How Creative Force Is Using Dust to Help Define “The Future of Work”

This article was written by Aaron Horwath, Director of AI Operations at Creative Force. Creative Force is an end-to-end software platform for managing content production workflows. The company uses Dust as its internal AI agent platform to power automation, insights, and innovation across teams.

A Coming Paradigm Shift

For years, software companies, including Creative Force, have worked to integrate AI into their products, with the promise of fundamentally transforming the user experience and the value delivered to customers.

Near the end of 2024, our People team, led by VP of People Astrid Riber Poulsen, recognized that AI was on the brink of triggering a similar paradigm shift for employees and the nature of work itself. It was clear to Astrid that organizations able to integrate AI effectively into their ways of working would lead a transformation in how knowledge work is conceived, executed, and delivered. And so, our team got to work.

In the first six months of our official rollout, the results are clear. We’ve achieved 93 percent adoption of Dust across our Sales, Marketing, and Customer Experience teams—the focus of our first major AI enablement push. An internal survey in September 2025 reinforced this progress: 95 percent of respondents from across Creative Force agreed or strongly agreed that they feel more capable of leveraging AI in advanced use cases than they did six months ago. Most importantly, we’re steadily eliminating the kinds of administrative tasks that drain motivation and distract from high-value work. The outcome is measurable improvement in productivity, performance, and return on investment, all while keeping employee wellness at the center of our strategy.

We’re still in the middle of our journey, but the challenges we’ve faced are the same ones many organizations will encounter as they begin integrating AI into their operations. To help other organizations with their AI journey, we’ve shared the key lessons we’ve learned so far and the insights that continue to shape how we approach this transformation and the role of AI tools, like Dust, in our work.

AI as a Change Management Challenge

Many organizations fall into the trap of treating AI integration as a purely technical challenge, handing it over to IT or engineering to “figure out.” In our view, this is, put simply, misguided.

AI is not just a technology challenge. It is a people challenge.

AI is not just a technology challenge. It is a people challenge. At its core, AI transformation changes how work gets done, how decisions are made, and what skills are needed to succeed. It redefines roles, reshapes workflows, and shifts what we expect from both humans and machines. These are not technical questions—they are organizational, cultural, and philosophical ones.

That is why, at Creative Force, our People team led our AI transformation effort. We recognized early on that successful adoption requires empathy, communication, and change leadership. The hardest part of AI adoption is not building the tools; it is changing habits, reshaping processes, and helping people build trust and confidence in a new way of working. 

To enable AI effectively, organizations need a cohesive, company-wide strategy for adoption, governance, and enablement—and one that starts with people first.

Guiding Principles: Understanding our Why

The development of our AI strategy began with Astrid Riber Poulsen working closely with the rest of our Leadership team to position the development of an internal AI strategy as a primary strategic priority for the company. We owed it to our employees, Astrid made clear, to get ahead of the coming shift and prepare them for what we came to call The Future of Work, and to do so required a cohesive, organization-wide approach rooted in shared purpose and alignment from the start.

In her pitch to the leadership team and Board of Directors, Astrid outlined three guiding principles that defined why an AI strategy was not just important, but mission-critical:

1. To Be AI Leaders, our People Needed AI Exposure.

If we were going to lead our industry in the adoption of AI, our own teams needed direct, hands-on experience using it. Internal adoption would build both exposure and fluency, allowing our employees to understand the technology deeply and, in turn, share that knowledge with our customers with confidence and credibility.

2. We Needed to Prepare People and Roles for the AI Shift.

We recognized that AI would change the skills required for success. Rather than waiting and reacting, we wanted to help our people stay ahead of the curve by creating opportunities to experiment, learn, and build confidence in the skills necessary to excel in the future of work.

3. We Needed to Redefine “Human Work.”

We anticipated the rise of a new category of “AI-Work”—tasks like administrative busywork, content creation, and manual data entry—that employees would expect AI to handle. By offloading these routine tasks to AI, we could free our teams to focus on the meaningful, creative, and value-generating activities that inspire them and drive the company forward.

Having defined why AI integration and enablement were business-critical, we next needed to articulate and define our beliefs about AI—clarifying the pillars of our strategy across security, upskilling, and the operational changes it would bring.

Strategy, Guardrails, and Governance

Organizations everywhere are searching for the most effective ways to enable AI adoption. At Creative Force, we knew we couldn’t realize our vision through a loose or unstructured approach—but we also couldn’t constrain innovation with overly rigid rules. We needed to strike the right balance: fostering creativity and experimentation while operating within a framework that was safe, cohesive, and aligned with our business priorities.

That meant defining, for ourselves, what our perspective and approach would be across several key dimensions:

• Redefining Work: What did we consider “Human Work” vs. “AI Work”? And how would we leverage AI to reduce administrative work and help our teams reinvest their time in higher-value activities?
• Innovation: How we would empower people to experiment with AI safely while keeping efforts aligned with company priorities?
• Adoption: How we would ensure AI adoption was deliberate, equitable, and supported across the organization, not just at the leadership level?
• Safety and Governance: How we would build confidence by establishing principles and guardrails for responsible and secure AI use?

We knew these questions could not be answered by the leadership team alone. Nor could they be solved solely within the People team. No set of guidelines or top-down directives would get us where we wanted to go. Success required an organization-wide effort shaped by a diverse set of perspectives that, together, would define the operational roadmap for how we would leverage this new technology in a way that reflected our values and ambitions.

And so we formed our Internal AI Enablement Group: a cross-functional team dedicated to shaping how AI would be explored, adopted, and embedded across Creative Force.

Answering the Big Questions

Our Internal AI Enablement Group is a cross-functional team, made up primarily of team leads and a few individual contributors, that meets every two weeks to explore, question, and define our organizational approach to AI. In the early days of the group, it served as a space where people from across Creative Force could share their perspectives, surface challenges, and shape our collective direction.

The group was deliberately diverse in its members’ skill sets and areas of expertise, including:

• Technical profiles: People comfortable working with APIs, automation tools, and system integrations.
• Enthusiastic learners: Individuals eager to experiment hands-on with emerging AI tools, even if they weren’t technical experts.
• Business leaders: Team leads who deeply understood their teams’ operations and could identify, surface, and prioritize the right problems to solve.

In our early meetings, we didn’t actually talk about AI at all. Instead, we began with a simple exercise. Together, we listed the operational challenges each team was facing. AI served as a useful Trojan horse, an entry point to much deeper conversations about how work was actually being done. We asked ourselves:

• What challenges are we facing today?
• What is the source of those challenges?
• Why do we do things the way we do?
• If we were a brand-new company, would we design this workflow differently?
• Should we keep doing it this way at all?

For this process to work, our group had to be willing to set aside egos and take an honest look at how our teams operated. That openness to question everything became one of the most valuable aspects of the initiative. It pushed us not just to add AI to existing processes but to rethink and redesign how we work when it made sense to do so.

“The process of reviewing our current operational challenges pushed us not just to add AI to existing processes but to rethink and redesign how we work when it made sense to do so.”

The outcome of these discussions was a consolidated list of roughly 25 to 30 operational challenges gathered from across the organization. We then evaluated each through three key lenses:

• Operational Impact: If we solved this challenge, what would the tangible impact be on the organization? Would it meaningfully improve efficiency, quality, or employee experience?
• True Problem or Manufactured One: AI can scale inefficiency just as easily as it can eliminate it. We needed to ensure we were solving real problems rooted in genuine operational friction.
• Solvable with AI: Not every challenge is an AI challenge. We focused on problems where AI had a high likelihood of making a measurable difference.

Organizing AI Opportunities: The K.A.P. Framework

For many teams, it can be difficult to know where to begin when identifying opportunities for leveraging AI. To make this process easier, we developed what we call the K.A.P. Framework—a simple but powerful way to focus on the areas where AI can have the most meaningful impact.

1. Knowledge at the Point of Need

AI has a powerful ability to connect and analyze data from multiple sources and deliver the right insights to the right person at the right moment. Tools like Dust enable interaction with data through chat interfaces, creating short, dynamic learning loops. Implemented correctly, this can reduce cognitive load, accelerate onboarding, and support faster decision-making. Whether it is accessing product knowledge or surfacing insights in the flow of work, the focus is on helping people get the information they need, learn, and act faster.

2. Automation and Efficiency

AI is a powerful engine for eliminating repetitive work and accelerating workflows. By automating administrative tasks, document creation, data entry, and analysis, teams free up time for higher-value activities. Efficiency gains in this area compound over time as employees reinvest their time in what we call value-generating activities.

3. Proactive Insights

AI allows us to move from a reactive model—where employees pull information when needed—to a proactive model, where insights are surfaced automatically at the right moment. This includes forecasting and surfacing early signals about customers or deals before they are even requested. The result is smarter, faster, and more forward-looking decision-making.

Our First Agent: Meet Tina

From those early discussions, one of our first high-impact opportunities emerged quickly. As an enterprise platform, our product is complex, and deep product knowledge was concentrated within just a handful of people. This created bottlenecks—those same experts were fielding the same questions repeatedly, leading to long feedback loops and delayed decisions. It also meant that new employees took longer to fully understand the software and become confident using it in their roles. It was clear that everyone needed a faster, more reliable way to access accurate information.

To address this challenge, we built Tina, our first bot in Dust. Trained on our product and industry knowledge, Tina became the go-to source for answers almost overnight. Sales and Marketing could instantly resolve technical questions, and new hires had smoother onboarding experiences because “ask Tina” became second nature.

The impact was immediate. Teams reclaimed hours once spent chasing information, and knowledge that had been locked in a few minds became accessible across the organization. In our Customer Support team, new hires reached full productivity 40 percent faster. Most importantly, Tina proved the value of starting small but focused, demonstrating value in a specific use case to the organization.

Tina was one of our very first agent, and while we are now tackling far more complex challenges, it remains a powerful example of how an operational pain point can be transformed into a practical, high-value solution with Dust—one that helped spark momentum for broader AI adoption across the company.

“Tina was one of our very first bots, and while today we are tackling far more complex challenges, it remains a great example of how an operational pain point can be transformed into a practical, high-value solution with Dust.”

From Early Wins to Broad Enablement

Building effective internal AI solutions was only half the journey. The other half was enablement. We discovered people come to AI with very different mindsets. Some are immediately excited and see the potential right away. Others need more time, repetition, and concrete examples before it clicks.

As we rolled out new tools, we grounded each in clear, operational value. Rather than talking about abstract benefits, we always connected the functionality with operational impact:

• “You no longer need to manually update our CRM/CSP. We will generate your call notes and push them automatically.”
• “You don’t have to spend hours creating documentation. You can do that in a few clicks in Dust.”
• “You can access customer insights by querying multiple data sources in one place, conversationally.”

We’ve learned that exposure is essential. Most people need to see several real examples before finding the one that clicks and unlocks their own understanding of AI’s potential. To accelerate that moment, we’ve invested heavily in enablement through hands-on sessions, one-on-one coaching, and team demos tailored to different roles.

Members of our AI group have become advocates, returning to their teams to showcase what they’ve built and the impact it’s made. Team leaders now use these tools live in meetings, demonstrating how AI can surface insights, automate tasks, and improve decision-making in real time. That consistent rhythm of examples has built confidence, inspired curiosity, and made adoption contagious.

Defining a New Era

The AI era is not on the horizon—it is already here. Massive transformations are happening inside a small subset of organizations, while many others are only beginning to explore what this means for them.

In the end, this transformation is about people more than technology. It’s about helping teams understand what’s changing, why it matters, and how they can grow through it. But doing that successfully requires a cohesive, company-wide effort. No single team or leader can carry it alone.

What makes this moment unique is that AI represents a rare alignment between people and business. When used thoughtfully, it can make work both more productive and more human. It can expand people’s range of skills, give them time to reinvest in meaningful, high-value work, and remove the tasks that don’t require human judgment or creativity. When technology takes on the non-human work, people are freed to reinvest in what only they can do: solving complex problems, collaborating, and creating connections that move the organization forward. That is where the real transformation begins.

Successful change starts with clarity, trust, and small, visible wins that build confidence over time. When we focus on those things, adoption follows naturally. The organizations that start now, that learn by doing and bring their people along, will be the ones shaping what comes next. 

Those who wait risk watching the future unfold without them.