Monetization & Revenue Models

1.Which of the following revenue models does your organization actively use today? (Select all that apply)

One‑time license / transactional sales

Subscription (seat / account-based)

Usage-based / consumption-based

Outcome-based / performance-based

Services (professional services / consulting)

Marketplace / revenue share with partners

Other (please specify)

2.How much have your monetization models changed in the last 3–5 years?

Very little or not at all

Somewhat – incremental tweaks (discounting, bundling)

Moderate – new pricing/packaging for existing offerings

Significant – new revenue models or new product lines

Transformational – fundamental change in how we monetize

Other (please specify)

3.What percentage of your organization’s total revenue is currently generated from AI‑native product streams?

0% – just beginning to plan or explore AI‑native offerings

1%–5% – early experimentation

6%–15% – establishing traction

16%–50% – significant portion of the portfolio

51%–99% – predominantly AI‑first

100% – fully AI‑native business

Other (please specify)

4.Which of the following best describes how you currently monetize your AI‑driven products and services? (Select all that apply)

Indirect / bundled: AI capabilities are bundled into existing products at no additional charge or are primarily used to support internal efficiency, not directly priced.

Simple recurring: AI is monetized via a straightforward recurring metric (e.g., per user, per seat, per account) without a usage or outcome component.

Hybrid (seat + usage): AI is monetized via recurring metrics with a usage component or threshold (e.g., per seat with overage/usage tiers).

Cost‑plus usage: AI is monetized via a dedicated usage‑based metric that primarily scales with internal cost drivers (e.g., compute units, API calls, tokens).

Outcome / value‑based usage: AI is monetized via a usage metric that scales with tangible outputs or business outcomes (e.g., number of qualified leads, transactions processed, cases resolved).

Multi‑model orchestration: Three or more of the above models coexist across different AI offerings or segments.

Other (please specify)

5.How would you describe operationalizing new pricing and packaging models from quote to revenue recognition at your organization?

Fully automated – end‑to‑end integration with minimal manual work

Mostly automated – good integration with some manual steps, limited impact on insight or timelines

Partially automated – integration exists, but manual work and data lags materially affect timeliness and accuracy

Poorly automated – largely manual processes and workarounds, frequent delays and reconciliation issues

Not automated or disconnected – monetization decisions and financial operations are mostly disconnected

Other (please specify)

6.What is the single biggest challenge you face when moving to new pricing and packaging models?

Securing sustained executive and cross‑functional sponsorship

Modeling and forecasting financial and workforce impacts (e.g., G&A, variable costs, capacity)

Defining, measuring, and agreeing on outcomes and value metrics

Adapting systems and processes (CRM, CPQ, billing, revenue recognition) quickly enough

Change management with field teams and customers (education, communication, adoption)

Other (please specify)

Revenue Attribution

7.How would you describe your revenue attribution approach across human, partner, and digital/AI channels?

We have no formal attribution model and do not systematically attribute revenue across roles/channels

We use simple single‑touch attribution (e.g., “deal owner” or last‑touch) for most revenue

We use rule‑based multi‑touch models (e.g., first/last/linear) across key roles/channels (sales, customer success, marketing, partners, digital/AI)

We use customized or position‑based weighted models across roles/channels (including partners and digital/AI)

We use data‑driven / algorithmic models that continuously learn from performance across human, partner, and digital/AI motions

8.How confident are you in the fairness and accuracy of your current attribution approach?

Extremely confident

Very confident

Somewhat confident

Not so confident

Not confident at all

Revenue Data, Insight & Performance Management

9.How would you rate the quality and consistency of your core revenue data across systems (customer, product, contract, region)?

Very poor – frequent errors and inconsistencies

Poor – significant issues that affect decisions

Adequate – usable but with known gaps

Good – generally reliable, with manageable issues

Excellent – highly reliable and standardized

10.Which best describes your revenue analytics maturity and ability to measure the impact of changes (e.g., pricing, GTM spend, headcount)?

Basic reporting – static P&L / revenue reports only; limited ability to measure impact

Descriptive analytics – dashboards by segment/region/product; some impact views, not fully trusted

Diagnostic analytics – root-cause / driver analysis; can attribute impact with reasonable confidence

Predictive analytics – forecasts, churn/upsell models; robust ability to model and anticipate impact

Prescriptive analytics – optimization, scenario simulation, what‑if; impact measurement is near real‑time and actioned

Financial Reporting & Close Process

11.How granular is your revenue reporting within your ERP, and how well does it support decision making?

Highly granular – revenue is available at detailed levels (e.g., SKU, contract, channel, agent), enabling confident, timely analysis with minimal additional data work

Moderately granular – core breakdowns (e.g., product line, region, customer segment) are available, but deeper analysis requires some additional modeling or BI work

Limited granularity – ERP provides mainly summarized views; significant rework in BI tools is required for actionable insight, adding time and reconciliation effort

Very limited granularity – ERP revenue data is too high‑level or inconsistent to support decisions; most decision‑grade reporting relies on offline models and manual reconciliation

12.How well can you reconcile operational metrics (ARR, NRR, bookings, pipeline) to GAAP/statutory revenue reporting?

Not well – numbers often disagree with no clear explanation

Poorly – frequent discrepancies and confusion

Adequately – reconciled with effort and ad hoc analyses

Well – consistent, explainable differences

Very well – fully aligned with transparent bridges

Alignment, Ownership & Ambition

13.Looking ahead 2–3 years, how ambitious are your plans to improve monetization, attribution, and financial reporting?

Low – maintain current state with minor optimizations

Moderate – targeted improvements in select areas

High – broad upgrade of processes and systems

Very high – comprehensive transformation of the revenue engine

Unsure / still under discussion