# Build Dynamic Products, and Stop the AI Sideshow

Eliza Cabrera · Jeremy Silva

AI Engineer World's Fair 2025

[Talk page](https://ai.engineer/talks/CB-4NKDYnRs-build-dynamic-products-stop-ai-sideshow) · [Video](https://www.youtube.com/watch?v=CB-4NKDYnRs)

## Build Dynamic Products, and Stop the AI Sideshow

AI becomes useful when it improves the product customers already need. A support inbox and Workday’s employee services show how embedded capabilities can grow into dynamic products.

### What does AI positioning actually differentiate?

[0:29](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=29s)

If every product leads with AI, what does that tell a customer about which product solves their problem? Eliza Cabrera approaches that question as a principal AI product manager at Workday, building its financial audit agent after working on policy-agent go-to-market, assistant early access, and early generative AI features. Jeremy Silva brings a data science and machine learning background, including language-model work before **GPT-3**, and leads product at Freeplay. Their shared concern is what happens when technological exploration becomes the product strategy.

Leading products, marketing, and strategy with AI initially signaled that a company was participating in a technological shift. Once everyone adopted the same positioning, that signal lost its distinction. Much of the resulting product work was valuable exploration: teams were building things to discover what the technology could do. But **discovering a capability is not the same as discovering a differentiated customer solution**.

![Slide titled “Stop the AI Sideshow” shows a motorcycle rider labeled “Product Strategy” and a sidecar passenger labeled “AI Strategy,” alongside explanatory text.](https://ai.engineer/api/talk-essay-media/3381d024c797cfbfec60376f77aeeb01a66e36cb872bb4459f4269210da0531c)

Stop the AI sideshow: product strategy drives, with AI strategy in the sidecar.

### From testing LLMs to delegating work

[2:08](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=128s)

The early sequence was understandable. After an initial encounter with LLMs, teams used chat interfaces, content editors, and existing applications to explore their boundaries. Multimodal inputs and outputs expanded what those experiments could accept and produce. Then vector databases and **retrieval-augmented generation (RAG)** offered ways to ground responses in relevant information, while larger context windows and more memory increased what a model could work with. The aim was greater accuracy, even where the underlying notion of truth remained contested.

Copilots reflected another constraint: people were more comfortable with an assistant beside them than with software taking over work or displacing jobs. As the appeal of automation became clearer, agents offered reasoning, tool use, and API calls to coordinate work across business problems. These approaches helped teams understand the technology. Their widespread adoption, however, meant that choosing chat, RAG, or agents could not by itself establish a differentiated strategy.

### How a separate AI strategy produces separate features

[3:51](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=231s)

Silva describes a recurring pattern from Freeplay’s work with enterprise companies: leadership correctly prioritizes AI, then creates a centralized AI strategy that runs alongside the core product strategy. Separate initiatives, and sometimes separate teams, naturally produce features that feel disconnected from the rest of the product. The organizational separation becomes visible in the customer experience.

Three decisions reinforce that separation:

- **Quarantine the risk.** Teams confine AI to isolated product corners because reliability is uncertain. The underlying question is real: can this feature work consistently enough to create customer value?
- **Start with the technology.** A team builds a chatbot because it demonstrates AI, without establishing that customers struggle with support. Another adds summarization without evidence that users face information overload.
- **Prescribe solutions from above.** Leadership chooses the feature instead of setting direction and allowing product teams to discover the right solution.

### Make reliability part of product planning

[5:41](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=341s)

**AI reliability belongs inside product planning.** Instead of isolating uncertain functionality, teams need evaluation systems, tests, and prototypes that make its risks assessable. Those practices help establish whether a proposed feature can deliver enough value, and what must improve before it can do so reliably.

Discovery still starts with the customer’s problem. Leadership supplies the strategic direction; product teams close to customers need room to experiment, prototype, and abandon unsuccessful approaches quickly. Aligning strategy, teams, and roadmaps makes that learning part of the main product effort. The resulting experience should feel cohesive: it can solve the problem better without needing to announce itself as AI.

### Crawl, walk, run in a shared support inbox

[7:27](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=447s)

Deep integration does not require rebuilding the entire product at once. Silva’s crawl, walk, run framework develops capability incrementally while establishing a foundation for later work. Consider his hypothetical customer-support SaaS company: it already has a mature shared inbox where support teams handle customer questions.

| Stage | Shared-inbox capability | Product change |
| --- | --- | --- |
| Crawl | Find previous, similar questions with semantic search | Improve existing functionality with little new interface |
| Walk | Prepare a suggested reply before the user arrives | Add contextual, personalized assistance |
| Run | Autonomously triage issues and respond across features | Rethink interfaces, workflows, and application architecture |

In the crawl stage, retrieved questions help a support representative ground a response. In the walk stage, a draft gives that person a starting point; new interface elements need not yet require a fundamental redesign. Running changes the scope of action: the agent operates across the product and feature set, so the core architecture and user experience must accommodate that work.

![Slide with three purple cards connected by arrows: Crawl, embedded AI features in a single application; Walk, contextual and personalized features across multiple products or tools; Run, dynamic, proactive, interoperable AI experiences across product ecosystems.](https://ai.engineer/api/talk-essay-media/b0a7de858b8577f5b73279a2f16d811b17d7cd6ccbf29564017ecfeb746712c9)

Crawl, walk, run: from embedded AI in one application to interoperable experiences across product ecosystems.

The stages accumulate. Search remains useful when the product starts drafting, and those capabilities become foundations for broader automation. **Even the crawl stage is embedded in the customer’s workflow.** It is a smaller integrated capability, not a disconnected feature waiting for a future redesign to make it useful.

### Workday Help: build the content foundation

[10:18](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=618s)

Workday’s example begins with employee self-service. Employees need quick answers to workplace questions; when an answer is insufficient, they need a case or a live support person. [Workday Help](https://www1.workday.com/content/dam/web/en-us/documents/datasheets/enus-hcm-da-workday-help.pdf) combines the two supporting systems: a knowledge base and case management. Its initial generative AI features improved the content that would supply those answers.

An author could upload a benefits policy more than twenty pages long and generate an employee FAQ or manager talking points in a consistent format. Cabrera describes enterprise content teams of roughly three to fifteen people, where maintaining a unified voice matters alongside reducing the work of writing each article. Generation was therefore part of an authoring workflow, rather than a separate destination for prompting a model.

Translation extended the same workflow. Cabrera reports that Workday Help supported translation into 34 languages in a couple of clicks. The author could keep a base article, generate English talking points, and manage French, Spanish, or Japanese translations as versions in the left-hand panel. The demonstration pairs policy content and manager talking points with translation controls, making multilingual content management part of the editor.

![A Crawl card sits beside a Workday parental leave policy containing a summary and manager talking points, with a Translations panel on the right.](https://ai.engineer/api/talk-essay-media/bc84aee63ac1343c0be5248c2c38df36583f04a17040daa9162e91638a02a23a)

Workday’s Crawl example shows a parental leave policy with its Translations panel open.

The interface was designed in 2023 for users who did not need to understand generative AI to benefit from it. It avoided making chatbots, prompt fields, and decorative sparkles the entire experience. Human review remained in the workflow, with an AI disclaimer and limited purple sparkles identifying AI functionality. Cabrera dates these Help features’ general availability to August 2024, the R2 release. This was the crawl stage: useful authoring and translation inside an existing product.

### From content authoring to contextual self-service

[13:31](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=811s)

Once content exists, the next problem is helping employees use it while completing a task. In Cabrera’s walk-stage example, a manager named Elaine is changing a location to San Francisco. She understands many of the form fields, but not all of them. [Workday Assistant](https://newsroom.workday.com/2024-09-17-Workday-Announces-New-User-Experience-Powered-by-AI) provides a contextual copilot across Workday, with suggestions informed by the page she is using.

That context raises the stakes for data processing. A help article contains sensitive customer content, but assistance inside a task can encounter personally identifiable information, pay, or compensation inputs. The expansion also crosses organizational boundaries: a platform capability must work across human capital management and financials, including benefits, procurement, and core HCM. Shipping it requires stronger coordination between leadership’s direction and the teams implementing individual workflows.

### Proactive assistance across the platform

[14:50](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=890s)

For the run stage, Cabrera connects the same employee and manager use case to Workday’s [Agent System of Record](https://newsroom.workday.com/2025-02-11-The-Next-Generation-of-Workforce-Management-is-Here-Workday-Unveils-New-Agent-System-of-Record), announced a few months before the talk. She describes agentic capabilities operating behind Workday Assistant, so users can benefit without understanding agents. The direction is greater autonomy and proactivity: listen for policy changes, then notify people with suggestions. The February 2025 announcement described the Agent System of Record and new agents as still in development; this example presents the direction of the experience, not a confirmed general-availability milestone.

The coordination burden grows with that scope. A capability that began inside one product within a SKU now extends across the core platform, requiring top-down strategy and bottom-up execution to connect multiple product experiences. Cabrera describes Workday’s customer reach as roughly 60% of the S&P 500; the contemporaneous announcement instead reports more than 60% of the Fortune 500. These are different company groups, and the figures describe customer coverage, not adoption of these AI features.

### Beyond yesterday’s roadmap

[16:08](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=968s)

Stopping the AI sideshow does not mean abandoning agents or the underlying technology. These capabilities are stepping stones through which an organization learns to build more useful experiences. Cabrera connects that progression to purposeful, vertical-specific products, echoing earlier talks by Sarah and Brian. The opportunity is to use the accumulated capability to address a broader problem space.

More powerful technology can leave a team solving the same old roadmap faster. Digitizing new data, including inputs from physical environments and spaces, can instead change which problems a product can address. Multimodal experiences make that especially compelling when they work together with little friction. Cabrera briefly identifies interoperability and reinforcement learning as relevant to agents; the broader destination is **dynamic products that respond to their environment**, extending what the product can do as its understanding of the surrounding world expands.

## Resources

- [Workday's Agent System of Record announcement](https://newsroom.workday.com/2025-02-11-The-Next-Generation-of-Workforce-Management-is-Here-Workday-Unveils-New-Agent-System-of-Record) — The February 2025 announcement explains agent governance and planned agents for financial auditing, policy, payroll and contracts.
- [Workday Assistant's contextual HR and finance experience](https://newsroom.workday.com/2024-09-17-Workday-Announces-New-User-Experience-Powered-by-AI) — Workday's 2024 announcement describes personalized answers, guided tasks and the planned rollout of its new Assistant.
- [Workday Help product overview](https://www1.workday.com/content/dam/web/en-us/documents/datasheets/enus-hcm-da-workday-help.pdf) — A later product overview covering HR knowledge management, case management, AI authoring and translation, and article review workflows.

## Slides

### Build Dynamic Products and Stop the AI Sideshow

Eliza Cabrera and Jeremy Silva introduce a talk on dynamic AI products.

[0:20](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=20s) [Title slide for a talk by Eliza Cabrera and Jeremy Silva.](https://assets.ai.engineer/sites/aiecode2025/sha256/5e3693b1d2ae8083100ec9c721116a35c82ce6a36cf236f6071671669a329526.webp)

BUILD DYNAMIC PRODUCTS
AND STOP THE AI SIDESHOW

PRESENTED BY ELIZA CABRERA & JEREMY SILVA

AI ENGINEER WORLD’S FAIR
AIE
Microsoft
smol ai
World’s Fair

### Stop the AI Sideshow

A motorcycle puts product strategy in the driver’s seat and AI strategy in the sidecar. The speakers argue that talking about AI and building products to explore technological breakthroughs are not enough for a winning strategy.

[1:22](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=82s) [A motorcycle and sidecar represent product and AI strategy beside text arguing that talking about AI alone is not enough.](https://assets.ai.engineer/sites/aiecode2025/sha256/38e26fd52a50b5e06be6d37badecfadbff8c47f79c65292346de5470ad02c359.webp)

STOP THE AI SIDESHOW

PRODUCT STRATEGY
AI STRATEGY

We know that you’re using AI, so is everyone else. It’s not 2023 anymore, and simply talking about AI for your product strategy & UX is not enough.

Looking back, we see the products we’re building are the result of us seeking to understand the tech breakthroughs... but this isn’t a winning strategy.

ELIZA CABRERA & JEREMY SILVA
AI ENGINEER WORLD’S FAIR

### We've Seen This Show Before

The speaker traces a progression from chat and content interfaces to copilots and agents, arguing that exploring AI capabilities alone does not produce a differentiated product strategy.

[2:13](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=133s) [A chatbot illustration accompanies a card describing LLMs, chat and content interfaces, and multimodal inputs and outputs.](https://assets.ai.engineer/sites/aiecode2025/sha256/c4dd1723b29e832985c207220118a4664b33aeba72381dd458514b855806e194.webp)

WE’VE SEEN THIS SHOW BEFORE

Chatbots & Content Generation
LLM
+ Chat/content UI
+ multimodal

BUILDING DYNAMIC PRODUCTS
AI ENGINEER WORLD’S FAIR

AIE
Microsoft
smol ai
World’s Fair

[3:17](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=197s) [A progression from chatbots and content generation to copilot assistants and agents or digital workers, with the technologies associated with each stage.](https://assets.ai.engineer/sites/aiecode2025/sha256/e9166dc9236816aa7acda323465f6f227d5463241a9292943cc40ceec6586487.webp)

WE’VE SEEN THIS SHOW BEFORE

Chatbots & Content Generation
LLM
+ Chat/content UI
+ multimodal

Copilot Assistants
+ RAG/vector DBs
+ large context
+ memory

Agents & Digital Workers
+ CoT reasoning
+ tool use APIs
+ orchestration

BUILDING DYNAMIC PRODUCTS
AI ENGINEER WORLD’S FAIR

AIE
Microsoft
smol ai
World’s Fair

### Causes of the AI Sideshow

In the speakers’ experience, centralized AI strategy becomes a sidecar to product strategy, with separate initiatives and sometimes teams producing bolt-on features.

[3:59](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=239s) [A motorcycle rider labeled product strategy carries a sidecar labeled AI strategy.](https://assets.ai.engineer/sites/aiecode2025/sha256/4d2c579cd892d72097ac9e3a34eac92ff8b3e5bf99dbff09346708df857dbb19.webp)

CAUSES OF THE AI SIDESHOW

PRODUCT STRATEGY
AI STRATEGY

BUILDING DYNAMIC PRODUCTS
AI ENGINEER WORLD’S FAIR

AIE
World’s Fair
Microsoft
smol ai

[4:04](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=244s) [A motorcycle labeled product strategy pulls a sidecar labeled AI strategy.](https://assets.ai.engineer/sites/aiecode2025/sha256/4cba35934c7b212788662b929dacb885643c814b4c54b6479d92e31182e1cde9.webp)

CAUSES OF THE AI SIDESHOW

PRODUCT STRATEGY
AI STRATEGY

BUILDING DYNAMIC PRODUCTS
AI ENGINEER WORLD’S FAIR

AIE
World’s Fair
Microsoft
smol ai

### Causes of the AI Sideshow: Quarantining AI

Companies may manage AI risk by keeping it separate from the core product. The slide depicts AI strategy as a sidecar attached to product strategy.

[4:28](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=268s) [The first cause is managing risk by keeping AI as a separate initiative or product; a motorcycle and sidecar depict product and AI strategy.](https://assets.ai.engineer/sites/aiecode2025/sha256/1bacac067690f40e7b3db18877f560a33a3211723a96f878f9b1208562e482a4.webp)

CAUSES OF THE AI SIDESHOW

1 Quaranting AI
Managing risk by keeping AI as a separate initiative or product

PRODUCT STRATEGY
AI STRATEGY

BUILDING DYNAMIC PRODUCTS
AI ENGINEER WORLD’S FAIR

Interface text:
AIE
World’s Fair
Microsoft
smol ai

### Crawl, Walk, Run

The speaker proposes building generative AI capabilities iteratively while laying a foundation for AI across the product suite.

[7:41](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=461s) [A black slide displays the title Crawl, Walk, Run.](https://assets.ai.engineer/sites/aiecode2025/sha256/7cd3a9591c8c046c0308c3e465f91c710269f1fe64dbacfa8ad68e3bd2b82a19.webp)

CRAWL, WALK, RUN

AIE
Microsoft
smol ai

Wo[illegible]

[8:00](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=480s) [Title slide reading “Crawl, Walk, Run.”](https://assets.ai.engineer/sites/aiecode2025/sha256/4e819358709341f388615ac2515ec3b4e797eddb58c9ddde704781402d05bf68.webp)

CRAWL, WALK, RUN

AIE
Microsoft
smol ai

Wo[illegible]

### AI-generated parental leave article

The Help knowledge base shows an AI-generated parental leave article with a summary and manager talking points. The speaker describes content generation as an early use of GenAI in Help.

[10:58](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=658s) [A parental leave article shows a summary and talking points for managers.](https://assets.ai.engineer/sites/aiecode2025/sha256/b8b2925b7e28c23a0b3888d4fe75e7007524c14179625c7c1f86e6dd88e64e08.webp)

Parental Leave Policy

Summary
This document outlines the Parental Leave Policy for employees of Elevate Corporation in Ireland. It covers eligibility criteria, leave duration, application procedures, and employee rights during and after the leave. The policy aims to provide guidance on taking time off to care for a child while ensuring a smooth transition back to work.

Key Talking points for Managers
1. Eligibility and Entitlement: Employees are eligible for up to 26 weeks of unpaid parental leave, which generally must be taken before the child's 12th birthday. Discuss specific scenarios like adoption, child's disability, or pro-rata entitlement based on the employee's tenure.
2. Leave Structure and Transfer: The leave can be taken in a single block, two separate periods, or on specific days agreed upon with the company. Each parent has a separate entitlement, and transfer between parents working at Elevate Corporation is possible. Clarify the options and any limitations.
3. Application Process: Employees must submit a formal written request at least 6 weeks before the intended leave, outlining the duration and structure of the leave. Upon approval, they need to log the leave in the company system. Guide them through the process and required documentation.
4. Postponement and Illness: The company may postpone the leave under specific circumstances, and employees need to be informed in writing. In case of illness during the leave, employees should contact their manager and provide medical certificates. Explain the procedures and implications.
5. Employee Rights: Employees remain employed during parental leave, accruing benefits and leave entitlements. They have the right to return to their previous role or a suitable alternative. Emphasize their job security and any potential adjustments upon return.
6. Policy Purpose: Reiterate that parental leave is intended for childcare purposes and not for sabbaticals or other employment. Ensure employees understand the policy's intention and use it accordingly.

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### Article translations

The translations panel shows the published English base article, no drafts, and an option to add a translation. The speaker describes using the panel to manage article versions in other languages.

[11:17](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=677s) [The parental leave article appears beside a translations panel listing its published base version.](https://assets.ai.engineer/sites/aiecode2025/sha256/60dd3c81876ca930f55034a7115437c0a3d6ad4866e109af0e182d5780642022.webp)

Summary

This document outlines the Parental Leave Policy for employees of Elevate Corporation in Ireland. It covers eligibility criteria, leave duration, application procedures, and employee rights during and after the leave. The policy aims to provide guidance on taking time off to care for a child while ensuring a smooth transition back to work.

Key Talking points for Managers

1. Eligibility and Entitlement: Employees are eligible for up to 26 weeks of unpaid parental leave, which generally must be taken before the child's 12th birthday. Discuss specific scenarios like adoption, child's disability, or pro-rata entitlement based on the employee's tenure.
2. Leave Structure and Transfer: The leave can be taken in a single block, two separate periods, or on specific days agreed upon with the company. Each parent has a separate entitlement, and transfer between parents working at Elevate Corporation is possible. Clarify the options and any limitations.
3. Application Process: Employees must submit a formal written request at least 6 weeks before the intended leave, outlining the duration and structure of the leave. Upon approval, they need to log the leave in the company system. Guide them through the process and required documentation.
4. Postponement and Illness: The company may postpone the leave under specific circumstances, and employees need to be informed in writing. In case of illness during the leave, employees should contact their manager and provide medical certificates. Explain the procedures and implications.
5. Employee Rights: Employees remain employed during parental leave, accruing benefits and leave entitlements. They have the right to return to their previous role or a suitable alternative. Emphasize their job security and any potential adjustments upon return.
6. Policy Purpose: Reiterate that parental leave is intended for childcare purposes and not for sabbaticals or other employment. Ensure employees understand the policy's intention and use it accordingly.

Call to Action

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### Parental leave article translation

The demo generates a French translation from the published parental leave article. The resulting draft opens in an editor, where a paragraph is selected for review.

[11:28](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=688s) [An Add Translation dialog selects French and generation from the base article.](https://assets.ai.engineer/sites/aiecode2025/sha256/0620ea864f5c1547e632950509fd84a4e03966729defad3e8eae0602cc669809.webp)

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Summary
This document outlines the Parental Leave Policy for employees of Elevate Corporation in Ireland. It covers eligibility criteria, leave duration, application procedures, and empl[illegible] to provide guidance on taking time off to care for a child while ensu[illegible]

Key Talking points for Managers
1. Eligibility and Entitlement: Employees are eligible [illegible] must be taken before the child's 12th birthday. Dis[illegible] rata entitlement based on the employee's tenure.
2. Leave Structure and Transfer: The leave can be ta[illegible] agreed upon with the company. Each parent has a [illegible] Elevate Corporation is possible. Clarify the options [illegible]
3. Application Process: Employees must submit a fo[illegible] outlining the duration and structure of the leave. U[illegible] Guide them through the process and required documentation.
4. Postponement and Illness: The company may postpone the leave under specific circumstances, and employees need to be informed in writing. In case of illness during the leave, employees should contact their manager and provide medical certificates. Explain the procedures and implications.
5. Employee Rights: Employees remain employed during parental leave, accruing benefits and leave entitlements. They have the right to return to their previous role or a suitable alternative. Emphasize their job security and any potential adjustments upon return.
6. Policy Purpose: Reiterate that parental leave is intended for childcare purposes and not for sabbaticals or other employment. Ensure employees understand the policy's intention and use it accordingly.

Call to Action

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BUILDING DYNAMIC PRODUCTS
AI ENGINEER WORLD FAIR

[11:55](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=715s) [French parental leave article in an editor, with the postponement and illness paragraph selected.](https://assets.ai.engineer/sites/aiecode2025/sha256/24724b416abcf42ae7962feefaabab9531335dbdd40ec064d0ee3462f5332214.webp)

en douceur vers le retour au travail.

Principaux points de discussion pour les managers
1. Admissibilité et droits : Les employés ont droit à un congé parental non rémunéré pouvant aller jusqu'à 26 semaines, qui doit généralement être pris avant le 12e anniversaire de l'enfant. Discutez de scénarios spécifiques comme l'adoption, le handicap de l'enfant ou le droit au prorata en fonction de l'ancienneté de l'employé.
2. Structure et transfert des congés : Le congé peut être pris en un seul bloc, en deux périodes distinctes ou à des jours précis convenus avec l'entreprise. Chaque parent a un droit distinct et le transfert entre les parents travaillant chez Elevate Corporation est possible. Clarifiez les options et les éventuelles limitations.
3. Processus de demande : Les employés doivent soumettre une demande écrite officielle au moins 6 semaines avant le congé prévu, en décrivant la durée et la structure du congé. Une fois l'approbation obtenue, ils doivent enregistrer le congé dans le système de l'entreprise. Guidez-les tout au long du processus et de la documentation requise.
4. Report et maladie : L'entreprise peut reporter le congé dans des circonstances particulières et les salariés doivent en être informés par;écrit. En cas de maladie pendant le congé, les salariés doivent contacter leur responsable et fournir des certificats médicaux. Expliquez les procédures et les implications.
5. Droits des employés : Les employés conservent leur emploi pendant leur congé parental, ce qui leur permet d'accumuler des avantages et des droits à congé. Ils ont le droit de retrouver leur poste précédent ou un autre poste approprié. Mettez l'accent sur la sécurité de leur emploi et sur les éventuels aménagements à leur retour.
6. Objectif de la politique : Réitérer que le congé parental est destiné à des fins de garde d'enfants et non à des congés sabbatiques ou à d'autres emplois. Veiller à ce que les employés comprennent l'intention de la politique et l'utilisent en conséquence.

Appel à l'action
Les employés qui envisagent de prendre un congé parental doivent se familiariser avec l'intégralité du document de [illegible]

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### French draft ready to publish

The French parental leave draft is open for editing beside the published English base article. The presenter frames translations as a way to manage content for large enterprise teams.

[12:00](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=720s) [Edited French parental leave draft with the Publish button visible.](https://assets.ai.engineer/sites/aiecode2025/sha256/a577d453f7da72e8a729f41b7b31b527ba00beaa97bd8d72f41b5ce6e40789d1.webp)

Politique de congé parental

Résumé

Ce document décrit la politique de congé parental pour les employés d'Elevate Corporation en Irlande. Il couvre les critères d'éligibilité, la durée du congé, les procédures de demande et les droits des employés pendant et après le congé. La politique vise à fournir des conseils sur la prise de congés pour s'occuper d'un enfant tout en assurant une transition en douceur vers le retour au travail.

Principaux points de discussion pour les managers

1. Admissibilité et droits : Les employés ont droit à un congé parental non rémunéré pouvant aller jusqu'à 26 semaines, qui doit généralement être pris avant le 12e anniversaire de l'enfant. Discutez de scénarios spécifiques comme l'adoption, le handicap de l'enfant ou le droit au prorata en fonction de l'ancienneté de l'employé.
2. Structure et transfert des congés : Le congé peut être pris en un seul bloc, en deux périodes distinctes ou à des jours précis convenus avec l'entreprise. Chaque parent a un droit distinct et le transfert entre les parents travaillant chez Elevate Corporation est possible. Clarifiez les options et les éventuelles limitations.
3. Processus de demande : Les employés doivent soumettre une demande écrite officielle au moins 6 semaines avant le congé prévu, en décrivant la durée et la structure du congé. Une fois l'approbation obtenue, ils doivent enregistrer le congé dans le système de l'entreprise. Guidez-les tout au long du processus et de la documentation requise.
4. Droits des employés : Les employés conservent leur emploi pendant leur congé parental, ce qui leur permet d'accumuler des avantages et des droits à congé. Ils ont le droit de retrouver leur poste précédent ou un autre poste approprié. Mettez l'accent sur la sécurité de leur emploi et sur les éventuels aménagements à leur retour.
5. Objectif de la politique : Réitérer que le congé parental est destiné à des fins de garde d'enfants et non à des congés sabbatiques ou à d'autres emplois. Veiller à ce que les employés comprennent l'intention de la politique et l'utilisent en conséquence.

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### French translation published

The French parental leave article is published alongside the English base article. The translations panel now lists both as published and offers an option to add another translation.

[12:08](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=728s) [Published French article with a success message and two published translations in the sidebar.](https://assets.ai.engineer/sites/aiecode2025/sha256/baa94cc983776b63b8a41594caae10a87312146a5c5129568bd8b6f61106c628.webp)

Politique de congé parental

Résumé

Ce document décrit la politique de congé parental pour les employés d'Elevate Corporation en Irlande. Il couvre les critères d'éligibilité, la durée du congé, les procédures de demande et les droits des employés pendant et après le congé. La politique vise à fournir des conseils sur la prise de congés pour s'occuper d'un enfant tout en assurant une transition en douceur vers le retour au travail.

Principaux points de discussion pour les managers

1. Admissibilité et droits : Les employés ont droit à un congé parental non rémunéré pouvant aller jusqu'à 26 semaines, qui doit généralement être pris avant le 12e anniversaire de l'enfant. Discutez de scénarios spécifiques comme l'adoption, le handicap de l'enfant ou le droit au prorata en fonction de l'ancienneté de l'employé.
2. Structure et transfert des congés : Le congé peut être pris en un seul bloc, en deux périodes distinctes ou à des jours précis convenus avec l'entreprise. Chaque parent a un droit distinct et le transfert entre les parents travaillant chez Elevate Corporation est possible. Clarifiez les options et les éventuelles limitations.
3. Processus de demande : Les employés doivent soumettre une demande écrite officielle au moins 6 semaines avant le congé prévu, en décrivant la durée et la structure du congé. Une fois l'approbation obtenue, ils doivent enregistrer le congé dans le système de l'entreprise. Guidez-les tout au long du processus et de la documentation requise.
4. Droits des employés : Les employés conservent leur emploi pendant leur congé parental, ce qui leur permet d'accumuler des avantages et des droits à congé. Ils ont le droit de retrouver leur poste précédent ou un autre poste approprié. Mettez l'accent sur la sécurité de leur emploi et sur les éventuels aménagements à leur retour.
5. Objectif de la politique : Réitérer que le congé parental est destiné à des fins de garde d'enfants et non à des congés sabbatiques ou à d'autres emplois. Veiller à ce que les employés comprennent l'intention de la politique et l'utilisent en conséquence.

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## Full transcript

[0:00](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=0s) [on-hold music] All right.

[0:16](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=16s) Well, thank you for joining us. We are here to talk AI products, and specifically dynamic products, which we'll unpack in the next twenty minutes or so. A little bit about us before we jump in.

[0:29](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=29s) I am Eliza Cabrera. I'm a principal AI product manager at Workday. I'm currently building with an incredible team, our financial audit agent. I also led go-to-market for our policy agent, as well as early access for our assistant, which is more like a copilot, as well as some of our early days kind of GenAI, uh, features as well.

[0:49](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=49s) My name's Jeremy Silva. Uh, I come from a data science machine learning background. Been building language models since the Dark Ages, pre-GPT-3 that is. Um, and now I lead product at a company called Freeplay, which exists to help teams build great AI products.

[1:03](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=63s) Awesome. Let's get into it. So if there's one thing that we want you to take away from this session, it's to stop the AI sideshow, which I know sounds a little bit counterintuitive.

[1:17](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=77s) We're all at an AI conference. All of us are talking about agents and AI. It's all over the place, right? So what exactly are we talking about here? Having AI leading your products, your go-to-market, your strategy, this was a really great approach when we were all trying to communicate that we were at the forefront of this technological

[1:38](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=98s) disruption, right? But now everyone is really kind of saying the same thing.

[1:45](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=105s) And if we look at the different products that have resulted, in hindsight, which hindsight's kind of twenty/twenty, right? We're able to see that what we've been doing is building product to try to figure out what these different technological breakthroughs can do for us.

[2:03](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=123s) So let's unpack what we're talking about here a bit.

[2:08](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=128s) So let's go back maybe post-ChatGPT, maybe for some of us in the room pre-GPT, but whenever your sort of aha moment was with LLMs, trying to figure out what you can do with the tech, what you can't, what the boundaries are.

[2:22](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=142s) We ended up using chat UIs, content UIs, existing applications, right, to be able to really test the boundaries of these LLMs. We were also using multimodal to see what different kinds of inputs and outputs we could use the technology for.

[2:40](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=160s) Then we realized we could ground the models. We had vector databases and RAG. We were trying to get to accuracy and truth, if we can agree on that. Um, we had larger context windows and increased memory.

[2:56](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=176s) We also weren't super, um, I would say, comfortable with having AI do work for us. So everything was a copilot, right? A buddy next to us who can help us get things done, but we don't want to be taking anybody's jobs away.

[3:11](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=191s) We don't want to be, um, sort of automating work until we realize that might actually be kind of nice [chuckles] to be able to have agents that can do things for us, to reason, to be able to use tools and various APIs to orchestrate across different business problems.

[3:31](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=211s) And this is the state and sort of space I would say that we're in right now. We're not saying that these different approaches are wrong, but they're an approach to understand the technology, and it's not going to build you a differentiated strategy because everyone is doing the same thing.

[3:51](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=231s) So why do we see these kind of like bolt-on, non-differentiated AI products persist?

[3:58](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=238s) By working across dozens of enterprise companies at Freeplay, we've noticed a common trend emerge, which is companies know they rightly need to prioritize AI, but the way they do that is via creating this sort of centralized AI strategy.

[4:10](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=250s) And what happens is this centralized AI strategy starts running as this sort of sidecar to their core product strategy rather than the two be deeply integrated. There's different initiatives, sometimes even different teams, and then naturally these sort of like bolt-on, non-integrated AI features and products start to proliferate.

[4:28](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=268s) So what are some of the causes of this sort of AI sideshow that we're talking about here? The first is that companies seek to mitigate the risk associated with AI by quarantining it to specific corners of the product.

[4:40](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=280s) Albeit there, like, is new risk here, right? There's this new reliability question you have to ask yourself, which is like, "Can I even get this feature to work reliably enough to drive value for customers?"

[4:52](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=292s) Second, we see teams prioritizing the technology over their customer needs. They become the hammer in search of the nail. Rather than trying to solve their customer problems by harnessing the technology, they're just trying to find any manifestation of that technology.

[5:07](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=307s) And we see this manifest in a bunch of predictable ways. We see teams building chatbots because chatbots demonstrate AI capability, not because customers are actually struggling with support. We see companies building document summarization, again, because it demonstrates capability, not because their users are suffering with information overload.

[5:26](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=326s) And finally, we see companies creating this kind of top down-- They're pushing solutions out from the top down rather than setting that top-level strategy and letting the bottoms-up discovery process, um, be the manifestation of that priority.

[5:41](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=341s) So how do you avoid the AI sideshow here? The key is to integrate and align your AI and your product, and integrating AI risk into planning is a critical part of that.

[5:53](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=353s) There is this new risk we're talking about, but instead of being-- shying away from that risk and trying to quarantine AI to specific corners of the product or specific teams, you need to deeply integrate that into your product planning.

[6:04](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=364s) And this will require, like, some new muscles here, right? Like, you need to kind of build these systems for evaluation, for testing, 'cause if you're doing good prototyping and testing, you can at least kind of wrap your arms around that risk and know how to handle it.

[6:18](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=378s) And then second, start with the customer problem. If you're inventing new problems to go solve with the advent of AI, you're probably gonna stray here. And finally, like we talked about, enable that bottoms-up discovery process for AI products.

[6:31](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=391s) It's likely your product folks who are boots on the ground every day, who understand the right solutions here, give them the space to experiment, prototype, and importantly, fail fast, but set that top-line strategy, and then allow the bottoms-up discovery process to take place.

[6:47](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=407s) This is how you ultimately manifest AI products that feel a nat- like a natural and cohesive part of the product experience, rather than feeling bolt-on. And that's alt- ultimately the, like, the hallmark of good, successful AI integration are AI products that need not announce themselves as AI, but rather just solve the customer problem better than what came

[7:07](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=427s) before. So the North Star that Eliza and I are talking about today are AI products that are deeply and dynamically integrated into your product ecosystem, but the only way you get there is by aligning your, your strategy, your teams, and your roadmaps accordingly, and importantly, avoiding the AI sideshow.

[7:27](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=447s) This is admittedly like an audacious North Star, and especially if you're kind of stuck in this sideshow model, like, how do you find your way out? This is where we think this crawl, walk, run approach comes into play.

[7:40](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=460s) We're all new to building generative AI products. Like, to some degree or another, we're all building the plane while we're flying it. The most successful teams we see here are those that crawl, walk, run their way into this new era of kind of generative AI products.

[7:56](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=476s) 'Cause what that allows you to do is it allows you to sort of build the capability iteratively while laying the fun- the foundation of that AI functionality throughout your product suite.

[8:06](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=486s) So I wanna, like, walk through an example here. Um, with an example, we'll take, like, an, a customer support SaaS company. Let's say they have, like, a shared inbox feature, um, that customer support teams come on to work out of.

[8:18](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=498s) Mature product, um, but they wanna start integrating AI. So in this crawl phase, you're starting to build embedded AI experiences. You're likely, in this phase, not building a whole lot of new product surface area.

[8:31](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=511s) Rather, you're just, like, adding AI on the back end and starting to kind of accentuate and accelerate the existing functionality you have. If we take that customer support example, that might look something like, you know, building a feature that uses semantic search to, like, surface previous similar questions to help the c- the user ground when they, like,

[8:48](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=528s) are responding to their customer. And then in the walk phase,

[8:53](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=533s) this is where we're starting to build more contextual and personalized AI experiences. Here we might actually-- We are starting to build, like, new product surface area, but we're probably not at the point yet where we need to, like, fundamentally rethink our core app architecture and our UX.

[9:07](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=547s) If we go back to that example, that might look something like, um, you know, building a feature that will, like, suggest a draft ahead of time so that when the, the user comes in, there's already a draft there ready to go for them to start from.

[9:20](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=560s) And then finally, where we land, when we really start to run, this is where we're building those dynamic, interoperable, and integrated AI experiences throughout our product suite. This is the stage where you do start needing to, like, re- fundamentally rethink your UI, your UX, and your app architecture because now your AI features, like, if we go back

[9:40](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=580s) to our customer support example, it might look like an autonomous agent that can triage issues, respond to customers, but importantly, it's operating across the product and, and feature set.

[9:49](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=589s) And in order to incorporate that kind of functionality, you do need to start rebuilding core surface area and, like, starting to revisit your UX. But importantly, along the way, you're not throwing out functionality.

[10:02](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=602s) It's building on top of each other. It's-- That functionality is building as you go, right? You're just extending on it. And importantly, even at the crawl phase, you're still building embedded functionality, not this sort of, like, bolt-on, non-integrated functionality.

[10:16](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=616s) So I'll pass it to Eliza now.

[10:18](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=618s) Yeah. So let's walk through a tangible example here, 'cause it-- there's a lot to unpack. So this problem space, I feel like everyone knows this. I've been living and breathing it for a few years.

[10:29](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=629s) But HR service delivery or employee self-service is all of us work in jobs or you're running a company. Your employees need to be able to get their questions answered quickly, and if they can't get those questions answered, they need help from a, a, a support person.

[10:45](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=645s) So through a case, this could be a live agent, et cetera. So we've spent a lot of time working in this space. This is also where some products have found product market fit, especially with early sort of GenAI solutions.

[10:57](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=657s) So where we started to, I would say, crawl with the technology, um, this was within our Help product. So Help has two components. There's a knowledge-base solution. There's also a case management solution.

[11:10](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=670s) And so early days, we took a look at the tech and said, "Where can we use GenAI really to affect change with customers?" And so I know knowledge base has become, like, the back end for GPTs and just a best practice.

[11:22](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=682s) But at the time, we said, "Okay, we've got content gen in here. We've got translations in here." This is the content that's fueling the answers to all of those questions that employees are asking.

[11:31](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=691s) And so there were two key features. So one was actually content authors. So they might come into an editor like this. They are going to upload, say, a policy doc.

[11:40](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=700s) So imagine a benefits policy, like, twenty-plus pages long. They don't wanna necessarily write that article, right? But they could have the AI ingest it, create an employee FAQ. In this case, we had talking points for managers, and they're able to get a consistent format.

[11:55](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=715s) So the other thing I would mention is we're thinking about content at scale. So this isn't for small sort of SMBs. This is large enterprise who have content teams of, say, like, three to fifteen people.

[12:06](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=726s) And so you need to have a united sort of voice around that content that's coming out. So- On top of that other feature, we put this translations, which you can see in the GIF here.

[12:16](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=736s) In just a couple of clicks, I can go in and translate into one of the thirty-four different languages that we support. And you see we added on the left-hand panel here, um, the ability to actually manage versions as well.

[12:29](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=749s) So I might have my base article, I'm generating talking points in English, and then I want to translate into French and Spanish, um, maybe Japanese. And you can see that you're managing those versions as well.

[12:42](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=762s) A couple of things I want to call out here. Yes, we're using GenAI and translations, but this isn't in your face sparkles and chatbots and text fields all over the place.

[12:54](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=774s) This was built for users who didn't know about GenAI, this is twenty twenty-three, wanted to be able to kind of get in and use the features without actually understanding the functionality.

[13:05](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=785s) And it also, you know, keeping that human in the loop, we want to have the disclaimer around AI, and so we make sure that we've got enough little purple sparkles [laughs] to let them know what they're using.

[13:16](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=796s) Um, but it's not the entire experience here. So this allowed us to go GA, um, in, uh, twenty twenty-four, um, R2, or August, I should say, um, and sort of, I would say, kind of crawling with the functionality.

[13:31](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=811s) So on top of that, so that's our, our content teams. So then we moved into what I would say is walking. This was, now we have our content drafted, but we actually need to solve the self-service problem.

[13:45](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=825s) So as a manager, I might need to come in, Elaine, in this case, is trying to do a location change to San Francisco, and she knows a lot of the fields, but not all of them.

[13:55](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=835s) And so she now has this sort of contextually aware co-pilot, Workday Assistant, that lives across Workday that she can sort of prompt. A lot of us are familiar with this functionality, but a couple of points I want to make here.

[14:08](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=848s) One, we have the contextually aware suggestions, so it knows what's happening when I'm on the page. Also, around the data processing, if you're looking at a help article, it's generally customer content, which is sensitive, but not nearly as sensitive as PII or personally identifiable information.

[14:24](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=864s) Think about these tasks more on, say, pay or compensation, things that are really sensitive, where employees are putting really sensitive information in. So this is the next level of, um, sort of walking with the capabilities.

[14:38](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=878s) The other piece I'd mention is that this was a platform capability, meaning that we had to be working across our suite. So we have HCM and financials, think benefits, procurement, um, core HCM, et cetera.

[14:50](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=890s) And so there's a higher level of sort of top-down and bottoms-up alignment that had to happen to get these capabilities out the door. Then finally, um, running. So extending the same use case here, you may have seen a few months back, we announced our Agent System of Record.

[15:09](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=909s) A subset of that functionality targeted towards, again, those employees and managers, was really around the agentic capabilities behind the Workday Assistant. So again, our users don't necessarily want to know or have the sort of technical expertise around agents, but we still have that work happening behind the scenes, where our assistant becomes a lot more autonomous, proactive, listening

[15:34](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=934s) to policy changes, notifying us with suggestions as well. And so you can see just thinking through this at scale, there's a much higher level of, I would say, sort of top-down strategy with bottoms-up e-execution that then happens, threading the needle across, um, these different product experiences.

[15:52](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=952s) So you can see here we've kind of gone from a single product within a SKU all the way across our sort of core platform, which, um, some of you may know or not, but we serve, uh, like sixty percent of the S&P 500, so a, a pretty broad group.

[16:08](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=968s) So where we would land with all of this, when we talk about not making AI a sideshow, we're not telling you to stop working on agents or stop caring about AI.

[16:20](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=980s) But understand that these are stepping stones in terms of teaching your organization, training up your organization, on what it means to actually be building impactful AI experiences. And so as you sort of, I would say, mature as an organization, ideally, where we want to get to is building dynamic products.

[16:41](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1001s) I'm hearing some of this today in some of the talks, if you heard Sarah or Brian talking earlier, about building purposeful, sort of vertical-specific, um, products. I think it's really interesting when we start thinking about dynamic products in terms of new problem spaces.

[16:58](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1018s) I don't know if anyone else feels this way, but sometimes I feel like we're solving yesterday's roadmap with just, like, a much more powerful technology. And so as we digitize new data, new inputs in terms of our environment and spaces, we can see the problem space of the products that we're creating really sort of extend.

[17:17](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1037s) Um, I think especially with multimodal, this is where this gets really compelling as well. When we have frictionless multimodal experiences that interoperate... I would say interoperability and RL are still pretty relevant within the agent sphere.

[17:31](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1051s) But when we think about dynamic products that are sort of responsive to your environment, um, this is where we really start to see, I would say, the next generation of products come into play.

[17:42](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1062s) So hopefully, this sparked a few thoughts, maybe some questions. Um, if you want to connect, feel free to, um, scan our QR codes. Happy to, uh, connect if you want to drop us a note.

[17:54](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1074s) We'll also be around the rest of the week, so happy to chat. And we are right at time.

[18:00](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1080s) Look at that.

[18:01](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1081s) Okay.

[18:01](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1081s) Thanks, everyone.

[18:02](https://www.youtube.com/watch?v=CB-4NKDYnRs&t=1082s) Thanks. [applause] [outro music]