SERVICES
Most businesses do not have an AI adoption problem. They have an AI value problem.
They have licences, pilots and employees experimenting with new tools. But work is still completed in much the same way, management cannot see a measurable financial return and nobody is sure what should happen next.
Maatix helps organisations identify where AI can create tangible economic value, make the business changes required to realise it, control the associated risks and implement the workflows that deliver the result.
AI AUDIT
The first AI decision is not what to buy. It is where to invest.
The problem
Leaders know AI matters but do not know where it can create enough value to justify the investment. Different teams suggest different tools and use cases. Existing pilots generate activity, but little evidence of economic impact.
The Maatix approach
We review high-volume, information-heavy and judgement-intensive workflows. Each credible opportunity is assessed for economic value, AI fit, implementation feasibility, strategic leverage and risk. The objective is to find the small number of opportunities worth pursuing—and reject those that are not.
Deliverables
- Three to five prioritised AI opportunities
- Evidence-led economic value ranges
- AI suitability, feasibility and risk assessment
- Human oversight and data-boundary requirements
- A clear recommendation on what to pursue first
AI Audit engagements start from £1,000.
AI BUSINESS TRANSFORMATION
AI will not transform a business that leaves the business unchanged.
The problem
Many organisations have accumulated tools, pilots and isolated use cases without deciding how AI should change the way the business competes, operates or grows. There is no shared view of the economic opportunity or clear order of implementation.
The Maatix approach
We connect business strategy to a clear AI vision and practical programme of change. Opportunities are sequenced by value, feasibility, risk and time to benefit. The result is not a technology roadmap. It is a business transformation plan in which AI has a defined economic purpose.
Deliverables
- Business-aligned AI vision and objectives
- Current-state and AI-readiness assessment
- Prioritised opportunities and business cases
- Sequenced transformation roadmap
- Target operating model
- Data, technology, capability and governance requirements
- Measures, milestones and decision points
AI GOVERNANCE & RISK MANAGEMENT
A policy that people cannot apply does not govern anything.
The problem
Employees may already be using AI with company, client or proprietary information. Management lacks visibility of which tools are being used, what data is being shared and whether important outputs are being checked.
The Maatix approach
We turn governance principles into controls that operate in real work. Tools and uses are classified, data boundaries made explicit and approval, testing and human-review requirements matched to the value of the workflow and consequences of error.
Deliverables
- Approved and prohibited tool classifications
- Permitted and restricted uses
- Data classification and handling boundaries
- Workflow risk classifications
- Human-review and approval requirements
- Ownership, testing, monitoring and escalation standards
- Practical guidance for employees
AI WORKFLOW DESIGN & IMPLEMENTATION
Buying AI tools does not change how work gets done.
The problem
Employees still copy information between applications, search through documents, update spreadsheets, prepare reports manually and repeat routine decisions. The organisation has tools, not a new way of working.
The Maatix approach
We establish how the process performs today, remove unnecessary steps and decide what should remain human, what should become AI-assisted and what can safely be automated. We then build the agents, integrations and automations required, with controls and human judgement designed in from the start.
Deliverables
- Current-state workflow map and performance baseline
- Redesigned target workflow
- Human, AI-assisted and automated task allocation
- Custom agents, automations and integrations
- Operational controls and human-review points
- Reliability, security and acceptance testing
- User adoption and post-implementation value measurement
OUR PRINCIPLES
AI is only worth doing when the economics work.
Business value before technology. We begin with the commercial or operational result, not the tool.
A 10X return hurdle. We pursue opportunities with a credible path to creating at least ten times their implementation cost in measurable economic value.
Redesign before automation. Automating a poor process produces a faster poor process.
Controls matched to consequences. Valuable adoption requires clear data boundaries, accountability and human judgement where errors matter.
Evidence before claims. We establish a baseline before implementation and measure the result after it.
Scale what works. Stop what does not. Successful workflows are extended. Weak initiatives are corrected or ended.