Keith Anderson

It says nothing about whether work changed: cycle times, cost per unit of output, or whether saved time was reinvested.

• The conflict they describe is not that finance leaders oppose AI spending, but that they are handed numbers that do not answer their question: whether unit economics changed and whether that can be proved.
• In their framing, login counts are the metric you present when you do not have the one you need.

An AI rollout reported 84% adoption on the vendor dashboard. The CFO asked what that meant for the profit and loss statement, and nobody in the room had the number.

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Measure net hours reclaimed per employee per week from AI-assisted work.

• "Net" matters: Anderson puts the share of saved time spent correcting or validating AI output at roughly 37%, so gross time saved is not the number to present.
• Multiply the loaded rate by net hours, headcount, and 52 weeks, then compare the total with license and implementation spend.
• They say the math is not complicated; what most organizations lack is a pre-rollout baseline and a disciplined measurement approach, which is why they fall back on the bar chart.

One hundred professionals at an average loaded rate of $80 an hour, each recovering three net hours a week, produce $1.25 million in recovered capacity annually.

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Boards want to see what was found, then decide whether they trust how it was found.

• They recommend a five-part order, kept to one page: the result in one sentence with a dollar figure or percentage; two sentences of context on the rollout, team, and period; three to five sentences of methodology covering the baseline, what counts as an eligible work event, and the formula; a before-and-after comparison using the same unit of output and time window; and one closing sentence on what is being recommended or requested.
• Put anything that does not serve one of those five parts in an appendix you do not print.
• Methodology is the section teams skip, but it makes the rest credible by answering the finance reviewer's unspoken question: how do I know this is real rather than a favorable reading of ambiguous data?

Anderson's model starts with the headline: AI reclaimed 4,200 hours in Q1 at a net value of $336,000 against $180,000 in total investment cost.

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It requires a measurement architecture built before deployment: capture baselines, define eligible task types, and agree on formulas before anyone can see how the results would land.

• Reconstructing a before-and-after comparison after the fact leaves teams presenting a rising adoption chart.
• For them, this is as much a priority as a methodological claim: the failure lies in preparation, not reporting.

The board review number Anderson describes is hours reclaimed multiplied by the loaded hourly rate, set against license and implementation cost. The formula can be audited and compared with the original investment thesis, but only if the baseline was captured in advance.

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One post asks readers directly how many teams captured a baseline before deploying. The posts also point to a newsletter at lab.workredesigned.co for the fuller framework, making them entry points to longer guidance.

Two posts close by asking readers what they will have instead of the bar chart and whether their rollout had a baseline before deployment.

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Start here to understand the question the rest of the work answers.

The post opens with a small scene: an AI rollout reported 84% adoption on the vendor dashboard, the CFO asked what that meant for the profit and loss statement, and nobody in the room had the number.

• Anderson unpacks what that figure almost always measures: the share of licensed employees who opened the tool at least once in the reporting period, sometimes defined as loosely as once in thirty days.
• It does not measure whether the work changed: cycle times, cost per unit of output, or whether saved time was put back into anything useful.
• The underlying conflict is worth quoting: boards and finance chiefs are not hostile to AI spending; they are hostile to numbers that do not answer their question.
• The alternative is a measurement architecture designed before the rollout, with baselines captured, eligible task types defined, and formulas agreed in advance, so the result can be audited against the original investment case.
• The post ends with a question for the reader: the next budget conversation is coming, and a rising bar chart will not carry it.

For brands planning a campaign

Suitable for AI platform and enterprise software campaigns aimed at finance and operations buyers rather than practitioners. A sponsored angle built on "replace your adoption metric with a defensible one" lands closer to how a CFO evaluates a renewal and gives the vendor a reason to publish measurement guidance instead of usage statistics.

It answers "what should this page actually say?" in a form you can copy.

Anderson argues that most AI investment one-pagers fail because they open with methodology instead of results.

• Boards want to see what you found, they write, and will then judge whether they trust how you found it.
• The five-part order: the result in a single sentence with a dollar figure or percentage; two sentences of context on the rollout, team, and period; three to five sentences of methodology covering the baseline, what counts as an eligible work event, and the formula; a before-and-after comparison using the same unit of output and time window; and one closing sentence on what is being recommended or requested.
• Their worked example is concrete: AI reclaimed 4,200 hours in Q1 at a net value of $336,000 against $180,000 in total investment cost.
• Anderson notes that methodology is the section teams skip—and the one that makes everything else credible—because it answers the question a finance reviewer has not asked out loud: how do I know this is real rather than a favourable reading of ambiguous data?
• Anything that does not serve one of the five parts belongs in an appendix you do not print.

For brands planning a campaign

Works as a publishing hook for vendors selling AI measurement, analytics, or services. A co-created one-page template—the structure plus a worked example in the sponsor's category—is a directly usable asset for the buyer's internal review and a stronger lead magnet than another adoption benchmark.

Read it to calculate a defensible value figure yourself rather than take one on trust.

This post turns the argument into a four-step calculation.

• Take base salary and multiply by 1.4 for fully loaded cost per year—salary plus taxes, benefits, and overhead.
• Divide by 2,080 productive working hours for a loaded hourly rate.
• Measure net hours reclaimed per employee per week from AI-assisted work; net matters because Anderson puts the share of saved time spent correcting or validating AI output at roughly 37%, so gross time saved is not the number to present.
• Then multiply loaded rate by net hours by headcount by 52 weeks.
• The illustration: 100 professionals at an average loaded rate of $80 an hour recovering three net hours a week comes to $1.25 million in recovered capacity annually, which can be set against licence and implementation spend.
• Anderson is explicit that the arithmetic is not complicated, but depends on a pre-rollout baseline and a disciplined measurement approach—steps most organizations skip, which is why they end up presenting the bar chart.
• The post closes by asking readers whether their own rollout had a baseline captured before deployment.

For brands planning a campaign

Useful for a campaign that wants to move a prospect from awareness to a self-serve action. Publishing the formula as a calculator, worksheet, or benchmarking tool gives finance-minded buyers something to run with their own numbers, and the 37% rework figure is a specific, arguable claim that invites response and discussion.

Useful as a quick orientation before reading the detailed posts.

The shortest of the four posts opens with the line that a CFO does not need another bar chart showing AI adoption going up, but one number she can defend—and that most AI program reviews do not have it.

• Anderson then summarises their work: how to calculate cost per hour reclaimed, what makes a before-and-after comparison auditable, and the one-pager format that leads with the result instead of burying it.
• The post is a signpost rather than a full argument; the substance sits in the longer posts on the same themes.

For brands planning a campaign

Shows the short-form version of the message: what a sponsored or co-authored social post would look like at this length. Useful as a model for a campaign that needs a compliant, low-friction post pointing to a longer asset.

The recurring claim is that adoption rates and login counts usually measure access, not changed work, and that budget renewals depend on showing unit economics, recovered hours, or cost per output. One post contrasts an “84% adoption” dashboard with the CFO’s P&L question; another calls for one number that can be defended in a board review; a third says finance funds business cases, not stories.

For brands planning a campaign

For a B2B SaaS launch into enterprise accounts, lead with an auditable outcome claim, not an adoption dashboard. Give sales and customer-success teams a CFO-ready result, a before-and-after comparison, and a methodology note so the buyer can defend the purchase internally.

Examples: Source 1 · Source 2 · Source 3

It stresses “net” because roughly 37% of saved time can go to validating or correcting AI output, and gives an example of 100 professionals at an $80 loaded hourly rate recovering three net hours per week, producing $1.25 million in annual recovered capacity. Another post ties the same logic to hours reclaimed multiplied by loaded hourly rate against license and implementation cost.

Use loaded hourly rate, net hours reclaimed, and annualization; set the result against license and implementation spend.

For brands planning a campaign

Package the formula as a simple calculator or worksheet for prospective buyers. Position the AI feature around net time captured—after correction work—and compare recovered capacity directly to total investment.

Examples: Source 1 · Source 2

It argues that boards want to see what was found before they decide whether to trust the method, and that the methodology section makes the result credible. Another post repeats the idea that the one-pager should lead with the result instead of burying it.

Result, context, methodology, before-and-after comparison, next step—kept to one page.

For brands planning a campaign

Campaign assets can mirror this structure. A launch case study, sales one-pager, or webinar deck that opens with a dollar result and explains the measurement method will match the format Keith Anderson’s audience is being taught to expect.

Examples: Source 1 · Source 2

Another makes the same point: most organizations skip the pre-rollout baseline, which is why they end up presenting a bar chart.

For brands planning a campaign

Position implementation and onboarding as measurement design, not just deployment. A B2B SaaS company could offer a pre-rollout baseline workshop or measurement plan as part of the enterprise package, making the ROI claim more credible from day one.

Examples: Source 1 · Source 2

One speaks directly to CHROs and VPs of Operations; others frame the reader as someone preparing for CFO or board scrutiny. The posts also point readers to a newsletter for the fuller framework.

For brands planning a campaign

Reach this audience through operations, HR, and finance-leader channels, and through the newsletter the posts point to. Build the message for a budget-review moment, not a general AI curiosity audience.

Examples: Source 1 · Source 2 · Source 3 · Source 4

The newsletter is referenced as the place for a fuller framework.

No video, transcript, or long-form article body was available in the posts here.

For brands planning a campaign

Co-created campaign content should be text-first, practical, and formula-friendly: explainer posts, worksheets, swipe files, and calculator copy. Video-first or highly produced formats would be less aligned with the posts.

Examples: Source 1 · Source 2 · Source 3 · Source 4

The posts use the #workredesigned hashtag and present the newsletter as a disclosed partnership, but do not state whether it is Keith Anderson’s own publication or an outlet they write for. Ask them to clarify the ownership and employment relationship.

Original disclosure

Subscribe to the newsletter where I break down what actually works in AI rollouts: lab.workredesigned.co

View example

Koffler's baton-in-the-parking-lot post argues that "we delivered everything in the contract" is a weak answer if none of it produced pipeline, and that form fills are not the same as customers — the same complaint Anderson makes about login counts standing in for changed work.

Koffler covers paid media agency operations: where spend goes, whom it targets, and how leads reach sales inside the client company.

• The focus is campaign and pipeline attribution, not AI rollout economics.
• Anderson measures hours reclaimed against license and implementation spend.

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Foote covers revenue operations and growth platform rollouts, advocating people-first leadership and manager communication. Anderson covers AI deployment, focusing on measurement architecture and finance-facing arithmetic.

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Viswanath draws on McKinsey research on value drivers and a PYMNTS Intelligence report on the cash conversion cycle; Anderson uses a board-review scenario.

Viswanath covers FP&A capital allocation, driver-based budgeting, and working capital. Anderson focuses on proving the value of AI investment to the people who fund it.

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They write about agency accountability and the marketing-to-sales handoff: who follows up on a demo request and how quickly, what sales says about the leads it receives, and whether a closed opportunity can be traced to the campaign that generated it. That fits a brief where the buying group has accepted the AI investment and the live argument is about pipeline attribution and who owns the handoff—not unit economics.

Koffler's posts do not answer the question this campaign is built on: whether an AI rollout changed unit economics.

• They offer no formula for turning usage into a defensible dollar figure.
• A vendor choosing Koffler gets a pipeline-accountability angle, but gives up a finance-facing measurement asset.
• Koffler writes for marketing and agency leaders, not the CFO and operations buyers in this campaign.
• The CFO-defensible number will have to come from the vendor.

Examples: Source 1 · Source 2

They frame flat marketing budgets as a structural ceiling rather than a dip, argue that measurement becomes the foundation of strategy once spend stops growing, and present fractional growth leadership as a way to build that discipline without permanent headcount. That fits a brief where a growth or revenue leader is defending spend in a flat-budget cycle, and the vendor needs a systems-and-people explanation for why a well-built rollout still stalls at adoption.

The subject is GTM measurement and operational adoption broadly, not AI rollouts specifically.

• Foote argues that these systems fail at the human layer rather than the architecture, making this a people-and-process argument.
• The adoption-versus-unit-economics framing and recovered-hours arithmetic that make an AI business case auditable come from Anderson's work, not Foote's.
• Foote writes for revenue operations or growth leaders, not the CFO buying group here.
• Choosing Foote trades a CFO-facing ROI argument for a broader measurement-and-adoption argument aimed one level away from the budget owner.

Examples: Source 1 · Source 2

The posts cite McKinsey on how a handful of operational drivers explain most performance variance, and PYMNTS data showing top performers convert cash roughly 20 days faster than lower performers. That fits a brief framed as an investment decision for a finance function already fluent in driver-based planning, with the finance team itself as the audience.

The work covers finance broadly but not AI adoption measurement, so it offers no method for translating AI usage into a number a CFO can defend.

• The posts are short commentary on cited reports, not a reusable template or worksheet a vendor can hand to a prospect—the concrete asset a campaign aimed at CFOs needs.
• The argument that adoption rates measure access rather than changed work, and the recovered-hours formula, come from Anderson.
• Choosing Viswanath trades an AI-specific, template-ready argument for a wider finance audience and broader capital-allocation framing.

Examples: Source 1 · Source 2