AI-Enhanced Investment Analysis: From Foundations to Fluency

Led by: Anthea Roberts

Understand what AI is, why this moment is genuinely different for investment markets, and how to think about human-AI collaboration in a fiduciary context.

What will be covered:

• How Large Language Models work — capabilities, limitations, and what they mean for investment professionals

• Mental models for human-AI collaboration: from actor to director, from athlete to coach, from writer to editor

• Why structured thinking matters more than ever when working with AI

• Fundamental shifts transforming how investment research and analysis works

• Near-term and medium-term developments that will affect investment workflows

• Strategic questions every investment firm should be considering

Outcome: A clear-eyed understanding of what AI can and cannot do, and a framework for thinking about where it fits in your investment process.

Led by: Anthea Roberts and members of the Dragonfly Team

Get hands-on with the Dragonfly platform and learn to use structured analytical lenses for investment analysis.

What will be covered:

• From basic prompting to sophisticated analytical frameworks — building real skill with AI tools

• Introduction to the Dragonfly platform and its structured thinking methodology

• The RRR (Risk, Reward, Resilience) framework —Dragonfly's proprietary methodology for rigorous analysis

• Using analytical lenses for investment research: PESTLE for macro analysis, SWOT for position assessment, Competitive Intelligence for
  market mapping

• Working with the Orchestrator to scope and direct analysis

• Revise, Extend, and Diverge — the structured pathways for deepening and challenging analysis

• Verification and critical assessment of AI outputs

Hands-on exercise: Analyse an investment opportunity using multiple Dragonfly lenses, then critique and refine the outputs.

Outcome: Confidence with the Dragonfly platform and practical experience applying structured lenses to real investment questions.

Led by: Sue Brake and members of the Dragonfly Team

Apply AI-augmented analysis to real investment challenges, then systematically challenge your conclusions — guided by a practitioner who has led some of the world's most sophisticated institutional investors.

What will be covered:

• The practitioner's journey: from traditional investing to AI-augmented analysis

• Deep-dive case study: "Is Factor Investing Still Robust in an AI World?"

  • How AI was used to conduct PESTLE analysis across macro factors

  • The "bifurcated future" thesis and what it means for investment strategies

  • Lessons from recent market events and what they reveal about AI-driven dynamics

• AI-augmented ESG analysis — moving beyond compliance checklists to substantive assessment of environmental, social, and governance risks and opportunities across portfolio holdings

• AI-augmented workflows for company deep-dives and sector analysis

• Devil's Advocate analysis — systematically challenging investment theses to expose hidden weaknesses

• Cognitive bias detection — identifying the biases that affect investment decision-making (anchoring, confirmation bias, herding)

• How institutional investors are thinking about AI adoption

Hands-on exercises: Build AI-enhanced investment analysis using Dragonfly lenses, then use Devil's Advocate and Cognitive Bias Detection to stress-test your own conclusions.

Outcome: Proven approaches for integrating AI into investment analysis — and the discipline to challenge your own outputs before they reach a decision-maker.

Led by: Sue Brake and Elizabeth McPherson

Stress-test investment theses against multiple futures, synthesise insights across analytical frameworks, and develop a practical approach to AI governance in your organisation.

What will be covered:

• Scenario planning for portfolio management — developing and testing against multiple future states using the Four Scenarios methodology

• Wildcard and shock scenarios — preparing for tail-risk events and black swan disruptions

• Scenario stress testing — testing investment decisions against multiple futures to identify vulnerabilities

• ESG stress testing — climate transition scenarios, detecting greenwashing, and assessing how ESG risks compound under different future states

• Pre-mortem analysis — "assume this investment has failed, now work out why"

• Multi-lens synthesis — integrating insights from across different analytical frameworks into a coherent, decision-ready narrative

• Applying the Green/Amber/Red framework — building a practical AI governance approach for your organisation: which investment AI use cases are green-light, which need guardrails (amber), and which require human-only judgement (red)

Outcome: The ability to stress-test investment thinking under multiple scenarios, synthesise complex analysis into clear recommendations, and implement a practical governance framework for AI use in your organisation.