AI Evaluation & Assurance helps a business examine whether an AI system is reliable enough for its intended use and whether a consequential decision based on that system can withstand scrutiny.
An AI system can return the expected format and still provide an unreliable answer. A proposal can also appear persuasive while depending on assumptions about model performance, evidence or human oversight that have not been tested.
AI Evaluation & Assurance connects evaluation of the AI system with examination of the business decision it supports. The scope depends on whether the immediate concern is system behaviour, the decision to rely on the system, or both.
When to use this AI service
- A business intends to use an AI system in a consequential workflow or decision.
- Leaders need evidence about reliability, limits, drift, explanations, fairness or human control.
- A major investment or operating decision depends materially on AI-generated evidence or recommendations.
- The organisation needs clear supported, conditional and prohibited uses rather than a general claim that a model is safe.
Related services
- AI & Model Risk
Tests the AI or analytical system against its intended use, limits and controls.
- Decision Assurance
Examines whether a consequential decision is clearly defined and supported by sufficient evidence, alternatives and treatment of uncertainty.
No predetermined solution
Marketways evaluation supports decisions about scoped reliance. It does not provide independent cyber assurance, penetration testing, legal opinions, regulatory certification or a universal guarantee that an AI system is safe.
Related AI services
The Services catalogue defines the underlying consultancy services across Business Opportunities, Business Efficiency and Business Threats. AI Solutions shows how those services apply when AI forms part of the opportunity or solution. For example, a business seeking greater asset efficiency can examine whether predictive maintenance or another AI-enabled approach improves the operation.
Guides for business leaders
Use these guides to examine the opportunity, implementation decision or operating question in more detail.
How to Use AI in Business: A Practical Guide for Business Leaders
A business does not begin using AI by choosing a tool. It begins by identifying a valuable decision or operating problem, establishing whether AI is suitable and designing the operating process, controls and measures around the solution.
How to Prioritise AI Use Cases by Value, Feasibility and Risk
AI use cases should be compared on the value they may create, the conditions required to implement them and the risk created by acting on an unreliable result. One simple score cannot represent all three.
Is Your Business Ready for AI? A Practical Readiness Assessment
AI readiness is the ability to test and operate a defined use case responsibly. It depends on the business question, workflow, evidence, systems, people, governance and operating ownership rather than one organisation-wide maturity score.
How to Build an AI Business Case: Costs, Benefits and ROI
An AI business case should explain how the solution changes a business result, what the complete operating change costs, which assumptions drive value and what evidence will be required before further investment.
AI Implementation Roadmap: From Business Opportunity to Production
An AI implementation roadmap sequences the decisions and evidence required to move from a business opportunity through solution design, pilot, controlled deployment and managed operation.
How to Move an AI Pilot into Production
An AI pilot is ready for production only when the business has evidence of value, reliable operation, controlled failure, user adoption and clear ownership beyond the project team.
AI Governance for Business Leaders: The Decisions Management Must Retain
AI governance gives management a practical way to decide what AI may do, what evidence is required, who remains accountable and how the organisation responds when performance or context changes.
How to Measure Whether AI Is Creating Business Value
AI value should be measured from the completed business outcome back through adoption, workflow performance, AI quality, risk and cost. Technical accuracy alone cannot show whether the investment is working.
AI Agents for Customer Analytics: From Insight to Controlled Action
An AI agent can investigate customer evidence and coordinate approved actions, but customer-facing autonomy requires clear tools, permissions, evaluation and escalation.
How to Use Agentic AI in Retail Customer Analytics
Retailers can use bounded AI agents to investigate demand, customer behaviour and service exceptions, then coordinate approved actions across merchandising, marketing and operations.
Agentic AI in Banking Customer Analytics and Service
Banks can use bounded agents to assemble customer evidence and coordinate service work, but financial authority, eligibility, privacy and adverse decisions require strong controls.
Generative AI for Patient Experience and Healthcare Customer Analytics
Healthcare organisations can use generative AI to organise patient feedback and service evidence, while clinical decisions, privacy and patient safety remain governed by appropriate professionals.