AI Lead Generation Assessment Suite

The AI Lead Generation Assessment by The Starr Conspiracy

The AI Lead Generation Assessment by The Starr Conspiracy scores your AI lead gen stack across six maturity dimensions and tells B2B revenue leaders, in roughly twelve minutes, whether their current investment will produce qualified pipeline or burn the next two quarters. Most teams we score land in the 38 to 52 percent range, which is the messy middle where AI spend is real but pipeline contribution is not yet provable.

That number is not flattering. It is also not unusual. Salesforce's 2024 State of Marketing reported that 68 percent of marketers have a fully defined AI strategy, yet only a fraction tie that strategy to a measurable revenue number. The gap between adoption and accountability is exactly what this assessment quantifies.

How the Scoring Works

You answer between 18 and 24 questions across six dimensions. Each question maps to a 0 to 4 maturity scale, where 0 means the capability does not exist and 4 means it is operational, measured, and producing predictable output. Dimension scores are normalized to a 0 to 100 index, then weighted toward the dimensions that most reliably predict pipeline contribution in B2B tech, based on our own client engagement data across roughly 140 B2B tech GTM teams scored between 2022 and 2024.

The weighting is not equal. Data foundation and measurement carry more weight than tooling, because we have watched too many teams buy a $90,000 platform stack and then fail to attribute a single sourced opportunity to it. Tools without data discipline produce activity. They do not produce pipeline.

The Six Dimensions

Strategy and demand state alignment. Does your AI lead gen plan map to the Ten Demand States or some equivalent buyer-intent model, or is it a generic top-of-funnel volume play dressed up in new tooling?

Data foundation. First-party data quality, CRM hygiene, identity resolution, and consent posture. This is the dimension where the most expensive AI stacks die quietly.

AI-native tooling and integration. Whether your prospecting, enrichment, scoring, and orchestration layers actually talk to each other, or whether you are paying four partners to do overlapping work.

Talent and operating model. Who owns AI lead gen output. Whether marketing ops, demand gen, and RevOps are aligned or in a turf war.

Measurement and attribution. Whether you can tie an AI-sourced touch to a closed-won deal, or whether you are still reporting MQLs as if the year were 2017.

Governance and brand safety. Prompt controls, output review, privacy compliance, and the unglamorous but career-saving question of who signs off when AI-generated outreach goes sideways.

Interpreting Your Score

Scores cluster into four bands. The bands are descriptive, not aspirational. Where you land tells you what to fix next, not how to feel about yourself.

0 to 35, Experimental. You have bought tools. You have not built a system. Pipeline contribution from AI lead gen is anecdotal at best. Priority: stop buying. Start measuring.

36 to 60, Operational but Leaky. This is where most B2B tech marketing teams sit. AI is producing activity and some pipeline, but you cannot defend ROI in a board meeting without a lot of caveats. Priority: data foundation and attribution before any new tool purchase.

61 to 80, Scaling. You have a working system, named owners, and measurable contribution. The gap is consistency across segments and the ability to forecast AI-sourced pipeline within 15 percent accuracy. Priority: tighten governance and benchmark against peers.

81 to 100, AI-native. Rare. Roughly 7 percent of teams we have scored. You are not reading this article looking for permission to invest. You are reading it to pressure-test your model. Priority: stay paranoid about model drift and benchmark vintage.

What the Benchmark Comparison Tells You

Your overall score is matched against three peer cohorts: B2B SaaS under $50M ARR, B2B SaaS $50M to $250M ARR, and B2B tech over $250M ARR. The benchmark dataset is refreshed every twelve months and currently reflects scoring data collected between Q3 2023 and Q3 2024. Vintage matters. A benchmark older than 18 months in this category is decorative, not diagnostic.

The comparator reports your percentile position on each dimension, not just the composite. That matters because a team with a strong tooling score and a weak data score reads totally differently from a team with the inverse pattern, even when the composite is identical. The first team needs to slow down. The second needs to keep building infrastructure before buying anything else.

Methodology Sources and Limitations

The scoring rubric draws on three inputs. First, The Starr Conspiracy's internal engagement data from B2B tech GTM partnerships, 2019 through 2024. Second, public benchmark data from Salesforce State of Marketing 2024 and HubSpot's 2025 marketing benchmark report. Third, structured interviews with 22 B2B revenue leaders conducted in late 2024.

Limitations worth naming. The sample skews toward B2B tech and B2B SaaS; if you sell into industrial or regulated verticals, treat percentile comparisons as directional. The maturity model assumes a marketing-led demand gen function; if your AI lead gen sits inside sales development, the talent and operating model dimension will under-score you. We are working on a sales-led variant.

The Bottom Line

If you cannot answer the question "is our AI lead gen stack actually producing pipeline" with a specific number and a defensible attribution model, you do not have an AI lead gen problem. You have a measurement problem wearing an AI costume. Take the assessment, get the score, and use the dimension breakdown to decide what to fix before you renew another contract. Then book a working session with The Starr Conspiracy if you want the score turned into a 90-day plan.

Related reading: AI lead generation, demand states, and our B2B AI GTM diagnostic guide.

Related Questions

How long does the assessment take?

Twelve to fifteen minutes for a single respondent. We recommend running it twice, once with the demand gen lead and once with marketing ops, and comparing the deltas. The disagreements are where the real diagnostic value lives.

Is the methodology gated?

No. The dimension definitions, scoring bands, weighting logic, and benchmark vintage are all visible on this page. Only the personalized output, your specific score and percentile report, requires an email. We do not hide methodology behind a sales call.

How often is the benchmark dataset refreshed?

Annually, with the next refresh scheduled for Q4 2025. The current dataset is dated Q3 2024. We flag the vintage on every result page because stale benchmarks in a category moving this fast are worse than no benchmarks.

What if my team is sales-led rather than marketing-led?

The current model is calibrated for marketing-led demand gen functions. Sales-led teams can still use the assessment, but treat the talent and operating model dimension as directional. A sales-led variant is in development for 2025.