Migrating Legacy Quotes to SAP CPQ with Graip AI

Migrating to SAP CPQ is already a major transformation. But the moment teams realize they need to rebuild years’ worth of legacy quotes, often stored across PDFs, spreadsheets, emails, or outdated CPQ tools, the project slows down fast.

And it’s not just about volume. Legacy quotes tend to be messy:

• Old product names
• Missing fields
• Pricing that no longer exists
• Configurations that don’t map cleanly to the current model

Legacy quote migration becomes one of the biggest hidden bottlenecks in any CPQ rollout, as described in our deep dive on CPQ implementation challenges.

Graip AI changes this dynamic completely.

Instead of manually re-entering or “best-guessing” old configurations, Graip reads legacy quotes, extracts their structure, maps them to your current SAP CPQ models, and rebuilds them, accurately and at scale.

In this article, I’ll walk you through how Graip AI accelerates this process, reduces errors, and helps companies achieve a smoother, faster SAP CPQ go-live.

Why Legacy Quote Migration Is Harder Than It Looks

Migrating legacy quotes isn’t just a data transfer exercise, it’s an interpretation challenge. Every historical quote is a snapshot of how your business used to operate: old catalogs, old pricing, old rules. None of which match your SAP CPQ structure today.

Different formats, inconsistent structures, missing data

Legacy quotes commonly exist as:

• PDFs exported from old systems
• Excel spreadsheets built by individual reps
• Word documents with free-form descriptions
• Emails or attachments that were never standardized

Some contain full configurations. Others contain only partial specs. And many refer to discontinued products or bundles.

This creates a massive interpretation burden, something humans do slowly and inconsistently.

Historical pricing and product shifts over time

Even if the quotes are readable, product logic rarely stays the same.

• Items get renamed
• Attributes evolve
• Bundles are reorganized
• Pricing models change

Graip AI’s ability to interpret historical data (similar to the logic behind AI interpretation of historical data) removes the guesswork.

High manual effort and high error risk

Teams often underestimate how long manual re-entry takes:

• Minutes per line item
• Hours per quote
• Weeks for an entire customer segment

Inaccurate migrations break trust with sales teams and slow down your SAP deployment, as seen in many CPQ implementation challenges.

How Graip AI Rebuilds Legacy Quotes Automatically

Graip AI eliminates the bottleneck by treating legacy quotes the same way it handles RFQs and tenders: it reads them, interprets them, and reconstructs them using AI-driven mapping.

Extracting configurations, attributes, and components

Legacy quotes come in every format, PDFs, XLSX files, custom-built templates, or exports from old systems. Graip AI uses the same extraction engine highlighted in our Graip AI document understanding capabilities to identify:

• Products or components selected
• Attribute values
• Quantities
• Discount or price lines
• Free-text notes affecting configuration

Even unstructured or inconsistent quotes are parsed with high accuracy.

Mapping legacy attributes to current SAP CPQ models

Graip maps extracted attributes to your active SAP CPQ models:

• Old product codes → current SKUs
• Deprecated bundles → modern equivalents
• Attribute names → updated naming conventions
• Add-ons → today’s product hierarchy

Your implementation team or partners providing integration and data mapping support can refine these during rollout.

Creating a CPQ-ready quote

Once mapped, Graip reconstructs the quote inside SAP CPQ:

• Product selection is populated
• Attributes are applied
• Dependencies validated
• Pricing recalculated
• Approval workflows triggered

The legacy quote becomes a fully compliant SAP CPQ quote, aligned with SAP’s configuration rules (as detailed in SAP CPQ modeling documentation).

Handling Inconsistencies: When Old Data Doesn’t Match the New Model

Legacy data is messy. Graip AI is built to interpret, resolve, or escalate inconsistencies.

Predictive matching using historical patterns

When Graip encounters unclear or unmatched data, it evaluates:

• Similar past configurations
• Customer segment patterns
• Deprecated-to-modern catalog links
• Attribute similarity and context

It proposes the most probable modern equivalent, using logic similar to how it handles historical data interpretation.

Flagging data gaps for human review

Graip flags unclear values and provides:

• Highlighted source text
• Suggested configuration options
• Confidence scoring

This allows humans to validate only edge cases.

Recommending the nearest valid configuration

When products are discontinued, attributes renamed, or rules changed, Graip:

• Suggests compliant alternatives
• Documents all adjustments
• Ensures alignment with CPQ constraints

This prevents broken or unusable data from entering SAP CPQ.

Migration at Scale: Bulk Upload or Continuous Cleanup

Graip AI supports large-scale and iterative migration strategies.

Bulk conversion of thousands of quotes

Graip processes high volumes of legacy documents in parallel. Teams have seen this reflected in large-scale conversion results.

Migrating active or high-value customers first

Phased migration is often more strategic:

• Renewals
• High-value accounts
• Long-cycle segments

Graip adapts to priority-based migration workflows.

Using migration as a catalog cleanup opportunity

Legacy migration often reveals:

• Redundant SKUs
• Unused attributes
• Outdated bundles
• Pricing inconsistencies

Teams responsible for product governance or integration support often use this opportunity to modernize the catalog.

Business Impact: Faster Go-Live, Cleaner Data, Less Stress

Graip AI turns a painful migration step into a strategic advantage.

Shorter implementation timelines

Automating migration eliminates months of manual re-entry. This directly accelerates go-live timelines, supporting smoother onboarding into SAP CPQ.

Reduced manual effort for presales and product teams

Presales and product experts spend less time retyping quotes and more time supporting revenue-generating work.

Fewer errors and higher data integrity

By ensuring:

• Accurate extraction
• Correct mapping
• Consistent pricing
• Full traceability

Graip aligns with best practices outlined in CPQ implementation preparation.

Clean data in = clean quoting out.