ticks up on the migration health dashboard, with both updating automatically as data lineage propagates the change downstream. |
Total - roughly 85 minutes.
In a traditional migration, the equivalent diagnostic typically takes one to two weeks.
The point is not the time, but that the entire diagnostic loop, from defect surfacing to root cause to fix, to impact analysis, to approval, to audit trail, runs inside a single coherent context. This is all without the need for a team to restructure what another did before it, or for a data refresh in between, so the cycle does not need to restart from scratch.
But the architecture is only half the story
Walk back through the stage-gate table and a pattern is obvious. The technical stages (ABAP, mapping, validation, reconciliation, lineage-based diagnostics) compress dramatically. But the stages where humans drive the work (operating model decisions, design workshops, UAT business sign-off, business owner confirmation at cutover, change management and training in hypercare and evolution) barely move at all in the analysis above. Whatever is not accelerated becomes the new bottleneck.
If the platform compresses ABAP remediation from 12 months to six weeks, but training audiences are still being mapped in spreadsheets, and design workshop outputs are still feeding into the technical pipeline three weeks later, the programme timeline is set by what did not compress. If reconciliation surfaces a discrepancy in 85 minutes but the relevant business owner is not in the same Ontology as the data, and the comms team takes a week to figure out who needs to know, the cycle is slower still. The compounding effect (better discovery feeding cleaner data migration feeding shorter testing feeding tighter cutover) only happens if the entire programme is moving at the same pace.
AI has to span every part of your programme – both internally and in terms of your external suppliers and consultants.
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Is it too late for us?
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Is it too late for us?
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Is it too late for us?
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Is it too late for us?
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Is it too late for us?
Most enterprises evaluating an approach like this one are not starting from a clean sheet. They are two years into a programme and twelve months from a target go-live they no longer believe in, looking at a cutover window with anxiety. The first reaction to the case set out above is often that it would have been useful eighteen months ago but is too late now.
But that’s not the case.
The minimum value of a contextual architecture on a programme that is already in flight is risk reduction. Whatever stage you are currently in, every stage gate that still lies ahead has acceleration potential.
ABAP remediation work that is still in progress can be re-prioritised against actual estate data rather than initial estimates. Data quality issues that have been surfacing through the programme can be modelled against the live transformation pipeline to predict which ones will fail at cutover. Integration testing that is still running sequentially can be re-architected to run in parallel.
Palantir can be deployed mid-programme, into the workstreams that have not yet been completed, and the gains accrue from that point forward. Programmes that have been running for two years and are still twelve months from go-live have recovered months of timeline within weeks of Palantir deployment.
The more substantive value is wave-on-wave compounding. Programmes that are 5% through their rollout (a handful of operating entities live out of hundreds) have most of the programme still to come. The next 95% can run materially faster because the lessons learned from the first wave can be embedded in the Ontology. The customisations that worked, the integrations that broke, and the data quality issues that the first business unit revealed are all embedded and applied to every wave that follows.
Even when a provider is committed to a specific delivery partner or timeline, the case for an independent assessment is strong. The output is a clear, data-grounded picture of where the existing programme will reach each problem, and what each problem will cost the organisation in cutover risk and evolution-stage opportunity cost.
This is something your incumbent consultancy cannot produce.
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Why Valliance
06
Why Valliance
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Why Valliance
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Why Valliance
06
Why Valliance
AI delivers vast improvements, but the architecture only delivers its full value when three things sit in the same delivery team:
AI-native consulting that operates at AI pace across both technical and consulting workstreams.
Deep SAP migration domain expertise across all four migration approaches and understanding of the in-flight reality most enterprises are living.
A team with real Palantir experience.
Several major consultancy firms have acquired Palantir capability, yet they have not systematically deployed it for clients because SAP migration is a significant revenue line, estimated to represent up to 10% of global revenues at the largest firms. Palantir compresses the technical migration timeline by 70% - and when their business model is based on charging for hours, there’s no incentive to speed processes up.
Valliance is structured differently. We are an AI-native team with deep SAP and Palantir expertise, with teams that deeply understand how to leverage the latter’s market leading Octopus architecture for data migrations of all kinds. We give you an independent assessment of your migration, and our work is structured so the majority of our fees are paid only on successful technical migration. We have no interest in creating complexity. We have every interest in removing it.
When we tell you that your ABAP estate is 40% larger than documented, or that three of your integrations carry critical cutover risk, or that your data quality issues will blow your go-live window, we are telling you because the data shows it, not because it extends our engagement.
Every recommendation we make is oriented toward the same outcome you are working toward: a clean technical migration, completed correctly, as fast as possible.
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What to do next
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What to do next
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What to do next
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What to do next
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What to do next
An independent assessment
Wherever you are in your migration we will produce a data-grounded view of your estate, covering: ABAP volume and remediation profile; integration count and parallel-test readiness; data quality baseline; cutover risk; evolution backlog and opportunity cost. This assessment will show what a realistic timeline looks like – with and without AI, and if you are mid-flight, the specific points at which Palantir can be dropped into the existing programme, with the recovery that can be achieved from there.
The assessment is delivered by an AI-native team, handpicked according to the skills you need for success.
A bootcamp
If your team wants to skill up on Foundry and AIP for SAP migration before committing to anything, we run an intensive bootcamp on the Octopus model. The brain, the six arms, AI FDE, the Connector, HyperAuto V2, the SAP-Palantir joint engineering partnership, and the worked examples that show what the model actually does in practice.
Either way, the conversation that follows is the one that matters. Whether the next migration you do or the one you are already in the middle of is delivered the way the previous generations were, or whether it earns back the next fifteen years of operational advantage.
It’s time to start the conversation - hello@valliance.ai
Let’s put AI to work.
This paper draws on Palantir’s published technical material on AI-accelerated migration; Valliance’s own published article “Accelerating SAP Migrations with Foundry and AIP” (Tigue & Parvin, May 2026); independent research from Horváth and other industry sources on SAP migration outcomes; the Valliance four-pillar positioning (“Valliance is for leaders building the future”); and direct SAP migration delivery experience across Greenfield, Brownfield, SDT and RISE programmes in oil and gas, FMCG, industrial manufacturing, and retail sectors. Specific quantitative claims about per-stage acceleration are sourced from Palantir, Panaya, Applexus, and SAP Joule benchmarks where indicated, and should be tested against any given organisation’s data complexity rather than treated as universal.
We can help you compress the technical migration that typically consumes years, with overall cost cut by as much as 70%.
We can help you compress the technical migration that typically consumes years, with overall cost cut by as much as 70%.
_Related thinking
_Related thinking
_Related thinking
_Related thinking
Future Enterprise
Ontologies
Future Enterprise
Ontologies
Aug 23, 2026
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5 Mins
Aug 23, 2026
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5 Mins
Aug 23, 2026
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5 Mins
Let’s put AI to work.
Copyright © 2026 Valliance. All rights reserved.
Let’s put AI to work.
Copyright © 2026 Valliance. All rights reserved.
Let’s put AI to work.
Copyright © 2026 Valliance. All rights reserved.
How to accelerate your SAP S/4HANA migration
Why most migrations overrun, the case for a new transformed approach to migration, and what it takes to keep up
A white paper by Valliance
·
Published May 2026
How to accelerate your SAP S/4HANA migration
Why most migrations overrun, the case for a new transformed approach to migration, and what it takes to keep up
A white paper by Valliance
·
May 2026
How to accelerate your SAP S/4HANA migration
Why most migrations overrun, the case for a new transformed approach to migration, and what it takes to keep up
A white paper by Valliance
·
May '26
Executive Summary
Chapters
1 to 7
_Executive Summary
AI is transforming every aspect of enterprise technology. Isn’t it time your S/4HANA migration evolved, too?
Your migration is still likely being run with the same methods that were used twenty years ago. That means manual assessment of hundreds of thousands of lines of ABAP, with sequential testing of integration landscapes that should run in parallel. Data quality issues that only surface in User Acceptance Testing (UAT) because no tool was profiling the full estate from day one, and point-in-time status reports built by the same advisory firm billing you to keep the programme running.
These methods work. They have delivered every SAP migration that has ever been completed. But they have also produced one of the worst track records of any major enterprise programme category. More than nine in 10 miss their schedule, programmes run 30% over time on average, and most exceed budget.
AI fundamentally changes this. With Palantir Foundry as the data layer, AIP as the AI layer, and the Ontology providing a single semantic model that spans ECC, S/4HANA and the surrounding application estate, discovery, mapping, transformation and validation run continuously and concurrently rather than in sequence. The Ontology built during the migration persists afterwards as the operational layer the business runs on.
This leads to true timeline compression:
Compression on ABAP remediation.
Panaya auto-fixes up to 98% of the routine syntax and table-structure changes needed to make custom code S/4HANA- compatible
70–98%
Compression of data mapping.
The Ontology eliminates the “paper mapping” exercise that has historically consumed the majority of explore-phase efforts.
80–90%
Compression of data mapping.
The Ontology eliminates the “paper mapping” exercise that has historically consumed the majority of explore-phase efforts.
80–90%
Compression in reconciliation and validation.
Continuous error surfacing with full lineage replaces end-gate validation.
60–80%
Compression in reconciliation and validation.
Continuous error surfacing with full lineage replaces end-gate validation.
60–80%
Accuracy of reconciled records within hours of starting a migration.
96%
Accuracy of reconciled records within hours of starting a migration.
96%
A programme is only as fast as its slowest workstream. The technical stages can compress, but the migration only fully compresses when the consultancy delivering is AI-native.
Valliance extends the same model into the human-paced workstreams.
End-users are mapped to functions, processes and data domains in the Ontology. Training audiences are identified automatically.
Communications are targeted by what is actually changing for each group. UAT is segmented by role.
The output of every design workshop flows straight into the technical pipeline.
The combined effect across technical and consulting workstreams is what compresses the whole programme by 70%. What’s more, (see Section 4 for a full breakdown of how this compression compounds across programme waves), the value continues to compound after go-live, in the most-overlooked stages of every SAP programme:
Evolution: the painful backlog of change requests that should make the implementation actually work for the business. This rarely lands on time, and the opportunity cost is bigger than the direct cost. Every quarter that change requests are deferred is a quarter the business runs on a less-capable system than the one it replaced.
Reintegration: the replumbing of analytics, BI, planning and the hundreds of connected applications that depend on the core. This is where most migration tooling is decommissioned, and where the next £50m of post-go-live spend lives. A Foundry-led migration arrives at both stages with the operational layer already in place; AI use cases are deployable in days rather than months, and the audit trail that proves the migration was complete and accurate (the evidence auditors look for on any major systems change) is generated as a by-product. This is where the lifetime compression claim earns its keep.
The 2027 deadline is the same for everyone. What differs is whether you meet it by paying in full for a manual migration, or whether you save 70% of the cost of the technical programme by reducing the timeline using AI, freeing up the rest of your budget to invest in change management.
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The 2027 deadline is not the most important consideration
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The 2027 deadline is not the most important consideration
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The 2027 deadline is not the most important consideration
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The 2027 deadline is not the most important consideration
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The 2027 deadline is not the most important consideration
SAP ends mainstream maintenance for ECC in December 2027. But behind the date, the more pertinent numbers define the overall migration timeline. A technical S/4HANA migration of any complexity requires 18 to 24 months of focused execution, followed by a stabilisation period before the business can operate confidently on the new core. For an enterprise that has not started, the window for a controlled, well-executed programme is already narrow. For an enterprise that is mid-flight and behind schedule, which is most of them, the runway is shorter still.
SAP has extended the maintenance deadline three times already and it may well move again. Extended maintenance is also available at a price. But that price amounts to significant multiples of standard support without any functional improvement, while the underlying system continues to age.
The wider context adds further challenges. With most large enterprises now in active migration, demand for experienced S/4HANA architects, data migration specialists, finance leads, testing teams and cutover engineers is running well ahead of supply.
30–50%
The expected rise in consulting rates for experienced S/4HANA resources across 2026–27, as demand approaches three times the available supply. Source: The Silicon Partners, Why 2026 is the Decision Year, April 2026. Based on 250+ SAP transformation programmes.
While the migration is running, the operational cost of the interim state quietly grows. Phased rollouts (the norm in any large enterprise) create extended periods during which ECC and S/4HANA operate in parallel. Master data, pricing conditions, bills of material and reporting estates have to be maintained in both systems. Reconciliation between them becomes a continuous operational expense, routinely adding millions in annual IT and business costs during the rollout window. This is all money that does not appear in the original business case, because it sits between programmes.
The real question for any board, CIO or CFO evaluating their S/4HANA position is not whether the deadline will move, but whether the chosen approach will compress the timeline enough to land cleanly, reduce dependence on increasingly expensive specialist labour, and contain the interim-state cost while the work is being done. If the answer is no, it’s time to change how the programme is run.
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Why do so many SAP S/4HANA migrations fail?
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Why do so many SAP S/4HANA migrations fail?
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Why do so many SAP S/4HANA migrations fail?
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Why do so many SAP S/4HANA migrations fail?
Too many SAP migrations are still running on legacy migration methods, when AI can drastically improve outcomes. Linear, stage-gated programmes have delivered every SAP migration that has ever completed, yet independent research and industry surveys consistently report that fewer than one in 10 S/4HANA migrations finish on schedule. Plus, overruns of 30% are the norm rather than the exception, and the majority exceed budget. This challenge is structural, rather than being primarily technical.
This section sets out three reasons for failure, showing how AI technology can be used to rectify the problem.
2.1 Most enterprises are exploring at least two approaches
Different parts of an enterprise estate need different approaches for migration. As such, most large organisations will end up exploring at least two, sometimes more. These approaches can be categorised as follows:
Greenfield programmes
Deliberate process re-engineering. The legacy system has been customised and configured to such an extent that any level of technical upgrade is simply impossible.
The answer is to redesign fresh on top of S/4HANA, with a big focus on keeping a clean core. A representative pilot is taken first, usually covering multiple operating entities that are neither the largest or smallest. The clean-core blueprint can then be rolled out, however it’s likely that different versions of that blueprint will still be required. Every new jurisdiction, joint-venture accounting requirement, and regulatory variation, will bend the template a little further.
The largest greenfield programmes in oil and gas, FMCG and industrial manufacturing have spent in excess of $500 million and are still working through their first wave of operating entities. While there’s a standard template for these programmes, it should be considered a starting point, rather than as the end state.
Brownfield programmes
A forced migration or upgrade, which usually occurs in one of two ways.
The first is a manual root-and-branch review of every line of custom code and every variant of standard transaction, with a programme of decustomisation that gets the system to a state where the technical upgrade can run cleanly.
The second is the SLO / SNP-style forced technical upgrade that pushes structural change at the kernel and base configuration level, dragging the system across to the new platform. The business process logic is preserved, but the technical foundation is replaced.
Brownfield is cheaper than greenfield, but the cutover is a big-bang event with very high stakes.
Selective Data Transition (SDT)
This is a brownfield programme with surgical greenfield characteristics.
Certain modules, master data structures, and business areas are redesigned during the technical upgrade. Master data structures for profit centre, account, and functional areas are realigned as part of the move.
The complexity is higher, but the optionality is greater.
RISE with SAP
This is the most controversial option. SAP takes full responsibility for hosting, infrastructure and the upgrade cycle, resulting in a standardised footprint.
It is often the right path for smaller, simpler organisations with less complex needs, or for a divisional unit within a larger group that can be carved out cleanly. The largest, most complex enterprises are unlikely to be on RISE in the near term, but individual divisional units within those enterprises may well be.
In any large organisation, there will be appetite for at least two of these approaches across different parts of the estate.
Enterprises often start with a greenfield approach, moving to brownfield at least in part, following complications, and are now finding the RISE pitch increasingly appealing for its simpler edges. A single-mode acceleration story (one that talks only about greenfield, or only about brownfield, or only about RISE) does not describe the reality, and neither does a single-mode delivery model.
2.2 The eight stage gates of an SAP migration: what really happens
The stereotypical perception of an SAP migration is split into six stages, but the reality is that there are eight. The eighth is hidden, and so too is the cost.
Stage 1, Discovery:
Understanding the legacy estate.
Most ECC systems have accumulated decades of customisation, custom ABAP code, undocumented business logic and master data scattered across modules with inconsistent definitions. Information is held in data dictionaries, integration documentation stored in PDFs, requirements captured in spreadsheets and compliance frameworks held in regulatory documents.
It takes months of SME interviews, manual documentation review and reverse engineering, producing an output with quality that varies depending on the patience of the team that did the work.
Stage 2, Business Case and Architecture:
The blueprint for migration.
The promises made at this stage almost always get compromised. In reality, the blueprint that is approved at month three usually bends by month fifteen, when the first operating entity exposes the customisation that the global template ignored. Everyone privately knows the timeline will slip, and the cost estimate ends up being revised at the first steering committee after go-live.
Stage 3, Design and Build:
Blueprint becomes reality, with challenges frequently ignored.
The clean core target comes up against the customisations that were used for the last 25 or 30 years, which were there for a reason. While some of those reasons will no longer be valid (whereby the customisations can be retired), many still will be, so the design will have to bend to accommodate them.
This is the phase when tricky feature requests are often put on hold, while a deferred requests list quietly grows.
Stage 4, Data Migration:
Data transfer takes place, and previously ignored problems come home to roost.
Everything that was missed in discovery, every customisation that was deferred in design and master data quality issue that the original ECC system was tolerating, surfaces here.
Cross-module relationships break as reference data fails to resolve. Period balances do not reconcile.
Each defect triggers an investigation that necessitates a walk back through the pipeline by a different team to the one that led the original transformation. Upstream data refreshes and the cycle restarts, delaying the process by weeks.
Stage 5, Testing and QA:
Six months of upstream optimism becomes six weeks of unplanned remediation.
User Acceptance Testing (UAT) identifies defects that should have been caught in unit testing; integration tests surface dependencies that should have been documented in design, and performance tests reveal architectural decisions that should have been challenged in the business case.
Each of these would be manageable on its own, but together, they consume the float that the programme plan never really had to begin with.
Stage 6, Cutover and Go-Live:
The highest-stakes moment in the organisation’s year.
or a brownfield programme delivering a major global ERP, the cutover takes the system down for 72 hours or more. During that window, the business grinds to a standstill, with every action that would have been scheduled through the system being put on hold. The team needs the data to be right the first time, or businesses risk massive disruption.
If reconciliation fails, the attempt is scrapped. A second is scheduled two or three months later, with another £20m to £30m on the price tag, confidence reduced, and risk heightened.
Stage 7, Hypercare and Optimisation:
The deferred action list comes back to bite.
The list that was growing through design and build now arrives in production. The deferred customisations and workarounds the business has been promised all arrive with the operations team in the months after go-live. Each one comes with a business case, a design change, a regression test cycle and a cost, and solving all of them together, can cost multiples of the original implementation.
Stage 8, Evolution and Reintegration:
The stage every vendor framework leaves out.
Evolution is the painful backlog of change requests that are needed to make the implementation actually work for the business.
These are the functions that were shelved during design, promised at hypercare, and which are never delivered on time. The direct cost of implementing these often mounts to tens of millions across the post-go-live years, but the opportunity cost is much higher. Every quarter these change requests are deferred is a quarter the business runs on a less-capable system than the one it replaced. As a quick fix, SMEs invent workarounds and manual processes proliferate. The new system becomes a bottleneck, and the benefits case that justified the original business case slips, quarter by quarter.
Reintegration is the replumbing of everything around the core. As underlying ERP structures are changed, ACDOCA tables are moved, and different S/4HANA structures are transferred to... all data warehousing, reporting and analytics platforms need to be replumbed as a result. Everything must be assessed, redesigned, rebuilt, retested and re-deployed.
That means every reporting estate that was built on top of the old data model: every business warehouse, analytics platform, planning system and compliance tool that depends on data flowing out of the ERP, not to mention the 1,000 connected application portfolio data sets. It also means every one of the hundreds, sometimes thousands, of connected applications that fed the old system and were fed by it.
For most large enterprises, this is a second big investment bucket queueing behind the first, which is often discovered halfway through the original programme and quietly costed into a separate budget line so it does not show up against the headline migration number.
Together, evolution and reintegration are where the next £50m to £100m of post-go-live spend lives. Most migration tooling is decommissioned before it gets there and the data context built during the migration is thrown away, with lessons learned scattered across people who are now back in their day jobs.
2.3 The wheel still goes nowhere.
Running faster does not change the destination.
Where AI is applied to migration programmes, it is typically bolted on at individual stages of the linear pipeline, for example AI-assisted ABAP code interpretation; AI-augmented data mapping; AI-generated test cases, or AI-driven documentation.
While each of these examples can be useful at the point applied, delivering measurable acceleration on its specific workstream, the overall structural problem is not addressed, so the track record remains unchanged.
A linear pipeline accumulates errors, with defects at every stage propagated forward and compounded. AI that compresses one stage from twelve weeks to six weeks moves the start of the next stage by six weeks, but it doesn’t eliminate the rework cycle that begins when validation fails at the end of the pipeline and the painstaking work to find the origin of the defect begins. The handoff between teams remains, with the context built by one team failing to transfer to the next, and the data model still differing between tools.
This is the hamster wheel problem. The wheel might run faster – the pipeline is more efficient in its individual stages – but the hamster is the same, i.e. the same cumulative outcome is achieved.
The fix is not faster stages; rather, the whole pipeline must be re-wired, with context preserved across the whole lifecycle. Validation becomes continuous and manageable, rather than being a high-stakes, all-or-nothing event.
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How AI changes the numbers
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How AI changes the numbers
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How AI changes the numbers
03
How AI changes the numbers
03
How AI changes the numbers
An AI-led migration approach with a central brain coordinating six specialised arms.
The solution to these migration challenges requires comprehensive contextual awareness maintained across the entire migration lifecycle, with specialised AI capability deployed where each stage actually needs it. This approach, realised through Palantir’s Octopus model, fundamentally changes the entire programme.
Independent ‘arms’, each capable of autonomous neural processing, are coordinated by a central ‘brain’, that maintains the full picture.
This approach has three integrated components:
The brain takes shape as an AIP Hivemind, an application that holds the migration’s unified context, and coordinates work across each of the six arms.
Each context piece is held by specific platform capabilities:
Legacy structures held as source datasets and virtual tables, with Data Lineage providing the cross-pipeline view;
Target requirements held in the Foundry Ontology, which names entities in business terms (Material, BusinessPartner, JournalEntry) independent of the source SAP tables;
Business rules held in Health Checks and Data Expectations at the pre-Ontology layer, and in AIP Logic and Foundry Functions at the Ontology layer;
Compliance standards held as Ontology objects and in Notepads alongside the data;
Delivery goals held as Ontology objects (Workstream, MigrationGate) so the same context covers both what is being migrated and when each piece must land.
The Ontology is the layer that matters most for an SAP migration.
It is the semantic layer that maps data from underlying systems into business objects with explicit relationships, properties and the actions that can be performed on them.
The Ontology is not a static data model. Instead, it supports both semantic elements (the nouns: object types, properties, link types) and kinetic elements (the verbs: actions, functions, governed permissions). It can be populated from ECC data, from S/4HANA data, or critically, from both at once. During the interim-state period of any phased migration, the Ontology sits across both systems, providing a single canonical view that downstream consumers (reporting, analytics, planning, compliance) read from. The dual-maintenance burden that typically dominates phased rollouts collapses into a design choice.
HyperAuto V2 is the SAP-specific accelerant.
HyperAuto offers out-of-the-box understanding of the standard SAP data model, including table relationships, schema mapping, and pre-canned cleansing transforms (null handling, type fixes, whitespace) for common objects (MARA, LFA1, KNA1, BSEG, EKKO). HyperAuto V2 is optimised for SAP migration specifically, supporting both ECC and S/4HANA with or without an SLT Replication Server. It removes the need for most of the bespoke pipeline work that’s usually required for anything that fits the standard SAP shape.
The Octopus arms map directly to the stages of a migration:
Arm 1 - Data Understanding: connects to sources, interprets schemas, parses legacy documents and data dictionaries.
Arm 2 - Code Interpretation: translates legacy ABAP and custom code to extract business logic, producing a structured Z-inventory that classifies every routine (retire, port, or refactor) with rationale.
Arm 3 - Transformation: maps legacy values to new standards, remapping hierarchies and organisational structures, with the heavy lifting in Pipeline Builder for expert-led work and in PySpark transforms for migration-specific logic.
Arm 4 - Validation: continuous validation metrics and real-time error identification, with failed records carrying a structured reason code surfaced via an Ontology property (reconciliation_status: RECONCILED | DELTA | UNRESOLVED).
Arm 5 - SME Interfaces: custom Workshop applications, conversational analytics through AIP Analyst, SME-authored business rules through AIP Logic.
Arm 6 - Upload and Execution: writes prepared data to S/4HANA via action webhooks for real-time push, scheduled exports for batches, BAPI, OData services, or LTMC staging tables. Foundry maintains live connections to both legacy and target systems through the parallel-run period, so business operations don’t stop on cutover weekend.
The whole thing is operated by AI FDE, Palantir’s AIP-powered agent that does the engineering work across all arms on behalf of a human Forward Deployed Engineer. AI FDE reads Data Lineage, writes transforms, edits the Ontology, drafts rules and validates its own changes against continuous integration checks. It also proposes work on a Global Branch or in a pull request. Decision making is retained by the human; the human FDE reviews the technical approach, and the affected SME reviews the business impact, before the change merges.
Supportability - There are two important things to note in relation to supportability:
First, the Foundry Connector 2.0 for SAP Applications is an SAP-certified add-on, installed via SAINT on the SAP application server. It supports SAP_BASIS 7.4 SP5 and above, covering both ECC and S/4HANA, and accesses data through SAP’s native application logic rather than at the database layer. Authorisations are governed by SAP user permissions, which can’t be bypassed. System load is checked before extraction begins, and SAP Basis teams can install and govern the connector through their existing processes.
Second, SAP and Palantir announced a formal joint engineering partnership in 2025. The partnership commits both companies to maintaining HyperAuto as the primary connector across the ECC to SAP Cloud ERP Private transition, and to deepening interoperability between SAP Business Data Cloud and Palantir’s Ontology and AIP. This will also support customers on the RISE with SAP programme,
These points mean that the model described here is not a workaround that requires customers to take an aggressive position relative to their ERP vendor. Instead, it sits inside the supported envelope of the SAP estate.
The Octopus model
An AI-led migration approach where the central 'brain' coordinates six specialised 'arms' - Valliance / Palantir May 2026
AI FDE
Operates Foundry across all six arms on the human FDE's behalf
Human always keeps control of decisions.
Worked Example
€184k reconciliation gap
€184k reconciliation gap
Traditional - 1-2 weeks
Octopus - 85 minutes
The Octopus model. Central AIP Hivemind brain holding the unified context (legacy structures, target requirements, business rules, compliance, delivery goals). Six radiating arms each labelled with stage of migration and key Foundry / AIP capabilities.
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Acceleration in practice, and where it stops
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Acceleration in practice, and where it stops
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Acceleration in practice, and where it stops
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Acceleration in practice, and where it stops
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Acceleration in practice, and where it stops
ABAP estate, data quality, and integration landscape, can all be assessed simultaneously, dramatically speeding up tech migration and reducing cost by as much as 70%.
The 70% claim listed above is a bold assertion that deserves explanation. This compression has four layers.
| Layer | What it is | Realistic compression |
|---|---|---|
| 1. Per-workstream technical | Specific technical activities where AI is genuinely transformative, including ABAP remediation (Panaya), data mapping (Ontology), validation and reconciliation (Palantir AIP). | 60–98% |
| 2. First-wave whole programme | A whole programme on its first wave, anchored by the activities that remain human-paced, such as operating model decisions, business sign-off, UAT, training, the SAP technical migration window itself. | 30–50% |
| 3. Multi-wave compounding | As the programme rolls forward, gains feed each other. Better discovery in wave one produces a cleaner Ontology for wave two and cleaner data feeds shorter testing, with shorter testing feeding tighter cutover. | ~70% |
| 4. Lifetime, including post go-live | Most enterprises evaluating an approach like this one are not starting from a clean sheet. They are two years into a programme and twelve months from a target go-live they no longer believe in, looking at a cutover window with anxiety. The common reaction to the case set out above is often to think a change would have been useful eighteen months ago, but is too late now. | Better than 70% |
Walking through the same eight stage gates, here is where the compression actually shows up:
| Stage | Per-workstream technical | First-wave whole programme | Notes |
|---|---|---|---|
| 1. Discovery | 60–75% | ~47% | AIP interpretation AI ingests all dictionaries, PDFs, and requirements docs at once. Months of SME interviews become weeks of structured profiling. |
| 2. Business Case & Architecture | 40–55% | ~38% | Scenario modelling, sizing, and impact analysis accelerates. Strategic decisions remain with human team members. |
| 3. Design & Build | 70–98% | ~60% | The biggest absolute saving in the programme, with Panaya on routine code, and Ontology on mapping. |
| 4. Data Migration | 75–90% | ~57% | Continuous validation replaces end-gate validation, with >96% accuracy within hours. |
| 5. Testing & QA | 40–55% | ~27% | UAT doesn’t compress, but defect-to-fix loop does, because impact analysis comes from lineage. |
| 6. Cutover & Go-Live | 30–45% | ~37% | Modest on time, the SAP DMO engine sets the floor, dramatically reducing the level of likely risk. |
| 7. Hypercare & Optimisation | 60–70% | ~47% | Full lineage means root cause analysis is instant. Smaller team, shorter period. |
| 8. Evolution & Reintegration | Structural | Structural | Value multiplication rather than compression. The Ontology stays constant, with change requests prioritised by simulated business impact and replumb starting from a working model. |
It’s worth exploring some of the above rows in more detail:
Stage 3, Design and Build. The biggest absolute time saving sits here.
ABAP remediation compresses 70%-98%. Panaya deployed for the routine syntax and table-structure updates needed to make custom code S/4HANA-compatible, CeleRITE and smartShift at 70%-80%. SAP Joule covers 40%-60% of standard ATC issues.
Data mapping compresses 80%-90% through the Ontology, which eliminates the paper-mapping exercise. Mock migration cycles compress because continuous validation surfaces errors at once with full lineage, rather than after each end-to-end run. For a complex brownfield programme, what was 28 weeks halves, becoming 10-14.
Stage 6, Cutover. Modest on time reduction but transformative in terms of managing risk.
Palantir’s automated reconciliation runs continuously with full lineage, surfacing discrepancies in real time rather than discovering them in days of manual spreadsheet comparison. The Go / No-Go decision is based on a programme health score derived from data, rather than on the opinion of whoever is most confident in the room. Cutover compression on the headline number is 30%-45%, but reduction in the risk of a failed go-live is the case that matters most, potentially eliminating £20m-£30m in re-attempt costs.
Stage 8, Evolution and Reintegration. The evolution backlog is prioritised based on simulated business impact rather than political weight.
A proposed change request can be modelled against the Ontology, the affected entities and processes identified, with the cost-benefit articulated against the actual estate rather than being based on estimates. Change requests are therefore delivered with AI assistance and enabled by the persistent Ontology. The opportunity-cost gap (the period during which the business runs on a less-capable system than before) closes in weeks rather than years, and the reintegration scope (analytics, BI, planning, the connected-application ecosystem) reuses the existing Ontology rather than rebuilding context from scratch.
This is where the lifetime compression claim earns its keep, and where the case for not decommissioning the migration tooling at go-live becomes obvious.
A worked example: 85 minutes versus two weeks
The capability is easier to see in an example than in a heatmap.
In the following instance, at week 8 of the S/4HANA migration, reconciliation surfaces a €184,000 gap on one cost centre between the source ECC balance and its transformed equivalent.
| Time | Step |
|---|---|
| 00:00 | SME opens the failing cost centre in the reconciliation app. 412 JournalEntry objects are displayed with their reconciliation status. |
| 00:05 | Filtering unreconciled entries shows 4 of the 412 failing. All four share the same posting key. |
| 00:30 | The human FDE asks AI FDE why these rows are excluded from the ACDOCA aggregation. AI FDE runs the transform code and the data lineage, and identifies an outdated filter clause introduced two weeks earlier. |
| 01:00 | AI FDE recommends a fix, which the human FDE approves. AI FDE applies the change on a Global Branch, runs the transform on branch data only, and opens a pull request. |
| 01:15 | Impact analysis reports show 4 JournalEntry objects changed, 1 cost centre affected, and the period balance shifts by exactly €184,000. |
| 01:20 | SME reviews and approves the pull request and branch merges to main. Audit trail records both approvals. |
| 01:25 | The reconciliation app shows the cost centre as reconciled. The workstream’s rolled-up reconciliation rate ticks up on the migration health dashboard, with both updating automatically as data lineage propagates the change downstream. |
Total - roughly 85 minutes.
In a traditional migration, the equivalent diagnostic typically takes one to two weeks.
The point is not the time, but that the entire diagnostic loop, from defect surfacing to root cause to fix, to impact analysis, to approval, to audit trail, runs inside a single coherent context. This is all without the need for a team to restructure what another did before it, or for a data refresh in between, so the cycle does not need to restart from scratch.
But the architecture is only half the story
Walk back through the stage-gate table and a pattern is obvious. The technical stages (ABAP, mapping, validation, reconciliation, lineage-based diagnostics) compress dramatically. But the stages where humans drive the work (operating model decisions, design workshops, UAT business sign-off, business owner confirmation at cutover, change management and training in hypercare and evolution) barely move at all in the analysis above. Whatever is not accelerated becomes the new bottleneck.
If the platform compresses ABAP remediation from 12 months to six weeks, but training audiences are still being mapped in spreadsheets, and design workshop outputs are still feeding into the technical pipeline three weeks later, the programme timeline is set by what did not compress. If reconciliation surfaces a discrepancy in 85 minutes but the relevant business owner is not in the same Ontology as the data, and the comms team takes a week to figure out who needs to know, the cycle is slower still. The compounding effect (better discovery feeding cleaner data migration feeding shorter testing feeding tighter cutover) only happens if the entire programme is moving at the same pace.
AI has to span every part of your programme – both internally and in terms of your external suppliers and consultants.
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Is it too late for us?
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Is it too late for us?
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Is it too late for us?
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Is it too late for us?
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Is it too late for us?
Most enterprises evaluating an approach like this one are not starting from a clean sheet. They are two years into a programme and twelve months from a target go-live they no longer believe in, looking at a cutover window with anxiety. The first reaction to the case set out above is often that it would have been useful eighteen months ago but is too late now.
But that’s not the case.
The minimum value of a contextual architecture on a programme that is already in flight is risk reduction. Whatever stage you are currently in, every stage gate that still lies ahead has acceleration potential.
ABAP remediation work that is still in progress can be re-prioritised against actual estate data rather than initial estimates. Data quality issues that have been surfacing through the programme can be modelled against the live transformation pipeline to predict which ones will fail at cutover. Integration testing that is still running sequentially can be re-architected to run in parallel.
Palantir can be deployed mid-programme, into the workstreams that have not yet been completed, and the gains accrue from that point forward. Programmes that have been running for two years and are still twelve months from go-live have recovered months of timeline within weeks of Palantir deployment.
The more substantive value is wave-on-wave compounding. Programmes that are 5% through their rollout (a handful of operating entities live out of hundreds) have most of the programme still to come. The next 95% can run materially faster because the lessons learned from the first wave can be embedded in the Ontology. The customisations that worked, the integrations that broke, and the data quality issues that the first business unit revealed are all embedded and applied to every wave that follows.
Even when a provider is committed to a specific delivery partner or timeline, the case for an independent assessment is strong. The output is a clear, data-grounded picture of where the existing programme will reach each problem, and what each problem will cost the organisation in cutover risk and evolution-stage opportunity cost.
This is something your incumbent consultancy cannot produce.
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Why Valliance
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Why Valliance
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Why Valliance
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Why Valliance
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Why Valliance
AI delivers vast improvements, but the architecture only delivers its full value when three things sit in the same delivery team:
AI-native consulting that operates at AI pace across both technical and consulting workstreams.
Deep SAP migration domain expertise across all four migration approaches and understanding of the in-flight reality most enterprises are living.
A team with real Palantir experience.
Several major consultancy firms have acquired Palantir capability, yet they have not systematically deployed it for clients because SAP migration is a significant revenue line, estimated to represent up to 10% of global revenues at the largest firms. Palantir compresses the technical migration timeline by 70% - and when their business model is based on charging for hours, there’s no incentive to speed processes up.
Valliance is structured differently. We are an AI-native team with deep SAP and Palantir expertise, with teams that deeply understand how to leverage the latter’s market leading Octopus architecture for data migrations of all kinds. We give you an independent assessment of your migration, and our work is structured so the majority of our fees are paid only on successful technical migration. We have no interest in creating complexity. We have every interest in removing it.
When we tell you that your ABAP estate is 40% larger than documented, or that three of your integrations carry critical cutover risk, or that your data quality issues will blow your go-live window, we are telling you because the data shows it, not because it extends our engagement.
Every recommendation we make is oriented toward the same outcome you are working toward: a clean technical migration, completed correctly, as fast as possible.
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What to do next
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What to do next
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What to do next
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What to do next
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What to do next
An independent assessment
Wherever you are in your migration we will produce a data-grounded view of your estate, covering: ABAP volume and remediation profile; integration count and parallel-test readiness; data quality baseline; cutover risk; evolution backlog and opportunity cost. This assessment will show what a realistic timeline looks like – with and without AI, and if you are mid-flight, the specific points at which Palantir can be dropped into the existing programme, with the recovery that can be achieved from there.
The assessment is delivered by an AI-native team, handpicked according to the skills you need for success.
A bootcamp
If your team wants to skill up on Foundry and AIP for SAP migration before committing to anything, we run an intensive bootcamp on the Octopus model. The brain, the six arms, AI FDE, the Connector, HyperAuto V2, the SAP-Palantir joint engineering partnership, and the worked examples that show what the model actually does in practice.
Either way, the conversation that follows is the one that matters. Whether the next migration you do or the one you are already in the middle of is delivered the way the previous generations were, or whether it earns back the next fifteen years of operational advantage.
It’s time to start the conversation - hello@valliance.ai
Let’s put AI to work.
This paper draws on Palantir’s published technical material on AI-accelerated migration; Valliance’s own published article “Accelerating SAP Migrations with Foundry and AIP” (Tigue & Parvin, May 2026); independent research from Horváth and other industry sources on SAP migration outcomes; the Valliance four-pillar positioning (“Valliance is for leaders building the future”); and direct SAP migration delivery experience across Greenfield, Brownfield, SDT and RISE programmes in oil and gas, FMCG, industrial manufacturing, and retail sectors. Specific quantitative claims about per-stage acceleration are sourced from Palantir, Panaya, Applexus, and SAP Joule benchmarks where indicated, and should be tested against any given organisation’s data complexity rather than treated as universal.
We can help you compress the technical migration that typically consumes years, with overall cost cut by as much as 70%.
We can help you compress the technical migration that typically consumes years, with overall cost cut by as much as 70%.
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