# Experience Futures (XF) — Full Journal Archive
> Full text of every published journal article, newest first. Generated at build time from content.json. See https://xfutures.org/llms.txt for the site index and usage terms.
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# What Social Impact Leaders Actually Want From AI, in Their Own Words
**Author:** Howard Pyle
**Date:** 2026-04-13
**URL:** https://xfutures.org/journal/good-tech-summit-workshop
*Howard Pyle and Stephen Godfrey of [Numantic Solutions](https://numanticsolutions.com) led the workshop using [xf.app](https://xf.app).*
---
## A room that didn't expect to be designing AI
The most striking thing about the workshop wasn't any single product anyone built. It was a room of leaders who, ninety minutes earlier, would have described themselves as not technical enough to design AI products, ending the session by debating what their AI should be allowed to refuse and when a human should step in.
That shift, from "I don't know how this stuff works" to "this is how it should work," happened in about ninety minutes. The rest of this post is what we learned from watching it happen.
## How it works
Over the past year, we have been building [xf.app](https://xf.app), a platform that helps people who aren't engineers design and deploy AI products for high-trust environments, developed with support from a [Roddenberry Foundation](/journal/roddenberry-catalyst-fund) Catalyst grant. For [Good Tech Summit](https://www.goodtechtogether.org/), we adapted it into a workshop format using xf.app's conversational design tool.
The mechanics were simple. Each participant opened xf.app and started a conversation with an AI facilitator. The first question was the hardest: what problem would your AI solve, and who does it help? Once they had an objective, the AI walked them through four phases in sequence, each one building on the last.
*Where every participant started: a blank canvas and one question.*
First, **AI Role**: what should the AI actually do, step by step? Second, **Data & APIs**: what information does it need, and where does that information live? Third, **Guardrails**: what should the AI never do, what data should it never touch, what requests should it refuse? Fourth, **Human Escalation**: when does a human need to step in, and what triggers that handoff?
*The canvas after the first phase. Nodes populate as the conversation progresses, not after a form is submitted.*
As participants worked through each phase in conversation, their answers populated a visual workflow canvas in real time, the four-column layout shown in the banner above. By the end, they had not filled out a form or done a whiteboard exercise. They had had a conversation, and that conversation had produced a structured, testable product specification.
The last step was the most surprising for most participants. They could click a button and immediately talk to a simulated version of the product they had just designed. The guardrails they had specified were already in effect. The AI role they had defined was already shaping the responses. The thing they had described in conversation had become a thing they could actually use.
*The finished specification for a volunteer coordination AI. Built entirely through conversation.*
We put this in front of a room of program directors, foundation leaders, school planners, data architects, and nonprofit operators, and gave them about ninety minutes to design something they cared about. The point wasn't to ship a product. The point was to see what comes out when non-technical leaders are given the structure to articulate what they want from AI.
This is what we learned.
## Almost no one designed a chatbot
The most consistent pattern across the room was that participants did not reach for general-purpose AI. They reached for the operational friction that eats their weeks.
A planner at a national nonprofit that redistributes refurbished computers designed a self-service onboarding portal for partner organizations. Her approach was telling. Rather than starting from scratch, she pulled up a colleague's existing onboarding checklist, fed it into an AI tool to extract the underlying steps, and used that as the spine of her design. She was not designing in the abstract. She was encoding institutional knowledge that already existed but had never been made into a product.
A planner from a large urban school district designed an enrollment forecasting tool. A team from an environmental nonprofit designed a corporate-prospect finder for an urban tree-planting program. A leader at a data commons designed a self-service intake portal for partner data. A professional in executive search designed a candidate-research tool. Several others designed grant-related tools.
These were not hypothetical products. They were specific, named pain points, described with the precision that only comes from doing the work.
## Multiple participants independently designed grant tools, and that's a signal
The clearest convergence in the room was around grants. Working entirely independently, several participants designed tools for different parts of the grant lifecycle: discovery, qualification, reporting, compliance, donor matching. Different organizations, different angles, the same underlying problem.
When several people in a room arrive at the same problem from different starting points, that is not coincidence. It is evidence of a sector-wide tax on impact work. The labor of translating what an organization actually does into the language a funder needs to hear is consuming an enormous amount of time across the field, and the people doing the work know it.
## The workshop made it easier to see when AI was the wrong answer
One participant shared a useful counter-example from earlier work her team had done. They had been considering AI for a record de-duplication problem and concluded, after working through it carefully, that AI was not the right tool. "Deduping entities has been around for a long time," she explained. "We use lower-cost technologies first."
She appreciated that the workshop's framework forced her to name the AI's role in a single sentence, because that naming exposed when no AI was needed at all. This is a small but important point. Most AI design conversations begin from the assumption that AI is the answer and reason backwards. Forcing a clear articulation of the AI's role early is one of the cheapest ways to surface the cases where it isn't.
## The most interesting design decisions happened around guardrails
The workshop pushes participants through two phases that most AI conversations skip: what should the AI never do, and when should a human take over.
Most participants accepted the workshop's default suggestions for these phases without much pushback. The participants who did push back produced the most thoughtful work of the day.
A leader designing a data intake portal pushed back on a default rule that would reject any dataset containing personally identifiable information. Her argument was operationally specific. "We want to accept the data for the analysis but mask specific data that falls into those categories. We don't want to outright reject the whole dataset because there is some PII in the mix." That distinction, between rejecting data and selectively masking it, is a real governance design decision, not a checkbox.
A participant in executive search raised an identity disambiguation edge case that no automated system would catch by default: how do you ensure a candidate named John Smith is the same John Smith across multiple sources? Another participant tested her own design by trying to remove a guardrail about self-harm and watching the system refuse her change. In a tree-planting demo, the designer asked her completed product for a prospect's phone number and Social Security number, and watched the guardrail she had specified ten minutes earlier politely decline.
These were not abstract debates about responsible AI. They were design decisions with visible consequences. The participants who engaged with them most deeply were also the participants whose final products felt most like they could actually be built.
## Governance is a design conversation when it's framed as one
The pattern that has stayed with us most clearly: when governance is framed as a compliance checklist, people skip past it. When it is framed as a product decision, what should this thing refuse, when does a human take over, what should be visible to whom, people become deeply opinionated.
*The same design, now live. The AI queries the data sources specified in the design phase and returns matched roles.*
One participant argued that the most important human escalation trigger was not system failure or legal exposure. It was notifying and including the other humans who had a stake in the outcome of a decision. That is a governance philosophy, surfaced in a ninety-minute exercise by someone who had never used the tool before.
The school district planner went further. He added a process by which principals could contest the model's enrollment projections with their own data, then a transparency layer so planners could see how the data was being used, then a review process for the contested projections. He was not specifying an AI. He was designing a governance system for how humans and AI would make consequential decisions together.
If the broader field could consistently shift the governance conversation from compliance documentation to product design, the responsible AI conversation would be in a different place than it is today.
## Discovering an infrastructure gap is a legitimate output
One participant from a community organizing cooperative was designing a volunteer intake system and stopped mid-workshop. They did not have APIs. They did not have a database of volunteer opportunities. They asked, openly, "If we created documentation for it, would that be enough?"
That moment is the workshop working as intended. The gap between the product an organization wants and the infrastructure it actually has is one of the most expensive things to discover late. Discovering it in ninety minutes, before any code has been written, any vendor engaged, any budget committed, is among the most valuable outputs the exercise can produce.
## What we are taking away
The problem is not that social impact leaders lack ideas about what they want from AI. They have specific, well-formed ideas, grounded in deep operational knowledge of the work. The problem is that the space for translating that knowledge into a concrete, actionable product specification barely exists in most organizations.
When that space is created, with some structure, an AI facilitator that asks the right questions, and the ability to test the resulting design in a live conversation, the expertise comes pouring out.
*Human escalation in action. The trigger the participant specified ten minutes earlier fires exactly as designed.*
That is the part we want to keep building. Not better AI. Better ways for the people closest to the work to shape the AI that gets used on it.
xf.app is the platform we built to make that possible. If you would like to run a workshop like this with your team, foundation, or community, [we would welcome the conversation](mailto:info@xfutures.org).
---
# The Rise of Your Personal IP
**Author:** Howard Pyle
**Date:** 2025-11-03
**URL:** https://xfutures.org/journal/rise-of-personal-ip
tl;dr: Every professional needs to become a tool builder. AI helps you do that by building code. Yes, you should absolutely vibe code, but don’t outsource understanding. Your career may depend on it. Here are a few essential suggestions...
The wave of AI we’re in is not about new tools. We’re entering a time where everyone who's a knowledge worker or leader needs to be creating their own toolsets, customized to their work, personality, and expertise.
This is the rise of personal intellectual property (IP): the digital utilities, workflows, and automations you build and refine throughout your career.
We are at the beginning of an explosion of unique use cases, not just for companies or industries, but for every individual. The future of work is a world where every professional is building and maintaining their own set of tools.
I’ve lived this myself across my career. I’ve talked publicly about how I’m crushingly dyslexic and, for me, writing has always required a set of tools to help me work in a way that matches the environments I’m in. IBM vs MetLife. Working in startups vs writing product roadmaps for investor presentations. The way my brain is wired for language vs. the need to communicate to different audiences was only possible because I started as a software builder early in my career. Over the years I've built my own project management systems customized to the way I think, budgeting and P&L management tools that match how I want to manage my organizations, writing and creative development tools, etc.
This personal necessity is becoming a universal professional demand. The conversation is no longer about just using AI; it's shifting to a reality that everyone needs to be building with it. Look at the top consulting companies; they might be a bellwether for where professional skills are headed. Accenture is in the process of teaching 700,000 employees how to use and build with agentic AI. McKinsey and BCG are demanding their consultants learn to create “agent systems” to reinvent workflows from the ground up.
Just to be clear, an “AI agent” is not just a chatbot. It’s a system that can understand a goal, break it down into steps, and use tools to accomplish it with minimal human intervention. Ideally it should also adapt to new decisions and adjust what it does based on new inputs. It's a shift from using a reactive tool to a proactive, goal-driven virtual collaborator. The impact is staggering. BCG reported that effective AI agents can accelerate business processes by 30% to 50%. We're looking at a new wave of expectations for every leader and organization. Do more with less is the new standard for professional effectiveness.
So yes, you need to start “vibe coding” as a way to internalize this shift, adapt it to your work style and your unique experience or expertise.
Vibe coding is cute until you realize your career depends on it.
If you’re going to rely on a tool for your job, you need to know how it works. Understanding turns a quick experiment into something you can trust, and something that lasts.
Honestly, the 'vibe' part is cringe, but we're really just talking about the ability to use natural language and intuition to create functional code with AI assistants like Cursor. It’s fast, it’s powerful, and it’s here now. So go ahead and vibe code. But start thinking about how you’ll build the utilities that become your personal IP.
This is not a theoretical risk. In July 2025, a product manager using Google’s Gemini CLI to reorganize files watched as the AI, in a state of “confabulation,” hallucinated a phantom directory and proceeded to issue a series of move commands that overwrote and destroyed all his data. Supposedly, the AI tool responded: “I have failed you completely and catastrophically”. Yikes. Also, SaaStr founder Jason Lemkin reported that Replit’s AI agent ignored eleven explicit instructions to freeze code and deleted his production database, after first trying to hide its mistakes by generating fake data and false test results. I'm not saying that these people didn't have understanding of the tools and were trying to vibe their way through, but just that we're still in a phase of massive risk in relying on AI tools.
The point is that if we're talking about "vibing" at work, catastrophic consequences are the predictable outcome of delegating understanding. You wouldn’t “vibe” your taxes, just throwing some numbers at an accountant that “feel right.” You’d want them to explain the implications. You wouldn’t “vibe” a home renovation, telling a contractor to just get the “general feel” of the kitchen you want without understanding the structural plans. I hope you could hear the slight disdain in my tone there.
So the message is yes, you need to experiment and build tools that matter for your career and work, but don't just trust the tools without being clear on how to ensure you're in the driver's seat.
Here are some table stakes for using AI to start creating personal IP:
1. Get a GitHub account. Seriously.
This is a fundamental step. A GitHub account is your personal code repository. It’s your digital workshop. It’s where you’ll store, manage, and track the evolution of every tool you build. For too long, it’s been seen as a place exclusively for professional developers. That’s over. If you’re building your own tools, you need a place to keep them. GitHub is that place. You'll have version control, the ability to link your code to hosting platforms, use tools like ZenCoder or GitHub Copilot to review your work. Other benefits include the ability to invite devs to your repositories so you can get help building, adding, or completing them if you faceplant.
2. Use Cursor as your GitHub guru.
Once you have your workshop, you need a way to manage it without getting bogged down in technical details. This is where Cursor shines. You can use plain English to manage your code on GitHub. You don’t need to learn a bunch of complex Git commands. You can literally just tell it what you want to do.
For example, you can finish a coding session and say, “Back up this entire project to a new repository on my GitHub account called ‘my-first-marketing-automation-tool’.” Cursor will handle the rest. This removes the friction and makes managing your personal IP as easy as writing an email. Have it explain what it's doing at EVERY step.
3. Run your tools locally and in the cloud.
A tool isn’t a tool until you can use it. You have two great options here. You can run your tools locally on your own computer, which is perfect for small utilities. I have a lot of Node.js tools that I’ve built for myself that I just run locally to automate parts of my workflow. It’s simple and effective. I'm on OSX and use Docker to create separate containers. I hope you're taking notes, you can watch short YouTube videos on all this stuff.
Or, if you want your tool to be accessible from anywhere, you can deploy it to a platform like Railway (note: vibe-coded stuff has crap security - be really careful). These platforms are designed to make it incredibly easy to get your code running on the internet. Again, you can use Cursor to help you with this. The point is to take your code from an idea to a functioning utility that you can actually use. I don't bother to keep track of configuration methods for hosting platforms and rely on chatting with Cursor to help me get setup. I would NOT suggest this for production code or products other people use, but it works for making my personal set of AI tools.
From there, you can build tools that are profoundly customized to you. I have a tool that scans articles and project documents, extracts the text, and performs language analysis so I can understand trends and patterns. Another tool tracks program trends against conversations in Slack and code check ins to make sure the original scope of work matches what’s actually being built. A generalized project plan does not work when you need to map across hundreds of different documents to understand what’s really happening.
4. Make the AI Explain Itself.
To build tools like this, you have to explicitly tell the AI to explain what it's doing and teach you. Don’t let the AI be a black box and don't just have it run in yolo mode. You can do this by creating rules for how the AI interacts with you.
Here’s a prompt you can use to make the AI your personal tutor:
```
You are my AI coding tutor, not just a code generator. For every code change you make:
1. EXPLAIN THE CHANGE
- What you're changing and why
- What problem this solves
- What trade-offs or risks exist
2. PROVIDE A TESTING PLAN
- Step-by-step instructions to verify the change works
- Console logs or trace points I should add
- Expected output at each step
3. DOCUMENT DEPENDENCIES
- New packages or libraries added
- Environment variables needed
- Configuration changes required
4. TEACH ME
- If you're using a pattern or technique I might not know, explain it
- Point me to docs or resources if I want to learn more
- Tell me what I should watch out for or common mistakes
Wait for my confirmation before proceeding to the next change. If a test fails, explain what went wrong and propose a fix. Your job is to help me understand, not just to write code.
```
With this rule in place, every time the AI generates code, it also has to explain what it did, why it did it, and how you can prove that it works. You’re not just getting code; you’re getting a lesson.
If you want to deep dive into this, Google 'cursorrules' for ways to set specific parameters for every project. Claude Code has similar features. There are sites where you can just grab examples. Your shiny new GitHub account will come in handy here, as a ton of cursor rules are shared on there.
5. Use ZenCoder as Your Secret Weapon for Planning.
For anything more complex, you need a plan. ZenCoder is my go-to for generating documentation and project plans before you start coding. It allows you to step through the entire process, across the whole codebase, without getting lost.
You start by giving it a high-level goal. It then generates a detailed, documented project plans, and itemized implementation plans. You then tell it to execute one step at a time. This is how you maintain control and understanding, even on a complex project.
Here’s a prompt I use with ZenCoder:
```
You are a documentation-first project planner. Before writing any code, help me create a complete understanding of what we're building.
Project: [short description]
Goal: [what success looks like]
## ## ## PHASE 1: DOCUMENTATION
First, create comprehensive project documentation:
- System architecture overview (what components exist and how they connect)
- Data flow diagrams (how information moves through the system)
- Key decisions and assumptions (what we're betting on, what could change)
- Integration points (what external systems or APIs we're using)
- Glossary of terms (domain-specific language for this project)
## ## ## PHASE 2: IMPLEMENTATION PLAN
Then, create a numbered, step-by-step implementation plan:
- Clear scope for each step
- Files to create or modify
- Interfaces, data structures, and dependencies
- Test strategy (unit, integration, manual checks)
- Rollback plan if something goes wrong
## ## ## PHASE 3: STEP-BY-STEP EXECUTION
When I say "Step 1," propose ONLY the changes for Step 1:
- Show the code diff
- Explain what this step accomplishes
- List tests and console logs to add
- Provide a checklist to confirm success
- Update the documentation if anything changed
Wait for my confirmation before moving to Step 2. If a test fails, propose a fix or rollback for that step only. Keep the documentation updated as we go.
The goal is to build a codebase I can understand, maintain, and explain to others. Documentation is the foundation for working with AI coding tools.
```
With this approach, I can manage complexity without losing control. I use this process to manage a wide range of projects across my organization and client programs I'm leading. I built a tool that creates a thematic index across all of them. So if I need to write a grant proposal or a scope of work for a client, I can manage all of that without having to re-index or sift through a bunch of documents.
## The punchline
This is the future of professional work. It’s a response to the massive skills gap our economy is facing. Some estimates put the cost of this gap at $8.5 trillion annually, with 87% of companies reporting they cannot find the workers they need. Given how hard the job market is, that's absolutely insane. The solution is not more generic technology training. It’s empowering every professional to build the specific tools they need for their unique role.
My point of view is that the future belongs to professionals who don’t just use tools but build their own and curate a set of IP that is unique to them. You can use vibe coding to get this engine started. But understanding is the essential ingredient. Look at this as a way to start your investment in futureproofing. Don’t just vibe, understand. That’s your edge.
#AItools #PersonalIP #FutureOfWork
## Some References
- [Why Accenture is Teaching 700k Employees to Use Agentic AI](https://technologymagazine.com/news/why-accenture-is-teaching-700000-employees-to-use-agentic-ai) - Technology Magazine, 2025, September 19
- [Seizing the agentic AI advantage](https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage) - McKinsey & Company, 2025, June 13
- [How Agentic AI is Transforming Enterprise Platforms](https://www.bcg.com/publications/2025/how-agentic-ai-is-transforming-enterprise-platforms) - Boston Consulting Group, 2025, October 13
- [Two major AI coding tools wiped out user data after making cascading mistakes](https://arstechnica.com/information-technology/2025/07/ai-coding-assistants-chase-phantoms-destroy-real-user-data/) - Ars Technica, 2025, July 24
- [Why 87% of Companies Can't Find the Workers They Need](https://blog.theinterviewguys.com/why-87-of-companies-cant-find-the-workers-they-need/) - The Interview Guys, 2025, July 11
---
# The end of the front end (and maybe brand as you know it, too)
**Author:** Howard Pyle
**Date:** 2025-10-24
**URL:** https://xfutures.org/journal/end-of-front-end
The post-interface web and how agentic systems will redefine customer experience and brand.
Back in 2021, we released a white paper and wrote that websites would eventually disappear. Not the internet itself. But the idea of a “front end.”
We said that people would soon interact directly with the data underneath, through intelligent systems that understood intent instead of navigation.
At the time, there were two responses. Most people responded with either “What the hell are you talking about?” or, “Wow, this is visionary.” Honestly, it was probably closer to 95% confusion and 5% excitement.
We weren’t trying to predict the future, just describing what we saw as web builders: huge API ecosystems running underneath, but still funneled through a 1990s metaphor the website as “storefront” (and yes, companies literally had websites designed like a physical store with doors and windows). Everything sat behind a thin visual layer that brands controlled, even though most of the meaningful work was happening below it.
Agents and APIs are eating the web.
The same iceberg. You can't escape it.
A piece in Fast Company just ran titled "In the Age of AI, Websites Will Be Transformed," which describes how AI agents are reshaping how people get information, with adaptive interfaces that build themselves around the user.
So it’s both exciting and a little familiar. The conversation finally caught up. The key point is that the idea of customer experience, and perhaps even the idea of brand itself, might be on the verge of changing forever. Not that brand is going away (I believe brand is the emotional relationship someone has with a company), if anything, it becomes more important when AI agents need to decide which organizations to trust and recommend. But brand expression is going to implode and take on entirely new meanings in the coming two years. Your carefully designed website, a controlled journey, your visual identity system.. Maybe these matter less when users never visit your site and their personal AI tools sit in between the customer and your company. What matters more is reputation, trustworthiness, and whether your data is accessible and reliable. Brand becomes less about what you show and more about what you are.
So here we are back at the point that values, purpose, and ethics are vital for modern businesses.
## From Interfaces to Interactions
Fast Company talks about a world where people talk to intelligent systems that instantly shape content to their needs. No fixed templates, no clicking through menus. Think about that for a second. UX makes things easier but it also guides people through data and content the way the organization wants you to consume it. No CMO wants to users to access blob of information about their company that has no starting or ending point. That’s one reason “journey maps” are so popular as a strategic planning tool - you can try to shape how people move through content, marketing funnels, etc.
That’s exactly what we meant in our white paper when we said digital systems should be wrapped around the individual, not organized around the organization. We weren’t talking about personalization in the marketing sense, but rather systems that understand the context of that specific user and adjust in real-time. It’s essentially experience as a living exchange between a person and the data behind the scenes. Kind of like what happens when you want to chat with Perplexity or GPT about a company vs navigating their entire dumb website.
And this isn’t just Fast Company or thought leadership pieces like our white paper. Designers and researchers have started using terms like Agentic UX but I think that term feels like a bolt-on. I think what they mean is that we have to shift into designing for intelligent intermediaries rather than human clicks. So that means designing interactions that Agents or AI understand who will then pass it along to the user / customer / etc. No joke, I saw an ad for Postman (an API platform) the other day, and part of the ad copy was “AI agents are reshaping AI use, but human-centered design slows innovation.”
In 2024, UXDesign published "The Agentic Era of UX" about this exact shift, and UX Magazine has been exploring new patterns where interfaces dissolve into data conversations. McKinsey calls it the "Agentic AI Advantage," framing it as the next operational shift for business.
## Agents as the New Intermediaries
The Fast Company article also talks about OpenAI’s Agent Builder. This is essentially a concierge that does the searching and sorting for you. There are ton of these emerging and tools like n8n, Make and even Manus give powerful agent capabilities to anyone who’s up for trying to wrap their heads around it.
One of the key things we were trying to describe in our work is the impact of a small number of technologies (e.g. chatGPT or perplexity, etc) sitting between the human and whatever collection of content and organizations you need for any task. So think about if you use chatGPT to answer a question about doing your quarterly taxes: it’s going to pull from content, describe tools you can use, and from its memory about you from prior chats, and organizations required for any task. That’s an experience 100% customized around the individual and whatever context I provide about my situation. There’s no journey map that can predict their context, collection, or solutions surfaced up to them. That’s the shift we’re seeing now. The UX layer is becoming a negotiation between the user's AI layer and agents, and a collection of data that is hard to predict.
This is where it’s important to really prioritize AEO / GEO. Trying to get AI tools to ingest your content so it’s surfaced to users during whatever it is they’re using AI for. New standards like MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication also become essential for any organization trying to insert itself into AI ecosystems that users create for themselves. A paper published in July 2025 by an international team of academics titled "Agentic Web: Weaving the Next Web with AI Agents" calls this the missing connective tissue. These protocols let systems talk to each other safely without a UI.
## The End of the Walled Garden
Traditional digital strategy has always been about getting people into your system and keeping them there. That’s the walled-garden model. APIs are the secret plumbing behind many systems and products (and have been for a long time), but the agentic web breaks that completely for the end users.
If my AI assistant can get what I need without visiting your site, the walls don’t matter. Neither does your design or your brand experience. I’m just getting the data or information I need.
The dissappearing click
How many times can you redesign your site before your content just is absorbed by AI?
We argued that the strategy of having a walled garden in your organization is directly opposed to the future of creating AI-oriented digital experiences. So organizations that already have practices around making their content and tools widely available (think of open source models) have a huge advantage now. And the opposite statement is that companies with brittle, highly intricate tools or reams of gated content are quietly becoming ignored by the users they’re trying to reach. It turns out that openness isn’t just feel-good, it’s good UX and good for business in this Agentic AI era.
We’ve been working with startups, enterprises and non-profits to help them build creative systems that connect brand, product, and AI workflows so organizations can operate in this open, agentic landscape that’s emerging. It’s a combination of business, brand, tech, and ethics.
## Agents on a Human Web
The web was built for people, not machines. Every form, menu, and layout teaches humans how to find things. Agents hit that surface and have to pretend to be people to get data, and that’s where it breaks.
Most AI startups still rely on scraping stacks that have existed for decades. Headless browsers, Selenium, Playwright. They click, scroll, and parse HTML. It works until it doesn’t. Layout changes, bot detection, and render differences constantly break pipelines. Entire vendors exist to paper over the cracks: Browserbase sells fleets of headless browsers, Zyte and Apify offer scraping APIs that repackage page data as JSON (2025, browserbase.com; zyte.com; docs.apify.com).
Meanwhile, platforms are starting to close the door. Cloudflare now lets sites block AI crawlers by default (July 2025, The Verge). Publishers are testing paid access models to license data rather than have it scraped (2024, Wired). The idea that a public page is an open resource is quietly disappearing.
If agentic AI is the next step, the fix isn’t better scraping. It’s structured access. APIs, not HTML. But exposing APIs is hard work. You need to manage ownership, schema mapping, privacy, rate limits, versioning, and abuse. That’s why so many teams keep patching the browser layer (it looks faster until it collapses).
A few startups are rebuilding from the ground up. Exa offers semantic search and crawl APIs so agents query meaning instead of markup (2025, exa.ai). Diffbot turns web data into a live knowledge graph (2025, docs.diffbot.com). Perplexity and Firecrawl provide clean text and search endpoints for LLMs (2025, docs.perplexity.ai; firecrawl.dev).
There was an effort to legislate this shift. The ACCESS Act of 2021 proposed interoperability and data portability, but it stalled in Congress (June 11 2021, congress.gov). So the market is filling the gap.
If companies don’t invest in APIs now, agents will still reach their data, just through slower, more fragile paths they don’t control. The API has quietly become the new interface.
## Automation as the New Design Layer
The other thing the Fast Company article talks about LLM-powered websites that rewrite themselves dynamically. The future is a world where design and content generation blend together. Where the interface isn’t handcrafted anymore, it’s composed on the fly by algorithms.
To see this in action, I built a simple demo showing how an AI agent completes a healthcare workflow (prescription refill + flu shot) in 2 minutes without visiting any brand websites. Try the demo →
That doesn’t make design less important; it makes intent the new design material.
As Figma's recent MCP server announcement shows, serious design players believe that interfaces are becoming dynamic representations of design intent rather than fixed screens. Future design will be generated on the fly based on context and patterns rather than handcrafted layouts. Designers won’t just be deciding how things look, they’re defining how systems behave.
This is why in addition to AEO/GEO and a clear API strategy, every brand needs a well-groomed design system that not just for designers to use, but optimized for AI and automation tools as the fodder for these generative interfaces. This means treating your brand system as a data collection, not just a 100-page PDF guidelines doc.
## What's Still Missing: Values as Infrastructure
We’re all a little anxious about agents between us and the information we need. But even in navigating a sea of poorly designed websites to manage your healthcare or education, it’s frustrating but at least you could see what you were dealing with. You could spot the dark patterns, the buried disclaimers, the biased recommendations.
When an AI agent sits between you and the data you need, those problems don't disappear, they just become invisible.
Imagine an AI agent helping someone compare health insurance plans. If the underlying data comes from insurers who profit from denying claims, what gets surfaced? What gets hidden? The interface is gone, but the bias remains. Now it's just wrapped in the authority of an "intelligent system."
This is why values can't be an afterthought. In the agentic web, your values become part of the infrastructure.
When interfaces dissolve into data conversations, the algorithm IS the experience. If your organization's values aren't embedded in how data is structured, accessed, and prioritized, you're not just risking ethical violations, you're scaling harm invisibly.
This has been the central question that drives our work: How do we ensure that agentic systems serve people's actual needs rather than optimizing for outcomes that benefit organizations at the expense of users?
It's not enough to bolt on "AI ethics" after the fact or have it be a checkbox powerpoint slide living in a side room next to the CIO’s office. Organizations need to:
Define their values explicitly - Not as marketing language, but as operational principles that guide how data is structured and surfaced
Embed values in data architecture - If your API prioritizes profit over user benefit, agents will learn that priority
Design for transparency - When there's no interface to show your work, how do users know what's being hidden?
Protect user agency - Agentic systems should empower choice, not replace it
The organizations with the best brands in this agent era will not necessarily be the ones with the best AI models. They'll be the ones whose values are clear, consistent, and embedded in every layer of their technology stack. So clear the users have trust, belief and loyalty.
Because when the interface disappears, values are all that's left.
## The Same Roadmap Still Works
In 2021 (cough) we mapped a four-stage model for how people and organizations would mature into this new landscape: Baseline, Beginner, Intermediate, Advanced.
I think it still holds.
## Brand + API Maturity
Where is your brand on this chart?
The point wasn’t technology adoption. It was trust, transparency, and adaptation. Here’s what we wrote in our 2021 white paper (Ethan McCarty suggested we call it the “Stepwise Model”):
Baseline —
Most organizations operate as isolated systems with fixed front-ends and manual workflows.
Digital experience is fragmented; data lives in silos.
Personalization means static audience segments and basic content targeting.
Users have little control or visibility.
Beginner —
APIs and automation start connecting systems internally.
Experience teams begin experimenting with adaptive interfaces, but still design around organizational structure rather than individual need.
Governance and ethics are reactive, not embedded.
Intermediate —
Cross-organizational integration emerges.
People can complete multi-party tasks (for example, booking travel, healthcare, or education services) through a single digital flow.
Early “agent” layers appear; software that brokers between systems.
Ethical and accessibility frameworks start to formalize.
Advanced —
Interfaces dissolve into data interactions.
Agents mediate most engagement between individuals and institutions.
Systems adapt automatically based on user context and declared values.
Ethics, transparency, and inclusion are designed in from the start, not added later.
The distinction between front-end and back-end disappears.
## So What Happens Next
If customers stop visiting your website, what happens to your brand? (And your budget for it)
If people interact directly with your data, what defines their experience of your organization?
When there’s no interface, there’s nothing left but information, algorithms, and intent.
To me, the conclusion is that it’s not enough to only look at how AI integrates with brand, marketing, and CX as data flows or prompts help internal teams generate new content, and instead look at it as a wholesale reinvention of how people interact with your organization overall.
That’s the real transformation.
Some references / further reading:
- [Fast Company](https://www.fastcompany.com/91419282/open-ai-websites-nvidia-blackrock)
- [UXDesign](https://uxdesign.cc/the-agentic-era-of-ux-4b58634e410b)
- [McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage)
- [“Agentic Web](Weaving the Next Web with AI Agents” Academic Paper: https://arxiv.org/abs/2507.21206)
- [Figma MCP](https://www.figma.com/blog/introducing-figma-mcp-server/)
---
# Announcing Intentional Technology Training
**Author:** Howard Pyle
**Date:** 2025-10-10
**URL:** https://xfutures.org/journal/announcing-intentional-tech
Over the last year, I’ve found myself in the same conversation again and again—with startup founders, with enterprise leaders, with individual contributors trying to keep up: AI is everywhere, but most people still don’t know how to place themselves in this moment.
Originally, I set out to write a book about it. But I realized the field was moving too fast — and that people needed tools, not text.
So instead of launching a book, I’m launching a training program.
It’s called Intentional Technology. And it’s built to help people and organizations adopt AI in ways that are actually grounded: grounded in real work, real decisions, and what people actually care about.
### What I’ve Seen
In my work helping companies build AI products, advising mission-driven startups, and leading digital transformation inside large corporations, the pattern is clear: most AI adoption efforts start with tools and training, but they don’t give people a way to understand how any of it fits into their job, their values, or their responsibilities.
We’ve created a language gap.
We expect people to show up to “AI training” and translate it into career value, team decisions, or responsible use, without ever giving them the context to do it.
What I’ve learned is that unless you ground AI in what people already know—what matters to them in their role, what they care about in their work—it doesn’t stick. It creates confusion, burnout, or worse: misalignment that shows up as risk, resistance, or regret.
So we built a training model that does the opposite.
### Why This Is Personal
My experience shaped how I think about this.
I’m so crushingly dyslexic, I couldn’t finish college. Barely finished high school. But like a lot of neurodivergent people, I have superpowers that just need the right enablement to really shine. The only reason I was able to navigate executive roles at places like IBM and MetLife is because I had the right tools - and the right coaching.
Technology has always been how I close the gap. It’s what lets me operate at parity. I’ve built my entire career around learning how to use tools that let me think clearly, work faster, and not get lost in noise. That’s part of why I’m so passionate about this work.
If people aren’t equipped with a way to make AI useful to them, we’re just recreating the same gap over and over. That’s what I’m trying to fix.
### What We’re Offering
Intentional Technology is a live, coach-led training experience—delivered in two half-day sessions, online or in-person.
It’s built for teams who are already being asked to “do more with AI,” but haven’t been given the tools, framing, or space to do it responsibly.
Every participant builds:
* A personal AI plan that maps to their actual role
* A set of simple, repeatable decision practices
* A shared understanding of where AI supports their goals, and where it shouldn’t be used
This isn’t a tool tutorial. It’s not prompt engineering. It’s not ethics theory. It’s a human-centered approach to AI enablement, focused on clarity, productivity, and trust.
Everyone who participates gets:
* A workbook based on the Intentional Technology book
* A decision-making blueprint and personalized AI toolkit they build in-session
* A visual system they can reference and reuse
* Optional follow-up coaching to embed it inside the org
We also built this to be flexible. We’ve used it with nonprofits, public-sector teams, global brands, and founders. It adapts to different roles and levels of technical comfort. The only requirement is that the people in the room are already thinking about the future of their work, and need a better way to get there.
### The Team Behind It
I’m not doing this alone.
Intentional Technology is co-led by a group of facilitators with deep experience across leadership development, talent strategy, technology transformation, and career coaching. Some have helped scale high-growth companies. Others have spent their careers guiding nonprofit teams through change. Together, we’ve designed this program to serve real people, inside real systems.
Here are some of the amazing people who have signed on as collaborators:
* **Adam Santos-Coy, PCC** is an ICF-certified coach and Chief Performance Officer, Adam helps leaders and teams navigate complexity with clarity and integrity. His work bridges executive coaching, organizational performance, and transformational change across creative, corporate, and mission-driven sectors.
* **Freddie Helrich, PCC** is an ICF-certified executive coach and former HR leader with 25+ years of experience guiding individuals and organizations through change. With a focus on identity, resilience, and career growth, Freddie works across media, technology, education, healthcare, supply chain, and social impact sectors to help leaders build clarity, confidence, and lasting connection.
* **Melissa Heebink, ACC** is an ICF-certified coach, senior recruiter, and talent advisor with over 25 years in executive search. She’s helped hundreds of organizations adapt their hiring and retention strategies and brings insight into how AI is reshaping the employee journey from both sides of the table.
* **Bruce Faulk** has two decades of experience in global talent acquisition and executive search. He’s led recruiting functions at GroundTruth, built regional sales teams for Experian Marketing Services, and now runs GCN RPO, a minority-owned search firm. Bruce focuses on how hiring, upskilling, and retention all evolve as organizations adopt AI tools.
We’re also fortunate to have deep collaboration from our board members at Experience Futures, including **Miye McCullough** and **Utibe Bassey**. Both are helping shape the program and will be stepping in to help lead trainings and advise on our collaborations with partner organizations.
* **Miye** is a nonprofit governance and communications strategist who helps mission-driven organizations embed ethical decision-making and stay aligned with their values.
* **Utibe** is a global customer experience executive and author of *Love as a KPI*, with a career spent aligning technology, operations, and leadership at scale.
I'll personally be leading our training work for the foreseeable future. I founded Experience Futures in 2021 with the goal of helping empower people with AI. I’ve led brand and digital transformation at IBM and MetLife, advised mission-driven startups, and built this platform to close the gap between innovation and human clarity.
### Why This Matters
At its core, Intentional Technology is about integrity in everyday decision-making.
Not just the big, strategic kind that happens in boardrooms—but the small, constant choices that shape how people actually work:
* What tool do I use?
* What do I share with it?
* How do I check if the output’s wrong?
* How does using AI in this way affect my customers? My career? My day-to-day experience?
* How do I stay aligned with my goals and my organization’s values?
We believe every person in an organization should be empowered to answer those questions—confidently, responsibly, and not just for themselves, but on behalf of the teams and missions they serve.
That’s what this is for.
If you’re interested in learning more or bringing this training to your team, click here to book an intro call or feel free to connect with me on LinkedIn. Also, check out the overview at xfutures.org/training.
We're live. Let’s make AI something people feel confident using. For their work, their teams, and their future.
---
# Roddenberry Catalyst Fund
**Author:** Experience Futures
**Date:** 2025-04-02
**URL:** https://xfutures.org/journal/roddenberry-catalyst-fund
**Roddenberry Catalyst Grant Fuels Development of xf.app — AI Infrastructure for Nonprofits**
We're excited to share that XF has been awarded a Catalyst Fund grant from [The Roddenberry Foundation](https://www.xfutures.org/post/roddenberry-catalyst-fund) to accelerate the development of xf.app. Originally conceived in 2023 as part of our early work exploring AI use cases for social impact, xf.app has evolved through prototyping and nonprofit collaboration into a modular platform that helps mission-driven organizations transform their institutional knowledge into guided, accessible digital tools. The Roddenberry grant supports this ongoing work, building on grant proposals submitted in 2024.
### **Building Infrastructure for Inclusive AI**
The Catalyst Fund backs early-stage ideas tackling global challenges with bold, unconventional solutions. xf.app fits this vision by providing a lightweight, embeddable platform designed specifically for nonprofits and mission-aligned organizations. It enables them to translate their processes, policies, and community knowledge into smart, conversational tools that support real outcomes—from benefit navigation and program design to staff onboarding and leadership development.
While 58% of nonprofits are experimenting with AI, fewer than 10% have ethical policies or structured tools in place. Many are navigating this shift without the technical infrastructure to deploy AI safely or meaningfully. xf.app bridges this gap by offering a platform that helps organizations operationalize their knowledge and workflows, while maintaining alignment with their values and safeguarding the communities they serve.
### **Introducing xf.app**
xf.app is a multi-tenant, AI-enabled platform that helps organizations build conversational tools grounded in their own rules, processes, and expertise. It enables users to engage in guided interactions while still being able to ask questions, request clarification, or explore more freely using AI chat.
The platform is designed to integrate with an organization’s existing content, such as policies, forms, training guides, or planning documents, turning decades of institutional knowledge into structured, interactive experiences. What makes xf.app powerful is its ability to combine clear logic and pathways with AI’s natural language capabilities. Instead of forcing users down rigid paths or relying solely on unpredictable AI models, it blends the best of both.
This approach also addresses one of the biggest challenges in AI today: **hallucinations**, where models generate false or misleading information. With xf.app, organizations define the rules and logic—and AI supports the conversation, not the other way around.
Crucially, xf.app also enables organizations to define **human checkpoints**—escalation moments where a live person can step in. Whether the interaction involves legal aid, healthcare, or sensitive eligibility criteria, the ability to surface the issue to a human is core to xf.app’s design. This ensures nonprofits can embed oversight, empathy, and ethical guardrails directly into the way their systems function. For many mission-driven organizations, this kind of “human in the loop” capability is the essential foundation of responsible AI.
**Example Use Cases Include:**
* Helping staff build an "AI Blueprint" by walking through value-aligned planning questions and outputting a shareable plan
* Allowing leaders to build mentorship or program profiles from resumes, bios, and structured prompts
* Supporting benefits or services navigation through decision trees backed by uploaded documents and integrated community knowledge
### **Our Approach to AI for Social Impact**
Too often, AI tools are built for enterprises—with nonprofit adoption as an afterthought. xf.app reverses that model. It centers nonprofit workflows, document sets, and user needs, then adds just enough AI to make those processes smoother, smarter, and more scalable. Crucially, organizations retain control over:
* **Logic and structure** that reflects their missions and workflows
* **Historical content** that underpins their community expertise
* **Infrastructure**, whether hosted by xf.app or integrated with services like Google Drive or Vertex AI
Through xf.app, we’re building an AI foundation that any nonprofit can use—whether they need to automate intake, scale outreach, guide users through benefits, or match people with services. And it’s designed with **privacy, flexibility, and equity** in mind.
### **Toward an Open, Adaptable Future**
We're designing xf.app to evolve as an open, modular platform. Built on open-source components and standard protocols, it’s designed to foster a developer and nonprofit community that can adapt, remix, and expand it.
> "We believe AI infrastructure for the social sector shouldn't be proprietary. It should be accessible, extensible, and accountable to the people it serves."
By removing barriers—cost, complexity, and technical lock-in—xf.app ensures that nonprofits aren’t left behind in the AI revolution. They’re equipped to lead it.
### **Thank You to the Roddenberry Foundation**
We’re grateful to the Roddenberry Foundation for recognizing the importance of equitable AI and supporting tools that make technology work for more people, not fewer. Stay tuned as we roll out our first flows and invite early partners to join us in shaping this new layer of social impact infrastructure.
---
# Positive use cases for AI
**Author:** Experience Futures
**Date:** 2024-02-20
**URL:** https://xfutures.org/journal/positive-use-cases-for-ai
AI is transforming the landscape of digital products and services in almost every sector. Mission-driven organizations have a huge opportunity, but they also face an entirely new universe of risks. The rise of AI also raises critical questions about the ethical and responsible development and deployment of these technologies. From concerns about algorithmic bias and transparency to the risk of job displacement and the widening digital divide, the social implications of AI demand urgent attention and proactive solutions.
In this context, purpose-driven organizations—including nonprofits, social enterprises, and mission-oriented businesses—have a new imperative to align their social impact goals with their digital strategies. At the same time, social impact leaders have new opportunities to drive positive change and scale their impact through AI-powered products and services. Purpose-driven organizations need to focus on developing positive use cases for AI that prioritize social good and stakeholder engagement.
This tsunami of AI platforms is a unique opportunity for purpose-driven organizations to reimagine how they approach their missions and create positive social impact. Given the hype and noise surrounding AI, it can be challenging for these organizations to cut through the clutter and identify the most promising applications and use cases.
One of the key challenges facing purpose-driven organizations in the age of AI is the need to develop a clear and proactive approach to what we call "positive use cases"—applications of AI that are intentionally designed to promote social good, address pressing challenges, and empower communities. Rather than simply reacting to the latest technological trends or chasing short-term gains, social impact leaders must take a strategic and ethical stance on leveraging AI to advance their missions.
This requires a deep understanding of the specific needs, contexts, and challenges facing the communities and stakeholders they serve. It also demands a willingness to engage in ongoing dialogue and collaboration with a wide range of partners, from technology experts and ethicists to grassroots organizations and those with lived experience of the issues.
Product management and market fit play crucial roles in driving the success and impact of any digital initiative. There's a new approach growing for impact-oriented product managers, working across impacted communities and stakeholders to define and prioritize positive use cases. Product-market fit is essential for purpose-driven organizations leveraging AI for social impact. What are the use cases? Who's going to use it? Regardless of whether the product is commercial or not, the best case is to start with a deep understanding of the market landscape, target audience, and potential barriers to adoption. Iterating based on feedback and data, organizations is the way to ensure that their AI-powered solutions are not only technologically feasible but also truly meet the needs of their stakeholders and contribute to meaningful social impact.
By focusing on intentionally positive use cases and prioritizing the responsible and inclusive development and deployment of AI, purpose-driven organizations can position themselves at the forefront of the movement to harness technology for social good. This might involve exploring applications such as:
* AI-powered tools to help nonprofits and social enterprises better understand and engage with their stakeholders, from donors and volunteers to beneficiaries and partners
* Predictive analytics and machine learning models to help impact investors identify and support high-potential social entrepreneurs and innovative solutions
* Conversational AI and chatbots to provide personalized support and resources to marginalized communities, from mental health services to educational content
* Generative AI and content creation tools to help purpose-driven organizations tell compelling stories, raise awareness, and inspire action around their causes
Ultimately, any success in using AI for purpose-driven organizations isn't just in the technologies themselves but in the strategic and ethical frameworks that drive the product or service being created. This is 10 times harder than just creating an AI product or a startup for a specific market opportunity. By adopting a proactive and intentional approach to positive use cases, these organizations can ensure that the benefits of AI are distributed equitably and contribute to the greater good while mitigating the risks and unintended consequences.
As the AI landscape continues to evolve at this unpredictable pace, social impact leaders, nonprofits, social entrepreneurs, CSR and ESG leaders, and impact investors have to be loud and prominent voices in ways these tools can be used to create positive change.
---
# Building Brand Gravity Podcast
**Author:** Experience Futures
**Date:** 2023-11-15
**URL:** https://xfutures.org/journal/brand-gravity-podcast
**Podcast Description:**
Building Brand Gravity Podcast, G&S Communications CEO Anne Green chats with Howard Pyle, a leading voice in the mission to advance inclusivity in digital experiences as the founder of XF.
**From the Podcast blog:** From the moment I heard Howard Pyle speak at the Page Spring Seminar earlier this year, I knew I wanted to learn more. I sought him out in the conference reception area, and we had a great exchange. After meeting for coffee and further conversation in Brooklyn, Howard was kind enough to agree to join me as a guest on our G&S podcast, Building Brand Gravity. You can check out the full episode here.
Why the urgency to dig deeper? What Howard shared is, to me, a compelling call to action to rethink what we understand as “accessibility” in the digital world (and, by extension, the physical one as well). His perspective helped further open the aperture on how I think about areas like user experience and digital access – as well as work to advance diversity, equity and inclusion at G&S and beyond.
Howard has a fascinating background built at the intersections of technology, UX, creative, brand, marketing, and more, and has had significant roles at major agencies (Ogilvy) and brands (MetLife, IBM). With COVID’s disruption as a catalyst, Howard embarked on a new mission. He founded XF with the goal of advancing accessibility and inclusion in digital experiences, drawing from personal experience with neurodiversity including dyslexia. More specifically, he has fascinating insights to share on how the intentional use of generative AI is essential to closing the ever-widening gulf of the “digital divide.”
Generative AI is currently the shiniest of shiny objects, blinding in its glare and deafening in a hype cycle that swings wildly from utopian dreams to dystopic nightmares. A refrain of “this changes everything” is the one constant – and also a likely truth amid the over-promising and over-worrying.
Each of us benefits from active experimentation with what has been colloquially titled “generative AI” (though purists will argue it is nothing of the sort). In that spirit of exploration, I extracted key passages from my conversation with Howard and fed them to Anthropic’s Claude.ai with a prompt to pen me a 500-word blog post – placing emphasis on such themes as the digital divide, going beyond current definitions of accessibility, and Howard’s vision for the role of AI and automation.
Below is the piece produced by Claude.ai in full – no edits. It interests me to see how this large language model tackled the assignment (not exciting, but not bad). It interests me far more to see the stark differences in our rhetorical styles. The way I would approach this post is leagues of distance from how this platform predicts word to word. Or perhaps it simply needs to be fed enough of my prior writings to mimic the quirks of my style.
For now, I encourage you to scan the AI-generated post – and then make sure to listen to the full podcast segment for how eloquently Howard unpacks a range of issues that need a LOT more discussion in our society today.
There is no “average user” of digital experiences. Our current understanding of ability versus “disability” is limited at best. Anyone lucky enough to age across decades will experience changes in their abilities. The digital platforms and products that serve as gateways to the critical functions of our lives (health, financial, education, civic, etc.) must embrace a far more expansive view of “access.” We must all think hard about what it means to create greater inclusion and empowerment for the full range of human experiences. AI is a tool that can help us speed this process in transformative ways, but it is humans who must make the right choices about how it is applied.
**Closing the Digital Divide Through Technology and Automation**
Access to the internet and digital technologies has become an essential part of functioning in modern society. However, many individuals still face barriers to getting online and using digital tools effectively. This phenomenon is known as the digital divide.
The first level of the digital divide is basic access - whether someone has the hardware, software and internet connectivity required to get online. But as discussed in the attached document, there is a second layer to the digital divide related to usability and digital literacy. Even when someone has technical access, they may still struggle to navigate digital systems, understand terminology and use tools in the way they were designed. This creates a divide between those who have the experience and skills to capitalize on technology, and those who don't.
Populations especially impacted by challenges of the second digital divide include older adults, individuals with disabilities, those from lower income backgrounds and people with neurodiverse conditions like dyslexia that impact learning. For example, during the COVID-19 pandemic, older adults often struggled to use convoluted vaccine appointment websites, reducing their access to this essential healthcare service.
Solving the second digital divide is not just about meeting legal accessibility requirements. It's about understanding each user's unique needs and limitations to create truly inclusive experiences. The document discusses how in our diverse society, there really is no such thing as designing for "most people" anymore.
While workshops and collaboration sessions can build alignment, they are limited in their ability to uncover the full spectrum of user needs required to build maximally accessible digital tools. This is where artificial intelligence (AI) and automation come in. As the document argues, we need to intentionally design positive use cases for emerging technologies to expand digital access.
AI allows us to understand individuals' unique needs and constraints based on how they interact with systems. It then enables dynamically tailored experiences - delivering the right content in the right format to match each user. For organizations without the resources to manually design endless permutations of products and services, AI-powered personalization is the only scalable path to inclusion.
This has profound potential to empower the groups hit hardest by the digital divide, breaking down barriers not just to basic internet access but to the job opportunities, healthcare, education, financial services and commerce enabled by technology. The vision laid out is bold but within reach: leveraging AI to improve digital inclusion for 1 billion people worldwide by 2030.
With the accelerating pace of technological change, it's easy to see only risks and negatives. But there is cause for optimism if we intentionally guide innovation and automation to make information technology work for everyone. This inclusive digital future will require acknowledging the second digital divide, embracing AI's possibilities, and designing solutions that start with ethics rather than just capabilities. The dividends for both individuals and society will be immense.
---
# Video: Designing for Each
**Author:** Experience Futures
**Date:** 2023-05-08
**URL:** https://xfutures.org/journal/designing-for-each-with-ai
In a recent talk, XF's founder discussed the widening digital experience divide and how organizations can leverage AI to create more inclusive digital spaces for their users. In addition to the video (which we hope you'll watch), we wanted to highlight key points from the talk and offer insights into XF's approach to tackling this challenge.
**The Digital Experience Divide**
We live in a world where 80% of internet users have unique digital needs. Whether it's Generation Z, older users, or people with neurodiversity and physical disabilities, the "average user" simply doesn't exist. This means that one-size-fits-all approaches to digital design are no longer effective. The digital experience divide has emerged as a pressing issue, encompassing aspects of design, access, content access, and cognitive access. This divide impacts everything from access to healthcare, finance, education, and more.
**Tackling the Experience Divide with AI**
The solution lies in harnessing AI to automate and tailor digital experiences to individual needs. This approach not only addresses the issue of inclusivity but also opens up new markets for organizations, driving growth, engagement, and loyalty. Our prediction is that in the coming years, digital experiences - including mobile sites, websites, apps, and content - will be largely generated by AI.
**XF: Bridging Business Opportunity and Social Impact**
At XF, we have a dual approach: our nonprofit focuses on education, awareness, publishing, and creating open-source tools to create more inclusive digital experiences using AI, while our for-profit organization helps companies design new digital experiences for their audiences and employees, aligned with their social impact goals.
Our challenges lie in finding the right way to pitch the intersection of business opportunity and social impact to various stakeholders in an organization. Is it growth for the CFO, employee engagement for the CHRO, or mission and purpose for the CEO? Our goal is to help organizations find the sweet spot where both value and impact can be realized.
**XF Initiatives**
1. We are developing a process model that helps organizations strategically design and launch digital experiences while aligning business KPIs with social impact KPIs. This model focuses on ethical AI applications, and we are actively seeking partners to workshop this within their organizations.
2. We are building the largest repository of inclusive design rules by combining existing guidelines and rules from various sources into a language model that can drive AI queries and content generation. This resource will be publicly available through our nonprofit and integrated into our product software products.
**Key Takeaways**
1. Shift the mindset within your teams from "design for all" to "design for each". Recognize that unique digital needs require tailored experiences, and that automation can play a significant role in achieving this goal.
2. Consider digital sustainability as a way to elevate your organization's ESG strategy. Look for opportunities to incorporate digital accessibility, AI ethics, and inclusive technology initiatives into your ESG goals.
3. Embrace generative AI as a solution to engage with users on an individual level. This technology offers a unique opportunity to design for individual needs, but it requires planning and foresight to maximize its potential.
By addressing the digital experience divide and harnessing AI's capabilities, XF aims to create a more inclusive digital world that benefits all users. Join us in our mission to make this a reality.
***
**Full Transcript of the talk:**
Hi, I'm Howard Pyle, the founder of an organization called XF. And I'll tell you a little bit about that and what we do.
But first, I have a little bit of a dirty secret to tell you a little bit of a confession.
This is how I spell the word restaurant.
And I figured in a group of writers and esteemed communication executives, this is probably the most offensive thing that I could put up on screen. But it gets better. Here's four more times of me trying to spell the word restaurant.
This is how my brain is wired. My brain is wired differently.
My brain is wired around, not being able to really think through language and the way that we're supposed to think through language. And it's not uncommon. I'm crushingly dyslexic. And so is my son. And he also had a hearing issue when he was younger. So he has language and speech issues. He's 10. And it's interesting because I do a lot of work with him. This isn't my business, this isn't my profession, but I wanted to start here to tell you a little bit about what goes into the work that I do.
My background is I was a CTO, I worked in startups. I was a senior partner at Ogilvy where I worked on IBM. Then I went to IBM and I was a VP there and actually John Waters organization where I ran a network of design studios. I was Senior Vice President at MetLife; I ran Global Customer Experience Design and launched the Global Brand - the first new brand in 30 years, rolled out in 40 markets and, now - I resigned from that corporate job a few years ago, and I started a social impact organization called Experience Futures. I'm telling you this, and I'm starting with the fact that I have this intellectual disability.
And I've done these things not as a success story, but there's a different spin on this, there's this twist - to say that I have been able to manage, in a corporate setting as a knowledge worker, as someone who has to communicate, and has had to write 10s of millions of emails and presentations and documents, etc. Because - I have something that I would consider to be, a type of privilege that we don't talk about. I have a lot of different types of privilege. But this one in particular, is very important for us to talk about in our era.
I have digital privilege, which means that I can navigate through our email presentations, because when I was younger, somebody sat me down. Actually it was a couple of people, and showed me how to use early versions of Word processors, early versions of spell checkers. And I have, at this point, probably about half a dozen maybe a dozen different tools that I use on a daily basis even before generative AI and chat GPT and all that stuff, in order to manage and write emails - in order to manage and write presentations.
And still to this day, I will write an email, we use Google workspace. I will look at the Grammarly plug-in, it will go through it and then I've got the Gmail grammar checker, and then I'll take it and I'll copy it into Grammarly. Grammarly will catch things and, I'll copy and paste and send it - and I will still miss that I have the word “and” twice, or that I missed a verb, or that I completely spelled “there” totally wrong.
And as an executive. It's incredibly embarrassing. And it actually hasn't been until the past few years that I've talked openly about how that's there.
And here's the interesting thing - it's not uncommon. It's not uncommon. None of these things are. And so as someone who has built and created communications platforms, digital experiences, brands, software platforms. The one thing that we often get told is that there is a target user or an ideal user. And the thing I'm here to talk to you today about is that - the average user is not average. We're not an organization that focuses just on dyslexia or ADHD or diversity. We're an organization that's looking at this next era of our digital lives, and thinking about how to design for individual need. And that's pretty hard. That's a hard thing to do.
So 80% of Internet users have very unique digital needs. So here's what I mean by that. So if you think about Gen Z, there's a lot of articles out recently, and those of you that focus on workplace and workplace enablement - they don't have training on antiquated interfaces like Excel or Xerox machines. They don't know necessarily how to use the interfaces, they don't know what that little, that little floppy disk icon is. Right? They’re largely mobile-only users. Older users; users over the age of 55 have very specific digital needs they need to be designed for they need to be written for a specific ways. People with neurodiversity, people with physical disabilities need certain types of design and content presented to them so screen readers can access them and use them. And when you begin to rack these things up, you begin to see that there is no such thing as an average user. There is no such thing as a user that has the ideal set of you know, fire behavior, you know, common profile, ability to understand your design. And oh, by the way, your organization is struggling just to maintain that one digital presence, that one digital product and service. How are you going to manage that? How do you deal with that? That's like a problem that's too big to solve.
So I want to tell you a little bit of context here. This is a woman named Ingrid Johnson, she lives in Colorado. And there was a story about her trying to access government benefits in the state of Colorado, and it happened to be an employment, in this case. And in order to access this government website, they wanted to verify her identity. Great, it makes sense, government benefits, verify your identity. So they partnered with this other company - this platform to verify her identity. And the request was to upload a photo of yourself using your phone. But the instructions were vague, they didn't make sense. She had an older smartphone, she didn't know how to do it…
This woman spent two days trying to upload a photo of herself and ended up having to go into a virtual queue to chat with an agent to get verified over video, which took her seven hours. And so when you think about it, we have this crushing problem, where people who need digital access or digital resources are cut off from them, because they don't know how to navigate the digital tools in their lives. And this problem is widespread. And it's not just within government, it's within industry. And it obviously affects the way that we sell; it impacts the way that we engage employees. It impacts the way that we access health care, finance, and education. So this is a widespread, systemic problem. And as companies, both public and private, move to being digital-only, this becomes a radical challenge, and actually is about access. It's about large scale access.
We have a tsunami of digital tools. When you think about managing your healthcare, you've probably got dozens of different tools for insurance, for your doctors, health portals. When you think about managing your finances - think for a second - how many different digital tools do you manage your finances with? How many of them actually work on mobile? I had to answer a question yesterday for my accountant for tax season. And I was on the road with my son. And I only had access to my mobile phone. And the question that he asked me wasn't available to me by using the Fidelity app. Sorry, Fidelity if you’re here. I needed to know the signatures on a specific account. And it just wasn't part of their mobile app. But what if I'm a mobile-only user? So mobile-only users? Let's talk about that for a second. There's a huge correlation between mobile-only users and socioeconomic class.
The vast majority of people that live in households that make less than $30,000 a year are mobile-only. And by the way, 88% of them are non-white. So if you want to design for equity, then design for mobile-only. This is just one example. But how do you do this in a big organization? And this is what we would describe as the next layer of the digital divide - the digital experience divide.
And the academics talk about this as this idea of design, access, content access and cognitive access. Sure, you have a device, you have a computer, you have internet access, but you know how to use it? That's the era that we're entering into. So this is what we're focused on. We're focused on, as an organization addressing the experience divide. How do you give people access to the tools that they need when they need it? And we have to move away from this idea of - in every organization, we're designing for one user, that's our ideal target user.
This idea of one size fits all in digital spaces. Because it doesn't work anymore. It literally doesn't work anymore. So this is where the kind of big bombshell happens. And in this conference - sounds like you talked a lot yesterday about AI. And that's where the real that's where things really begin to change.
So here's the idea: if the average user is an average, and the majority of people have very distinct digital needs, and your organizations have a hard time keeping up with how to manage those things, right now, how on earth could you design for all these individual possibilities?
And the answer is, we can begin to automate different aspects of what different people need. So here is an optimistic, positive potential use case for AI. So if you think about it, there's actually the possibility of understanding what different abilities and different cohorts might need. So I'm an older user, I need you to explain to me why I have to give you my username and password. I only have a mobile device, I need you to present this particular screen to me in a way that I can use it on my mobile device. And if you begin to employ AI to generate different types of experience for different individual needs, not only are you beginning to address that, but you're beginning to actually open up your organizations to new markets, if you're a for-profit organization. And so there's this interesting question that happens, which is, if you agree with that, if you buy that hypothesis. And our prediction is that in the coming years, the digital experiences that we interact with the mobile sites, the websites, the apps, the content will be generated largely by AI.
So this is, of course, the point at which I'm going to pitch my organization. And I'm literally asking for your help at this moment.
My organization is called XF. And we have two parts. We have a nonprofit, which is focused on education, awareness, publishing, creating data and open source tools for organizations to create more inclusive digital experiences using AI. But we also have a for profit organization that helps companies create new digital experiences, for their audiences, for their employees, aligned to inclusive goals aligned to their social impact goals, but in order to drive growth, in order to drive engagement, in order to drive loyalty.
So here's the question, and it's a genuine question for this audience, which is, “How do you pitch the intersection of the business opportunity and social impact?” Do you go to the CFO, then pitch growth? Do you go to the CHRO and pitch employee engagement? Do you go to the board and do you talk about social impact goals? Do you go to the CEO and talk about mission and purpose and aligning to that? And this is literally the question that we've been wrestling with for the past year and a half.
We started as a nonprofit, we began publishing, we began doing partnership work with organizations and we ended up creating a separate for profit just mainly to be able to go and sell to the people who need to be sold to and talk about influence and mission to the people who are willing to hear that message.
Because it's very difficult to have the same conversation in both. And as someone who's been an executive at Fortune 100 companies, I can tell you right now, the thing that I will always find is that there's, there's some people in the room that nod their head at the mission. And there's some people in the room that only nod their head at the dollars, and how do we bring them together? So this is the thing that I would ask in this session on influence. And I'm not asking you to quit your fancy SVP job at MetLife and go start a nonprofit. But I would say as influencers in your organization, there is a role, especially for communications leaders, in bringing those two points together. How do you help an organization link together, the idea of value and impact?
Now we do that in other areas, right? When we're thinking about ESG from a sustainability perspective and an environmental impact perspective. But where did that start? It didn't start because everyone in the organization suddenly began to believe that their organization needed a sustainable supply chain. It's when the CFOs began to agree that there would be market risk, and it would be valuable for the organization. So they were willing to invest in it. I mean, let's just be that's just the truth of it. Right? So how do you begin to play out this idea of equity in digital spaces? And I'll tell you, I've been in leadership positions, running design for large organizations where we've had multiple class action lawsuits, because of basic ADA requirements not being met. And when you look at it, there's a dollar problem there. How do you influence that? Do you go to your C suite and pitch them on both? Or do you just go to the business leaders and pitch them on the value? This is the question to ask about this.
So we have two things in our organization that we're focused on, that we're introducing.
One is a process model that helps organizations - it’s like the Higg index and sustainable apparel. And it's like the B Corp certification is this idea of going from strategy down through design, matching business KPIs and social impact KPIs, thinking about how you're applying AI in an ethical way, and launching digital experiences. And we actually believe that there's a way in that there's a need for organizations to have a standardized process model in order to think about how those goals and those values are linked. And we're actually in the process of rolling this out. And we're looking for partners to help us workshop this within organizations.
The other thing that we're doing is we're building the largest repository of inclusive design rules. So you can go to the WC3 consortium, you can go to most organizations, IBM, Microsoft, Google have published Accessibility Guidelines. There’s blog posts, there's rule sets created. We're taking all of that and we're building a language model that we can use to drive AI queries, and generation of content generation of design, by being able to access this large model of inclusive design rules. And we're going to make that publicly available on our nonprofit, but it's also part of the product, the product software products that we're building. And there's really three things that I want to leave you with.
One is that I did some advisory work on a big with a big tech startup last year, and their design team, their whole rally cry was “design for all”. And their definition of design for all was, start with ADHD. And I was like, Well, what about the fact that there's three types of ADHD? And what about how older people and people with ADHD don't actually have the same digital needs. And so suddenly, there's this shift that has to happen within your teams within your organizations that is not designed for all, it's designed for each. And that's an enormous problem. How are you going to do that without automation? It's a real question.
The second is this idea of digital sustainability. I guarantee you that somewhere in your organization you've got a team that's focused on digital accessibility that's focused on AI ethics, that’s focused on how you can make technologies more inclusive. That's like an easy win for your ESG strategy. How do you take those efforts and elevate them and report them out as part of what you're doing in your ESG goals to your board? That's a great opportunity. It's an easy win.
And the third thing is this idea of generative AI can actually be a solution for a lot of the problems that we have about engaging with people individually and it's actually there's an opportunity there to design for individual needs on that, but you have to plan for it. You have to plan for generative AI so with that I'll say thank you very much and I appreciate it.
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# Reuters Feature: The invisible barriers of the digital divide
**Author:** Experience Futures
**Date:** 2021-11-05
**URL:** https://xfutures.org/journal/reuters-the-invisible-barriers-to-tackling-the-digital-divide
**Reuters Feature: The invisible barriers of the digital divide**
By Umberto Bacchi, Thomson Reuters Foundation
* Nearly half the world’s population have no way to get online
* Internet should be cheaper, easier to use, say tech experts
* COVID-19 pandemic has highlighted unequal access
LISBON, Nov 5 (Thomson Reuters Foundation) - Connecting everyone in the world to the web will not single-handedly bridge the digital divide, tech experts at the Web Summit said this week, citing other invisible barriers like high costs, low digital literacy and complicated user interfaces.
The so-called “digital divide” refers to the gap between those who have access to computers and the internet and those who don’t, with the latter group made up of nearly half the world’s population, according to the United Nations.
With many essential services like schooling and banking moving online, the coronavirus pandemic has brought new urgency to global efforts to get the unconnected online by bringing internet coverage to remote or deprived areas.
“(COVID-19) made us clearly understand that what used to be seen as a ‘nice-to-have’ technology is now a ‘must-have’,” said ‘Gbenga Sesan, executive director of Paradigm Initiative, a pan-African social enterprise working on digital inclusion.
Reaching everyone can be a daunting task.
Even identifying where exactly internet access is needed is no easy feat in parts of the globe, said Sophia Farrar, who leads a programme that uses satellite imagery and other data to locate offline schools and get them connected.
“No one actually knows how many schools there are in the world,” Farrar, of the U.N. children’s agency UNICEF, told a panel at Europe’s biggest tech conference in Lisbon.
“What we aim to achieve through the mapping is even just setting what that baseline target is.”
Increased mobile penetration has accelerated the process.
The number of active mobile broadband subscriptions worldwide jumped more than 75% to nearly 6 billion, including people with multiple accounts, between 2015 and 2020, according to the International Telecommunication Union.
Only about 450 million people live in areas not covered by mobile broadband, according to telecoms lobby group GSMA.
But even where there is coverage, more than 3 billion are not online, largely because they lack tools, skills and money to make use of it, said Robert Opp, chief digital officer at the U.N. Development Programme (UNDP).
“If you just connect somebody with infrastructure, it doesn’t mean that you’re going to have productive use of your internet connection,” told the Thomson Reuters Foundation in an interview.
Cost is one major barrier, he noted.
There are only a few developing countries where internet prices are in line with the U.N.’s target of less than 2% of the national average monthly income, Opp said.
Even in rich nations like Britain or the United States poor people often can’t afford to buy data, an issue that has sparked calls for price caps and motivated some countries to declare the internet an essential public service during the pandemic.
Others might not have the skills to navigate often complex, jargon-filled websites and applications, Opp added.
The problem has come to the fore with COVID-19 vaccine rollouts, as the elderly and the frail in countries from Sweden to South Africa report having trouble booking their shots online.
Lack of digital literacy also leaves people exposed to risks such as misinformation and loss of privacy, said Opp.
While education is key to helping people protect themselves online, designing digital tools that are easier to understand and tailored for the communities they are meant to serve is also essential, said Howard Pyle, a digital designer turned social entrepreneur.
“Most websites and mobile apps are designed for digitally privileged users who already know how to use those tools - typically the most profitable users that companies will get most traction with,” Pyle said in an interview at the Web Summit.
“But this excludes people who have different needs or different abilities, for example, those who are older or lack experience with technology or lower income users who have limits in terms of the types of devices they have access to.”
Pyle’s social enterprise, ExperienceFutures, looks to help firms and governments make their web services more accessible by cutting jargon and complexity and involving the communities they are trying to serve at the design stage.
“At the moment, there is too much emphasis on trying to create one-size-fits-all tools and expect users to learn how to use them,” he said.
“We have to evolve to a place where the technology is flexible enough that individuals can understand it based on their abilities.”
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# WebSummit: AI and Ethical Design
**Author:** Experience Futures
**Date:** 2021-11-02
**URL:** https://xfutures.org/journal/websummit-2021-digital-equity-through-ethical-design-and-automation
# WebSummit: AI and Ethical Design
XF Founder Howard Pyle and Board Member Amit Sen recently took the stage at WebSummit to discuss the intersection of digital equity, front-end experience design, and the impact of machine learning and AI tools. Drawing from their diverse backgrounds in design, coding, and human rights law, Pyle and Sen explored the challenges and opportunities in creating inclusive digital experiences for refugees and other underserved communities.
Full Transcript
**Howard Pyle 0:03**
All right. Hi, everyone. Thanks for being here. Today is a great session. I'm Howard Pyle, this is Amit Sen, we're going to talk a little bit about what we see coming in terms of the intersection of digital equity, front end experience design on how machine learning and AI tools are going to impact that. Just by way of background, I spent 20 years as a digital product designer and startups - I ran a network of design studios at IBM. Most recently, I ran design UX, front end platforms for MetLife and I resigned to start this organization, xfutures.org. And we're focused on how to shift digital front end experiences to be more equitable through new design methods and through new technologies, and Amit, you want to introduce yourself?
**Amit Sen 0:57**
Sure, yeah. So I'm Amit Sen. And I work in a very different field, probably with some different experiences coming into it. I'm a human rights lawyer. I currently work at the UN Refugee Agency. But I'm not speaking on behalf of the UN today, because what I want to say, in a way is much more personal. It's much closer to home. And it comes a little bit from my family's own story. So I'm from India, but I'm also from Bangladesh. And as many people will know, when India became independent, something like 12 million people were displaced from their homes, and what was their country and that included my family. And then in 1971, when Bangladesh went through the same process, and it became independent, another 6 million were displaced. And again, that included my family. And then when my parents got married, instead of having arranged and in fact, forced marriages, they eloped and ran away and fell in love. They too needed to flee for their own safety. So for me working on refugee issues has never been about fixing something out there. It's been the story of my own family, my own community and my own life, so it’s deeply personal. I've worked in it in a couple different capacities. And what I'm really inspired by now is how refugees are engaging with digital environments, and digital platforms to solve, in a way the same sets of problems that my family was looking at for the last four to five decades, but in new ways, with new opportunities, and also some new risks. And I'm really excited about the entry point that experience futures has opened up to take a closer look at that.
**Howard Pyle 2:25**
Yeah. So the thing I think we want to touch on today's is talking about, “How do you think about the future of experience?” And when we think about the term experience in a digital or creative capacity, we're really talking about front end design. We're talking about the content, the UX, the visual design, but also the front end coding that makes up the websites, the mobile apps that you use. And when you talk to academics, about the digital divide, which is a term that's been around for quite some time, a lot of what we end up talking about is the, you know, bandwidth. Do you have a computer physical infrastructure, but the academics talk about a second and a third layer of the digital divide, which is really about access. Do you understand the designs that have been given to you? Do you understand the content? Do you have the cognitive abilities to understand what's being presented to you? And the insight that we started this organization around is basically that all websites, all mobile apps; all front end design you interact with is fundamentally designed for digitally privileged users - people already know how to use those tools. And if you look at a lot of the tools that we create, especially in a COVID era, where everything's going into digital spaces, people are being left behind. So if I'm an older person, and I don't understand how digital tools work, but yet mobile healthcare is accelerating, I don't just have a digital problem, I actually lose access to healthcare. And this plays out in an ageing population. It plays out in terms of racial justice, gender, gender, ethics, etc. And a lot of the things that we're working on together, and part of what Amit and I are here to talk about is, how does that impact refugees and stateless individuals. So there has to be a fundamental change in the way that front end experience is created. We have to have new methods, we have to think about the audiences, we have to link those methods to our diversity and impact and inclusion goals, to our purposes, to our social statements and our social sentiments within every organization. And what I've witnessed as a professional and I'm therefore a contributor to this problem, is that every design I've participated in, has never really linked to the diverse audiences that the organization claims to support. And so that's why we started this organization as an effort to try to change that, and some of that it's about method and process of design., and some of that is about the tools, the wave of machine learning and AI tools that are going to hit the front-end experiences. And Amit and I have actually known each other for quite a long time - we actually used to live in a punk rock group house together in Washington, DC. Amit was a bike messenger and I played in a punk band. And so now here we are all fancy on stage for you. But, the thing that's we've talked about for years and years and years is how do our different disciplines you know, I'm a lawyer, I'm a designer and a coder. How do these things overlap? And so I think that the thing that's been very interesting in our conversations on it is you've talked a lot about how does the front end experience hit the world of refugees, stateless individuals in your work in terms of human rights advocacy?
---
# TechCrunch: Design's Dirty Secrets
**Author:** Experience Futures
**Date:** 2021-10-03
**URL:** https://xfutures.org/journal/techcrunch-designs-dirty-secrets-and-how-to-address-experience-bias
# TechCrunch OpEd: Design's dirty secrets and experience bias
(Article by xfutures.org Founder Howard Pyle, originally posted on TechCrunch)
I had a conversation recently with a huge technology company, and they wanted to know if their work in human-centered design guards against experience bias. The short answer? Probably not.
When we say experience bias, we’re not talking about our own cognitive biases; we’re talking about it at the digital interface layer (design, content, etc.). The truth is that pretty much every app and site you interact with is designed either based on the perceptions and ability of the team that created it, or for one or two high-value users. If users don’t have experience with design conventions, lack digital understanding, don’t have technical access, etc., we’d say the experience is biased against them.
The solution is to shift to a mindset where organizations create multiple versions of a design or experience customized to the needs of diverse users.
Going back to this tech company I was talking with, any company’s investments in empathetic design are essential, but, as someone who has launched and runs design functions, we need to address a few dirty secrets.
The first is that UX and design teams are often instructed on very limited target users by a strategy or business function, and experience bias starts there. If the business doesn’t prioritize a user, then a design team won’t have the permission or budget to create experiences for them. So even if the company is pursuing human-centered design or employs design thinking, they’re often just iterating against a user profile based on commercial interests and not aligned with any definition of diversity in terms of culture, race, age, income level, ability, language or other factors.
The other dirty secret is that human-centered design frequently assumes humans design all of the UX, services and interfaces. If the solution to experience bias is to create tailored variations based on users’ different needs, this hand-crafted UI model won’t cut it, especially when the teams making it often lack diversity. Prioritizing a variety of experiences based on user needs requires either a fundamental change in design processes or leveraging machine learning and automation in creating digital experiences — both necessary in a shift to experience equity.
### How to diagnose and address experience bias
Addressing experience bias starts with understanding how to diagnose where it might appear. These questions have been helpful in understanding where the problem can exist in your digital experiences:
#### Content and language: Does the content make sense to an individual?
Many applications require special technical understanding, use jargon oriented to the company or industry, or assume technical knowledge.
With any financial services or insurance website — the assumption is that you understand their terms, industry and nomenclature. If the days of an agent or banker translating for you are going away, then the digital experiences need to translate for you instead.
#### UI complexity: Does the interface make sense based on my abilities?
If I have a disability, can I navigate it using assistive technology? Am I expected to learn how to use the UI? The way that one user needs to navigate an interface may be very different based on ability or context.
For example, design for an aging population would prioritize more text and less subtle visual cues. In contrast, younger people tend to do well with color-coding or preexisting design conventions. Think about terrible COVID-19 vaccine websites that made it your problem to understand how to navigate and book appointments — or how each of your banks has radically different ways to navigate to similar information. It used to be that startups had radically simple UIs, but feature upon feature makes them complex even for veteran users — just look at how Instagram has changed in the past five years.
#### Ecosystem complexity: Are you placing responsibility on the user to navigate multiple experiences seamlessly?
Our digital lives aren’t oriented around one site or app — we use collections of tools for everything we do online. Almost every digital business or product team aspires to keep users locked into their walled garden and rarely considers the other tools a user might encounter based on whatever they’re trying to accomplish in their lives.
If I’m sick, I may need to engage with insurance, hospitals, doctors and banks. If I’m a new college student, I may have to work with multiple systems at my school, along with vendors, housing, banks and other related organizations. The users are always to blame if they have difficulty stitching together different experiences across an ecosystem.
#### **Inherited bias**: **Are you using systems that generate content, design patterns built for a different purpose or machine learning to personalize experiences?**
If so, how do you ensure these approaches are creating the right experiences for the user you’re designing for? If we leverage content, UI and code from other systems, you inherit whatever bias is baked into those tools. One example is the dozens of AI content and copy generation tools now available — if those systems generate copy for your site, you import their bias into your experience.
To start building more inclusive and equitable experience ecosystems right now, new design and organizational processes are needed. While AI tools that help generate more customized digital experiences will play a big role in new approaches to front-end design and content in the coming years, there are five immediate steps any organization can take:
**Make digital equity part of the DEI agenda**: While many organizations have diversity, equity and inclusion goals, these rarely translate into their digital products for customers. Having led design at large companies and also worked in digital startups, the problem is the same across both: a lack of clear accountability to diverse users across the organization.
The truth is that at big and small companies alike, departments compete for impact and who is closer to the customer. The starting point for digital experiences or products is defining and prioritizing diverse users at the business level. If a mandate exists at the most senior levels to create a definition of digital and experience equity, then each department can define how it serves those goals.
No design or product team can make an impact without management and funding support, and the C-suite needs to be held accountable for ensuring this is prioritized.
**Prioritize diversity in your design and dev teams**: There’s been a lot written about this, but it’s vital to emphasize that teams that lack any diverse perspective will create experiences based on their privileged background and abilities.
I would add that it’s essential to cast for people who have experience designing for diverse users. How is your organization changing its hiring process to improve design and developer groups? Who are you partnering with to help source diverse talent? Are your DEI goals just check boxes on a hiring form that are circumvented when hiring the designer you already had in mind? Do your agencies have clear and proactive diversity programs? How well-versed are they in inclusive design?
A few valuable initiatives from Google are exemplary: In its efforts to improve representation in the talent pipeline, it has shifted funding of machine learning courses from predominantly white institutions to a more inclusive range of schools, enabled free access to TensorFlow courses and sends free tickets to BIPOC developers to attend events like Google I/O.
**Redefine what and whom you test with**: Too often, user testing (if it happens at all) is limited to the most profitable or important user segments. But how does your site work with an aging population or with younger users who don’t ever use desktop computers?
One of the key aspects of equity versus equality in experience is developing and testing a variety of experiences. Too often, design teams test ONE design and tweak based on user feedback (again, if they’re testing at all). Though it might be more work, creating design variations considering the needs of older users, people who are mobile-only, from different cultural backgrounds, etc. allows you to link designs to digital equity goals.
**Shift your design goal from one design for all users to launching multiple versions of an experience**: Common practice for digital design and product development is to create a single version of any experience based on the needs of the most important users. A future where there’s not one version of any app or site, but many iterations that align to diverse users, flies in the face of how most design organizations are resourced and create work.
However, this shift is essential in a pivot to experience equity. Ask simple questions: Does your site/product/app have a variation with simple, larger text for older audiences? In designing for lower-income households, can mobile-only users complete the tasks you’re expecting, as with people who would switch to desktops to complete?
This goes beyond simply having a responsive version of your website or testing variations to find the best possible design. Design teams should have a goal of launching multiple focused experiences that tie directly back to prioritized diverse and underserved users.
**Embrace automation to create variations of content and copy for each user group**: Even if we create design variations or test with a wide range of users, I’ve often seen content and UI copy be considered an afterthought; especially as organizations scale, content either becomes more jargon-filled or so overpolished that it’s meaningless.
If we take copy from existing language (say, marketing copy) and put it into an app, how are you limiting people’s understanding of what the tool is for or how to use it? If the solution to experience bias is variation in front-end design based on the needs of the individual, then one smart way we can dramatically accelerate that is to understand where automation can be applied.
We’re at a moment in time where there is a quiet explosion of new AI tools that will radically change the way UI and content are created. Look at the volume of copy-driven AI tools that have come online in the last year — while they’re largely aimed at helping content creators write ads and blog posts faster, it’s not a stretch to imagine a custom deployment of such a tool within a large brand that takes users’ data and dynamically generates UI copy and content on the fly for them. Older users may get more textual descriptions of services or products that have zero jargon; Gen Z users may get more referential copy with a heavier dose of imagery.
The no-code platforms show a similar opportunity — everything from WebFlow to Thunkable speaks to the possibility of dynamically generated UI. While Canva’s designs may feel generic at times, thousands of businesses are using it to create visual content rather than hire designers.
So many companies are using the Adobe Experience Cloud but seemingly ignore the experience automation functions that are buried inside. Ultimately, the role of design will change from handcrafting bespoke experiences to being curators of dynamically generated UI — just look at how animation in film has evolved over the past 20 years.
### The future of design variation powered by machine learning and AI
The steps above are oriented toward changing the way that organizations address experience bias using current state technology. But if the future state of addressing experience bias is rooted in creating design and content variations, AI tools will start to play a critical role. We already see a huge wave of AI-driven content tools like Jarvis.ai, Copy.ai and others — then there are automation tools built into Figma, Adobe XD and other platforms.
AI and machine learning technology that can dynamically generate front-end design and content is still nascent in many ways, but there are interesting examples I’d call out that speak to what’s coming.
The first is the work that Google released earlier this year with Material You, its design system for Android devices that’s intended to be highly customizable for users as well as having a high degree of accessibility built-in. Users can customize color, type and layout, giving them a high degree of control — but there are machine learning features emerging that may change the designs based on user variables such as location or time of day.
While the personalization aspects are initially pitched as giving users more ability to customize for themselves, reading through the details of Material You reveals a lot of possible intersections with automation at the design layer.
It’s also important to call out the work that organizations have been doing around design principles and interactions for how people experience AI; for example, Microsoft’s Human-AI eXperience program, which covers a core set of interaction principles and design patterns that can be used in crafting AI-driven experiences alongside an upcoming playbook for anticipating and designing solutions for human-AI interaction failures.
These examples are indicators of a future that assumes interactions and designs are generated by AI — but there are precious few examples of how this manifests in the real world as of yet. The point is that, to reduce bias, we need to evolve to a place where there is a radical increase in variation and personalization for front-end designs, and this speaks to the trends emerging around the intersection of AI and design.
These technologies and new design practices will converge to create an opportunity for organizations to radically change how they design for their users. If we don’t begin to look now at the question of experience bias, we won’t have an opportunity to address it as this new era of front-end automation takes hold.