Key Points
- UK-based management consultancy Clarasys has launched CAM.ai, an artificial intelligence-enabled transformation methodology designed to accelerate business transformation.
- The methodology re-engineers Clarasys’ established 15-year consulting approach by integrating AI into business transformation processes.
- Clarasys reports that early engagements using CAM.ai have reduced project delivery timelines from months to weeks.
- Consultants can configure and test solutions directly within clients’ environments, reducing reliance on separate technical delivery teams.
- The methodology initially supports platforms including Salesforce, Stripe and Zuora.
- Chief Executive Officer Matt Cheung says CAM.ai is designed to extend consultants’ capabilities rather than replace human professionals.
- AI supports analysis, configuration, testing and migration, while human specialists retain responsibility for business decisions, requirements, quality and governance.
- Clarasys is expanding CAM.ai’s platform coverage and introducing additional Model Context Protocol (MCP) connectors.
- The launch reflects the consultancy’s ambition to promote responsible, outcome-led AI adoption across professional services.
What Is CAM.ai and Why Has Clarasys Introduced the New Methodology?
UK-based management consultancy Clarasys has launched CAM.ai, an artificial intelligence-enabled transformation methodology intended to accelerate business transformation programmes by bringing AI-supported analysis, configuration and testing closer to the consultants responsible for understanding clients’ requirements. The new approach re-engineers the company’s established consulting methodology, with Clarasys reporting that projects using CAM.ai have already reduced delivery timelines from months to weeks. Rather than positioning artificial intelligence as a replacement for consultants or simply adding new technology to existing processes, the company aims to combine AI capabilities with human judgement, governance and accountability throughout the transformation process.
According to a report published by Consultancy.uk on 30 October 2026, the launch comes amid continuing debate about the role of artificial intelligence in the consulting industry. While some discussions focus on whether AI will eventually replace traditional consulting expertise, others examine how firms can incorporate the technology into their existing service models.
Clarasys is proposing an alternative approach through CAM.ai, which it describes as a methodology that extends consultants’ capabilities and connects business decision-making more directly with technical implementation.
The launch also highlights a wider shift in professional services towards using AI to improve project delivery, reduce delays, and help organisations see the practical results of transformation decisions earlier.
How Does CAM.ai Change the Traditional Business Transformation Process?
Traditional business transformation programmes often involve several stages, including initial discussions, requirements gathering, documentation, technical development, testing, validation and migration. These stages may involve different teams, creating gaps between the decisions made during client workshops and the eventual delivery of a working solution.
According to Consultancy.uk’s report, Clarasys believes these delays can weaken project momentum. When organisations have to wait for extended periods to see how their decisions translate into practical changes, maintaining engagement and responding to changing business priorities can become more difficult.
CAM.ai is designed to address this challenge by shortening the time between business discussions and the implementation of solutions.
Under the methodology, consultants who lead client workshops can also configure and test solutions within the client’s own environment. This reduces the need to transfer requirements to a separate technical delivery team before implementation can begin.
Matt Cheung explained that this represents a significant change in the pace of business transformation programmes. He indicated that consultants could move from a conversation on Monday to demonstrating a working version of the proposed change by Friday.
The approach is intended to create a more direct relationship between business decisions and technical delivery. Instead of spending substantial time preparing specifications for other teams to interpret, consultants can remain involved throughout the process and see how decisions are translated into operational changes.
This model could also help businesses identify potential problems earlier. By making working versions available sooner, organisations have more opportunities to review proposed solutions, challenge assumptions and adjust requirements before committing to a wider implementation.
For businesses managing complex digital projects, the reduction of delays between planning and execution could be particularly relevant. It may allow decision-makers to assess whether a proposed solution addresses the original business problem before additional resources are committed.
Which Technology Platforms Can Consultants Use Through CAM.ai?
Clarasys has identified Salesforce, Stripe and Zuora among the leading technology platforms on which consultants can configure and test solutions through the CAM.ai methodology.
These platforms provide different capabilities across customer relationship management, payment processing and subscription-based business operations.
By enabling consultants to work directly within clients’ environments, Clarasys aims to reduce the separation between business consulting and technical implementation.
The approach is particularly relevant to organisations undertaking changes involving customer-facing systems, financial processes, subscription management and operational workflows.
For example, a business considering changes to its customer management processes may need to examine how those changes affect its existing technology environment. Under a traditional delivery model, consultants might document the requirements before transferring them to a technical team for configuration and testing.
CAM.ai seeks to bring these activities closer together. Consultants can use AI to accelerate the technical work while remaining responsible for ensuring that the resulting configuration reflects the client’s requirements.
However, Clarasys has not presented the methodology as an unrestricted replacement for specialist technical oversight. Its approach retains human review and approval at important stages, ensuring that AI-generated configurations are checked before progressing.
The company is also extending the number of platforms that CAM.ai can support, suggesting that its initial platform coverage is not intended to represent the final scope of the methodology.
Will CAM.ai Replace Human Consultants With Artificial Intelligence?
No. Clarasys has explicitly positioned CAM.ai as a way to extend consultants’ capabilities rather than replace human professionals. The methodology is built around combining artificial intelligence with the experience, judgement and accountability of the people responsible for delivering business transformation.
In his comments published by Consultancy. Matt Cheung said:
“We’ve taken a different approach to replacing consultants, or bolting new technology onto old processes. 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.”
Cheung also explained that the approach would provide consultants with more opportunities to apply human judgement earlier and more frequently during transformation programmes.
Under the proposed operating model, Clarasys’ change management specialists will continue to take responsibility for several essential activities.
These include:
- Defining business problems: Consultants will identify the issues organisations are trying to address and establish the objectives of transformation programmes.
- Challenging assumptions: Human specialists will assess whether initial requirements and proposed solutions are appropriate for the organisation.
- Shaping requirements: Consultants will translate business needs into clear requirements that guide the design and implementation process.
- Governing quality: Experienced professionals will maintain oversight of the work and ensure that the proposed changes meet agreed expectations.
- Applying professional judgement: Consultants will remain involved in important decisions where business understanding, organisational context and human assessment are required.
Artificial intelligence will accelerate activities between these stages, particularly analysis, configuration, testing and migration within pre-agreed designs and client-owned test environments.
This division of responsibilities is central to the methodology. Rather than allowing technology to make decisions independently, Clarasys intends to use AI to reduce time-consuming work while retaining human responsibility for the direction and quality of transformation projects.
The approach also reflects a broader discussion within professional services about how organisations can introduce AI without weakening professional standards, client relationships or accountability.
How Will CAM.ai Improve Quality Control and Accountability?
The use of artificial intelligence in business transformation creates questions about accuracy, oversight and the reliability of automated outputs. These concerns become particularly important when AI-generated configurations are used within operational business systems.
Clarasys has incorporated human review into its CAM.ai methodology to address these issues.
According to Consultancy.uk, a Clarasys expert will review, test, and approve every piece of AI-generated configuration before it progresses to the next stage.
This means that AI-generated work will not automatically be treated as an approved outcome. Human specialists will remain responsible for checking whether the configuration meets the requirements established during the business design process.
The company also intends to maintain end-to-end traceability across transformation programmes.
Every requirement, decision and change will be traceable from the original business conversation through to the tested outcome. This is intended to provide a clearer record of how decisions were made and how they influenced the final solution.
Such traceability is important for organisations that need to understand the relationship between business requirements, technical changes and implementation results.
It can also help project teams identify where a proposed change originated, what decisions influenced its development and whether the delivered configuration has been tested against the agreed requirements.
The methodology therefore combines faster technical execution with continued professional oversight.
For organisations introducing AI into operational processes, this relationship between automation, quality assurance and accountability is an important consideration. It also creates a connection with Management, Artificial Intelligence (AI) and IT and Computer Science, particularly where business leaders and technical teams need to coordinate AI-enabled transformation initiatives.
What Results Has Clarasys Reported From Early CAM.ai Engagements?
One of the most significant claims associated with the launch is the reduction in delivery times already reported by Clarasys.
According to Consultancy.uk, early engagements using CAM.ai have compressed delivery timelines from months into weeks. The company also stated that these improvements have been achieved without introducing a separate technology delivery partner or changing the people clients work with throughout an engagement.
This is an important part of Clarasys’ proposition because traditional transformation programmes can involve several teams working across different stages of delivery. Each handover can create additional communication requirements, interpretation challenges and delays.
By bringing more activities into the same consulting engagement, CAM.ai aims to reduce these interruptions.
The reported improvements cover several aspects of project delivery:
Shorter implementation periods: AI-supported activities can help consultants move from business discussions to working configurations more quickly.
Fewer team handovers: Consultants can remain involved in both understanding business requirements and configuring solutions, reducing dependence on separate technical delivery teams.
Earlier feedback: Clients can review working versions of proposed changes sooner, allowing them to identify problems and refine requirements.
More direct collaboration: Business stakeholders and consultants can spend more time discussing decisions and less time transferring information between teams.
Greater continuity: Clients can continue working with the same consulting professionals throughout the engagement rather than relying on multiple delivery partners.
However, the reported reduction in delivery times represents Clarasys’ early experience with the methodology. Consultancy.uk’s report does not provide a detailed breakdown of the number of engagements, individual project durations,s or independently verified performance measurements.
The results should therefore be understood as the company’s reported early outcomes rather than a guarantee that every transformation programme will achieve the same reduction in delivery time.
Why Does CAM.ai Focus on Shorter Feedback Loops?
A central principle behind CAM.ai is the reduction of the time between making a business decision and seeing the practical result of that decision.
In conventional transformation programmes, considerable time can pass between workshops, documentation, technical development, validation and migration. During this period, business priorities may change, stakeholders may reconsider their requirements, ts and project teams may discover that their initial assumptions need further examination.
Clarasys believes that reducing these gaps can improve the overall transformation experience.
Matt Cheung highlighted this issue when discussing the limitations of traditional consulting deliverables.
He said:
“Clients do not hire us simply to produce a specification. They hire us to understand the business problem, make the right decisions, and carry those decisions through to working change.”
He added that CAM.ai strengthens this connection by enabling more conversation and reducing time-consuming documentation.
This distinction is important because a specification alone does not necessarily demonstrate that a proposed solution will work in an organisation’s operational environment.
By enabling faster configuration and testing, CAM.ai seeks to make transformation outcomes more visible while there is still an opportunity to change direction.
The shorter feedback loop also allows clients to test assumptions earlier. If a proposed configuration does not meet operational requirements, consultants and stakeholders can identify the issue and make adjustments before the programme progresses further.
For organisations operating in changing commercial environments, this approach may help maintain alignment between business objectives and technology implementation.
It also reinforces the importance of Product Management, where understanding user requirements, testing proposed solutions,s and responding to feedback are essential parts of successful product and service development.
What Is Clarasys Planning for the Future of CAM.ai?
Clarasys is continuing to develop the methodology following its launch, with plans to extend CAM.ai’s coverage across additional enterprise technology platforms.
According to Consultancy.uk, the company is adding further Model Context Protocol (MCP) connectors to enterprise platforms as part of this expansion.
MCP is a protocol designed to help AI applications connect with external tools, services, and data sources through a more consistent integration approach.
For enterprise environments, these connections can be relevant because business transformation frequently involves multiple systems that need to exchange information or support connected workflows.
Expanding CAM.ai’s platform coverage could therefore help Clarasys apply its methodology across a wider range of client technology environments.
However, the report does not identify all the additional platforms being targeted, provide a specific implementation timetable or disclose how many MCP connectors will be introduced.
The expansion nevertheless indicates that Clarasys views CAM.ai as an evolving methodology rather than a fixed consulting solution limited to its initial technology platforms.
The company’s wider ambition is to establish a more connected and responsive approach to AI adoption across professional services.
According to Clarasys, the objective is to use AI to make transformation more connected, responsive and valuable while keeping human expertise, governance and accountability at the centre of delivery.
This ambition places the launch within a broader shift towards outcome-led AI adoption, in which businesses assess artificial intelligence not simply through its ability to automate individual tasks but through its contribution to measurable operational and organisational improvements.
What Does the CAM.ai Launch Mean for the Consulting Industry?
The introduction of CAM.ai highlights how management consultancies are reconsidering the relationship between professional expertise, technology implementation and artificial intelligence.
For many consulting firms, AI adoption involves finding ways to improve research, documentation, analysis,s and internal productivity. However, Clarasys is positioning its methodology around a more integrated delivery model that connects business advice with technical execution.
This distinction could become increasingly important as clients expect consulting partners to demonstrate how recommendations translate into practical improvements.
Businesses undertaking digital transformation do not necessarily need additional reports or lengthy specifications. They also need solutions that address operational problems, work within existing technology environments and deliver outcomes that can be evaluated.
CAM.ai attempts to respond to these requirements by allowing consultants to participate more directly in configuration and testing activities.
The methodology also raises an important consideration for the wider consulting market: whether AI-enabled delivery can improve project speed without reducing the quality of professional judgement and client oversight.
Clarasys’ position is that the two objectives can work together. AI can accelerate technical activities, while experienced consultants remain responsible for understanding business needs, challenging assumptions and approving changes.
Nevertheless, the long-term effectiveness of this approach will depend on its application across different organisations, project complexities and technology environments.
Its reported early results provide an indication of potential benefits, but further evidence will be needed to assess how consistently the methodology performs across larger and more varied transformation programmes.
For consulting businesses and their clients, the development of CAM.ai adds another example of how artificial intelligence is influencing established service-delivery models.
Who Founded Clarasys and What Services Does the Consultancy Provide?
Clarasys is a London-based management consultancy specialising in project management, process improvement and digital transformation.
According to Consultancy.uk, the business was founded in 2011 by Matt Cheung, Claudia Meyer-Scott and Chris Hamilton.
The consultancy has developed expertise in change management and digital services, supporting organisations with business improvement and technology-related transformation.
Matt Cheung serves as the company’s Chief Executive Officer and is one of its co-founders. His comments about CAM.ai form a central part of the company’s explanation of why it developed the methodology and how it intends to use artificial intelligence in client engagements.
The launch builds on Clarasys’ existing consulting approach rather than introducing an entirely separate service model. Its stated objective is to adapt its established methodology to an AI-enabled working environment while maintaining the professional expertise and accountability expected by clients.
The development is particularly relevant to Management and Leadership, Artificial Intelligence (AI) and Data Science and Visualization, where understanding organisational change, applying emerging technologies and interpreting business information are important capabilities for professionals involved in transformation programmes.
As more organisations consider AI-enabled business transformation, the ability to coordinate people, processes and technology will remain an important part of implementing change effectively.
What Are the Main Takeaways From Clarasys’ CAM.ai Launch?
Clarasys’ launch of CAM.ai represents an attempt to bring artificial intelligence closer to the practical delivery of business transformation programmes.
The methodology combines AI-supported analysis, configuration, testing and migration with the continued involvement of experienced consultants. Its central proposition is that organisations can reduce delays between business decisions and working solutions without removing the human expertise responsible for quality and accountability.
The early results reported by Clarasys suggest that some engagements have reduced delivery timelines from months to weeks. The company is also expanding the methodology’s technology coverage through additional enterprise platform integrations and MCP connectors.
However, the wider impact of CAM.ai will depend on how effectively it performs across different client environments and whether the reported improvements can be sustained as adoption expands.
For the consulting industry, the launch highlights an increasingly important question: how can artificial intelligence accelerate professional services while preserving the judgement, governance and accountability that clients expect?
Clarasys is seeking to answer that question by integrating AI into its established consulting methodology rather than treating the technology as a replacement for traditional expertise.