How UserTesting Unlocked Company-Wide AI Adoption

UserTesting's exec team reframed legal, security, and privacy from gatekeepers to advisors — clearing the path for enterprise ChatGPT, employee-built custom GPTs, and an AI council company-wide.

Compliance Reframed as Enablement

Repositioned legal as AI advisor

4–6 months

Implementation Time

Kaj van de Loo

Founder

CA1 Team LLC

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The Challenge

UserTesting, a market-leading enterprise SaaS company, feared being disrupted by AI-native startups capable of matching their product at a fraction of the cost. Productivity was constrained by legacy workflows, and attempts to adopt AI tools were blocked by undeclared concerns from legal, security, and privacy teams — leaving the organization paralyzed before any meaningful change could begin.

What They Built

Kaj led a structured company-wide AI adoption program beginning with a deliberate pre-alignment step: engaging legal, security, and privacy teams as advisors rather than gatekeepers, clarifying that executive leadership — not compliance functions — owned the risk. An AI council was formed with senior cross-functional representation. Enterprise ChatGPT licenses were rolled out to all employees, internal data sources were integrated into custom GPTs, and employees were invited to submit and implement AI improvement ideas. Hackathons at OpenAI's San Francisco offices accelerated collaboration and built momentum across the organization.

Kaj Van De Loo began with a deliberate pre-alignment step, engaging legal, security, and privacy teams as advisors before any technology was introduced. The key move was clarifying that executive leadership — not compliance functions — owned the risk decisions. This repositioning removed the veto power these teams had been quietly exercising and surfaced undeclared concerns that could be addressed directly rather than allowed to quietly block progress. With organizational alignment secured, the program proceeded in structured phases. An AI council was formed with senior cross-functional representation to govern and champion adoption. Enterprise ChatGPT licenses were rolled out to all employees. Internal data sources were integrated into custom GPTs, giving employees AI tools grounded in the company's actual context. Employees were then invited to submit AI improvement ideas, which could be implemented directly. Hackathons held at OpenAI's San Francisco offices accelerated collaboration and built visible momentum across the organization. The full program unfolded over six to twelve months.

AI Role

AI tools — primarily enterprise ChatGPT with internal data source integrations — are used across all employees to augment daily work and automate specific workflows. Custom GPTs built by employees and leadership perform tasks such as generating personalised customer stories and automating professional services intake processes, while the broader platform enables teams to embed AI into their specific functional workflows.

AI Model

ChatGPT

Infrastructure

Integration Points

N/A

Impact

Compliance Reframed as Enablement

By repositioning legal, security, and privacy teams as advisors rather than approvers, the program removed the most common organizational blocker to AI adoption — allowing the initiative to move forward quickly and without internal friction.

Employee-Driven AI Innovation at Scale

Custom GPTs built bottom-up by employees — including one built by the CEO to generate personalized customer stories — became widely adopted. One use case automated a professional services intake process that had caused repeated friction between sales and delivery teams.

Organization Ready for AI at Scale

The program left UserTesting with the habits, infrastructure (data-connected custom GPTs), and cultural readiness to accelerate AI adoption continuously — including a clear path toward customer-facing AI self-service.

Technology Utilized

Implementation Complexity

Time Savings

Revenue Growth

Headcount Avoidance

The technical implementation — enterprise ChatGPT licences and custom GPT configurations with internal data sources — is relatively accessible. The significant complexity is organisational: constructing a governance structure, forming an AI council, running hackathons, and managing the political dynamics of repositioning compliance functions from blockers to advisors requires sustained executive sponsorship and change management expertise.

Best Fit For

Best for mid-sized VC- or PE-backed enterprise software companies that are category leaders beginning to feel competitive pressure from AI-native startups and want a governance-first approach to company-wide AI adoption — rather than piecemeal tool rollouts.

Kaj van de Loo

Founder

CA1 Team LLC

Repeat CTO, CPTO, and Chief Innovation Officer in VC-backed, public, and PE-owned SaaS companies.

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Industry:

Technology & Software

Business Function:

Executive & Strategy

Legal & Compliance

Operations

Company Size:

1,001-5,000

Project Cost:

Cost not disclosed

Ownership:

Private Equity-Backed

Organization Type:

Private Company

AI Pattern:

AI Workforce Enablement

Value Type:

Time Savings

Revenue Growth

Headcount Avoidance

AI Model:

Frequently Asked Questions

How did UserTesting unlock company-wide AI adoption?

The experts began with a deliberate pre-alignment step, engaging legal, security, and privacy teams as advisors rather than gatekeepers and clarifying that executive leadership — not compliance functions — owned the risk. With alignment secured, they formed a cross-functional AI council, rolled enterprise ChatGPT licenses out to all employees, integrated internal data into custom GPTs, and invited employees to submit and implement AI ideas. Repositioning compliance as enablement removed the most common organizational blocker to adoption.

What AI tools and approach drove the company-wide adoption?

The work centered on AI workforce enablement using enterprise ChatGPT and custom GPTs. Enterprise ChatGPT licenses were rolled out to all employees, internal data sources were integrated into custom GPTs to ground tools in company context, and hackathons accelerated collaboration and momentum.

What results did UserTesting achieve?

Compliance was reframed from approver to enabler, removing the most common adoption blocker; employees built bottom-up custom GPTs that became widely adopted — including one that automated a professional-services intake process that had caused friction between sales and delivery; and the company was left with the habits, infrastructure, and cultural readiness to keep scaling AI.

How long did the AI adoption program take?

The full program unfolded over roughly six to twelve months, proceeding in structured phases after the initial pre-alignment work.

Who is this AI enablement approach best for?

Mid-sized VC- or PE-backed enterprise software companies that are category leaders feeling competitive pressure from AI-native startups and want a governance-first approach to company-wide AI adoption rather than piecemeal tool rollouts.