§ IV — Industry Framework Management Consulting Version 1.0
The Management Consulting Industry Framework
Management consulting firms operate at the intersection of three pressures: client demand for AI-enabled efficiency in research and synthesis, the obligation of confidentiality across concurrent engagements, and the partnership-economic question of how AI changes the leverage ratio on which the firm is built. The Raydorf Management Consulting Framework adapts the seven dimensions of the Standard to these pressures.
§ I Evaluation
What the Management Consulting Framework evaluates
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Strategy & Leadership
Managing partner or executive committee accountability. AI strategy reviewed at partnership level on a defined cadence. A named individual responsible for the firm's AI programme. Engagement-level governance integrated with the firm's wider risk function.
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Governance, Risk & Compliance
Written AI policy aligned with the EU AI Act, applicable industry codes, and the confidentiality terms of the firm's standard engagement letter. Risk classification of AI use cases by engagement type. Engagement letter language addressing AI involvement and the handling of client materials by deployed AI tools.
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Data & Knowledge Infrastructure
Engagement-level sensitivity labelling. Retention discipline aligned with client agreements and applicable data protection regimes. A queryable institutional knowledge base populated through a defined engagement-close process. Knowledge separation between engagements maintained in the architecture, not by assertion.
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Workflow Redesign & Operations
Identified workflows where AI is the default first pass — typically primary research, market sizing, document analysis, presentation drafting, and the synthesis of expert interviews. Documented before-and-after metrics on engagement delivery time, quality, and the partner-leverage ratio.
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Talent & Operating Model
AI fluency across consultants and operational staff. A designated operator role for the firm's AI infrastructure. Hiring and progression criteria that reflect AI-era competencies. Defined training cadence covering both capability and the specific obligations of engagements involving regulated client populations.
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Client Experience
Disclosure practice for AI involvement in engagement deliverables. Client-facing AI features where appropriate. Demonstrable engagement-delivery improvements attributable to AI maturity, reported alongside the engagement's defined success criteria.
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Measurement & Accountability
Cycle-time, quality, and utilisation metrics. Logged human oversight on AI-assisted client deliverables. An audit trail sufficient to reconstruct AI involvement on any engagement, at any time, in support of internal quality review or post-engagement client enquiry.
§ II EU AI Act
EU AI Act considerations specific to consulting practice
Most uses of AI in management consulting fall outside the EU AI Act's high-risk categories. Certain engagement types engage the regime more directly, however: human-resources consulting where AI is used in candidate evaluation, public-sector engagements supporting eligibility determinations, education-sector work where AI is used in the assessment of natural persons. Consultancies advising clients on the deployment of high-risk AI systems may also acquire deployer-side obligations through their participation in the client's programme. The framework includes screening criteria to surface these uses, and the EU AI Act Readiness Attestation evaluates the firm's response to them.