Mark Maynard

Turning complex problems into working intelligent systems.

Every organisation I work with faces the same challenge: how to move from ambition to evidence. These examples show what happens when AI transformation begins with clear analysis, responsible design, and real proof of value.

Internal Knowledge Assistant

Challenge:

Critical knowledge was buried across documents, email, and shared drives. Staff were wasting hours finding accurate information, and expertise disappeared when people moved roles.

Approach:

I analysed how information moved through the organisation and identified where knowledge was being lost or duplicated. We designed an internal AI-powered assistant that indexed trusted sources, applied context, and delivered instant, verified answers through a secure chat interface — with permission controls and governance built in.

Outcome:

Information retrieval time dropped by over 60%. Onboarding time fell. Decisions improved because everyone had access to consistent answers. The assistant is now being extended to other functions.

Data Preparedness for an Advertising Company

Challenge:

An advertising company wanted to apply AI across client work, but data was scattered, unstructured, and effectively ownerless. There was energy but no foundation.

Approach:

I ran a full data and governance readiness assessment: mapped data sources, defined ownership, created a metadata and classification model, and designed a governance framework for compliant access and use. This became the blueprint for safe AI integration.

Outcome:

The company now has an auditable, permissioned data layer and a roadmap for AI automation and analytics. They know what's possible, what's risky, and what's next.

Competitive Intelligence Agent (placeholder)

Challenge:

Organisations were reacting too slowly to competitor moves and market shifts.

Approach:

(Placeholder for agent-based monitoring / enrichment system.)

Outcome:

(Placeholder for measurable strategic speed / awareness.)

Each project follows the same principle: analyse deeply, design intelligently, prove results, and scale responsibly. The goal is always the same — deliver systems that show real value before investment, then build governance and confidence for long-term success.