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NewsMaterial4 min readDisconfirmAI Editorial

Dayforce CDO Carrie Rasmussen: Time Saved by AI Agents Must Be Priced or Reduced

At the Discover 2026 conference, Dayforce Chief Digital Officer and CIO Carrie Rasmussen provided an example of team-level AI economics: when AI agents improve a 10-person team's efficiency by 20-30%, but token costs equal 20-30% of their salaries and benefits, management must decide if the freed-up time is worth retaining. At the same event, Dayforce CFO and Chief People Officer noted that AI disrupts linear approval processes in HR, technology, and finance, turning transformation decisions into cross-functional business design issues. Rasmussen also argued that software vendors lacking conversational AI interfaces are already outdated.

2026-11-11 01:24 GMT+8

Carrie Rasmussen, Chief Digital Officer and CIO of Dayforce, cited an example from her own management at the Discover 2026 conference to illustrate the economic trade-offs of AI agents: If an AI agent boosts the efficiency of a 10-person team by 20-30%, while token costs exactly match 20-30% of those 10 employees' salaries and benefits, is that 20-30% of freed-up time actually worth the price? If the company believes this time can be converted into higher-value strategic work, the team headcount can remain unchanged; if the company feels 'this area of the business doesn't need to be so strong, being a B-player is fine,' then return the excess staff to the corporate pool, reducing the 10 people to 8. She emphasized this was 'an example within my own jurisdiction to show how these trade-offs operate,' not a validated pricing model.

The value of this example lies not in the numbers themselves, but in shifting the decision on deploying AI agents from 'should we buy this tool' to 'who prices the saved time.' Jeremy Johnson, Dayforce's Chief Financial Officer, pointed out at the same conference that enterprise technology transformation used to follow a three-step process: HR defines requirements, IT evaluates the technology, and Finance approves the budget. AI shatters this linear flow because introducing an agent involves workforce planning, technology selection, and cost structure simultaneously, requiring all three functions to sit together and make the same decision.

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Chief People Officer Amy Cappellanto-Wolf described this cross-functional decision-making as a 'business design issue.' She gave a hiring example: previously, HR only looked at what talent was needed; now, they must first ask 'does this job really require hiring another person? Can an agent do it? If an agent can do it, what is the cost comparison? Where is human judgment required?' She believes these questions fall outside the purview of any single function. HR's unique opportunity is to help the business think about 'how work gets done,' including deciding what should be human-led, what should be AI-assisted, what should be automated, and what she considers the most unique option—what should be stopped.

Rasmussen further extended this logic to vendor assessment. She believes AI will first eliminate point solutions—small vendors with single functionalities—because core platforms (Salesforce, NetSuite, Microsoft, and Dayforce itself) now have extension capabilities, allowing enterprises to add features directly on the platform without separate procurement. Her other screening criterion is more intuitive: if a software company's product interface lacks a conversational AI entry point, it is already outdated. 'It's just that OpenAI chat box, the simplest page, everyone copied it. If you don't see it, you feel like this product is so ancient.'

However, this approach has several clear boundaries. First, the correspondence between token costs and salaries/benefits is Rasmussen's illustrative estimate; she did not disclose specific calculation methods, measurement periods, or applicable conditions. Second, the model assumes a linear correlation between efficiency gains and cost savings, but in reality, agent output quality, error rates, and human supervision costs significantly impact net benefits. Third, the option to 'return staff to the corporate pool' may face execution obstacles in markets with strict labor laws. Finally, this logic primarily applies to relatively standardized, quantifiable work scenarios; for creative, complex judgment, or highly contextualized work, 'saved time' itself is difficult to price.

Nevertheless, Rasmussen's remarks are noteworthy because they represent a forming direction in enterprise practice: the authority to judge the economics of AI agents is shifting from IT departments to business leaders. Her own approach is to first map all roles—'What skill directions will future business analyst roles migrate to? Do the people in these positions have the ability to become forward-deployed engineers?'—and then use this map to talk with employees, telling them where the direction lies and what needs development, rather than announcing job eliminations after the fact.

She simultaneously acknowledged this might be 'one of the hardest conversations to have.' Over the past two years, Dayforce has done extensive change management—AI marathons, external speakers, open tools—which Rasmussen believes is part of what the company should do, but she also returns ownership to employees: 'You cannot wait for the company to take you where you need to go. Especially for technical roles, you must use personal time to pull yourself up to speed.' She added that this does not mean learning only outside of work; there are internal channels too, but 'now is really the time to ensure you are not falling behind.' This statement places the responsibility allocation of AI transformation on the table: the company provides tools and direction, and employees provide adaptation speed.

Sources

  • diginomica.comdiginomica↗
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