AI GRCGRC & ADOPTION USE CASES
AI use cases: healthcare, financial services, technology, and Media
AI Use Cases defined by client needs
We help financial services organisations design, prioritise, and implement AI use cases across asset management, banking, insurance, and wealth management. Our team combines deep financial services expertise with applied artificial intelligence, machine learning, and technology capabilities, with a strong focus on risk management, regulatory compliance, and responsible AI deployment across global markets.
We support clients throughout the full AI use case lifecycle, from identifying high-impact, compliant AI opportunities to embedding AI into core operating models and decision-making processes. Much of our work comes from client referrals and repeat engagements, reflecting our successful delivery of AI adoption programmes, regulated AI governance frameworks, digital transformation initiatives, sanctions and compliance automation, and post-merger AI integration.
Our core strength is translating AI strategy into real-world business outcomes. We enable organisations to move beyond experimentation by defining clear AI use cases, governance controls, and accountability models that allow AI solutions to scale safely and sustainably. Through T3, we actively facilitate collaboration between industry and academia, ensuring AI use cases are grounded in both technical excellence and practical application.
HEALTHCARE
Medical device manufacturer integrating high-risk AI for diagnostic equipment.
- A medical device manufacturer is ingesting AI into its diagnostic equipment to improve detection accuracy and accelerate clinical decision-making.
- The client’s diagnostic equipment falls under regulation Annex III of the EU AI Act (High-Risk) and EU Medical Device Regulation (MDR) 2017/745, requiring strict conformity assessments and risk management.
- The use of complex, opaque AI models creates a clinical accountability gap, with the risk of undetected misdiagnoses due to limited explainability and insufficient audit trails.
- Algorithmic Guardrails: Established automated safety "kill-switches" that freeze AI output if diagnostic confidence scores drop below a predefined clinical threshold.
- Technical Documentation Pipeline: Built an automated audit trail system that captures real-time data inputs and decision logic to satisfy high-risk regulatory requirements.
- Human-in-the-Loop (HITL) Protocol: Designed a secondary verification layer where high-risk anomalies are automatically flagged for mandatory specialist review.
MEDIA
Media house implementing Generative AI for content production at scale.
- AI tools were used unevenly across departments, creating high brand safety exposure due to the absence of clear standards and operating procedures.
- Staff were constantly asking chatbot what they were allowed to do and what not.
- Staff were spending a lot of time creating similar use cases but with uneven output and off brand.
- There was lack of a unified AI approach increased the risk of non compliance with EU AI Act Article 50, which requires clear labeling of AI generated content.
- Lack of formal controls increased the risk of unintentionally publishing inadequately verified or manipulated AI assisted content, with potential exposure under the UK Online Safety Act.
- Define clear AI standards, approved tools, and allowed use cases to eliminate uncertainty and reduce brand and compliance risk.
- Standardise workflows, prompts, and disclosure practices to ensure consistent, on brand outputs and EU AI Act Article 50 compliance.
- Centralise use case inventory and organise relevant for lessons learned (bad ROI use case) and use case scoring
- Embed technical and editorial controls, including verification steps and labeling, to mitigate misinformation and Online Safety Act exposure.
- Enable teams through role specific guidance, reusable assets, and ongoing oversight to reduce duplication and improve quality.
FINANCE
Wealth Manager using AI to assist advisors with HNW product selection.
- AI optimises for mathematical logic, not regulatory suitability, producing recommendations that look rational but fail to meet best interest, appropriateness, or client specific constraints.
- Firm risk frameworks are unintentionally bypassed, as models do not respect internal product governance and exclusions.
- Failure modes appear as over sophistication rather than errors, with AI suggesting technically valid but strategically, ethically, or reputationally inappropriate instruments.
- Human oversight absorbs the risk, turning advisors into filters who must validate, explain, or discard outputs, reducing efficiency and blurring accountability.
- AI tools generated product suggestions that deviated from firm approved standards and failed to exclude atypical or high risk recommendations, especially around edge cases.
- AI systems were re positioned from autonomous recommendation engines to constrained decision support tools, with regulatory suitability, best interest, and client specific constraints implemented as hard, non overridable controls rather than advisory signals.
- Firm risk frameworks and product governance rules were encoded directly into the AI workflow, ensuring approved product universes, exclusions, and escalation thresholds were enforced by design rather than relying on post hoc human review.
- Validation layers and sanity checks were introduced to prevent over optimisation, blocking technically valid but strategically, ethically, or reputationally inappropriate recommendations, particularly in edge case scenarios.
- Human oversight was redefined from risk absorption to accountable sign off, restoring efficiency while clarifying ownership and materially reducing the likelihood of off standard or high risk product recommendations.
SAAS / TECH
AI-powered video interviewing and candidate assessment platform used by Fortune 500 companies for recruitment decisions.
- Proprietary "black box" algorithms with no explanation of candidate scoring; candidates couldn’t contest decisions or request human review.
- Unclear retention policies with indefinite storage of facial analysis data; no clear consent mechanisms.
- Faced lawsuits under Illinois BIPA and EEOC scrutiny; no framework for emerging state AI laws; lost major clients due to fairness concerns.
- Analysis showed potential bias against neurodivergent candidates and non-native speakers.
- Only internal audits conducted; no independent third-party validation of algorithmic fairness across protected demographics.
- Implemented explainable AI features; candidates receive clear scoring breakdowns and can request human review of AI decisions.
- Quarterly third-party algorithmic audits across demographic groups; discontinued facial expression analysis due to bias concerns.
- Clear 30-day data retention limits; explicit opt-in consent; compliant with state biometric privacy laws (Illinois BIPA, Texas CUBI).
- Formal high-risk AI classification; comprehensive documentation; compliance with NYC Local Law 144 (automated employment decision tools).
- Proactive compliance with Colorado AI Act and other state regulations; reduced legal exposure and improved brand reputation.
AI TRAININGTRAINING AND PROMPT ENGINEERING
AI training in EU AI Act Training and Prompt Engineering for Asset Managers
GAMING COMPANY
Online gaming company using AI for player behavior analysis, dynamic difficulty adjustment, and in-game content recommendations.
- Development teams were unaware which AI systems qualified as high-risk under the EU AI Act and had no understanding of prohibited practices.
- AI systems were deployed without risk classification, technical documentation, or conformity assessments.
- Player profiling systems potentially violated transparency requirements, with no age verification for emotion manipulation features.
- No designated AI governance roles existed, leaving compliance responsibilities unclear across product, legal, and engineering teams.
- Exposure to potential fines of up to €35M (7% global turnover) and inability to launch new AI features in the EU market without compliance clarity.
- Over 4000 staff trained on EU AI Act requirements, enabling teams to self-assess AI systems and distinguish high-risk from minimal-risk applications.
- Established AI risk classification workflows, technical documentation templates, and conformity assessment procedures.
- Disabled emotion-manipulating features for minors, implemented transparency notices for AI-driven recommendations, and introduced proper record-keeping systems.
- Appointed an AI governance officer and implemented a clear RACI matrix defining compliance responsibilities across teams.
- Confident EU market operations, with new AI features designed for compliance from inception and competitive advantage achieved through trustworthy AI positioning.
FINANCIAL PROMPT ENGINEERING
Mid-sized financial advisory firm (500 employees) without proper Prompt Engineering Course.
- Inconsistent outputs: Staff used ChatGPT and Claude haphazardly without knowing how to use them effectively within their ecosystem, unaware of available applications, integrations, or how to connect to internal databases to generate insights and reports.
- Compliance risks: Client-facing content was generated without proper oversight, creating potential regulatory violations in financial advice.
- Productivity gaps: 40% of staff avoided AI tools due to frustration, while those using AI spent 60% of their time fixing outputs.
- Brand risks: Instances of hallucinated financial data appeared in client reports, with inconsistent tone failing to meet the firm’s professional standards.
- No governance: Each department created its own prompts, with no quality control, templates, or shared best practices.
- 85% of staff now understand which AI tools to use for specific tasks, with training on connecting to internal CRM, portfolio systems, and databases to generate accurate, data-driven insights and reports.
- All client-facing AI content follows approved prompt templates with built-in regulatory guardrails, supported by verification workflows that eliminate regulatory violations.
- AI tool usage increased from 40% to 85% of staff, with a 75% reduction in time spent editing outputs and average time savings of 8 hours per week per employee.
- Hallucination incidents dropped to near-zero through verified data retrieval prompts, and a consistent professional tone was achieved using standardized firm-wide templates.
- A centralized prompt repository with over 50 finance-specific templates was introduced, supported by a network of Prompt Champions providing ongoing peer support and clear quality control processes across all departments.
