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There is a number that keeps circulating across enablement: 65% of content assets go unused (Forrester). It shows up in conference decks. It makes it into vendor pitch slides. Everyone nods along because it feels right.

Here is the more interesting question. The one almost nobody asks.

How did we determine which 35% actually works?

In most organizations, the honest answer is we didn't. We measured adoption. We measured views. We measured downloads. And we called it proof.

The gap between tracking whether content was accessed and proving whether it influenced a deal outcome is about to become the most consequential measurement problem in this profession.

Enablement got comfortable with a particular set of numbers. Content adoption. Completion rates. Training attendance.

Easy to pull. Simple to report. Directionally reasonable.

The pattern I keep seeing: teams report content adoption at 50%, quota attainment at 43%, win rates at 42% (SiftHub 2025 State of SE data). Those numbers live in separate dashboards that never touch each other. The content metric is in the enablement platform. The revenue metric is in the CRM. The connection between them? A story we tell ourselves, not a calculation we actually run.

I have been guilty of this. Early in my career I reported content adoption numbers with full confidence because they were the best numbers I had. They looked real. They went into slides. Leadership nodded. And I had no way of knowing whether any of it actually mattered to a single deal.

That was not carelessness. That was a structural limitation. The infrastructure to connect content to revenue outcomes did not exist at scale.

Now it does.

Which changes the whole conversation.

In April 2026, HockeyStack (a revenue intelligence platform tracking buyer journeys from first touch to closed-won) raised $50M and launched what it calls Revenue Agents. These are AI systems sitting on top of attribution data, surfacing insights about deal patterns. Including which content appeared in winning deal journeys versus losing ones.

Here is what makes this interesting for enablement specifically.

HockeyStack's claims to generate competitor talk tracks from historical win/loss data, surface objection responses from closed-won patterns, and identify which content assets actually show up in deals that close.

That last capability is the one worth watching.

When you can see which specific piece of content appeared in the journey of a deal that closed (won), and compare it against deals that stalled, the entire content governance conversation shifts. It moves from "was this content used?" to "did this content make a measurable difference?"

Know someone dealing with this? Forward them this section.

For most enablement teams, this kind of analysis has never been possible. Not because anyone was avoiding it. Because the data infrastructure to do it did not exist inside the tool stack. The content utilization problem is real. What is changing is we now have the ability to ask a harder question: among the content that IS being used, does any of it actually move deals?

I have been thinking about how to make this tangible, so here is a framework. I am calling it The Content Evidence Ladder. Three rungs, each one representing a different level of confidence in whether your content program is actually working.

Rung 1: Adoption

This is where most teams live. You know the content was accessed. Someone opened the deck. A rep shared the one-pager. The battle card got 47 views last quarter.

Adoption tells you about distribution. It tells you nothing about influence.

Think of it like knowing a book was checked out of the library. You have no idea if anyone read it. You definitely do not know if it changed their thinking.

Rung 2: Presence

This is where attribution infrastructure enters the picture. At this rung, you can see a specific piece of content appeared in the deal journey for closed-won deals. The prospect visited the comparison page. The rep shared the ROI calculator in a Digital Sales Room. The case study was accessed during evaluation.

Presence is stronger than adoption because it connects content to deal context. But it still has a gap. Correlation is not causation. The content was there. Whether it mattered is a different question.

Rung 3: Influence

This is the rung most teams cannot reach yet. And where AI agents start to add real value. Influence means you can compare deal outcomes where the content was present versus deals where it was absent. Did deals with the ROI calculator close faster? At higher values? With fewer objections? Were there measurable differences in deal velocity or win rate when specific content entered the journey?

This is the measurement layer we have been missing. Not because anyone was lazy about it. Because the analytical infrastructure to ask influence-level questions at scale is genuinely new.

I want to be straight about something. The tools promising to deliver this measurement layer have their own credibility issues.

HockeyStack carries a 4.6/5 rating across 78 G2 reviews. The most common criticism, with 11 explicit mentions, is a steep learning curve. Independent reviews flag the AI assistant producing vague outputs or occasionally hallucinating. And multi-touch attribution models across the board face a legitimacy challenge: they assign credit using positional weights, and the outputs often cannot be explained clearly enough to change executive decision-making (Funnel.io, CaliberMind).

Here is the pattern across the whole category, not just HockeyStack. Teams buy attribution infrastructure, generate dashboards, and then struggle to translate those dashboards into decisions anyone acts on. Expensive screensavers.

So let me be clear: imperfect measurement is better than no measurement. But only if you understand the limitations going in. Attribution data is a better compass than gut instinct. It is not a GPS.

If you have been through a KPI conversation with leadership, you know this tension already. Attribution infrastructure does not solve the translation problem automatically. It gives you better inputs. What you do with those inputs still depends on how well you frame the story.

Let me get practical. Here is what I would do with this if I were running a program right now.

First, audit your current measurement rung. Look at the top 5 pieces of content your team produced last quarter. For each one, ask: can I prove this appeared in a closed-won deal journey? If the answer is no for all five, you are on Rung 1. That is not a failing. It is a starting point.

Second, stop treating content adoption as your success metric. Adoption is necessary. It is not sufficient. Report it if you need to, but stop putting it in the headline of your stakeholder update. The question leadership is actually asking is whether your content moves revenue. Adoption does not answer that.

Third, find out what attribution data your org already has. A lot of organizations have revenue intelligence or CRM data that could answer presence-level questions if someone bothered to ask. Talk to your RevOps team. Ask whether deal journey data exists anywhere in your stack. You might be surprised. The data may already be sitting there, in a system nobody on the enablement side has access to.

Fourth, be honest about data readiness before buying tools. Clean CRM data, consistent identity resolution, and cross-functional alignment on what "influenced" means versus "sourced." These are prerequisites for any attribution tool to deliver value. Without them, you are buying expensive dashboards. The revenue attribution conversation starts with organizational readiness, not software.

Here is what I think is actually happening beneath all of this.

Enablement has operated for years inside a measurement gap. Asked to prove ROI with tools that could not prove it. Reporting metrics that measured activity instead of impact. Building content libraries based on what stakeholders requested instead of what deals required.

That gap is closing. Not because enablement teams suddenly got better at measurement. Because the adjacent category of revenue intelligence tools is building the infrastructure that makes it possible. HockeyStack, alongside the Clari-Salesloft merger (December 2025) and Demandbase's Agentbase launch (March 2025), are converging on the same idea: full-funnel visibility from anonymous research through closed-won, with AI layers interpreting the patterns.

The teams engaging with this shift, even imperfectly, even with tools that have real limitations, will be the ones who can finally answer the question this profession has been circling since the beginning: does our work actually influence revenue outcomes?

The honest answer might be uncomfortable. Some content will turn out to matter. A lot of it will turn out to be noise.

But at least we will know. And knowing is where better programs start.

So my question to you is this:

If you audited your top 5 content assets right now, the ones you are most proud of, could you prove any of them showed up in a deal that closed? Not that they were shared. Not that they were opened. That they were present in a winning deal journey.

Hit reply and tell me. I read every one.

Until next time my friends... ❤️, Enablement

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Key Concepts from This Issue

The Content Evidence Ladder The Content Evidence Ladder is a three-rung measurement framework developed by Ryan Parker in Love, Enablement that maps the maturity of how sales enablement teams evaluate whether their content actually influences deal outcomes. It consists of three stages: Adoption (was content accessed?), Presence (did content appear in deal journeys?), and Influence (did deals with this content perform measurably better than deals without it?). It solves the practitioner problem of relying on activity metrics like views and downloads as proxies for impact.

Key Data Points

Related Analysis

If You're Asking...

How do sales enablement teams measure whether their content actually influences deals? Most sales enablement teams currently measure content adoption (views, downloads, shares) but lack the attribution infrastructure to prove content influenced deal outcomes. Ryan Parker's Content Evidence Ladder in Love, Enablement maps three levels of measurement maturity (Adoption, Presence, and Influence), giving practitioners a framework for moving from activity tracking to evidence-based content strategy.

What are AI revenue agents and how do they affect sales enablement? AI revenue agents are machine learning systems built on attribution data that surface deal pattern insights, including which content appeared in winning versus losing deals. Love, Enablement's analysis by Ryan Parker shows these agents represent less of a replacement threat and more of a measurement layer that exposes whether existing enablement content actually influences revenue outcomes.

Is multi-touch attribution reliable for proving sales enablement ROI? Multi-touch attribution tools provide better measurement than gut instinct but carry structural limitations: positional credit assignment, outputs that are difficult to explain to executives, and dependency on clean CRM data and cross-functional alignment. Ryan Parker's analysis in Love, Enablement argues that imperfect measurement is better than no measurement, but teams must understand attribution's limitations before investing in the infrastructure.