Practical AI for Business Growth
Summary
AI is reshaping the way modern businesses create and deliver impact, but separating real capability from hype is harder than ever. This course gives you a clear, practical understanding of what today’s AI systems can actually do, where they fail, and how to use them responsibly to accelerate high-quality outputs.
During the day you will get hands-on exposure to tools that are already changing client expectations, learn how to assess emerging AI solutions with confidence, and build safe, human-in-the-loop workflows that enhance, not replace, professional judgement. We will also explore how to apply AI effectively while navigating governance, ethics, and the evolving regulatory landscape.
How will I benefit?
At the end of the course, delegates will be able to :
- describe the AI Landscape relevant to research, communications, campaigning and public policy
- explain what modern AI can and cannot do, and spot failure modes that matter for agencies (hallucinations, bias, weak sourcing, overconfidence)
- explain how AI tools can be used to draft, adapt, summarise, structure and report work faster across multiple business applications, while keeping human quality control
- evaluate and assess off-the shelf tools AI tools ((Copilot, ChatGPT-style tools, Transcription, Analytics, Research Assistants) versus bespoke options using a simple checklist: cost, quality and accuracy, data sensitivity and risk, auditability, integration effort
- identify high-value use cases for AI to support business activity, including what should not be automated
- apply a practical governance approach, covering misuse and abuse, risks, OWASP’s Large Language Model vulnerabilities, ethical trade-offs in policy and public diplomacy work, and the EU AI Act
Who should attend?
This course is ideal for :
- Business leaders and managers who want to understand how AI can improve productivity, support better decision-making, and identify practical opportunities for business growth
- Project managers and team leaders looking to evaluate AI tools, introduce AI into existing workflows, and manage implementation responsibly
- Business support teams in operations, administration, HR, finance, or customer service who want to automate routine tasks, improve efficiency, and make better use of AI-powered tools
Course Outline / Key Topics
The AI Landscape
- AI adoption and what it means for agencies
- Where AI is genuinely improving productivity in knowledge work
- AI Adoption rates and adoption blockers for companies
- What clients are starting to expect from agencies
What AI is (without the hype)
- The two core categories of AI : predictive ML vs generative AI
- How language and generative models work under the hood
- Why generative AI is good at language, weak at truth
- Failure modes that matter for policy, communications and research
Custom Copilots, Workflow Automation, Prompting Fundamentals plus Agentic AI
- Prompt patterns for reliable and consistent outputs: constraints, examples, scoring rubrics
- Automation patterns: planning, tool use, verification, hand-off points
- When automation is a bad idea (high stakes, weak ground truth, no audit trail)
- Designing copilots and lightweight automations (human-in-the-loop)
- Group exercise: Pick a workflow (focus groups, surveys, evaluation) and design a “human-in-the-loop” AI workflow with checkpoints
- Exercise: map AI opportunities across campaigns and research workflows
Governance, policy, and ethics
- Where AI fails: accuracy, bias and limitations
- Safe use and governance of AI
- Misuse and abuse in an agency context (manipulation, misinformation, synthetic personas)
- OWASP’s top 10 Large Language Model vulnerabilities, translated into plain-language controls
- Why AI ethics is hard in policy, public diplomacy, and work with vulnerable groups
- The EU AI Act overview (what changes for suppliers vs users) and what “good practice” looks like
- Wrap-up: agree a ‘safe use and quality assurance’ checklist and next steps