Time AI Copilot

WorkRippling · Time policies

Time AI copilot

Explain, create, and validate policies; answer “why” outcomes

In-context answers that translate configuration into plain language
In-context answers that translate configuration into plain language

Project snapshot

My contribution

Role

Lead Designer, Time

Focus

Identified high-ROI use cases, designed flows/prompts/UI, created 3 prototypes, aligned with Platform for shared Copilot patterns

With

Platform Design/Eng • Time Eng (prototype spike) • Leadership reviewers

Project details

Company
Rippling
Product area
Time policies
Discipline
AI framework · Product design
Timeline
Jun 2025 concept → Q4 2025 build
Status
Platform rollout in Q1 2026
Platform
Shared Copilot patterns entry points, response types, citations
Validation
Support-ticket analysis explainability clusters set the ROI targets
Prototypes
3 plus an engineering proof of concept
Checkout the Rippling AI launch announcementOpens in a new tab

00 Overview

Time policies (Time tracking, Overtime, Breaks, Time off) are rich and jurisdictional—great power, little clarity. I defined an AI Framework for Time: a copilot that explains policies, creates/edits them via natural language with real-time compliance checks, and answers employees’ “why” outcomes (e.g., no OT applied). The work aligned with Platform AI primitives now shipping across Rippling features, with Time implementation starting Q4 2025 to reduce policy-related support tickets and increase trust.

01 The problem

Great power, little clarity

Time policies (Time tracking, Overtime, Breaks, Time off) are rich and jurisdictional. The configuration is powerful; the outcomes are hard to explain.

  • Complexity

    Global workforce management is hard—customers struggle with country/state rules

  • Support burden

    Routine “what/why/how” questions pull Eng, managers, and support off higher-value work

  • Configuration fear

    Admins hesitate to customize defaults; risk of misconfiguration

  • Decentralized AI efforts

    Needed Time-specific use cases to guide platform investment

FIG. 01Three policy configuration screens, each one scrolling far past the fold

02 Solution highlights

Explain. Create. Check.

A copilot that explains policies, creates/edits them via natural language with real-time compliance checks, and answers employees’ “why” outcomes (e.g., no OT applied)

TIMEPOLICY

Create / Edit

“Create UK maternity policy with 100% pay for 12 weeks”

Check

Real-time compliance checks with citations

Explain

“Why no OT today?”

TIMEPOLICY

Create / Edit

“Create UK maternity policy with 100% pay for 12 weeks”

Check

Real-time compliance checks with citations

Explain

“Why no OT today?”

FIG. 02The three capabilities, and how they hand off to each other
AI explanation and policy-editing video
Plain-language answers; compliant edits; proactive risk scanning
FIG. 03Plain-language answers; compliant edits; proactive risk scanning
  • Explainability (in-context)

    “Summarize UK maternity,” “Explain AU annual leave payout at termination,” “Why no OT today?”

  • Creation & editing

    NL setup: “Create UK maternity policy with 100% pay for 12 weeks”; real-time compliance checks with citations; what-if scenarios

  • Proactive risk

    Run compliance across policies; flag gaps; offer compliant presets and trade-off notes

  • Reusable patterns

    Standard entry points, chat panel behaviors, response cards, sources/retention/guardrails; shared across Rippling features

  • 03 Process

    How the case got made

    1. Step 03.A Feasibility

      Could plain language summarize a policy?

      Engineering proof of concept for plain-language AI summaries

      Time AI feasibility primary product UI concept
      Time AI feasibility product UI concept three
      FIG. 04The first proof of concept and a later concept exploration, side by side
    2. Step 03.B Discovery

      Where the confusion actually lived

      Support analysis → “explainability/why/how” ticket clusters; biggest ROI areas

      ROUTINE QUESTIONS REACHING MANAGERS AND SUPPORTHOW

      How is Overtime calculated?

      WHY

      Am I eligible for a payout due to a missed break?

      WHAT

      How much time off can I take at year-end?

      Explainability

      BIGGEST ROI AREA

      FIRST PROOF: PLAIN-LANGUAGEPOLICY SUMMARIES
      ROUTINE QUESTIONS REACHING MANAGERS AND SUPPORTHOW

      How is Overtime calculated?

      WHY

      Am I eligible for a payout due to a missed break?

      WHAT

      How much time off can I take at year-end?

      Explainability

      BIGGEST ROI AREA

      FIRST PROOF: PLAIN-LANGUAGEPOLICY SUMMARIES
      FIG. 05Support themes behind the paradoxRoutine what, why, and how questions clustered around explainability

      Research

      The pain points

      The current reality for our customers is a paradox: Rippling provides the power to configure highly customized policies, but this power often comes at the cost of clarity. Our policy creation forms, while comprehensive, are incredibly complex. This leads to:

      Confusion and errors

      Customers often find it challenging to grasp the implications of each setting, leading to mistakes that can result in non-compliance or unexpected financial burdens

      Increased support burden

      This confusion results in a significant number of customer support tickets, as administrators seek assistance to understand or rectify their policy configurations. This not only drains our resources but also frustrates our customers.

      Hesitation to customize

      While we offer ‘Rippling Default Policies’ that comply with statutory minimums, such as a UK Maternity Leave policy, customers frequently wish to enhance them—like raising maternity pay from 80% to 100%. However, the fear of non-compliance or misinterpreting the effects of their changes often holds them back, forcing them to choose between providing better benefits for their employees and sticking to a rigid default.

      Initial strategy

      Our CEO challenged the team to devise a strategy for reducing customer support tickets. As the company explored ways to integrate AI experiences into our product, I recognized that enhancing explainability through AI capabilities was a key part of the solution.

      Common questions such as, “How is Overtime calculated?” “Am I eligible for a payout due to a missed break?” and “How much time off can I take at year-end?” frequently reach Managers, who often struggle to interpret policies set by others in the organization. AI could play a crucial role in addressing these inquiries.

      Explainability

      The initial focus was on enhancing product feature explainability to clarify Time policies. An engineering partner developed a proof-of-concept using AI to generate clear and digestible policy summaries. Ultimately, our vision is to enable users to input prompts for policy creation and to explore how AI can elevate Rippling’s policy engine, empowering our customers.

    3. Step 03.C Use cases

      Mapping the moments AI could help

      Mapping admin journeys

      Create a new policy

      1. 01

        Prompt to create a policy

        A user would simply begin chatting with the AI agent about a policy they want to create

        1. It could be a simple prompt like:

          • “I want to create a Vacation Policy with 15 days off for each employee that pays them 100% of their salary”
          • OR complex like:
          • “I want to create a Maternity Leave Policy for employees in my Manchester office in the UK”
        2. We can also guide the admin:

          • Select a country or countries for this policy
          • Select which leave type

          Create a 15 day vacation policy

      2. 02

        Rippling AI creates a simple policy

        Once I send my prompt, Rippling AI would get to work and create an initial policy draft for me. The policy could potentially be seen in various ways, maybe as a summary, a grid/table, or as a form

      3. 03

        I review the policy draft

        I can review the policy and continue chatting with the AI agent to refine the policy and to make sure that I can close any gaps

        1. I can ask clarifying questions to the AI Agent like:

          • “How much money does this policy cost the company?”
          • “What is the average cost per employee”
          • “Is this amount of time off standard or should I add more days?”
        2. I can refine my policy with additional prompts like

          • “Add 2 extra days of vacation after 1 year of service”
          • “Add expirations 1 year after the employees accrue vacation”
        3. The AI can help me fill the gaps by surfacing questions like:

          • “Would you like for employees to have the full 15 days available upon hire or do you want to accrue as they work?”
          • “I want them to accrue the hours as they work”
          • “Okay that’s great, how days should they accrue per day/hour worked? A standard accrual for most companies your size would accrue .5 hours per 1 day of work. Assuming an 8-hour work day, an employee would need to work 240 days to accrue the full 15 days of vacation.”
          • “That seems too long, let them accrue 1 hour per 1 day of work”
      4. 04

        I can also do a compliance check

        This is particularly helpful if you want to create this vacation policy with 15 days off for all employees across your company, but you have employees in Canada and Australia

        1. I can ask the AI Agent to cross check this policy with statutory laws in countries where I employ people

        2. The AI can then help me sort out where I’m non-compliant. For instance:

          • “Are there any issues with this policy I should be aware of?”
          • “It looks like you employ people in Canada and Australia, the 15 days of time off in this policy would be non-compliant. Would you like to create separate vacation policies for those countries with the statutory minimum days off and pay structure?”
      5. 05

        I can create scenarios to test out my policy

        I could select a specific employee from my company, select a start and end date, and run a scenario to see how this policy would play out. The UI could surface a visual to show me when how many days of time off this employee has accrued by the start date of vacation, or if I select an earlier date it could let me know that the employee wouldn’t have enough balance to take time off etc.

        1. Because we know how much the employee gets paid, we can also surface how much they would get paid for this time off. This would be exceptionally beneficial for more complex policies where complex pay rules apply

      Save policy

      The board also drew a dashed return from every stage to the one before it: the admin can go back and revise at any point before saving

      FIG. 06Time AI admin journey and use-case mapWhere an AI agent could help at each stage of creating a policy
    4. Step 03.D Concepts

      From explanation to action

      3 prototypes around Create/Edit • Explain • Check

      Intelligent policy creation
      Proactive compliance & risk management
      Play clip: Transparency & employee empowerment
      FIG. 07Three prototypes, one per capability
    5. Step 03.E Platform alignment

      Making the pattern reusable

      Contribution to system patterns (entry points, response types, citations)

      SHARED COPILOT SYSTEM

      Entry points

      Chat panel

      Response cards

      Sources + guardrails

      Data CloudHRISTimeSpendITEARLY IMPLEMENTATIONSCONTRIBUTED PATTERNSBROADER ADOPTION
      SHARED COPILOT SYSTEM

      Entry points

      Chat panel

      Response cards

      Sources + guardrails

      Data CloudHRISTimeSpendITEARLY IMPLEMENTATIONSCONTRIBUTED PATTERNSBROADER ADOPTION
      FIG. 08Shared Copilot anatomyAlready live in Data Cloud and HRIS, extended by Time, now reaching Spend and IT
      Panel pattern
      Agent thinking pattern
      Input collection pattern
      Create record operation pattern
      Prompt box pattern
      Panel header pattern
      14%
      FIG. 09Platform design-system patterns (Pebble)
    6. Step 03.F Implementation

      From framework to product

      Implement in Time, Q4 2025; Rippling beta launch Mar 26; measure ticket reduction, setup time, resolution rate

      Time AI Copilot implementation and final view
      Play clip: Time AI Copilot
      FIG. 10Play clip: Time AI Copilot

      The Copilot patterns didn’t stay a framework. In Time Simplified, the redesign of Time as a standalone product, I carried them into Timesheets: managers can ask why an overtime total came out the way it did and get the policy rules and the days behind it, in the product where the question comes up.

      Explain with AI, in Timesheets
      From Time Simplified: Explain with AI in Timesheets, in development, with the policy rules and daily breakdown behind an overtime total
      FIG. 11From Time Simplified: Explain with AI in Timesheets, in development, with the policy rules and daily breakdown behind an overtime total

    What I learned

    1. Trust starts with explainability and the “why”
    2. Tie AI to domain objects + citations; don’t ship a generic chat
    3. Platformizing early keeps AI consistent—and safer—across products
    4. The strength of AI lies in clarity and action—diagnosing and fixing, surfacing unknowns, and guiding smarter setup
    Adrian shared the Time Product org’s framework for AI, looking at how it will support the creation and understanding of often complex policies. [He] did an awesome job framing the opportunity and showing the concept through a realistic scenario. Great guidance for other teams shaping their AI initiative
    — Ryan Lucas, VP of Design, Jun 2025