Frank Spillers UX Inner Circle

10 things AI changes about software design in 2026

Microsoft

Intel

Department for Work and Pensions

Capital One

Nike

Frank Spillers

Frank is a 25-year UX & Service Design expert helping teams adapt to AI-First UX

Get the core shifts that redesign how teams think, plan, and deliver AI-enabled products.

10 shifts every team must make to build for AI services

Learn design habits that support AI-First UX and keep users, context, and intent in the loop.

Scale your team's AI process

See where sprint planning, refinement, and decision points must reset for safer, more reliable AI experiences.

How AI changes your Agile workflow

Use a simple checklist your team can adopt to reduce risk, manage drift, and design user-led AI services.

Apply tested patterns and behaviors

This course teaches you how to adapt your UX research and design practices to deliver AI-based functionality.


World-renowned Leader in UX and Design

Center Centre – UIE

Jared Spool

Guides UX professionals on delivering well-designed products and services.


Cohort 9

Cohort 10

Ensure that every new AI capability your organization delivers improves the lives of your users and customers. AI is changing everything, and it’s currently impossible to imagine a world without it. The romantic viewpoint speaks to all the new things we’ll do with it — how it’ll make all our lives so much better.Yet you'll need to invest significant effort to realize the full potential of your AI functionality. UX and Design can (and should) play a substantial strategic role in how AI is infused into your organization’s products and services.How do you ensure that every new AI capability your organization delivers improves the lives of our users and customers?You can’t let AI be something that just happens to your products. You must intentionally design your AI capabilities to respect AI’s limitations while delivering exceptional experiences to the people you design for.

UX & Design for Today's AI Functionality

Section 1: Delivering Strategic UX & Design into the AI World (Lecture: 2 hours)

Section 2: Bringing AI Value through Intelligent UX & Design (Lecture: 1 hour)

Section 3: The Strategic UX & Design Process in an AI World (Lecture: 3 hours)

Learn how to adapt UX and design practices to deliver AI-based functionality.

For UX professionals seeking a user-centered approach to implementing AI.

Experienced Product and UX leaders who are now building and designing AI capabilities into their products and services.

Senior UX and Individual Contributors who want to take a leadership role in their org to ensure that UX is given attention with AI efforts.

Professionals ready to drive strategic thinking throughout their org on how to implement and deliver innovative, high-quality AI-infused UX.

Go from trial-and-error prompting to systematic AI-assisted building. Ship functional projects with a workflow you can use on every project

Cohort 2

AI Design Leader |  x-Uber/Google/Amazon

Stanford University

Amazon

Google

Uber

Rupa Chaturvedi

AI Design Leader | AI enablement for UX orgs | ex-Uber, Google, Amazon, Stanford

Cohort 1

The job posting asks for a designer who can research, prototype, code, and ship. One role, three people's worth of expectations. Design teams got smaller. The work did not. And now every product roadmap includes AI features that need to be designed by people who were never trained to design for probabilistic systems.The pressure is coming from two directions. Inside your organization, you are expected to move faster with fewer resources while somehow incorporating AI tools into workflows no one has figured out yet. Outside, the profession itself is shifting. AI generates interfaces, writes copy, produces prototypes in minutes. The execution work that built your career is compressing. The question is no longer whether designers need to adapt—it is whether you are building the skills that will matter in two years.Claude Code for Designers answers both pressures at once. You move faster because you can build functional prototypes yourself. You stay relevant because directing AI to build is the new execution skill. You leave with a deployed project and a workflow system you can use on anything that comes next.

Master Claude Code. Build functional projects. Develop a reusable workflow.

An AI-First Build Workflow: From project scoping through deployment

A Reusable Workflow System: Templates, skills, prompt libraries, MCP configs

A Deployed Application: Ship a functional project from your own idea

Portfolio artifacts and fluency to discuss AI-assisted design in interviews

Design-to-Code Translation: Turn your design system into functional components

This course assumes working knowledge of design systems, user research, and product workflows.

5+ years of professional design experience

Experience using AI assistants in your workflow. The course moves quickly and assumes baseline comfort.

Familiarity with AI tools (Claude, ChatGPT, Copilot)

Product Designers. You design in Figma and wait on engineering. You want to build and ship functional prototypes yourself.

Design Leaders. Your team needs to move faster and integrate AI workflows. You want firsthand fluency before rolling this out.

MXDAI Alumni. You have the strategic frameworks for AI design. Now you want the execution skill to match.

By the end of this course, you’ll transition from a software engineer to a confident AI engineer—able to identify the right problems, design

AI Engineer & Consultant for F-500 firms

Qualcomm

Wipro

Sujeet Mishra

AI Engineer | Entrepreneur | AI Consultant & Trainer for Fortune-500

AI/ML | Search | Ex-Microsoft, Yahoo

Apple

Yahoo

Prasenjit Mukherjee

Adjunct Faculty | Quantum ML & Big Data | Ex-Microsoft, Yahoo | IIT Madras

For software engineers, moving into AI engineering is not just about learning new tools—it’s a mindset shift.Over the past year, we’ve worked with engineering teams, from Fortune 500 companies to unicorn startups, helping them adopt AI-native and agent-based systems. One pattern shows up repeatedly: engineers don’t struggle because they can’t build, but because AI systems behave very differently from deterministic software.⚙️ Engineers must learn how to design, evaluate, and operate probabilistic systems, reason about failures and trade-offs, and think end-to-end—from use-case identification to production and scale.Many engineers hesitate because they believe AI engineering requires deep ML or statistics expertise. In reality, modern AI engineering is about system design, evaluation, observability, and production readiness—skills engineers already have but need to apply in a new context.🚀 This course provides a practical 6-week roadmap to make that transition by building and extending a real, production-grade agentic system.By the end, you won’t just understand AI systems—you’ll think and operate like an AI engineer.For queries, write to us: ai-education@theseuslab.ai

A 6-week roadmap for software engineers to become production-ready AI engineers

Use LLMs to 10x day-to-day engineering productivity

Design and build agentic systems using LangChain/LangGraph

Build reliable agentic systems by designing for evaluation and observability

Take AI ideas from use-case discovery to production and scale

Apply everything you learn to a real, end-to-end capstone project

Basic understanding of SDLC concepts like databases, APIs, frontend/backend systems, and servers

Familiarity with Software Engineering Fundamentals

Some programming experience, even if not recent—or willingness to spend ~8 hours pre-course to learn Python (we will provide resources)

Prior Programming Exposure (or Willingness to Ramp Up)

Openness to learning, experimenting, and thinking differently

Curiosity and Willingness to Experiment

Software engineers looking to transition into AI engineering roles and build production-grade AI systems

AI engineers and data science professionals who want to get a hands-on experience of building, evaluating and scaling agentic AI applications

Product managers and Tech leaders seeking a practical, system-level view of how production-ready AI applications are designed and shipped

Shift from ad hoc AI efforts to disciplined, structured, workflow-driven collaboration before significant investment.

Workshop Lead: Netflix, Google, LinkedIn

Netflix

LinkedIn

Paul Bryan

I help organizations design and develop AI products that deliver business value.

July 2026 Cohort

<blockquote>AI applied to the wrong workflows leads to chaos. This course helps Enterprise teams launch successful AI pilots by aligning on workflows, roles &amp; responsibilities, risks, guardrails, and AI functional requirements before major investments.Industry reports suggest that over half of enterprise AI pilots never scale to production. Many pilots stall because the workflows were not ready for AI.This course addresses the real bottleneck: cross-functional alignment on key details before committing to technical debt.You will leave with:<ul><li>An AI Workflow Canvas: A structured way to identify where AI can move the needle in your organization</li><li>An AI Workflow Scorecard: A shared evaluation framework your cross-functional team can use to assess value, feasibility, data readiness, and risk</li><li>A Thin-Slice Prototype: Storyboards, scene cards, and an action flow diagram that aligns stakeholders across PM, DS, UX, Eng, and Biz on exactly what the AI system will do, feasibility and risk, and where humans are needed in the loop.</li></ul></blockquote>

Identify High-ROI Workflows Ready for AI

Identify high-friction workflows suitable for Enterprise AI

Prioritize AI initiatives using structured evaluation criteria

Clarify roles and decision rights before pilot launch

Surface feasibility and governance risks early

Make AI behavior visible to cross-functional stakeholders

Define readiness criteria before committing to a pilot

AI product team members (PM, UX, DS, ENG) who need a shared framework to align on workflows, feasibility, and responsibility before building

Product and business leaders accountable for Enterprise AI outcomes who must prioritize workflows and reduce risk before committing budget

AI governance, legal, and risk stakeholders who need structured ways to surface constraints, guardrails, &amp; ownership early in AI initiatives

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Introduction: 10 Things AI Changes About Software Design

The End of "AI as a Feature" and "Human Replacement" Narratives

Shift 1: From Traditional QA to Continuous AI Observability

Shift 2 & 3: Designing for AI "Drift" and the Core Skill of Problem Framing

Shift 4: Designing Dynamic and Adaptive AI Interactions

Shift 5: The Importance of Implementing Upfront Guardrails

Shift 6 & 7: Using Real-Time Data and Prioritizing Human-Centered AI

Shift 8: Prototyping With AI as a Design Partner

Shift 9: Boosting Personas with Intent and Context Models

Shift 10: Planning for Intervention, Repair, and Conclusion

Product managers who drive strategy, development, and launch of products.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

10 things AI changes about software design in 2026

What you'll learn

Why this topic matters

You'll learn from

Frank Spillers