AI Strategy & Enablement Consultant Job at IQbusiness

Purpose:

• The AI Strategy & Enablement Consultant is a client-facing role supporting the Enterprise Modernisation and AI teams. The role combines business analysis, consulting and AI advisory capabilities to help organisations identify, prioritise and progress AI opportunities.
• The core mission is to translate business challenges into practical AI use cases and enable clients to move from interest and experimentation towards structured, responsible and value-focused adoption.

Working alongside AI strategists, solution architects, delivery leads and client stakeholders, the consultant will:

• Demystify AI and Generative AI in clear, business-relevant language.
• Support AI opportunity discovery, use-case definition and prioritisation.
• Gather, analyse and document business requirements, processes and stakeholder needs.
• Contribute to AI maturity assessments, strategies, roadmaps and adoption plans.
• Translate business needs into structured inputs for solution and delivery teams.
• Support responsible AI, governance, change and value-realisation activities.

Key Responsibilities:

Demystifying AI & Business Enablement 

• Support the design and delivery of AI and GenAI awareness, discovery and enablement sessions. 
• Explain core AI concepts, capabilities, limitations, risks and value levers in practical business language. 
• Research and prepare relevant industry examples, use cases and discussion material. 
• Adapt content to the client context, maturity, priorities and audience. 

AI Opportunity Discovery & Use-Case Shaping 

• Facilitate or co-facilitate discovery workshops with cross-functional stakeholders. 
• Analyse business challenges, processes, pain points and opportunities for AI-enabled improvement. 
• Capture and structure potential AI use cases using agreed templates and canvases. 
• Assess use cases against business value, feasibility, data readiness, risk, adoption and change impact. 
• Support prioritisation backlogs and develop clear use-case narratives, problem statements and expected outcomes. 

Business Analysis & Requirements Definition 

• Plan and conduct stakeholder interviews, workshops and requirements-gathering activities. 
• Document current-state and future-state processes, user journeys, business rules and requirements. 
• Translate business needs into clear artefacts for architecture, product, data and delivery teams. 
• Support the definition of acceptance criteria, success measures and traceability from problem to outcome. 
• Identify dependencies, assumptions, constraints, risks and information gaps. 

AI Strategy, Maturity & Roadmap Support 

• Contribute research, analysis and structured insights to AI strategy and maturity engagements. 
• Support baseline assessments covering business readiness, data, technology, governance, people and adoption. 
• Assist in developing practical AI roadmaps that sequence opportunities, foundations and enablement activities. 
• Prepare client-ready recommendations, presentations and decision material. 
• Maintain roadmaps, initiative backlogs, action registers and supporting documentation.

Solution & Delivery Enablement 

• Work with strategists, architects and technical specialists to translate prioritised use cases into solution requirements. 
• Support problem framing, user-journey definition, data-readiness analysis and solution option assessment. 
• Participate in proof-of-concept, pilot and mobilisation activities. 
• Support evaluation planning, user acceptance, feedback gathering and continuous improvement. 
• Help maintain alignment between the original business need, proposed solution and intended value. 

Responsible AI, Governance & Risk Support 

• Apply awareness of privacy, security, model risk, bias, explainability and human oversight when shaping use cases. 
• Support the documentation of governance requirements, decision points, controls and escalation paths. 
• Coordinate input from business, risk, legal, compliance, security and technical stakeholders. 
• Ensure responsible and human-centred AI considerations are reflected in recommendations and delivery artefacts. 

Adoption, Change & Value Realisation 

• Support stakeholder analysis, change-impact assessment and adoption planning. 
• Contribute to communication, learning, enablement and feedback activities. 
• Help define adoption, usage, business-outcome and risk indicators. 
• Track agreed actions, benefits and learning from pilots and implemented use cases. 
• Use insight and feedback to support roadmap refinement and continuous improvement. 

Practice Development & Capability Building 

• Contribute to reusable AI advisory assets, templates, use-case libraries and delivery methods. 
• Capture lessons, case studies and repeatable patterns from client engagements. 
• Stay current on AI and GenAI developments, enterprise adoption patterns and responsible AI practices. 
• Share knowledge across the EMA and AI teams and support internal capability-building activities. 

Minimum Requirements

Essential

• Typically 3-6 years of experience across business analysis, management consulting, digital transformation, product, process improvement or change-related roles.
• Practical exposure to AI, Generative AI, data, analytics, automation or digital-transformation initiatives.
• Experience gathering requirements, analysing processes and translating business needs into structured recommendations or delivery artefacts.
• Experience facilitating or supporting workshops with business and technical stakeholders.
• Strong written communication and experience preparing presentations, reports, process maps or business cases.
• Ability to work across multiple disciplines and contribute effectively within consulting or project environments.

Advantageous 

• Experience in management consulting, AI advisory or enterprise transformation. 
• Exposure to Microsoft Copilot, enterprise GenAI tools or AI-enabled business solutions. 
• Experience with AI use-case assessment, maturity assessments, roadmaps or adoption programmes. 
• Business analysis, Agile, product, design thinking or change management certification. 
• Experience in regulated or complex enterprise environments. 
• Exposure to responsible AI, governance, data privacy, security or risk requirements. 

Skills & Competencies 

AI, Data & Digital Understanding

• Sound understanding of AI and GenAI concepts, common enterprise use cases, capabilities and limitations.
• Awareness of the AI solution lifecycle, including opportunity discovery, data readiness, design, evaluation, deployment, monitoring and adoption.
• Working understanding of responsible AI, privacy, security, governance and human oversight.
• Ability to learn new AI tools, methods and market developments quickly.

Business Analysis & Consulting

• Requirements gathering and documentation.
• Process analysis and mapping.
• Use-case discovery, problem framing and prioritisation.
• Business case and value articulation.
• Structured problem solving, research and synthesis.
• Roadmap, backlog and action-plan development.

Communication, Facilitation & Stakeholder Engagement

• Clear written and verbal communication with business and technical audiences.
• Confident workshop facilitation and stakeholder interviewing skills.
• Strong presentation development and structured storytelling.
• Ability to build trust, manage differing perspectives and maintain shared understanding.
• Comfort engaging senior stakeholders while recognising when to draw on more experienced specialists.

Delivery, Change & Collaboration

• Collaborative approach across strategy, architecture, delivery, change and client teams.
• Strong organisation, attention to detail and follow-through.
• Awareness of Agile, product or iterative delivery practices.
• Practical understanding of adoption, change impact, learning and benefits tracking.
• Ability to manage ambiguity, competing priorities and evolving requirements.

Mindset

• Curious, pragmatic and commercially aware.
• Analytical and comfortable working between business and technology.
• Ethically grounded, with a commitment to secure, responsible and human-centred AI.
• Collaborative, low-ego and willing to learn from specialists.
• Focused on practical outcomes, client capability and measurable value.