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Book demoHow ThoughtFox Uses Cassidy to Deploy AI Transformation at Enterprise Scale
ThoughtFox is an enterprise AI transformation consultancy working with mid-to-large organizations across Europe.
Their work spans seven dimensions of organizational change — from leadership alignment and governance through to skills development, operating model redesign, and value realization. They're not a software vendor. They're in the room with clients, helping organizations figure out how to make AI actually work across the business.
Part of what makes that work land is that ThoughtFox doesn't just advise on AI adoption. They practice it.
Ben Churchill, co-founder and lead AI transformation consultant, brought Cassidy into the practice after evaluating the alternatives for an enterprise client engagement. Two years later, Cassidy is how ThoughtFox runs critical parts of its own operations — and how Churchill deploys AI automation for enterprise clients across Europe.
The Challenges ThoughtFox Needed to Solve
ThoughtFox operates at the intersection of fast-moving technology and slow-moving enterprise change. That creates a specific set of pressures most consultancies don't face:
- Regulatory knowledge moves faster than any team can track manually. The EU AI Act, GDPR updates, enforcement guidance — staying current on what's required versus what's simply good practice is a full-time job on top of client work.
- Valuable research disappears. Articles, governance books, expert commentary — useful in the moment, gone within days. No system for capturing it and making it reusable.
- Enterprise clients need non-technical teams to own their automation. The people closest to a problem can't wait on IT or engineering to build every workflow. The platform has to be accessible enough that anyone can build with it.
- Security can't be an afterthought. Clients in financial services and healthcare handle PII and sensitive operational data. Whatever platform ThoughtFox deploys has to meet enterprise security requirements before any capability conversation begins.
Building a Living Knowledge Base for AI Governance Research
For ThoughtFox, staying genuinely current on AI regulation isn't optional. It's the product.
Why Manual Research Wasn't Cutting It
Marie Toft, ThoughtFox's AI adoption lead, describes a research workflow most knowledge workers will recognize.
"The way you're reading stuff on LinkedIn and it's kind of in and gone and you're not capturing it."
Books on AI governance, regulatory updates, expert commentary — all coming in faster than any individual could meaningfully retain and connect. Every client conversation about EU AI Act compliance, every governance workshop, every maturity assessment drew on the same pool of expertise.
When that expertise wasn't being systematically captured, the team was effectively starting from scratch more often than it should.
The EU AI Act alone has:
- Multiple implementation phases with different deadlines
- Distinct enforcement timelines across member states
- A meaningful gap between what's legally required and what simply represents best practice
Navigating that accurately across multiple client engagements simultaneously required a system — not just individual research habits.
How Cassidy Solved It
ThoughtFox built a dedicated knowledge system in Cassidy's Knowledge Base.
Governance books, regulatory research, EU AI Act guidance, enforcement updates, curated articles — all uploaded, continuously synced, and available to every agent and workflow the team builds. No re-uploading. No starting from scratch.
On top of that foundation, Toft is building an EU AI Act compliance agent — an assistant that can surface the distinction between legal requirements and best practice on demand, grounded in the actual source material ThoughtFox has accumulated.
The approach reflects something ThoughtFox tells clients consistently: governance and capability building have to happen at the same time. The Knowledge Base isn't just a research tool. It's the foundation that makes every other workflow more accurate.
What Changed
Research that used to disappear now compounds.
Every new piece of content added to the Knowledge Base makes every existing workflow smarter. "When you go back to it every time, it's got it all and it's putting it in context," Toft says.
When a client asks where they stand on EU AI Act compliance, the answer doesn't require an overnight research sprint. It comes from a system that's been accumulating and connecting the relevant material continuously.
Deploying AI Automation Across an Enterprise Without Touching IT
The second use case is where Churchill's enterprise client work shows what Cassidy makes possible at scale — and why the platform stood apart from everything else he evaluated.
The Enterprise Problem Most AI Tools Don't Solve
Churchill's first major Cassidy engagement was with a large enterprise client handling sensitive personal and health data across sales, marketing, and finance.
Two requirements were non-negotiable from the start:
- Enterprise-grade security. PII and DHI data meant compliance couldn't be retrofitted later. It had to be built in from day one.
- Non-technical adoption. The people who understood the work best weren't technical. Getting them to build and maintain their own workflows — without routing everything through IT — was the only path to adoption that would actually stick.
Churchill evaluated the alternatives carefully.
N8N required meaningful technical skill and additional security configuration to meet the client's compliance requirements.
Copilot Studio was the obvious choice for a Microsoft-heavy organization. In practice, Churchill describes it plainly: "unhelpfully useless." It wasn't close enough to be real competition.
"It ended up being quite an easy decision," he says.
Meeting People Where They Already Work
Churchill's team deployed Cassidy Workflows directly into Microsoft Teams — the interface the client's teams were already using every day.
No context switch. No new tool to learn. Cassidy showed up where the work was already happening.
"The way that you could deploy Cassidy Workflows into Teams was really helpful for us because it was a Microsoft shop," Churchill says. "People were accessing that through familiar interfaces. So that became really powerful."
The no-code Workflow builder handled the second requirement. Non-technical staff across departments could describe what they needed and build toward it — no engineering support, no IT ticket, no delay.
Multi-step automations. Business logic. Internal policy references. Routed outputs. All built by people who had never written a line of code.
"The fact that non-technical people could deploy usable workflows in Cassidy was the game changer for us," Churchill says.
Two Years In, Across Three Departments
The engagement has now run for over two years.
Back-office processes that previously ran manually now operate on triggers. Information that required someone to compile manually gets surfaced automatically. Teams across sales, marketing, and finance started creating and iterating on their own Workflows — without IT involvement.
"Lots and lots of time savings in various dimensions," Churchill says.
More meaningfully: people "really just starting to get used to the fact that they can do their own AI automation."
That shift — from AI as something IT manages to AI as something every team member reaches for — is what ThoughtFox measures as a genuine transformation outcome. It's what separates a deployment that compounds over time from one that stalls after the initial rollout.
"What makes Cassidy work for our enterprise clients is the combination of genuine flexibility and security," Churchill says. "We can build agentic workflows and AI systems that integrate directly into the tools and applications they're already using, and do it knowing the security and compliance piece is already taken care of."
What This Means for Enterprise AI Transformation
ThoughtFox's experience with Cassidy reflects something they see consistently across client engagements.
The organizations that get the most from AI aren't the ones with the most sophisticated technical infrastructure. They're the ones that remove the barriers between the people who understand a problem and the tools that can solve it.
A Knowledge Base that turns scattered research into queryable institutional memory. Non-technical teams building and owning their own automations. Governance and capability building happening simultaneously.
These aren't separate wins. They're connected.
"Good governance allows you to unlock," Churchill says. "Without doing that, you not only don't get the full power of the models, you also put yourself at massive risk. Governance actually goes with performance."
ThoughtFox partners with mid-to-large enterprises across Europe on AI transformation — from initial maturity assessment through to full deployment across all seven dimensions. To explore how Cassidy supports that work at the automation layer, book a demo and see what your highest-value Workflows could look like.
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