Understanding Artificial Intelligence
The world is changing, don’t get left behind
UNDERSTANDING ARTIFICIAL INTELLIGENCE (AI)
Understanding AI Adoption for Managers and Business Leaders
Duration: Monday to Thursday 09:00–14:00,
Friday 09:00–12:00Location: Central London
The British Academy of Professional Development
$5,500
COURSE OVERVIEW
UNDERSTANDING ARTIFICIAL INTELLIGENCE (AI) is a transformative 5-day programme designed to help managers and business leaders adopt AI confidently and responsibly to improve marketing performance, productivity, and operational efficiency.
This comprehensive course moves beyond traditional “tech-only” conversations and focuses on the real leadership decisions that determine whether AI creates value or creates risk. Participants will learn practical ways to use AI to strengthen brand awareness, improve customer communication, streamline management workflows, and support faster, higher-quality decision-making.
The programme also addresses the realities leaders must manage: intellectual property and rights, data and confidentiality, bias and misinformation, reputational risk, and the governance required to protect the organisation while enabling innovation.
By the end of the programme, participants will be able to lead AI adoption with clarity, select and govern tools appropriately, and embed responsible AI practices that support sustainable organisational excellence.
Target Audience: Senior leaders, executives, and managers who want to adopt AI in their organisations to improve marketing, productivity, and efficiency while managing risk, reputation, and responsible use.Duration: Monday to Thursday 09:00–14:00, Friday 09:00–12:00Location: Central London
LEARNING OBJECTIVES
By the end of this programme, participants will be able to:
Understand the AI capabilities most relevant to business leaders (including generative AI) and apply them to real organisational priorities
Develop high-value AI use cases for marketing, brand awareness, productivity, and operational efficiency, with clear success measures
Master practical AI-enabled workflows and management tools that improve planning, communication, reporting, and decision-making
Create an AI governance approach that addresses intellectual property, confidentiality, data protection, and responsible use
Sustain AI adoption by managing reputational risk, building trust, and leading change through clear implementation strategies
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What AI is (and isn’t): practical definitions for business decision-making
Generative AI in the workplace: where it helps and where it can harm
The leader’s role in AI adoption: clarity, accountability, and decision rights
Mapping organisational opportunities: marketing, operations, customer service, and leadership workflows
Readiness assessment: people, data, risk appetite, and governance basics
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Using AI to strengthen brand awareness: consistency, messaging, and content planning
AI for marketing productivity: campaigns, copy variations, and rapid iteration (with quality controls)
Audience insight and positioning: using AI to support research and segmentation responsibly
Reputation management basics: avoiding misinformation, tone risks, and brand damage
Building a marketing use case: objectives, KPIs, review processes, and approvals
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AI for productivity: meeting preparation, summarising, drafting, and prioritisation
AI for operational efficiency: process mapping, SOP creation, and workflow optimisation
Management tools and decision support: dashboards, reporting prompts, and structured thinking frameworks
Human-in-the-loop practices: quality assurance, escalation, and accountability
Measuring impact: time saved, quality improved, risk reduced, and adoption indicators
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Intellectual property and rights: ownership, licensing, and safe content practices
Confidentiality and data handling: what must never go into AI tools and why
Common dangers: hallucinations, bias, security exposure, and over-reliance
Reputational risk management: policies, training, and incident response basics
Governance essentials: tool approval, vendor due diligence, audit trails, and role-based access
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Designing a phased rollout: pilots, scaling, and continuous improvement
Building an AI adoption roadmap: people, process, technology, and governance alignment
Change leadership: communication, training, resistance, and building confidence
Personal AI leadership development planning: strengths, gaps, and next steps
Implementation strategies: 30/60/90-day plan, governance cadence, and accountability