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# Design as an algorithm, not as a feature

True design is an algorithmic process, not a cosmetic feature. Learn how Volkswagen used a three-bot AI architecture to slash enterprise procurement time from 90 to six minutes.

- **Speakers:** Kaya Sohyun Kim
- **Event:** World Congress 2022
- **Published:** June 15, 2022
- **Duration:** 39:27
- **URL:** https://www.wearedevelopers.com/videos/471-design-as-an-algorithm-not-as-a-feature

## Summary

At Volkswagen, developing software for complex enterprise needs demonstrates that design is an algorithmic process rather than a mere cosmetic feature. An initiative to automate "minor sourcing" tasks—procurement deals under 10,000 euros—resulted in the creation of a specialized AI digital buyer team. This project underscores how true product design goes beyond simply creating a new interface; it addresses the underlying objective, much like realizing people want clean clothes rather than just a better washing machine. By treating design as the shortest distance connecting user needs, business processes, and technological constraints, development teams can effectively navigate rigid enterprise environments.

The AI procurement solution relies on a three-bot architecture: an NLP search bot to interpret user requests, a one-to-many communication bot to solicit supplier quotations, and an RPA bot to seamlessly integrate selected offers back into SAP. Building this system required extensive service design and co-creation with key stakeholders, proving that traditional user-centered design often asks the unfair question of "what do you want?" regarding unprecedented technology. Instead, the team utilized "user-inspired design," actively refining workflows and anticipating corporate complexities. Crucially, the designers had to act as holistic moderators who deeply understood the domain, from historic data taxonomy issues to specific operational compliance constraints.

A standout insight from the project is how strategic design decisions can effectively bypass deep-rooted systemic problems. When faced with missing data taxonomies or unpredictable global delivery operations, the team utilized creative workarounds—such as leaning on human intelligence for visual descriptions and supplier-provided timeframes—rather than attempting to overhaul decades of legacy data. Through rigorous real-world testing across global plants, this pragmatic approach drastically reduced the average procurement time from 90 minutes to just 6 minutes per order. Ultimately, viewing design as a continuous line rather than an isolated "plugin dot" enables product teams to forge cohesive, functional software regardless of inherited architectural limitations.

**Keywords:** ai procurement bot, enterprise software development, minor sourcing operations, robotic process automation, natural language processing, service design principles, user-inspired design, sap workflow integration, stakeholder co-creation, data taxonomy challenges, procurement workflow optimization, dialog flow architecture, strategic design workarounds, legacy it system constraints, enterprise usability testing

## Chapters

1. **Understanding the true source of software design principles** (00:00) — Various specialized design fields all stem from a single fundamental set of principles.
1. **Business case for automating minor sourcing at Volkswagen** (01:28) — Replacing human buyers with automated systems saves significant costs on low-value procurement negotiations.
1. **Automating procurement with intelligent natural language processing bots** (02:57) — Three specialized bots work cooperatively to interpret employee needs, negotiate with suppliers, and generate actionable orders.
1. **Moving from user-centered to user-inspired software design** (05:17) — Focusing on the fundamental objective of a product leads to better solutions than simply fulfilling user requests.
1. **Conducting context research for complex system and data environments** (07:53) — Optimizing information architecture requires a deep understanding of physical environments, subsequent user actions, and backend data limitations.
1. **Navigating stakeholder complexity through co-creation and early negotiation** (13:25) — Identifying the varied motivations of users, purchasers, and influencers ensures the final product satisfies all necessary decision-makers.
1. **Validating product direction with thorough visualizations and clear hypotheses** (15:30) — Creating high-fidelity visual representations helps establish measurable goals for successful technology adoption and sustainable vendor diversity.
1. **Designing dialogue flows and selecting robust underlying bot frameworks** (18:51) — Modeling conversational pathways directly influences technical decisions around internal security architectures and external third-party bot frameworks.
1. **Overcoming bad historical data using strategic human intelligence workarounds** (21:46) — Bypassing missing database taxonomies by leveraging user attachments and human supplier judgment naturally prevents hard technical failures.
1. **Validating intelligent bot performance through continuous live user testing** (24:08) — Conducting live trials of the automated workflow practically reveals substantial time savings and uncovers unexpected supplier interaction patterns.
1. **Defining design as an algorithm connecting multiple business domains** (31:55) — Strategic design functions as the optimal algorithmic pathway aligning complex technology, business logic, compliance, and core user expectations.
1. **Answering questions on frontend-first architecture and core service design** (34:44) — Prototyping visually prior to building complex backends fundamentally reduces risk while directly reinforcing the core concept of sequence planning.

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