A process-led field guide for internal forward deployed engineers. Map the operation, investigate the causes of lost value, size the opportunity and inspect how the work could change.
By the Forward Deployed Team · Updated 24 October 2026 · Illustrative scenarios; not customer results.
Start with the operating outcome. Follow the end-to-end process, identify a specific cause of lost value, then test the intervention. Eight process families below contain 24 working hypotheses; the visual investigations that follow illustrate selected mechanisms.
Demand, specification, purchasing, process effort and loss. Distinguish spend removed from capacity released.
03 / Capital resilience
Improve capital decisions
Asset use, investment discipline, liquidity and resilience. Preserve service and essential controls.
Evaluate every opportunity through: people and responsibilities · process and standard work · data and information · controls and decision rights · technology and adoption. AI is one possible enabler.
These are starting hypotheses, not diagnoses. Establish the baseline and the burden of proof before selecting a tool. All process families stay open as you scroll.
Marketing / Sales / 01
Market insight and strategy
01Research
02needs
03segments
04proposition
05portfolio
01.01
Broad segments may hide different customer needs
01.02
Unverified assumptions may guide product investment
01.03
Repeated research may fail to inform an actual decision
Proof or rejection: what evidence is needed?
Use customer interviews, lost-deal evidence and controlled proposition tests. Reject segments without stable needs or actionable differences.
Where AI could contribute
Synthesize permissioned research with traceable quotes and unknowns; teams validate with customers.
Example sizing model / Research and planning capacity
Annual research projects × verified preparation hours released × loaded research cost
Low / base / high illustration: 20 projects × 5 / 10 / 15 hours × $100 = $10k / $20k / $30k annual research capacity value.
Research usefulness requires customer validation. Any resulting revenue or margin change needs its own causal evidence and full-cost calculation.
This sizes preparation effort. Any effect on win rate, selling price or time to order is separate and needs controlled evidence; do not add the same hours to multiple initiatives.
Normalize product, segment, competitive context and full cost; inspect price waterfalls. Reject uplift that ignores demand response or contract obligations.
Where AI could contribute
Compare terms and flag exceptions; commercial owners test price changes and approve offers.
Size the opportunity / Annual contribution opportunity
Estimate released processing capacity from your own workload. This is an effort model, not a prediction of AI savings. Use the separate formulas above for cash, capital, margin, materials and energy.
Formula: tasks × minutes ÷ 60 × realization × hourly cost − running cost; subtract one-time cost in year one. Financial savings require an actual budget change. No inputs leave your browser.
Method and sizing notes
The opportunity structure combines three value lenses—cash release, cost optimization and capital resilience—with process, contract, working-capital, customer-management and investment analysis. Historical cost-transformation and finance operating-model references inform the process coverage. Every hypothesis requires evidence, a test that could reject it and accountable validation.
Historical transformation benefit ranges are not current benchmarks or AI effect sizes. This guide uses transparent low/base/high sensitivity examples instead. The eligible baseline, proposed change and realization route must be established for the organization. The examples are not additive, forecasts or customer results.
Prioritize by net value, strength of evidence, feasibility, time to value and service or control consequences. Deduct implementation, transition and recurring costs; assign one owner and one benefit record to prevent double counting.
01
Marketing / Sales / investigate the opportunity
Segmentation & relevance
The working hypothesis
Broad campaigns may spend on audiences with different needs and little incremental response.
What would establish or reject the case?
Run consent-aware randomized tests with holdouts; compare incremental contribution after media and service cost.
Human action: Review segment-specific messages using controlled evidence; avoid sensitive inferences.
AI / analysis & workflow
Customer interview synthesis
OPERATING INPUT → REVIEW OUTPUT
Consented interview notes and stated buying needs
Group setup effort and reliability themes, preserving quotes and contradictory evidence.
Validate: Compare the draft interpretation with a verified sample and record missing or incorrect conclusions before changing the process.
What would weaken the hypothesis?
The proposed change fails the measured outcome, shifts work elsewhere, loses required controls or costs more than the verified benefit. Investigate those results before scaling.
02
Marketing / Sales / investigate the opportunity
Acquisition economics
The working hypothesis
Channel conversion reports may credit demand that would have converted without additional spend.
What would establish or reject the case?
Use randomized or geographic holdouts where feasible; reconcile acquisition, gross margin and retention.
Human action: Compare 80-dollar attributed CPA with 125-dollar incremental CPA; reconsider allocation.
AI / analysis & workflow
Experiment interpretation
OPERATING INPUT → REVIEW OUTPUT
Holdout results, acquisition cost and contribution
Compare incremental margin with uncertainty; avoid treating attributed orders as causal lift.
Validate: Compare the draft interpretation with a verified sample and record missing or incorrect conclusions before changing the process.
What would weaken the hypothesis?
The proposed change fails the measured outcome, shifts work elsewhere, loses required controls or costs more than the verified benefit. Investigate those results before scaling.
03
Marketing / Sales / investigate the opportunity
Lead qualification & handoff
The working hypothesis
Sales teams may lose time when lead context is incomplete and routing ignores fit or availability.
What would establish or reject the case?
Review accepted, rejected and missed leads; test routing and measure response time and conversion without excluding useful prospects.
Measure the consequence
Response time; accepted leads; conversion
Enablers to investigate
Seller interviews; CRM events; qualification criteria; text classification
WORKED EXAMPLE 03Illustrative scenario
A high-fit inquiry waits in the wrong queue
ILLUSTRATIVE WORKING RECORD / MARKETING-SALES-03
Inquiry
Declared need
Routing
L-201
Multi-site deployment
Enterprise queue
L-202
Single-site trial
Trial specialist
L-203
Need unclear
Ask for context
ILLUSTRATIVE WORKING RECORD / MARKETING-SALES-03
Inquiry
Declared need
Routing
L-201
Multi-site deployment
Enterprise queue
L-202
Single-site trial
Trial specialist
L-203
Need unclear
Ask for context
Review interpretation Suggest an owner and evidence gaps; a seller reviews the qualification.
Suggest an owner and evidence gaps; a seller reviews the qualification.
Explore the applications
AI use cases: how could the work change?
Two visual examples · inputs, interpretation and human decisions
+
An illustrative AI application: inspect the source records, the interpretation and the human decision.
AI / records & interpretation
Lead qualification & handoff: evidence assist
SOURCE RECORD → REVIEW NOTE
Inquiry summary: declared need, timing and consent are captured with source links.
Link evidence · flag uncertainty
Inputs: Seller interviews; CRM events; qualification criteria; text classification.
Human action: Suggest an owner and evidence gaps; a seller reviews the qualification.
AI / analysis & workflow
Follow-up preparation
OPERATING INPUT → REVIEW OUTPUT
Declared customer need and approved product information
Draft the missing-context question and a relevant follow-up for the seller to approve.
Validate: Compare the draft interpretation with a verified sample and record missing or incorrect conclusions before changing the process.
What would weaken the hypothesis?
The proposed change fails the measured outcome, shifts work elsewhere, loses required controls or costs more than the verified benefit. Investigate those results before scaling.
04
Marketing / Sales / investigate the opportunity
Pipeline & forecast
The working hypothesis
Forecasts may be optimistic when stages are updated without evidence of buyer progress.
What would establish or reject the case?
Backtest by stage, cohort and horizon; review missing buyer milestones with sellers and compare calibration.
Measure the consequence
Forecast calibration; stage aging; win rate
Enablers to investigate
Seller and buyer interviews; CRM history; deal evidence; forecasting
WORKED EXAMPLE 04Illustrative scenario
A late-stage deal lacks a confirmed buying step
ILLUSTRATIVE WORKING RECORD / MARKETING-SALES-04
Buyer milestone
Seller record
Confirmed?
Proposal
Sent
Yes
Procurement step
Expected this month
No
Decision date
Assumed
No
ILLUSTRATIVE WORKING RECORD / MARKETING-SALES-04
Buyer milestone
Seller record
Confirmed?
Proposal
Sent
Yes
Procurement step
Expected this month
No
Decision date
Assumed
No
Review interpretation Expose missing evidence and uncertainty; probability is a discussion input.
Expose missing evidence and uncertainty; probability is a discussion input.
Explore the applications
AI use cases: how could the work change?
Two visual examples · inputs, interpretation and human decisions
+
An illustrative AI application: inspect the source records, the interpretation and the human decision.
AI / records & interpretation
Pipeline & forecast: evidence assist
SOURCE RECORD → REVIEW NOTE
Deal evidence: proposal sent is recorded, procurement date unconfirmed → review stage.
Link evidence · flag uncertainty
Inputs: Seller and buyer interviews; CRM history; deal evidence; forecasting.
Human action: Expose missing evidence and uncertainty; probability is a discussion input.
AI / analysis & workflow
Pipeline evidence checking
OPERATING INPUT → REVIEW OUTPUT
Buyer-confirmed milestones and CRM history
Ask for confirmation of the procurement date before a seller advances the stage.
Validate: Compare the draft interpretation with a verified sample and record missing or incorrect conclusions before changing the process.
What would weaken the hypothesis?
The proposed change fails the measured outcome, shifts work elsewhere, loses required controls or costs more than the verified benefit. Investigate those results before scaling.
05
Marketing / Sales / investigate the opportunity
Pricing & discount leakage
The working hypothesis
Discounts may vary with approval habits rather than willingness to pay, competitive position or service economics.
What would establish or reject the case?
Normalize product, segment and terms; test price changes with suitable controls and monitor churn, fairness and margin.
Human action: Review price and service terms with the deal owner; demand response remains an assumption.
AI / analysis & workflow
Price-waterfall reconciliation
OPERATING INPUT → REVIEW OUTPUT
Approved quote, credits, rebates and service terms
Expose the realized net price and contribution after all concessions.
Validate: Compare the draft interpretation with a verified sample and record missing or incorrect conclusions before changing the process.
What would weaken the hypothesis?
The proposed change fails the measured outcome, shifts work elsewhere, loses required controls or costs more than the verified benefit. Investigate those results before scaling.
06
Marketing / Sales / investigate the opportunity
Retention & service recovery
The working hypothesis
Preventable service problems may drive churn before retention teams see the underlying issue.
What would establish or reject the case?
Study de-identified reasons and service history; test targeted issue resolution with a holdout and monitor customer experience.
Customer interviews; service events; cancellation reasons; text themes
WORKED EXAMPLE 06Illustrative scenario
A renewal reminder arrives before an unresolved repair
ILLUSTRATIVE WORKING RECORD / MARKETING-SALES-06
Customer event
State
Action
Support case
Repair unresolved
Service owner
Renewal date
In 30 days
Coordinate timing
Retention offer
Not tested
Resolve issue first
ILLUSTRATIVE WORKING RECORD / MARKETING-SALES-06
Customer event
State
Action
Support case
Repair unresolved
Service owner
Renewal date
In 30 days
Coordinate timing
Retention offer
Not tested
Resolve issue first
Review interpretation Resolve the issue before sending an offer; do not equate a risk score with a decision.
Resolve the issue before sending an offer; do not equate a risk score with a decision.
Explore the applications
AI use cases: how could the work change?
Two visual examples · inputs, interpretation and human decisions
+
An illustrative AI application: inspect the source records, the interpretation and the human decision.
AI / records & interpretation
Retention & service recovery: evidence assist
SOURCE RECORD → REVIEW NOTE
Reason synthesis: recurring setup failures appear across support conversations; validate the theme.
Link evidence · flag uncertainty
Inputs: Customer interviews; service events; cancellation reasons; text themes.
Human action: Resolve the issue before sending an offer; do not equate a risk score with a decision.
AI / analysis & workflow
Service-recovery briefing
OPERATING INPUT → REVIEW OUTPUT
Permissioned case history and unresolved customer issues
Summarize the open repair and accountable owner before any retention outreach.
Validate: Compare the draft interpretation with a verified sample and record missing or incorrect conclusions before changing the process.
What would weaken the hypothesis?
The proposed change fails the measured outcome, shifts work elsewhere, loses required controls or costs more than the verified benefit. Investigate those results before scaling.
07
Marketing / Sales / investigate the opportunity
Content & sales enablement
The working hypothesis
Sellers may recreate materials because approved content is hard to find or lacks context for customer needs.
What would establish or reject the case?
Observe preparation tasks; pilot source-linked draft assembly and verify accuracy, approvals and customer usefulness.
Measure the consequence
Preparation time; factual corrections; content use
Human action: Draft from approved facts and identify unknowns; a seller checks every claim.
AI / analysis & workflow
Claim verification
OPERATING INPUT → REVIEW OUTPUT
Draft content and approved supporting evidence
Flag the unsupported performance statement and link the nearest verified fact.
Validate: Compare the draft interpretation with a verified sample and record missing or incorrect conclusions before changing the process.
What would weaken the hypothesis?
The proposed change fails the measured outcome, shifts work elsewhere, loses required controls or costs more than the verified benefit. Investigate those results before scaling.
08
Marketing / Sales / investigate the opportunity
Trade promotion & execution
The working hypothesis
Promotional spend may leak through inconsistent terms, weak execution or sales shifted from other periods.
What would establish or reject the case?
Reconcile terms, claims, execution and baseline demand; test incremental margin after cannibalization and fulfillment cost.
Human action: Separate the gross uplift from trade spend and displaced sales before approving future spend.
AI / analysis & workflow
Promotion claim reconciliation
OPERATING INPUT → REVIEW OUTPUT
Approved products, dates and channel claims
Flag a product outside the promotion window for a commercial reviewer.
Validate: Compare the draft interpretation with a verified sample and record missing or incorrect conclusions before changing the process.
What would weaken the hypothesis?
The proposed change fails the measured outcome, shifts work elsewhere, loses required controls or costs more than the verified benefit. Investigate those results before scaling.
Questions & answers
Before testing an opportunity
Where can AI help in marketing / sales?
Potential areas include segmentation & relevance, acquisition economics, lead qualification & handoff, pipeline & forecast, pricing & discount leakage, retention & service recovery, content & sales enablement, trade promotion & execution. Each is a hypothesis to test against operating evidence.
Are these savings or measured results?
No. All figures, records and overlays are illustrative scenarios. Validate the baseline, net cost, feasibility and service outcomes before making a business case.
Which enablers are needed?
Start with process interviews, reliable source records and a measurable baseline. Depending on the task, useful enablers include document extraction, retrieval, classification, forecasting and workflow integration. Assign a person to review exceptions.
What should a first pilot establish?
A pilot should establish whether a specific intervention changes the measured outcome against a credible baseline, how often it fails, who reviews exceptions and what it costs to operate.
