Position to Fly

AI Strategy

Enable AI to manage, identify and resolve risks in your business

Help assist those firms with no AI strategy to semi-agentic and autonomous AI management, to enable them to manage their whole risk within a risk management or internal audit structure.

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The four-phase model

A clear view of how to use AI within risk management from a messy environment of pilots to running a staged production environment.

SustainableAssessEvaluateImprove

  • Assess

    Evaluate the full AI inventory to map the use of AI across workstreams

  • Evaluate

    The use of AI solutions in specific detail across Gen AI, agentic AI, data contents and its impact on revenue, cost management, improved compliance and positive branding.

  • Improve

    Suggest enhancing or rationalising AI to deliver its objective that is secure and controlled.

  • Sustainable

    Create a sustainable framework across the AI environment that is cost effective, controlled and agile.

Our end-to-end solutions includes:

  • Agree your business objectives
  • Understand your inventory of AI skills (including shadow AI)
  • Prioritise your AI roadmap by initiatives and key performance metrics
  • Create policy, governance and risk and trust framework leadership governance
  • Develop AI initiatives from your roadmap
  • Engender key stakeholders to drive pilots in the short and medium term
  • Realise the key performance over the use of AI
  • Identify rationalisation of key decisions through returns from AI
  • Create an established framework into use of AI and change agenda

Tactical solutions

Creation of AI governance

  • Develop of clear and understandable AI policies
  • Formulation of key AI practitioners and decision makers
  • AI solutions based across the business including needs from sales, operations, risk and reporting
  • Creation of overall AI heatmaps and key areas for performance and further decision making

Specific use cases and initiatives

  • Improving and educating stakeholders expectations on AI benefits and risks
  • AI proof of concepts showing AI benefits
  • Data management hurdles to overcome data quality issues
  • Identify training needs and suggested courses/on the job training needs
  • Improvements in realisation of existing AI initiatives

Building trust in AI architectures

  • Creation of an AI orchestration map and interfaces with other systems
  • Documenting AI policies and procedures
  • Performing Internal Audits or quality assessments on use of AI
  • Managing use of specialists to further improve AI proliferation
  • Manage Shadow AI usage on creation of tactical usage