London, 29 October, 2026
Clarasys, the award-winning, employee-owned management consultancy, has today launched CAM.ai, an AI-accelerated transformation method that bridges the gap between strategy and delivery, taking organisations from conversation to working change with unprecedented speed, lower costs and reduced risk.
CAM.ai connects strategy directly to delivery. It combines human expertise and the proven Clarasys Agile Method (CAM), which has underpinned hundreds of the firm’s transformation programmes over the last 15 years, with AI-enabled configuration, analysis and testing. This allows change specialists to move from a workshop conversation to working, testable software in the client’s own environment within days, eliminating the traditional hand-off between the people who define requirements and the separate technical teams who build them.
Transformation can lose momentum when too much time passes between decisions, documentation, build, validation and migration. Long gaps between an idea and seeing it take shape can create frustration and make it harder to sustain focus. Rapid visible change after a workshop conversation gives clients early evidence of what works and the opportunity to shape the solution while the context is still fresh. CAM.ai also empowers organisations to execute lower ROI projects that have been blocked.
“There’s a lot of noise right now about AI replacing consultants, and an equal amount about consultancies bolting AI onto old ways of working without really changing anything,” said Clarasys CEO Matt Cheung. “We’ve taken a different approach. CAM.ai re-engineers our 15-year methodology for an AI-enabled way of working, while retaining the same expertise, governance and accountability our clients have always hired us for. We think that’s what redefining consulting for an AI world actually looks like: more time to apply human judgement earlier and more often, so clients can build the right thing, see it working sooner and reduce the risk of committing to the wrong solution. In practice, this means having a conversation on Monday and seeing a working version of the change by Friday.”
Unlike approaches that simply add AI to existing processes, CAM.ai is designed to shorten the feedback loop between business need and working change. Change management experts continue to frame the problem, challenge assumptions, shape requirements and govern quality. AI accelerates the work between those points, including analysis, configuration, testing and migration within pre-agreed designs and client-owned test environments.
The result is a different way to work: less time producing documents for another team to interpret, more time making decisions with the people who will deliver and use the change. A shorter feedback loop helps clients test assumptions earlier and respond as priorities evolve.
“Clients do not hire us simply to produce a specification,” Cheung continued. “They hire us to understand the business problem, make the right decisions and carry those decisions through to working change. CAM.ai strengthens that connection. It means more conversation, less time-consuming documentation, and a better transformation experience for everyone involved.”
Central to CAM.ai’s design is a governance model in which AI never touches client production environments and aligns with existing deployment, security and governance processes. A Clarasys expert reviews, tests, and approves every piece of AI-generated configuration before it moves forward. Every requirement, decision and change is traceable end to end, from the original business conversation to the tested outcome.
Early engagements using CAM.ai have compressed delivery timelines from months into weeks, without adding a separate technology delivery partner or changing the people that clients work with throughout the engagement. Clarasys is now extending CAM.ai’s platform coverage, adding further Model Context Protocol (MCP) connectors to enterprise platforms including Salesforce, Zuora and Stripe.
The launch reflects Clarasys’ broader ambition to take the lead on what responsible, outcome-led AI adoption looks like in professional services: using AI to make transformation more connected, more responsive and more valuable, while keeping human expertise, governance and accountability at the centre.