Barrett King

A program that only counts what a partner brings in is an internal scorecard, not a growth engine.

• They propose replacing it with net revenue retention on partner-attached accounts compared with NRR on all other accounts, and treating the difference as the program's contribution.
• King gives three reasons: finance already owns NRR, so no one has to trust a metric the partner team invented; a cohort comparison avoids the sourced-versus-influenced argument; and partner attachment is a recorded fact, not an assertion.

King points to the wider services market: about a quarter of McKinsey's global fees now come from outcome-based pricing, Bain's AI-tied work is roughly 30% of revenue and climbing toward 50%, and BCG expects AI work to reach about 40% of revenue. Against that, they set the partner-program staples: sourced deals, tier badges, portal logins, and MDF spend.

King says the metric is not settled. They would rather defend a smaller number that withstands scrutiny than a bigger one that does not, and are starting to build a cohort comparison at Ottimate.

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The stronger comparison, they say, looks within a customer segment and tracks the same account before and after partner attachment, rather than comparing partner customers with everyone else and calling it proof. King says plainly that this is harder and has not yet been done cleanly.

After giving three reasons the NRR comparison would survive a budget meeting, King raises the concession they say most CFOs reach within seconds.

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They see the same three moves across four examples: activity to outcome, self-attested to verified, and point-in-time claim to lifecycle measurement.

Google Cloud relaunched its partner network in the first quarter, replacing activity-based metrics with outcome-based tiers and automated tracking of partner contribution across the customer lifecycle.

• Salesforce collapsed four consulting tiers into two and 170 badges into 28, tying status to demonstrated delivery capability.
• Microsoft replaced self-attested customer references in its Copilot specialization with an independent audit.
• WRITER built a three-tier program where advancement depends on demonstrated customer impact rather than deal volume.
• HFS Research, which King cites rather than speaks for, described the Salesforce change as a great thinning and noted that CIOs can choose partners based on verified delivery outcomes instead of tier badges.

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Implementation was a one-time fee with a defined end, while operating capability is a standing relationship that gains value the longer it runs.

• King points to integration governance, securing enterprise data flows, and maintaining AI performance over time as areas where partner engagement is growing.
• Automation remains fragile at the seams, where data quality and timing matter, so manual workarounds persist long after the core workflow is automated.
• Those seams, they say, are partner territory—and have become more valuable because more now runs through them.

King cites McKinsey's finding that early adopters of AI-integrated ERP environments see EBIT improvements of 5% or more, and argues that value comes from the system continuing to work under real conditions, not from the install. They also place an obligation on vendors: a partner program that gives partners only something to install has handed them a business with a shrinking floor.

King identifies the unresolved question: how to price operating capability instead of implementation. They say they have not seen anyone solve it cleanly and ask practitioners how they did it.

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King invites readers to test the argument in public and credits the people and sources that sharpened it.

In the sourced-pipeline post, King credits Ty Lingley with the fair counter-question—what holds up under CFO scrutiny instead—names the article behind the claim, thanks the Partnership Leaders Claude connector for research help, and asks whether a cohort comparison has made it past anyone's finance team and what they were asked for.

• The AI post asks how others priced operating capability.
• A separate post asks whether sourced pipeline is still the right headline metric for a partnership in 2026, or whether it persists because it is the easiest thing to count.

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If a program gives partners only something to install, they say, it has handed them a shrinking business and will then blame them for underperforming. King notes that they and most vendors have been slower on this than they would like to admit.

King describes working with product leadership at Ottimate to define Partner Activation around how joint go-to-market can speed a customer's time to value and extend the platform into partner organizations. They invite people in fintech, accounting, bookkeeping, or AP delivery firms to get in touch.

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It answers the question a newcomer is most likely to have: should sourced pipeline still be the headline metric for a partnership in 2026? Read it first for the claim, King's supporting points, and the question they put to readers.

King argues that the biggest professional-services firms are repricing their own work around outcomes: about a quarter of McKinsey's global fees now come from outcome-based pricing, Bain's AI-tied work is roughly 30% of revenue and climbing toward 50%, and BCG expects AI work to reach about 40% of revenue.

• They contrast that shift with typical partner programs that still count sourced deals, tier badges, portal logins, and MDF spend.
• Measuring what a partner brings in without measuring what the customer gets produces an internal scorecard rather than a growth engine—and internal scorecards are the first thing a CFO cuts.

For brands planning a campaign

Use this as the opening argument for a brief asking a partner leader to move away from sourced-pipeline reporting. Interview questions should ask candidates to name the customer-outcome metric they would put in its place and explain how they would defend it to finance.

King moves from diagnosis to a specific, finance-friendly metric, with the strongest objection already on the table.

King addresses what holds up under CFO scrutiny by pointing to recent moves by large partner programs: Google Cloud relaunched its partner network, replacing activity-based metrics with outcome-based tiers and automated tracking of partner contribution across the customer lifecycle; Salesforce collapsed four consulting tiers into two and 170 badges into 28, tying status to demonstrated delivery capability; Microsoft replaced self-attested customer references in its Copilot specialization with an independent audit; and WRITER built a three-tier program where advancement runs on demonstrated customer impact rather than deal volume.

• They see the same three moves in all four: activity to outcome, self-attested to verified, and point-in-time claim to lifecycle measurement.
• King proposes reporting net revenue retention on partner-attached accounts against NRR on everything else, and names the main objection: some of that gap is selection bias, because partners attach to healthier, better-resourced customers.
• The strongest version compares within segment and looks at the same account before and after attachment.

For brands planning a campaign

Use this to design interview questions that test whether a candidate can defend a partner metric under CFO scrutiny, including how they handle selection bias. It also gives a fintech partner GTM team a concrete alternative to debate: net revenue retention on partner-attached accounts versus NRR on everything else.

That distinction frames how they think about partner activation, pricing, and vendor product decisions.

King argues that the claim AI kills the channel is wrong in a specific way: setup work disappears, while the work of keeping things running grows.

• They point to integration governance, securing enterprise data flows, and maintaining AI performance over time as areas where partner engagement is moving, and cite McKinsey's finding that early adopters of AI-integrated ERP environments see EBIT improvements of 5% or more.
• King distinguishes partners who sold hours—implementation was a one-time fee with a defined end—from those who can operate something on a customer's behalf, creating a standing relationship that grows more valuable over time.
• They also put an obligation on vendors: a partner program that only gives partners something to install has handed them a business with a shrinking floor.
• King says they are working with Product leadership at Ottimate to define Partner Activation and asks how others have priced operating capability.

For brands planning a campaign

For a fintech partner GTM strategy, use this to argue that activation and ongoing operation, not implementation, should be the partner offer. Ask a candidate how they would price an operating-capability partnership and what vendor-side product changes it requires.

They propose replacing it with net revenue retention on partner-attached accounts compared with NRR on everything else, and treating the difference as the program's contribution.

• The approach has three advantages: finance already owns NRR, so nobody has to trust a number the partner team invented; it compares cohorts rather than making a credit claim, sidestepping the sourced-versus-influenced argument; and attachment is recorded as a fact in the system rather than asserted.
• King says this is the approach being built at Ottimate.

For brands planning a campaign

This is the closest published thinking to a fintech campaign aimed at shifting partner metrics.

• A fintech hiring to shift those metrics can treat it as King's proposal and test whether the design holds up in a CFO conversation.
• King has said the clean version has not been done yet.

Examples: Source 1 · Source 2

Google Cloud relaunched its partner network in Q1, replacing activity-based metrics with outcome-based tiers and automated tracking of partner contribution across the customer lifecycle.

• Salesforce collapsed four consulting tiers into two and 170 badges into 28, tying status to demonstrated delivery capability.
• HFS Research described the change as a great thinning that lets CIOs pick partners based on verified delivery outcomes.
• Microsoft replaced self-attested customer references in its Copilot specialization with an independent audit.
• WRITER built a three-tier program where advancement depends on demonstrated customer impact rather than deal volume.
• King distills the pattern into three moves: activity to outcome, self-attested to verified, and point-in-time claim to lifecycle measurement.

For brands planning a campaign

A useful benchmark set for a partner GTM strategy seeking external precedent rather than one person's opinion. A campaign could compare the fintech's current program design with these three moves and use the gaps as its argument.

Examples: Source 1

Implementation was a one-time fee with a defined end, while operating capability is a standing relationship that gains value the longer it runs.

• They point to integration governance, securing enterprise data flows, and maintaining AI performance over time as areas where partner engagement is moving.
• Automation remains fragile at the seams where data quality and timing matter, King argues, so manual workarounds survive long after the core process is automated.
• McKinsey work is cited for early adopters of AI-integrated ERP environments seeing EBIT improvements of 5% or more, with the value coming from the system continuing to work under real conditions rather than from the install.
• King also says that if a vendor program only gives partners something to install, it has handed them a shrinking business.

Separate partner work by its economics: one-time implementation with a defined end, versus standing operating relationships that grow more valuable over time.

King names the unresolved question: how to price operating capability instead of implementation. They say they have not seen anyone solve it cleanly and ask practitioners how they did it.

For brands planning a campaign

For a fintech partner GTM plan covering AP and accounting firms, this reframes the offer from deployment projects to ongoing operating accountability. Ask King whether they have a pricing model for that shift or are still working it out.

Examples: Source 1

The stronger comparison, they say, looks within segments and tracks the same account before and after attachment, rather than comparing partner customers with all customers and calling it proof. King says this is harder and has not been done cleanly anywhere yet, and would rather defend a smaller number that survives questions than a bigger one that does not.

Compare within customer segments and examine the same account before and after partner attachment, rather than comparing all partner-attached accounts with all other accounts.

King distinguishes a documented gap from proof of impact and says the rigorous version remains unsolved in practice.

For brands planning a campaign

This is the material's strongest signal of analytical rigor. Ask for a worked before-and-after design on a real account set: King has stated the standard and acknowledged that the hard version remains unbuilt.

Examples: Source 1

King credits Ty Lingley for asking the fair counter-question, names the article behind the claim, thanks a research connector, and closes each post with a direct question—for example, whether a cohort comparison has made it past a finance team and what they asked for, or how others priced operating capability. They also use the posts to describe current work at Ottimate, including defining Partner Activation with product leadership to accelerate customer speed to value.

State a contrarian claim, acknowledge the challenge, cite comparable programs or research, then ask readers for evidence that would disprove or refine the argument.

For brands planning a campaign

King's default is a debate-starter that draws responses, suited to co-marketing formats built around practitioner discussion. For long-form explainers or benchmark reports, ask King to develop that format: the posts here are short, not extended writing.

Examples: Source 1 · Source 2 · Source 3

They describe this as current work rather than a finished engagement. The posts do not state their job title; ask King to confirm it.

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They credit it as a research tool, not as a sponsorship, membership, or commercial partnership.

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Chris M. examines CRM object references — Salesforce's OpportunitySplit percent, validated to total 100% and owned by a user seat; HubSpot's 33 standard objects, with no partner object among them; and Oracle's partner percentage, defined as credit unrelated to the sale and off by default — to show there is nowhere to record what a partner contributed.

• Chris asks whether the deal would have closed without the partner.
• King makes the parallel argument at the metric level, proposing net revenue retention on partner-attached accounts as the measure to report instead.

Chris M. argues from primary vendor documentation and a longer institutional story about what the sales funnel was built to throw away.

• The numbered, multi-part series discloses that Chris is building tools in the space.
• King argues from what large vendor programs and cited analyst research now do, and proposes one measure to report rather than explaining why the underlying tools cannot represent a split.

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Danishevskiy models the three-year total cost of ownership of a subscription CRM against building and maintaining a custom one across three B2B SaaS scenarios, then lists five questions to distinguish a RevOps agency that delivers a working system from one that delivers a cleaner CRM and the same pipeline. King applies the same approach to partner programs, asking what the scorecard proves about the customer rather than what the partner brought in.

Danishevskiy's frame is cost and vendor or agency selection, drawn from work at Selleris.

• The posts end with a checklist for readers to run before comparing pricing.
• King's frame is partner-program design and the economics of partner services; the posts end with an open question rather than a set of criteria.

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Koopal-Patel argues that SaaS layoffs cut support for partners serving the mid-market and SMB, segments now buying on rapid ROI and total cost of ownership.

• The Know, Grow, Assess, Measure framework lists sourced pipeline, influenced deals, activated partners, and use case depth under measurement, and asks how to define success and stick to it.
• This overlaps with King's case for replacing sourced pipeline.

Koopal-Patel sets out ordered framework steps for vendors building a program, along with the staffing and distribution consequences of cutting partner support, and maps specific partner types to market segments. King focuses on one metric replacement and on AI shifting partner work from implementation to operating capability.

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Their posts examine why partnership measurement has been stuck for twenty years: CRM object schemas that cannot record a partner's share of a deal, the gap between what Salesforce, HubSpot, AWS, and Microsoft co-sell APIs actually store, and the counterfactual question of whether a deal would have closed without the partner.

• A second post argues that partner ecosystems are not sales functions and that forcing them into a sales funnel discards the introductions, delivery capacity, product knowledge, risk absorbed, and trust the funnel was never built to hold.
• For a brief whose sticking point is whether an outcome-based metric can be recorded in the systems at all, that structural argument is more useful than restating the metric case.

The work is a builder's critique, not a ready scorecard.

• Chris M. says they build tools in this space and spends part of the argument against their own position, so a brand should weigh the commercial interest behind the analysis.
• The posts explain why measurement is broken and identify the counterfactual as the missing question.
• Ask for a finished cohort method or a finance-defensible number; a campaign still needs a way to turn the problem into a metric a finance team will sign off on.

Examples: Source 1 · Source 2

One post models a three-year total cost of ownership for building a custom CRM versus buying HubSpot across three company sizes, with AI-assisted development already assumed, and lands on figures such as SMB at $61K versus $225K and enterprise at $2.8M versus $6M.

• The other gives a five-part test for choosing a RevOps partner: diagnose before quoting, build rather than advise, plan for the partner's own exit, cover the full revenue loop, and tie accountability to outcomes instead of hours.
• If the call to action is how to choose a partner accountable to outcomes, that checklist is more directly usable than an argument about which headline metric to use.

The focus is partner selection and build-versus-buy economics, not the partner-attached metric itself.

• The quantified comparisons concern owning a CRM, so ask for the partner-measurement specifics to build around them.
• This is a procurement and cost angle; King supplies the outcome definition, the selection-bias caveat, and the cohort method.

Examples: Source 1 · Source 2

One post traces where the enterprise-SaaS partner-team cuts landed: GSIs and the top 1% of technology partners were largely protected, while the PAMs, PSMs, and enablement leads serving the other 99% were cut.

• Those partners served the mid-market and SMB; they stop learning how to sell the product and eventually stop thinking about the vendor, while mid-market buyers move to more agile stacks.
• The second post lays out a Know, Grow, Assess, Measure framework for building an ecosystem, including how partners are tiered and measured and the warning that changing the scoreboard every year leaves you unable to tell what works.
• For a brief about rebuilding partner coverage or where to put enablement investment, that is closer to the decision than a metric definition.

The measurement guidance stays at the framework level: commit to a scoreboard and define readiness, specialization, and co-sell maturity. Ask for the numeric cohort method and finance-defensible retention delta King proposes.

Examples: Source 1 · Source 2