Podcast Summary: Work For Humans
Episode: What Happens When AI Removes Friction from Work | Aaron Horwath
Host: Dart Lindsley
Guest: Aaron Horwath, Director of AI Operations at Creative Force
Date: January 13, 2026
Overview
This episode of Work For Humans explores how AI can fundamentally reshape the work experience when thoughtfully integrated. Dart Lindsley interviews Aaron Horwath, who details how Creative Force embraced a “work as product” philosophy—putting employee experience on par with business needs—to guide their successful AI adoption. Rather than chasing flashy tools or automation for its own sake, Creative Force began by mapping employees’ work, identifying “friction” points, and using AI to eliminate drudgery, empower non-technical staff, and dramatically speed up innovation cycles. Their people-first approach has made work more rewarding, enhanced retention, and positioned the company at the vanguard of AI-enabled productivity.
Key Discussion Points and Insights
1. Context: Creative Force and Work as a Product
(05:39, 12:34)
- Creative Force is an end-to-end content production platform for photo studios aiming to streamline workflow, enhance data insight, and eliminate operational pain points.
- Aaron's team didn’t merely seek efficiency; they deliberately designed work to be more meaningful and enjoyable, treating employees as customers of the “work product.”
Quote:
“We’ve changed the way that our employees work and we've also eliminated a lot of the things that people don’t enjoy... We allow you to spend more time on the parts of the job you enjoy, and less on things you don’t.”
— Aaron (10:21)
2. People Before Tech: AI Enablement Strategy
(07:17, 13:04)
- The push for AI didn’t originate in IT, but in People Operations (HR), recognizing both employee development and business competitiveness.
- Leadership formed a cross-functional AI enablement team, not focused on “AI tools,” but on mapping out current pain points, repetitive tasks, and sources of joy/drain in work.
Quote:
“We talked about what are the current operational challenges... the tasks and work that staff need to execute but isn’t necessarily rewarding, or the things that they want to do.”
— Aaron (13:04)
3. The Bubble Chart: Mapping Work and Identifying AI Opportunities
(14:07, 16:39)
- Used a two-axis bubble chart (Core to Mission vs. Business Value) to map every task according to value, core-ness, enjoyment, and future interest—with individual employees defining their own work categories.
- Outputs were color- and size-coded, including an innovation: a blue sticky for “future work I want to do.”
- This exercise clarified which tasks offered low value or satisfaction and should be eliminated, kept human, or automated.
Quote:
“We did it as a one-on-one activity... how do people think about breaking up their work in their own minds?”
— Dart (16:06)
4. Three AI Use Case Categories (CAP Framework)
(20:36, 23:44, 25:00)
K: Knowledge at the Point of Need
- AI bots provide instant access to company, product, or industry data, shortening learning/onboarding curves and empowering all staff to answer questions in the moment.
- Example: A salesperson asks a bot about integrations during a call and gets an answer in seconds.
A: Automation
- Repetitive, structured tasks like generating reports, meeting summaries, or documentation are automated, freeing up employee time for higher-order thinking.
- Example: Meeting notes, business cases, and marketing content drafted by bots and reviewed by humans.
P: Proactive Insights
- AI synthesizes data (e.g. sales, customer engagements) and pushes recommendations, warnings, or actions to users, effectively acting as a team of specialized “bot assistants” managed by an AI overseer.
- Example: Bots monitor deals overnight, generate dashboards, and prep draft emails or suggestions for human review.
Quote:
“My job ... is to create as many of these people as possible. These people who can cut that loop all the way down... that's super powerful.”
— Aaron (38:57)
5. Transforming the Innovation Loop
(32:20, 36:52, 37:05)
- AI flattens traditional hierarchies and accelerates the “relay race” between idea, feedback, and product by equipping non-technical employees to build solutions directly—democratizing technical skills.
- Example: A non-technical employee builds and deploys a web ROI calculator in days, instead of months and multiple teams.
Quote:
“If you’re an organization leader right now, what is the big competitive advantage? ... Even a very optimized relay race, you’re probably not going to out compete somebody [with this new model]...”
— Aaron (36:52)
6. Organizational Change Management
(43:28, 47:29)
- Leadership buy-in and explicitly defining the “why” were crucial—AI isn’t to replace people, but to make work better.
- Transparency, cross-functional ownership, and ongoing education/demonstrations supported successful adoption.
- Feedback showed employees felt empowered, less “stuck,” and able to contribute more broadly.
Quote:
“People feel very empowered to execute tasks and do work they previously wouldn’t have the domain knowledge to do… there’s just a lot less getting stuck.”
— Aaron (51:39)
7. Tools and Tech Stack
(47:43, 48:33)
- Dust.tt: Enables easy building and chaining of bots for knowledge management.
- N8N: A workflow engine connecting platforms and automating multi-step bot activities.
- Emphasis on open, flexible tools that let employees experiment and build.
8. Impact on Skills, Employee Growth, and the Future of Work
(32:20, 40:58, 54:45)
- AI lets non-technical people augment their capabilities—now soft skills are layered with technical execution unlocked by AI.
- The future may see “domain agnostic” knowledge workers: problem solvers first, specialty second, enabled by AI to float between disciplines.
- New career incentives: retention is up as employees see work focused on what matters to them, and freedom to innovate with fewer barriers.
9. Philosophy: Human Work vs. AI (57:25, 58:44)
- Art and human expression still matter—a bot-written product lacks the intent of human-created work, especially in creative domains.
- Most employees are happy to let AI handle transactional or dull tasks and focus their human energy on creativity and collaboration.
Quote:
“The point of art is to communicate something about the human experience ... the AI is just a probability machine.”
— Aaron (57:28)
10. Advice for Organizations Starting Out
(43:28, 47:29)
- Begin by clarifying your why, map employee experience and business impact, and form a diverse internal AI team.
- Don’t chase tools—focus on meaningful challenges first, and build tool adoption around real needs.
- Invest in enablement and ongoing dialogue; encourage experimentation, recognize that adoption is a learning curve.
Quote:
“Focusing first on business objectives ... before you jump to tooling, what are our challenges, what do we want to achieve for our people, and what would be the business impact?”
— Aaron (44:06)
Notable Quotes & Memorable Moments
- “We started to really see the opportunity for it to improve the work experience of employees. That was the part that I got excited about.” (04:34)
- “It’s a qualitative change in how things happen...” (37:05, Dart, reflecting on the paradigm shift in organizational learning/response loops)
- “You could imagine in a few years ... AI could turn people into kind of domain agnostic knowledge workers, where you more look at an organization in terms of the level of problem that you’re solving.” (54:45)
- “My job is all about trying to have as much fun as possible in every variation of fun.” (65:46, Aaron on meaning at work)
- “There’s just a lot less getting stuck because of a lack of knowledge in one area or another.” (51:39)
Timestamps for Important Segments
- 03:33 – Why AI was initially avoided and why it’s now compelling
- 05:39 – What is Creative Force? Aaron's background
- 13:04 – Forming the AI Enablement Team and initial steps
- 14:07 – The Bubble Chart activity for mapping work friction
- 20:36 – The CAP Framework: Knowledge at Point of Need, Automation, Proactive Insights
- 32:20 – Non-technical employees gain technical superpowers
- 36:52 – Example: Drastic reduction in time from idea to product
- 43:28 – Steps for organizations at the start of their AI journey
- 47:43 – Tech stack specifics
- 54:45 – The future of skillsets: domain agnosticism
- 57:25 – AI vs. Human creation: the “art” of work
- 65:46 – Aaron’s personal motivation: Making work fun
Tone and Language
The episode strikes a balance between optimistic curiosity and pragmatic strategy. Both host and guest are candid, reflective, and practical: not starry-eyed about AI’s “magic,” but deeply excited about the human-centered ways it can transform both business outcomes and employee experience. They maintain a conversational, sometimes playful, always intellectually engaged tone.
Conclusion
This episode offers a detailed, eminently pragmatic roadmap for companies seeking to leverage AI to create better work rather than simply cut costs. The lessons: start with real employee needs, foster cross-functional ownership, make your motives explicit, and use AI as an “exoskeleton”—empowering, not replacing, your people. The result is faster learning, unleashed innovation, and a workplace where human judgment and creativity are central.
Listen for:
- Concrete, replicable frameworks for mapping “friction” in work
- Examples of practical AI tool adoption
- Reflection on the philosophical meaning of work
- Guidance for HR or People teams who want to lead, not follow, in the AI revolution