AI Workflow for Deal Desk Review

TL;DR

Deal desk review should make non-standard deals predictable. The workflow should identify the exception, route the right approver, and stop sales from committing terms before approval.

What is deal desk review?

Deal desk review is the approval process for commercial deals that fall outside standard pricing, scope, terms, or risk boundaries.

Who is this workflow for?

• Service businesses, SaaS companies, agencies, consultants, construction companies, and professional firms with recurring sales or proposal work.
• Teams where buyer-facing material depends on scattered notes, folders, and informal approval.
• Operators who need more speed without letting automation create commercial risk.
• Managers who want clearer evidence before sales sends assets, proposals, or terms.

What breaks in the manual process?

The manual process usually breaks when speed beats evidence:

• approval happens through scattered messages;
• discount thresholds are unclear;
• legal sees the deal too late;
• rep promises are missing from review;
• the buyer waits while owners debate;
• the final decision is not logged.

The workflow should make the recommendation or draft reviewable before it reaches the buyer.

How does the AI-enabled process work?

The workflow gathers source evidence, checks approved rules or assets, prepares the recommendation or draft, and flags anything that needs commercial, legal, pricing, scope, or proof review.

AI prepares the work. The accountable owner still approves customer-facing claims, pricing, scope, legal terms, proof, and delivery commitments.

What does this look like in practice?

Example scenario: A rep requests a larger discount and custom payment schedule for a strategic account. The workflow checks deal summary, discount, payment terms, contract changes, margin impact, approver threshold, and rep promise. It prepares deal desk brief, approval route, risk note, decision log, and a flag for any non-standard term.

What decision rules should govern this workflow?

• Route deals to review when they exceed discount, legal, security, payment, term, or scope thresholds.
• Allow standard deals to proceed without extra review.
• Require evidence for exceptions, not just rep preference.
• Log approval, rejection, and revision reasons.
• Do not let the rep commit non-standard terms before approval.

What are the implementation steps?

1. Trigger: A deal includes a discount, custom term, non-standard scope, legal/security request, payment exception, multi-year structure, or verbal promise outside standard policy.
2. Inputs collected: deal summary and account context, requested exception type, pricing, discount, and margin details, contract or legal term changes, security or compliance request, scope or delivery exception, required approver and approval threshold, rep promise and supporting evidence.
3. AI/system action: The system checks source evidence, applies the approved rule, drafts the output, and identifies review exceptions.
4. Human review point: Sales, finance, legal, security, or leadership reviews discounts, custom payment terms, legal changes, security reviews, non-standard scope, multi-year terms, and any verbal promise already made to the buyer.
5. Output generated: deal desk intake brief, approval route and required approver, risk and margin note, decision log with approval, rejection, or revision, measurement event for approval cycle time, exception volume, and rework rate.
6. Follow-up or next action: The owner approves, edits, routes, sends, logs, or blocks the output based on the evidence.

Required inputs

• deal summary and account context.
• requested exception type.
• pricing, discount, and margin details.
• contract or legal term changes.
• security or compliance request.
• scope or delivery exception.
• required approver and approval threshold.
• rep promise and supporting evidence.

Expected outputs

• deal desk intake brief.
• approval route and required approver.
• risk and margin note.
• decision log with approval, rejection, or revision.
• measurement event for approval cycle time, exception volume, and rework rate.

Human review point

Sales, finance, legal, security, or leadership reviews discounts, custom payment terms, legal changes, security reviews, non-standard scope, multi-year terms, and any verbal promise already made to the buyer.

Risks and stop rules

Stop when evidence is missing, the asset or claim is not approved, the recommendation changes price or scope, the draft creates a customer commitment, or legal, security, delivery, or proof claims need owner review.

Best first version

Start with one intake brief: deal summary, exception type, requested approval, supporting evidence, approver, and decision log.

Advanced version

Add source confidence, approval routing, asset performance feedback, pricing thresholds, legal clause libraries, delivery-risk scoring, and monthly exception review after the basic workflow is stable.

Related workflows

• Proposal Creation
• Pricing Approval Routing
• Proposal Compliance Review
• Statement Of Work Creation
• Sales Handoffs

Measurement plan

• Approval cycle time.
• Exception volume by type.
• Rework or resubmission rate.
• Discount exception rate.
• Legal or security review rate.
• Approved versus rejected exception count.

FAQ

What is deal desk review?

Deal desk review is the process for approving non-standard deals before sales commits pricing, terms, scope, or contract language to the buyer.

What should trigger deal desk review?

Discounts, custom payment terms, legal changes, security reviews, non-standard scope, multi-year terms, and verbal promises should trigger review.

What should AI prepare for deal desk?

AI should prepare the deal summary, exception type, supporting evidence, risk note, approver route, and decision log.

What is the simplest first version?

Start with one intake brief: deal summary, exception type, requested approval, supporting evidence, approver, and decision log.

How should deal desk review be measured?

Track approval cycle time, exception volume, rework, discount exceptions, legal review rate, and approved versus rejected exceptions.