Turn contract data into a CFO advantage with Analyzer in Acrobat Studio

CFO organizations depend on data, but important business data still sits inside contracts. 

A CRM may show the value of a customer relationship. The contract contains the terms that govern it, including termination rights, price holds, renewal provisions, and payment terms. An ERP shows what was billed, while the agreement may contain acceptance criteria, credits, rebates, or other obligations that affect how finance manages the transaction. 

Accessing this information across thousands of agreements takes time. Finance teams have to find documents, locate relevant terms, capture the information, and reconcile it with data in other systems. 

Analyzer in Adobe Acrobat Studio uses generative AI to turn collections of contracts and other business documents into structured data. Teams define the information they need and run that analysis across a document collection. Extracted values include citations to the source, so reviewers can verify the underlying language. 

This gives Accounting, Financial Planning & Analysis (FP&A), Revenue Operations (RevOps), Deal Desk, and Risk and Audit teams access to contract data that has traditionally required significant manual review. 
 

Give finance a clearer view of contract data 

Consider a common question: Which customer agreements contain termination-for-convenience clauses? 

Answering it manually can require someone to locate the contracts, identify variations of the relevant language, record the results, and compare those findings with information in other systems. 

Finance teams face the same task for payment terms, price holds, renewals, rebates, acceptance provisions, billing terms, and other contract attributes. 

Analyzer can extract defined attributes across a document collection and organize the results as structured information. Teams can search and filter the results, export the data, and use citations to review the source language. Analyzer also provides REST APIs for incorporating extracted document data into enterprise applications and workflows. 

For CFO organizations, this provides another source of data about the commitments, obligations, and risks behind financial transactions. 

Accounting: expand contract coverage 

Accounting teams need to identify contract terms that could affect revenue recognition, billing, commissions, or financial reporting. 

Volume makes this difficult. Reviewing thousands of agreements manually consumes accounting capacity, so teams may focus on larger transactions, known exceptions, or samples. 

Analyzer can apply defined criteria across the contract population and surface relevant provisions for review. Each extracted value links back to source content, giving accountants direct access to the contract language they need when making an accounting judgment. 

AI handles more of the repetitive search and extraction. Accounting professionals stay focused on interpretation and decisions. 

Adobe's revenue assurance team uses Analyzer to review roughly 18,000 customer contracts annually for non-standard terms, including termination-for-convenience and price-hold provisions. The team can review the full contract population in Analyzer. 

FP&A: bring contract data into the forecast 

Some inputs that affect forecasts remain inside executed agreements. 

Which customers have contractual price increases next year? Which agreements have upcoming renewal dates? Where do customers have termination options? Which contracts include price holds or minimum commitments? 

These terms can affect assumptions used in planning and forecasting. 

Analyzer can extract these terms across a contract portfolio and make them available as structured data alongside information from CRM, ERP, and planning systems. An analyst assessing renewals can examine the renewal and termination provisions in executed agreements. Teams evaluating future pricing can identify contractual price holds or escalation provisions across the portfolio. 

FP&A can use this data to test assumptions, identify contractual risks, and determine where a forecast needs closer review. 

RevOps and Quote-to-Cash: compare contracts with operational data 

Quote-to-Cash moves information across several teams and systems. A negotiated term can be recorded incorrectly or omitted during those handoffs. 

A payment term may differ from the value entered in CRM. A billing provision may not reach the ERP. A renewal condition may be absent from the system of record. Some commercial commitments may exist only in an exhibit. 

Analyzer can extract these terms from executed agreements so RevOps and Quote-to-Cash teams can compare them with operational data. This can help teams find discrepancies in payment terms, billing provisions, renewal conditions, and other commercial information. 

Adobe uses Analyzer for this work internally. Its finance team reconciles contract details against CRM data each quarter. Analyzer identifies discrepancies, reducing manual review across thousands of contracts per quarter. 

Analyzer's REST APIs can also make extracted document data available to downstream applications and workflows. This allows contract information to feed the systems used to manage the customer relationship after signature. 

Deal Desk: use contract history to improve deal processes 

Executed contracts provide Deal Desk with a record of the terms customers requested, the exceptions Adobe accepted, and the provisions that recur across negotiations. 

Finding those patterns manually requires reviewing a large number of past agreements. 

Analyzer can identify recurring non-standard terms across the contract population. Deal Desk can then measure how often an exception appears and use that information when evaluating standard language, approval policies, or self-service processes. 

Adobe's Deal Desk uses Analyzer across roughly 7,500 contracts annually to identify non-standard terms that appear frequently enough to consider for standard, self-service language for sales. 

Contract history gives Deal Desk evidence for deciding where existing standards create repeated review work and where a change could shorten the deal process. 

Risk and Audit: examine more of the contract population 

Risk and Audit teams often have more agreements available for review than their teams can examine manually. 

Analyzer can apply defined extraction criteria across a document collection and identify agreements with specific clauses, dates, obligations, values, or other characteristics. Reviewers can use citations to inspect the source language for each result. 

A team could identify agreements that may be missing a required provision, locate contracts with particular termination rights, find commitments above a defined threshold, or compare contract information with a system of record. 

This gives Risk and Audit teams information about a larger contract population before they decide where detailed human review is needed. Reviewers retain responsibility for interpreting the contract and determining the appropriate response. 

Use contract data across finance workflows 

A contract term can affect several finance functions. For example, Accounting may identify a non-standard provision that affects the treatment of a transaction. FP&A may need the same provision when evaluating a forecast assumption. RevOps may need to check whether the term appears correctly in CRM. Deal Desk may want to know how often it occurs across other agreements. Risk and Audit may need to identify the broader population affected by it. 

Without structured contract data, each team may conduct its own search or maintain separate spreadsheets. 

Analyzer provides a common set of extracted, source-linked contract information that can be exported or accessed through APIs. Teams can use the same underlying contract data for different finance workflows while returning to the signed agreement when they need to verify a result. 

Increase capacity for work that requires finance expertise 

Contract volume adds work across accounting, planning, commercial operations, and risk functions. A meaningful share of that work consists of locating documents, finding clauses, and transferring information into spreadsheets or systems. 

Analyzer can handle more of the extraction work and give finance professionals the relevant source language for review. 

Adobe's Business Model Strategy team uses Analyzer to detect specific deal types and bulk-extract key terms across roughly 20,000 contracts annually. Adobe estimates that the workflow saves about 4,000 hours of manual work each year. 

Those hours can be used for analysis, exception review, and other work that depends on finance expertise. 

Use contract data across the CFO organization 

Contracts contain data that can affect financial reporting, forecasts, commercial operations, and risk management. Accessing that data at scale has historically required substantial manual work. 

For CFO organizations, the value comes from better access to the terms behind financial and commercial activity. Teams can review more contracts, identify discrepancies earlier, and spend less time extracting information by hand. 

Your contracts already contain valuable business intelligence. Analyzer helps put it to work. 

Learn how Adobe teams use Acrobat Studio and Analyzer to turn documents into business insights and reduce manual work in Speed, scale, trust: How Adobe uses Acrobat Studio to turn documents into business insight. 

To learn how Analyzer could support your finance organization, reach out to your Adobe account team or contact us for more information. 

Product information: Analyzer overview | Analyzer developer overview | Analyzer REST API overview