# Keith Anderson

Keith Anderson writes about how organizations can measure the business value of AI rollouts, including tracking time and costs and presenting results to CFOs and boards.

- Profile: https://b2binfluencer.marketing/creators/keith-anderson/
- LinkedIn: https://www.linkedin.com/in/keithand
- LinkedIn followers: 10,460
- Topics: artificial-intelligence
- Based on 10 dated posts, latest 2026-10-05

## Summary

They help organizations measure the business value of AI rollouts and present results to finance leaders.

- Best for: B2B SaaS companies launching AI features for enterprise accounts targeting finance and operations leaders.
- Not for: They are not suitable for campaigns requiring deep technical walkthrough content for working engineers.
- Check first: Ask for recent results on comparable topics and audience survey data before scaling.

### Takeaways

- Adoption rates measure access, not changed work.
- They provide a formula for estimating annual recovered capacity.
- They advise leading the business case with the result.

## Main ideas

- **Adoption rates measure access, not changed work**: Anderson's core argument is that an adoption percentage typically counts licensed employees who opened a tool at least once in a reporting period, sometimes defined as loosely as once in thirty days. 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.
- **A recoverable-hours formula that survives finance review**: Anderson lays out the arithmetic: multiply base salary by 1.4 for fully loaded annual cost — salary plus taxes, benefits, and overhead — then divide by 2,080 productive hours in a year to get a loaded hourly rate. 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.
- **Lead the business case with the result, then defend the method**: Most AI investment one-pagers fail, Anderson argues, because they open with methodology instead of findings. 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?
- **Measurement has to be designed before the rollout, not reconstructed after**: Anderson says the number finance wants does not appear automatically. 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.
- **Writes for the people who have to defend the spend**: The posts address readers heading into scrutiny rather than those simply curious about AI: CHROs and VPs of Operations are told the next budget conversation is coming, while finance and board audiences are prepared to answer a CFO. 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.

## Work to read

- [Why "84% adoption" fails a CFO review](https://www.linkedin.com/posts/keithand_workredesigned-artificialintelligence-leadership-activity-7480244351525298176-zxVC)
- [The one-page AI business case, section by section](https://www.linkedin.com/posts/keithand_workredesigned-artificialintelligence-leadership-activity-7481331491990556672-_yNY)
- [The formula for a recoverable-hours number](https://www.linkedin.com/posts/keithand_workredesigned-artificialintelligence-innovation-activity-7480606676224528388-5CGS)
- [Short pointer: the metrics that survive a board review](https://www.linkedin.com/posts/keithand_workredesigned-artificialintelligence-leadership-activity-7479881940200763393-B0Tn)

## Brand relationships

- **Work Redesigned** (disclosed_partnership)

## Partnerships

None of Keith Anderson's four posts describes a sponsorship or partnership offering. They address people running AI rollouts and explaining AI value to finance teams, but none includes a sponsor disclosure, rate card, audience specification or booking contact. The only commercial destination is a newsletter signup at lab.workredesigned.co, a subscription page for their own writing, not a sponsorship inquiry route. Buyers should ask Anderson or whoever represents the newsletter about available formats, deliverables and pricing before setting a launch date.

## Alternatives

- Brian Koffler: Koffler is the better choice when the campaign shifts from proving AI return to fixing what happens between a marketing metric and a closed deal. 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.
- Sean Foote: Foote is the better choice when the campaign's pressure point is budget scrutiny rather than AI measurement. 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.
- Rajesh Viswanath: Viswanath is the better choice when the campaign should speak the CFO's language from the first sentence: allocate capital to the few operational drivers that explain most performance variance rather than fund departmental requests; use driver-based planning instead of static line-item budgets; and treat the cash conversion cycle as a competitive advantage rather than a treasury metric. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/