BBC 2027 Agenda

How to get involved earlier, change the conversation and shape the work

Business analysts can add significant value before an initiative formally starts. Yet all too often, BAs are brought in after key decisions have already been made, the scope has started to solidify, and a preferred solution is already taking shape.

Getting involved earlier creates an opportunity to ask different questions, challenge assumptions and help shape the work before the direction is fixed. But getting involved earlier isn’t just about asking for a seat at the table. We need to build the credibility, relationships and influence that make people want to involve us.

In this interactive, hands-on session you’ll explore:

1. Practical ways to increase your visibility and build credibility with stakeholders
2. Techniques for moving conversations beyond predefined solutions and towards underlying needs and outcomes
3. Approaches for navigating organizational dynamics and influencing what happens next

You’ll leave the session with practical techniques for increasing your strategic influence and helping shape work earlier, when there is still time to make a difference.

Learn foundational process modeling, then apply modern design principles to real-world scenarios. This hands-on workshop teaches you to discover process context and to create BPMN models. It then shows you how to redesign processes for flexible, empowered decisions, built-in innovation, end-to-end value stream thinking, and AI readiness.

Whether you are an experienced analyst or new to process work, you will learn modern design principles that traditional process mapping ignores. These skills enable you to identify where current processes fail organizational needs, scope improvement initiatives, and design workflows ready for today’s dynamic business environment. All content is designed for immediate application in your organization.

Learning Objectives:

• Process context and BPMN fundamentals
• Seven modern design principles that traditional process mapping ignores
• How to identify where processes fail modern organizational needs

Organizations often struggle to turn strategy into meaningful business outcomes. A key reason is the disconnect between business architecture and business analysis practices. When these disciplines operate in silos, gaps emerge between strategic intent, planning, requirements, and delivery, limiting the value organizations can achieve.

In this interactive, hands-on tutorial, you’ll learn how business architects and business analysts can work together to create stronger alignment across the strategy-to-execution lifecycle. Through practical exercises and real-world examples, you’ll explore how each discipline contributes unique perspectives while sharing a common goal: delivering business value. Leave with actionable techniques, artifacts, and collaboration approaches that improve traceability, strengthen decision-making, and help organizations transform strategy into measurable results.

Learning Objectives:

• Explain how business architecture and business analysis work together to connect strategy, execution, and business value.
• Identify common gaps and risks created when these disciplines operate independently and apply approaches to improve alignment.
• Apply practical techniques and artifacts to trace strategic objectives through initiatives, requirements, and delivery outcomes.

Most organizations believe business analysis delivers requirements, process models, and documentation. Yet projects rarely fail because artifacts are missing. They fail when stakeholders leave with different assumptions, expectations, and interpretations.

This session challenges attendees to rethink the true deliverable of business analysis: shared understanding.

Through practical examples and lessons learned, participants will explore how trust, psychological safety, facilitation, stakeholder dynamics, and feedback loops influence decision quality and organizational outcomes. Attendees will learn techniques for surfacing hidden concerns, navigating power dynamics, reducing misalignment, and clarifying ambiguous language.

Participants will leave with practical techniques to create alignment and surface risks earlier.

Learning Objectives:

• Analyze stakeholder behavior through trust, pressure, and competing priorities
• Build feedback loops that surface risks and concerns earlier
• Apply facilitation techniques that create shared understanding and alignment

AI tools can fail spectacularly or provide genuine assistance when you’re mapping business processes. In this hands-on workshop, you’ll be guided through a series of exercises to add an AI assist to your process mapping toolkit. Never fear, this isn’t about replacing your mapping skills – it’s about equipping you to leverage the things AI is good at so you can spend your time eliciting, analyzing, evaluating, validating, and mapping process knowledge.

Bring your favorite diagramming and genAI tools on your Mac or PC: Lucidchart, Visio, Miro, draw.io, Claude, ChatGPT, Copilot… your choice. We’ll experiment together, then share and compare results. As a bonus, you’ll learn about Mermaid syntax and even find out where the name came from!

Learning Objectives:

• Evaluate AI capabilities in major diagramming apps
• Understand where you should – and shouldn’t – use generative AI when mapping processes
• Get hands-on experience using AI to assist with process mapping

“Don’t just discuss AI transformation. Step into it.”

Organizations around the world are investing heavily in Artificial Intelligence. Yet many initiatives struggle. Not because of technology, but because organizations fail to fully understand the business problems before deciding on a solution.

This interactive learning experience places participants in the middle of a realistic AI transformation initiative where they must investigate, interview stakeholders, reconcile conflicting perspectives, and recommend the best path forward.

Rather than teaching Business Analysis through presentations, participants learn by experiencing the challenges of organizational transformation firsthand. The workshop combines immersive role-play, collaborative investigation, and practical facilitation techniques to demonstrate how an outcome-driven mindset can significantly improve AI-enabled business change.

Learning Objectives: 

• Lead AI transformation initiatives with a clear focus on business outcomes rather than technology.
• Elicit the real business problem and assess AI-related risks before committing to a solution.
• Apply an outcome-driven mindset to prioritize value delivery over solution development.

In the world of business change, surprises are not usually pleasant. However, unintended consequences are not unusual, resulting in shortfall of performance and delays sometimes measured in months and years. This is apparent to all of us who work to deliver complex transformations. As we tackle more and more complicated business issues and generate growingly interconnected designs, we can become our own worst enemies as we try to get it right quickly under pressure. We must become better prepared to get it right the first time. To thrive, we must get a grip on the knowledge of how the disparate pieces fit and how the disruptive impact of changes in any one domain on the others can be avoided before we start building solutions. Knowledge of the principles and practices of Business Architecture to achieve cohesive outcomes is essential for all business designers. With a solid grounding provided in this workshop we can deliver the right transformation and avoid unwanted consequences of the changes we are making.

1. The challenge of complexity
2. What has to change? – the dots
3. Connecting the dots
4. A Business Architecture Framework that avoids surprises?
5. Trying it out: Working sessions

This session will be an interactive workshop engaging all attendees to discover better ways to make a difference that matters.

Learning Objectives

• Understand the complex moving parts of business transformation
• Examine the interconnections that have inherent risk
• Decide what is important and prioritize your efforts

You were trained to discover requirements by asking, listening, and writing it all down. That made sense when building software was slow and expensive.

Building a working, clickable prototype is now nearly free. You can stand up a working prototype in minutes, and a working prototype pulls out what people actually need faster than any interview, user story, or spec. Put something real in front of stakeholders and the reactions pour out, and those reactions and feedback are critical!
AI helps us write faster, but it doesn’t mean that writing requirements is still the best way to collaborate.
This workshop treats AI rapid prototyping as what it really is: a discovery and elicitation technique. You will build and use prototypes to surface hidden assumptions, expose disagreement, and align technical, business, and vendor teams fast.
Please note: This is a bring-your-own-laptop workshop. We recommend that all participants bring their own laptop.

Learning Objectives: 

Master AI prototyping as an elicitation skill, and shift from documenting requirements to discovering them in real time.

• Reframe prototyping as the new front end of discovery
• Use prototypes to elicit what people struggle to put into words
• Know the difference between discovery and delivery prototyping
• Prototype across UIs & Workflows, Reports, Data & APIs, features, and full apps
• Facilitate prototyping across business, tech, and vendor teams

AI can generate ideas, write proposals, and create polished presentations in seconds. But it still cannot guarantee that anyone will care. In this highly interactive workshop, we will explore the behavioural science behind why some ideas capture attention and change decisions while others are ignored. Through surprising experiments, real-world product stories and hands-on challenges, participants will discover how framing, emotion, numbers, novelty, visual thinking, metaphor, language and delivery shape human behaviour. You will see how the same message can produce very different reactions depending on how it is communicated. Using Paddy’s SENND framework (Stakes, Emotion, Numbers, Novelty and Decisions), you will experiment with different ways to communicate an idea, build your own Product Story, and leave with practical techniques for making complex ideas easier to understand, harder to ignore and more likely to influence stakeholder decisions.

Learning Objectives:

• Apply behavioural science principles to influence stakeholder attention, judgement and decisions.|
• Use visual, verbal and metaphorical techniques to make complex ideas more persuasive.
• Apply SENND, the five ingredients (stakes, emotion, numbers, novelty and decisions), to structure persuasive messages.

Most AI work inside organisations is automation of an operating model you already have. Faster versions of what you already do. But the real deal is the business you could be running instead.

This tutorial is a working session in creative reinvention. You take a business you know well, reimagine how it creates and captures value, then redesign the operating model that would have to exist to deliver it.

You deliberately work with mixed hats. Executives, product and change leaders, designers, architects and analysts. A visual Open Source language (EDGY) helps create the shared understanding you need to decide and design, made explicit in an overarching graph repository. AI joins you as a creative partner throughout, once it knows enough about your enterprise to be useful rather than generic.

You leave with several credible options for a revamped business model, a reconfigured operating model for the one you choose, and a narrative for the way to get there.

Learning Objectives: 

• Co-create credible new business model options for your own organisation.
• Redesign an operating model to deliver a changed value proposition.
• Use AI with repositories as a creative partner, not to document automations.

AI is only as powerful as the data beneath it. Without clear terminology, well-defined rules, and trustworthy data, even advanced AI can produce inconsistent or misleading results. This tutorial helps participants move from fragmented, ad hoc data toward a disciplined approach that supports reliable, scalable AI.

Through interactive case studies and exercises, participants will see how different interpretations of the same data can lead to conflicting answers, and how shared vocabulary, concept models, and business rules can resolve that confusion, including in free-text, natural-language content. We will also cover governance practices that build trust in data, including validation, controlled vocabularies, and metric rules.

Finally, we will show how AI can support this work. Using an AI-enabled assistant built on proven methodology, we will demonstrate how well-prepared AI can draft concept models and business rules, helping participants apply these techniques to their own data.

• Collect and define shared vocabulary and rules for consistent data interpretation.
• Improve the quality and consistency of unstructured data for reliable AI outcomes.
• Diagnose and address common data quality issues that undermine AI.
• Use AI to help build concept models and business rules from your own material.

AI is expanding across the enterprise, disrupting and conflating traditional roles. It’s an opportunity for BA’s to become enablers of the agentic enterprise. How do we grasp it?

Agents need context, an entirely different set of knowledge and requirements than legacy applications. To deliver context, we must explain the operating model logic that agents will execute. Business Architecture is our scaffold, and BAs need ‘just enough’ of it for our purpose.

So, what changes when designing for agents and what is ‘just enough’ Business Architecture for project-centric Business Analysis?

In this hands-on session we answer both. Using industry scenarios and AI prompts, you will generate a ‘plausible’ draft of a ValueStream Canvas, the Capabilities that support it and the agents to orchestrate it.

Plausible is the operative word. Nothing counts until validated. We’ll work through what validation means and who owns it.

The deliverable: Your first agent charter and the method to write your next

Learning Objectives: 

• Use AI prompts to generate a ‘plausible’ business architecture foundation for your next project
• Apply operating model logic to design and specify agent charters
• Position yourself as custodian of the operating model