BBC 2027 Agenda

Design. Transform. Improve.

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

Organizations often begin AI initiatives by selecting technology before fully understanding the business opportunity.

This session presents a practical, business-first framework for moving from process improvement opportunities to decision-ready AI use cases. Attendees will learn how to conduct business discovery, map experiences and processes, assess data readiness, and determine whether AI adds meaningful value beyond process improvement or traditional automation. The session also explains how to evaluate technical options, address human oversight, risk, cost, security, and support, and apply governance based on the use case’s scope and impact.

Participants will leave with a repeatable approach for avoiding AI for AI’s sake, developing evidence-based recommendations, and connecting technology investments to measurable business value.

Learning Objectives: 

• Apply business discovery techniques to identify and prioritize opportunities.
• Assess whether AI adds value beyond process improvement or automation.
• Develop decision-ready AI recommendations addressing value, data, ownership, risk, and governance.

Operational bottlenecks and administrative overhead drain valuable business capacity and slow organizational growth. This practical session shows business analysis and operational leaders how to safely integrate AI-driven workflows to eliminate friction and scale capability. Attendees will explore how to audit high-effort processes, apply essential governance, and streamline routine operations without disrupting existing enterprise architecture. You will leave with an actionable blueprint to transform administrative friction into measurable capacity across your organization.

Learning Objectives:

• Audit Process Bottlenecks: Identify key administrative and operational friction points across business workflows to reclaim team capacity.
• Apply Governance Frameworks: Evaluate AI tools and digital workflows using a structured governance approach to ensure privacy, compliance, and scalability.
• Map Automated Workflows: Construct a practical blueprint to integrate AI-driven automation into daily operations without disrupting existing business architecture.

Your organization chart shows where people sit—but where does AI belong? As AI evolves from assistant to agent, simply inserting it into existing jobs and processes may automate yesterday’s operating model rather than design tomorrow’s. In this interactive session, attendees will work through a practical approach connecting value, capabilities, decisions, processes, human/AI allocation, and governance. Working with a familiar business capability, participants will decide which activities belong with humans, AI-assisted humans, AI agents, or traditional automation. Then comes the harder question: who owns the outcome when AI does the work? Attendees will leave with a practical method for designing human + AI operating models intentionally rather than allowing technology to define them by default.

Learning Objectives:

• Determine appropriate human and AI participation.
• Design decision rights and accountability for human-AI operating models.
• Apply capability-based thinking to redesign work around business value.

AI made ambiguity expensive, and “ontology” is suddenly everywhere. It’s being used to mean two very different things, by people who don’t realize they’re disagreeing.

A business ontology captures what the business means: the concepts, their real definitions, and how they connect. A technical ontology makes those concepts formal and machine-processable, so systems can store them, exchange them, and reason over them. Both are necessary. They are not interchangeable, and treating one as a substitute for the other is why so many data and AI programs stall.

This session separates the two, shows how they work together, and explains why the business ontology has to come first. It informs everything downstream: systems, data, processes, and above all AI. Along the way we’ll connect today’s ontology talk back to concept modeling, and show business analysts exactly where their work belongs.

Learning Objectives:

• What a business ontology is, what a technical ontology is, and how to recognize which one you’re actually being asked to build.
• How the business ontology hands off to the technical ontology and the strengths of each.
• How shared business meaning impacts downstream systems, data, processes, and especially AI

Research suggests that up to 80% of features in software products are rarely or never used. AI has accelerated shipping faster than ever, yet most of what gets built fails to deliver real value.

As Business Analysts, we’re fuelling what Paddy calls “The Failure Economy.” A world where teams are rewarded for shipping more, even when it’s the wrong things.

In this session, Paddy draws on 25 years across Business Analysis, Product Management, and leading product teams in complex environments to explore what discovery really is and why it’s not just a product management discipline.

Discovery is a universal skill that applies whether you’re shaping requirements or making any decision where uncertainty is high, and the cost of getting it wrong is high.

You’ll learn what Product Discovery Thinking actually means, get an overview of popular discovery frameworks and when to use them, and explore the human skills to master discovery thinking.

Learning Objectives: 

• Understand what discovery is and why it’s a universal skill, not just a product management activity
• Know what Product Discovery Thinking means and how it differs from simply doing discovery activities
• Identify the human skills to master effective discovery and how to start developing them

Every AI presentation promises transformation; almost none tell you what breaks when you hand judgment calls to a model. Unpack a practical framework for deciding which decisions in the product lifecycle — discovery, prioritization, requirements, QA — belong to AI, which belong to humans, and which need both in sequence. Using real product scenarios (feature prioritization, requirements elicitation, customer insight synthesis), this session shows how Six Sigma’s DMAIC discipline can be repurposed as a governance model for AI-assisted product work, preventing the two failure modes leaders are quietly panicking about: over-trusting AI outputs, and under-using AI capacity out of fear. Attendees leave with a decision matrix they can apply Monday morning to their own product or BA workflows.

Learning Objectives: 

• Leverage a decision matrix to classify tasks as AI-led, human-led or hybrid
• Customize a AI governance checkpoint system for your project
• Identify failure patterns from AI-assisted requirements gathering and how to work around them

Standing up an enterprise data products practice requires far more than modern technology—it demands a fundamental shift in operating model, governance, and organizational culture. In this case study, leaders from DaVita share how they transformed data delivery from reactive “ticket-taking” into a high-velocity, reusable data product engine.

Attendees will learn how to:

• Design an operating model that unites agile product delivery with enterprise architecture and governance 

• Master intake and prioritization to balance long-term strategic roadmaps with urgent business demands 

• Shift from one-off bespoke reporting to a reusable product portfolio that accelerates time-to-value 

• Build the trusted, governed data foundation necessary to scale enterprise AI and intelligent automation 

Walk away with practical, field-tested lessons to lead sustainable data transformation across complex organizations.

Learning Objectives:

• Taming the Intake Chaos — Transitioning from “IT Ticket-Taker” to a Governed Product Factory
• Bridging the Cultural Divide — Aligning Modern Data Product Model with Legacy IT & Enterprise Architecture
• The Multiplier Effect — Measuring Success via Reusability and Speed-to-Output

AI can already generate requirements, user stories, and test cases. The next opportunity is more fundamental: an AI agent that helps steward the business intent. In this session, attendees will learn how to move from AI-assisted artifact creation to an agent that maintains alignment across business objectives, requirements, processes, rules, data, implementation, and tests. They will explore practical patterns for having AI challenge assumptions, detect gaps and inconsistencies, perform impact analysis, maintain traceability, and proactively identify changes that threaten business outcomes while keeping ownership of business decisions and outcomes firmly with the BA.

Learning Objectives: 

• Recognize the role of AI beyond artifact generation
• Apply AI patterns to maintain SDLC alignment
• Design a BA-centered model of AI stewardship

Curious what “vibe coding” actually means? It’s not programming. It’s telling AI what you want done and watching it do the work.

In this hands-on session, you’ll go beyond writing and analyzing with AI. You’ll direct it: point it at a messy folder and have it organize, rename, move, and clean up your files, then summarize what’s in them and find what you need. From there, you’ll puts that organized foundation to work on a creative or analytical challenge, using AI to build something that you can use.

Working in small teams on a shared scenario, you’ll explore:
•⁠  ⁠How to get AI to take action on your behalf, not just answer questions
•⁠  ⁠How to turn a pile of files into something searchable, summarized, and usable
•⁠  ⁠How to judge and steer AI when it’s doing the work for you
•⁠  ⁠What “vibe coding” makes possible for people who don’t write code

Please bring your own laptop if possible. Provided devices are limited. Come with questions, leave having made AI do real work for you.

Learning Objectives:

• Direct AI to organize, rename, clean up, summarize, and search a messy folder of real files.
• Judge and steer AI mid-task, spotting when it goes off course and correcting it before the work compounds.
• Turn that organized foundation into something usable, built with a team and without writing code.

I can generate a hundred requirements from a single stakeholder transcript in minutes with AI. Sure, that’s wonderful, but here’s the problem: my human brain has no capability to decide if anything’s missing in the pile I just generated. It’s physically impossible for a brain to catch gaps or conflicts in a list that long. Meanwhile, developers build ahead of me anyway: it’s so cheap and fast to build now that they’ll generate code whether or not I’ve told them what to build.

Drawing on lessons from building an AI-powered requirements platform (and using it to build itself), I’ll walk through why BA work isn’t disappearing, it’s intensifying. The job becomes a thought exercise: razor-sharp prioritization instead of documentation, use cases that tie features into something usable instead of one-off feature lists, and mapping requirements to visual models so you can slice and dice a hundred generated requirements into something your brain can process.

Learning Objectives: 

• Identify which parts of BA practice remain irreplaceably human as AI generates requirements faster.
• Apply a use-case-first approach to steer AI-assisted development toward real value, not speed.
• Use visual model-mapping techniques to catch gaps and conflicts AI-generated requirements create.

An end-to-end collaboration case study based on the experience of Information Systems.

What happens when cybersecurity enters the conversation too late?

In many organisations, Business Analysts define the problem and requirements, delivery teams build the solution, and cybersecurity reviews it towards the end. The result can be rework, delays, friction and lost value. Delegates won’t just hear the story but they will experience the difference.

The session will provide a practical framework for integrating security thinking across problem definition, requirements, design, development, QA, acceptance and operations without requiring BAs to become cybersecurity specialists.

Learning Objectives: 

• Recognise the value lost through late security engagement. 
• Use an end-to-end collaboration model in their own initiatives.
• Apply security-informed questions within everyday BA practice.

We often see a manual process taking too much time and immediately think: let’s automate it. But if the process has unclear business rules, inconsistent definitions, data issues, or too many exceptions, automation can simply make those problems harder to see and fix.

In this session, I’ll share five questions I’ve learned to ask before automating a business process. We’ll look at how to decide whether the process is worth automating, what needs to be standardized first, how to get agreement on business rules, how to validate the output, and who should own it once it is live. Attendees will leave with a simple framework they can use to evaluate their own processes before moving from manual work to automation.

Learning Objectives: 

• Identify whether a business process is truly ready for automation.
• Apply a practical framework to standardize, define, and validate processes before automation.
• Establish clear ownership and controls that keep automated processes reliable after launch.

Curious what “vibe coding” actually means? It’s not programming. It’s telling AI what you want done and watching it do the work.

In this hands-on session, you’ll go beyond writing and analyzing with AI. You’ll direct it: point it at a messy folder and have it organize, rename, move, and clean up your files, then summarize what’s in them and find what you need. From there, you’ll puts that organized foundation to work on a creative or analytical challenge, using AI to build something that you can use.

Working in small teams on a shared scenario, you’ll explore:
•⁠  ⁠How to get AI to take action on your behalf, not just answer questions
•⁠  ⁠How to turn a pile of files into something searchable, summarized, and usable
•⁠  ⁠How to judge and steer AI when it’s doing the work for you
•⁠  ⁠What “vibe coding” makes possible for people who don’t write code

Please bring your own laptop if possible. Provided devices are limited. Come with questions, leave having made AI do real work for you.

Learning Objectives:

• Direct AI to organize, rename, clean up, summarize, and search a messy folder of real files.
• Judge and steer AI mid-task, spotting when it goes off course and correcting it before the work compounds.
• Turn that organized foundation into something usable, built with a team and without writing code.

AI builders are often handed policy, legal guidance, risk preferences and stakeholder intent, but not one authorized operating answer. When Legal permits several options, Risk prefers one, Operations another, and Product a third, Engineering still needs a rule it can implement. This session shows how Decision Architecture closes that gap before code turns unresolved organizational judgment into system behavior. Attendees will learn to distinguish legal permissibility from business authorization, identify legitimate Decision Authority, and trace an approved operating decision into a builder-facing specification and conformance requirement.

Learning Objectives:

• Distinguish legal permissibility from business authorization in consequential AI workflows.
• Identify when builders are inheriting unresolved organizational judgment.
• Map an authorized operating decision into testable implementation requirements.

Business Analysts can feel pressure to know every answer, especially when presenting, facilitating solution scoping, or leading discovery sessions. That pressure can cause us to focus more on trying to impress others than being ourselves, become defensive when our thinking is challenged, or feel responsible for providing the solution.

This session explores how authentic communication and intentional listening can help BAs build trust. We’ll discuss how to present confidently without feeling like we need to have every answer, facilitate conversations by asking thoughtful questions while allowing stakeholders to own their decisions, and stay present during discovery sessions. We’ll also explore how giving stakeholders space to explain their challenges can help us understand their needs, ask meaningful questions, and follow up on anything that still needs clarification.

Learning Objectives: 

• Apply techniques for managing nervousness, being yourself when presenting, and responding to challenging questions without becoming defensive or feeling like you need to have all the answers.
• Recognize when the Business Analyst’s role is to facilitate rather than provide the solution, and use thoughtful questions to bring the right people together, guide the conversation, and help stakeholders make informed decisions.
• Practice intentional listening during discovery sessions by staying present, giving stakeholders space to explain their challenges, and asking clarifying questions without feeling pressured to understand every detail right away.

Let’s go back to basics and redefine what product innovation really means. AI has made it easier than ever to build, faster, cheaper, more of everything, which makes it easier than ever to fall into the feature factory trap and mistake activity for value. So how do we build products that scale, that customers actually love, and that deliver real value, with AI in the mix?

This session revisits product innovation through the lens of customer-centricity. We will explore the art of product innovation, review real case studies, unpack the common antipatterns that derail product teams, and examine the different voices shaping product development, including where AI genuinely helps and where it just adds noise. Through discussion and analysis, participants will leave with practical ways to make sure what they are building truly delivers value and put customer-centricity at the heart of innovation while leveraging AI

Learning Objectives: 

• Appreciate the different actors and factors that drive product development, and where AI fits among them
• Apply the art of customer-centricity in product development leveraging AI
• Understand how to spot and escape the “feature factory” trap, now accelerated by AI

Curious what “vibe coding” actually means? It’s not programming. It’s telling AI what you want done and watching it do the work.

In this hands-on session, you’ll go beyond writing and analyzing with AI. You’ll direct it: point it at a messy folder and have it organize, rename, move, and clean up your files, then summarize what’s in them and find what you need. From there, you’ll puts that organized foundation to work on a creative or analytical challenge, using AI to build something that you can use.

Working in small teams on a shared scenario, you’ll explore:
•⁠  ⁠How to get AI to take action on your behalf, not just answer questions
•⁠  ⁠How to turn a pile of files into something searchable, summarized, and usable
•⁠  ⁠How to judge and steer AI when it’s doing the work for you
•⁠  ⁠What “vibe coding” makes possible for people who don’t write code

Please bring your own laptop if possible. Provided devices are limited. Come with questions, leave having made AI do real work for you.

Learning Objectives: 

• Direct AI to organize, rename, clean up, summarize, and search a messy folder of real files.
• Judge and steer AI mid-task, spotting when it goes off course and correcting it before the work compounds.
• Turn that organized foundation into something usable, built with a team and without writing code.

AI can speed up business analysis, but it can also produce confident answers that are incomplete, misleading, or simply wrong. This session looks at five failure patterns that appear when AI is used for analysis, including false patterns, missing business context, over-interpreted personas, automation bias, and decision abdication.

Participants will learn a practical Human Validation Loop built around evidence, context, assumptions, alternatives, and decision ownership. The framework can be used to review AI-generated insights before they influence requirements, prioritization, customer analysis, or business decisions.

The focus is not on AI tools. It is on knowing when AI is useful, when it needs to be challenged, and where experienced business judgment still matters.

Learning Objectives: 

• Recognize five common failure patterns in AI-assisted business analysis.
• Apply a human validation loop to challenge AI-generated recommendations
• Decide which analysis tasks need human judgment before action.

Project success is only possible when stakeholders speak the same business language.  The Business Terms Model (BTM), also known as the “Conceptual Data Model” (which is not the preferred term and we’ll explain why), captures a Common Business Language of terms for a particular business initiative.

With more and more data being created and used, combined with intense competition, AI, strict regulations, and rapid-spread social media, the financial, liability, and credibility stakes have never been higher, and therefore the need for a Common Business Language has never been greater.

Follow along with data modeling expert Steve Hoberman and appreciate the value of a common set of business terms and how a data model can visually depict these terms with precision. Be able to position the conceptual data model more strategically within your organization, aligned with other essential business deliverables.

Join Steve for this fun and interactive session and learn:

• To appreciate the value of a Common Business Language and how a data model can visually depict terms with precision.
• To position the BTM within the context of the Align > Refine > Design framework.
• To read and build a BTM, and leverage AI to write better term definitions.

Some change fails because nobody saw the problem. More often, someone saw it and couldn’t get it heard.

BA’s hold something nobody else holds: whether the pieces add up. Architects, developers, project managers and sponsors can each do their job perfectly and the organisation still gets the wrong thing. But that contribution doesn’t announce itself, so it isn’t valued like the others — and BAs who push get called difficult. Many retreat into capturing decisions and circulating documents. That version of the role is being automated. It isn’t coming back.

Drawing on over 25 years across mining, pensions, licensing and government services — including a £2m+ programme stopped before it spent money on benefits nobody could evidence — Karen shares six influence moves for getting analysis acted on, and a frank framework for telling a skill problem from an environment problem.

Two structured peer exercises. Six techniques, starting with the recommendation table you can use on Monday.

Learning Objectives: 

• Apply six influence techniques — including a decision table with an explicit recommendation — to get analysis acted on at the point decisions are made.
• Articulate your distinct contribution to senior stakeholders in terms of change outcomes, and distinguish it clearly from project management.
• Assess whether an organisation’s conditions allow you to do the job properly, and choose a deliberate response rather than gradually reducing scope to fit.

Join Jared & Jen for a fully immersive workshop that throws you headfirst into the kind of tangled, high-stakes project scenarios that make experienced business analysts earn their keep. You’ll join the BA team supporting a fictional client who is wrestling with falling customer satisfaction, overwhelmed store teams, and a leadership mandate to “figure out what needs to change.”

Inspired by escape room challenges and choose-your-own-adventure storytelling, every decision your group makes shapes what happens next. Misread a stakeholder? The plot thickens. Nail your problem framing? A door swings open. You’ll collaborate under pressure, debate trade-offs in real time, and apply your analytical instincts to puzzles that feel refreshingly, uncomfortably real. There are no passive observers here.

This isn’t passive learning. It’s active, collaborative, and occasionally uncomfortable in the best way. This isn’t another slide deck. This is your mission — and the clock is already ticking.

Learning Objectives: 

• Recognize stakeholder tensions and competing definitions of value
• Apply problem framing, discovery, analysis, prioritization, and change thinking to a realistic business scenario
• Select appropriate BA techniques based on the question being explored

Organizations are racing to automate work with AI, but AI often exposes problems technology did not create: unclear processes, inconsistent knowledge, undocumented expertise, conflicting rules, weak handoffs, and fuzzy decision ownership.

This practical session introduces a Business Before AI framework for determining whether a business process is actually ready for AI. Participants will learn how to examine the knowledge, processes, rules, decisions, roles, and human judgment behind a workflow before automating it. Through real-world examples and an interactive assessment, attendees will identify where AI can create value, where business foundations must first be strengthened, and what should remain human.

Learning Objectives: 

• Diagnose business weaknesses that undermine successful AI implementation.
• Analyze processes, knowledge, rules, and decisions for AI readiness.
• Apply a framework to prioritize what to fix before automating.

What if business analysts could reduce three weeks of BA effort into one day – without surrendering judgement to AI? This presentation will explain how a human-led, multi-agent approach was able to accelerate business analysis, while keeping the BA in the driving seat. Emma and Tom will showcase a real-world example of a blended team of BAs and agents they used with UK government. They’ll explain how they interacted with agents to analyse problems, elicit requirements, build prototypes, and create high-quality artefacts; while keeping all critical decisions in the hands of BAs. They will share the lessons, limitations and findings from this work, alongside the governance principles and human checkpoints needed for responsible adoption. Attendees will leave with a practical understanding of how a blended team of BAs and agents can accelerate delivery, alongside transferable techniques for balancing speed, evidence, and human judgement in their own organisations.

Learning Objectives: 

• Appreciate how to mobilise blended teams of BAs and agents to accelerate delivery
• Understand how to steer AI agents, while ensuring BAs make all critical decisions
• Apply ways to balance speed, evidence and human judgement

Join Jared & Jen for a fully immersive workshop that throws you headfirst into the kind of tangled, high-stakes project scenarios that make experienced business analysts earn their keep. You’ll join the BA team supporting a fictional client who is wrestling with falling customer satisfaction, overwhelmed store teams, and a leadership mandate to “figure out what needs to change.”
Inspired by escape room challenges and choose-your-own-adventure storytelling, every decision your group makes shapes what happens next. Misread a stakeholder? The plot thickens. Nail your problem framing? A door swings open. You’ll collaborate under pressure, debate trade-offs in real time, and apply your analytical instincts to puzzles that feel refreshingly, uncomfortably real. There are no passive observers here.
This isn’t passive learning. It’s active, collaborative, and occasionally uncomfortable in the best way. This isn’t another slide deck. This is your mission — and the clock is already ticking.

Learning Objectives: 

• Recognize stakeholder tensions and competing definitions of value
• Apply problem framing, discovery, analysis, prioritization, and change thinking to a realistic business scenario
• Select appropriate BA techniques based on the question being explored

Let’s fix this situation where AI writes the spec and more, but no one really reads it, understands the change, or analyzes it! New features, change requests, bugs, defects: whatever lands in your backlog, AI now helps produce it at speed. Specs get generated, code and tests built by Ai agents, and nobody slows down long enough to read them, picture the change, or ask the hard questions. In this session, we will learn practical techniques to bring the right conversations back into fast-moving, AI-powered work. We’ll explore techniques that turn stakeholder expectations into testable AI behavior. You’ll also see how to use AI as a live sparring partner in the room, so it speeds up the thinking while your stakeholders do the deciding. You’ll leave with a toolkit you can use and a renewed sense of the most valuable thing we bring to fast-moving work: getting the right people to understand and commit to what’s being built.

Learning Objectives: 

• Recognize how AI-accelerated delivery creates an illusion of alignment, and spot the warning signs in their own backlog.
• Facilitate reviews that surface misunderstandings in minutes, and are not reviewing documents!
• Use AI as a live facilitation partner to generate shared understanding and commitment

Everyone talks about the value of business architecture. Far fewer talk about how to actually get it started. Whether you’re a team of one, building support from scratch, or trying to move business architecture from concept to reality, this session will share practical lessons from a real-world implementation journey.

Learn the strategies, quick wins, and common pitfalls that helped transform an emerging practice into a trusted partner for strategy execution. You’ll hear what worked, what didn’t, how to gain traction with stakeholders, where to focus first, and how to deliver measurable value while building foundational architecture assets. Through honest stories, proven techniques, and actionable takeaways, you’ll leave with a roadmap you can apply immediately in your own organization.

If you’ve ever wondered, “Where do I start?” this session is for you.

Learning Objectives:

• Assess organizational readiness and determine the best starting point for a new business architecture practice.
• Prioritize early business architecture deliverables that create visibility, credibility, and stakeholder engagement.
• Create an actionable roadmap to grow business architecture capabilities while delivering business value from day one.

The new core system and the mobile app that integrated with it passed testing. The integration worked in production. It still failed on launch night and caused a 6-hour operational shutdown.

The cause was the Reality Gap: the distance between what everyone believed to be true and what was actually true. Everyone believed the testing accounted for operational reality. It did not.

In this session, attendees will learn how deadline pressure creates the Reality Gap, where to add Reality Check stops to their delivery cadence, and how testing artifacts against operational reality finds the gaps before launch does.

Learning Objectives: 

• Recognize how prioritizing the appearance of progress over honest appraisal creates the Reality Gap.
• Identify the appropriate points in the delivery cycle to plan Reality Check stops.
• Test artifacts against operational reality with stakeholders at each stop.

How cool would it be if everyone came with an instruction manual? You’d just have to press the right buttons (or say the right words) and there would be no misunderstandings or conflicts.

However, everyone is different so we must figure out how to work with each other.

Fabricio & Scott will provide a model for different ways that people think, and you’ll use that model to characterize your thinking style. Then you’ll compare your style with others and we’ll aggregate the results for the group. Our goal is to leverage similarities and differences so we can collaborate more effectively.

Next we’ll flip script from working with others to how others work with you. We’ll explore ways to think about yourself from the outside so you can guide people toward easier communication. We’ll even help you write a “user guide” for yourself, so your colleagues—and your friends & family—know what makes you tick.

Plan to think, talk with others, laugh a lot, and walk away with new insights.

Learning Objectives:

• Learn about thinking styles and how to work with people who think differently
• Create a personal user guide  to help others deal with you more effectively
• Enhance collaboration by leveraging knowledge about ourselves and our brains

As AI agents shift from helping to acting, three questions become unavoidable: What is this agent authorized to do? Under what conditions may it act? Who is accountable when it fails?

While these questions are new to many teams, regulators and auditors have asked them of critical operations and risk models for years. This talk turns regulators’ questions into a checklist any team should use on their own AI. It also highlights why the business analyst, who maps processes and separates decision logic, is uniquely positioned to ensure the agent is governed before it acts, not after it fails.

Learning Objectives:

• Three governance questions to ask of any AI agent
• How regulators worldwide already govern autonomous systems
• Business analysis skills that make AI governable

Most teams either skip change management entirely or apply the full playbook regardless of project size- most things need something else. This session teaches the fundamentals of change management, then shows how to right-size the plan to the actual scope and risk of your initiative. With AI implementations specifically, communication planning becomes the most critical piece: people need to understand what’s changing, why, and what it means for their work well before rollout. Walk away knowing what change management actually is, how to plan just enough of it, and how to build a communication plan that earns AI adoption instead of resistance.

Learning Objectives:

• Understand the fundamentals of change management
• Right-size a change management plan to the actual scope, risk, and behavior change of a project
• Understand the importance of communication for AI change

The best decision today may not be the best decision tomorrow. Priorities shift, assumptions prove wrong, new evidence emerges, and expected value changes. Yet organizations often continue following decisions long after circumstances suggest they should reconsider.

In this interactive session, participants will explore how to recognize when a decision needs to be challenged, distinguish evidence from assumptions, and overcome the tendency to stay committed to an original plan. Through an evolving business scenario, participants will consider when to continue, adapt, expand, pause, or stop an initiative. Leave with an Adaptive Decision Canvas and practical questions to help make better decisions as conditions change.

Learning Objectives: 

• Recognize when changing evidence or assumptions warrant reconsidering business decisions
• Evaluate new information to determine whether expected outcomes and value remain achievable
• Apply an adaptive decision framework to determine when to continue, adapt, expand, pause, or stop

How cool would it be if everyone came with an instruction manual? You’d just have to press the right buttons (or say the right words) and there would be no misunderstandings or conflicts.

However, everyone is different so we must figure out how to work with each other.
Fabricio & Scott will provide a model for different ways that people think, and you’ll use that model to characterize your thinking style. Then you’ll compare your style with others and we’ll aggregate the results for the group. Our goal is to leverage similarities and differences so we can collaborate more effectively.
Next we’ll flip script from working with others to how others work with you. We’ll explore ways to think about yourself from the outside so you can guide people toward easier communication. We’ll even help you write a “user guide” for yourself, so your colleagues—and your friends & family—know what makes you tick.
Plan to think, talk with others, laugh a lot, and walk away with new insights.

Learning Objectives:

• Learn about thinking styles and how to work with people who think differently
• Create a personal user guide  to help others deal with you more effectively
• Enhance collaboration by leveraging knowledge about ourselves and our brains

Ideas are easy to generate; earning approval and delivering value are harder. This practical session gives business analysts and transformation leaders a clear framework for turning a promising opportunity into a credible business case. Attendees will learn to define the business problem, assess organizational readiness, compare costs and benefits, address risk and governance, and establish measures of success before implementation begins. Through realistic examples, Jamie will show how to separate genuine value from hype and communicate a recommendation decision-makers can trust. Participants leave with a repeatable approach they can use to evaluate, strengthen and present their next initiative.

Learning Objectives: 

• Apply a practical framework to evaluate opportunities and organizational readiness.
• Analyze expected value, costs, risks, and measurable success criteria.
• Build a persuasive business case aligned with strategic priorities.

AI can generate in seconds what once took hours, but someone still has to read it, interpret it, evaluate it, question it, and decide what to do with it. At the same time, the sheer volume of AI-generated information can leave us overwhelmed. This session explores two sides of AI-enabled cognitive strain: too much information to process and a growing temptation to avoid the effort of processing it at all.

Participants examine how AI may be changing not only how much information we receive, but how willing we are to engage in the deeper thinking required to turn information into understanding.

Learning Objectives:

• Recognize how AI can contribute to cognitive overload
• Examine when AI is reducing unnecessary work versus replacing productive thinking.
• Apply practical strategies for managing AI-generated information while remaining actively engaged in thinking, reviewing, and decision-making.

The research is done. The options are on the table. And the meeting ends with ‘let’s take another look at this next week.’ Sound familiar? This session gives you a practical, repeatable path from ambiguity to action.

Drawing on IIBA’s BABOK Guide and hard-won experience leading investment operations, we will walk the full path from a fuzzy request to a committed decision. You’ll practice framing a problem statement that defines scope and end conditions, facilitate structured idea generation using “How might we” techniques, and evaluate options on business value, cost, risk, and user experience. You’ll leave with tools you can use on Monday: a solution matrix, a cost-benefit framework, and a Point of View template for presenting a recommendation executives can act on.

We’ll close on the hardest part — why commitment matters more than unanimity — and how to timebox, prototype, and move forward with conviction without perfect information.

Learning Objectives:

• Decide Better: Frame the problem so precisely that stakeholders buy into your recommendation before you make it
• Move Faster: Set guiding principles and evaluation criteria up front to cut deliberation time and break deadlock
• Deliver Greater Value: Translate options into cost-benefit terms that connect directly to business outcomes

An AI-generated prototype has impressed the executives, and they want it released. It looks complete—but is it? In this immersive simulation, teams represent product, business analysis, development, testing, operations, and risk. As new evidence emerges, participants uncover hidden assumptions, missing requirements, conflicting stakeholder expectations, and gaps in acceptance criteria. Each team must decide what the prototype proves, what still needs to be understood, and whether the solution is ready to advance. The experience culminates in a readiness hearing where teams defend a release, revise, or stop recommendation. Participants leave with practical techniques for using prototypes and AI to accelerate discovery without allowing a polished interface to become a substitute for analysis, shared understanding, or testable requirements.

Learning Objectives:

• Analyze AI-generated prototypes to uncover hidden assumptions and missing requirements.
• Develop acceptance criteria that make proposed behavior clear and testable.
• Determine when a prototype is ready to advance and when it is not.

AI pilots are easy; production is not. This session gives business analysts, architects, and transformation leaders a practical playbook for taking AI agents from demos to trusted enterprise systems. Drawing on an enterprise-scale deployment with two agent personas, one customer-facing and one for infrastructure operations, we walk through the four capability gaps that decide whether AI output can be trusted: capture (complete, well-structured context into the model), validation (checking AI output before it acts, including automated review by a second model), guardrails (tool permissions and blast-radius limits), and observability (measuring quality at scale so drift is caught before customers are). We also cover human-in-the-loop approvals for high-risk actions, evaluation at volume instead of demo-day anecdotes, and lessons of redesigning work around AI that is right most, but not all, of the time. Attendees leave with a capability checklist they can apply the week they return.

Learning Objectives:

• Describe the four capabilities (capture, validation, guardrails, observability) that separate production-grade AI from demos
• Apply tool-permission and human-in-the-loop approval patterns to limit AI agent risk in core business processes
• Use volume-based evaluation to measure AI output quality and catch drift before it reaches customers

Discover how Colorado connected agency databases into a unified data ecosystem to power smarter workforce solutions. Learn how the state linked workforce, higher education, and public assistance data to drive better policy decisions while keeping personal information strictly protected. Attendees will gain clear insights into combining multi-agency data securely while protecting privacy and federal compliance standards. The session highlights simple approaches for replacing legacy files with visual reporting tools, overcoming federal data gaps using state wage records, establishing shared agency governance.

Learning Objectives:

• Explore effective ways to combine multi-agency data securely using modern cloud tools.
• Apply privacy frameworks to ensure FERPA compliance and PII masking across cross-agency dataset linkages.
• Overcome inter-agency governance barriers to transition research data lakes into sustainable production environments.

An AI-generated prototype has impressed the executives, and they want it released. It looks complete—but is it? In this immersive simulation, teams represent product, business analysis, development, testing, operations, and risk. As new evidence emerges, participants uncover hidden assumptions, missing requirements, conflicting stakeholder expectations, and gaps in acceptance criteria. Each team must decide what the prototype proves, what still needs to be understood, and whether the solution is ready to advance. The experience culminates in a readiness hearing where teams defend a release, revise, or stop recommendation. Participants leave with practical techniques for using prototypes and AI to accelerate discovery without allowing a polished interface to become a substitute for analysis, shared understanding, or testable requirements.

Learning Objectives:

• Analyze AI-generated prototypes to uncover hidden assumptions and missing requirements.
• Develop acceptance criteria that make proposed behavior clear and testable.
• Determine when a prototype is ready to advance and when it is not.

Most organizations measure activity because activity is easy to count: calls made, tickets closed, utilization, cycle time. Those metrics were built for an industrial operating model where output scaled with effort, and they encode an assumption that no longer holds.

AI breaks it. When a system produces activity at near-zero marginal cost, an operating model that rewards activity will produce more of it, faster, without producing more value. Most AI programs are not failing. They are succeeding at the wrong thing.

This session covers the design work that comes first. It introduces Key Value Indicators as a replacement for activity-based KPIs, and a five-layer sequence for AI-native operations: data, shared definitions, governance, deployment, measurement. Attendees will identify where their own measurement system rewards motion over value, and leave with a method for redesigning it that preserves accountability rather than removing it.

Learning Objectives: 

• Diagnose where activity metrics reward motion instead of customer value
• Design Key Value Indicators that replace activity-based KPIs without losing accountability
• Sequence data, definitions and governance before deploying AI into operations

In a rapidly changing landscape driven by AI and market volatility (VUCA), business analysts must actively direct their career trajectories rather than react to disruption. This session introduces a practical blueprint that integrates emerging technologies with human-centered leadership. Using the Career Health Belief Model, BAs will assess personal risk, identify professional barriers, and cultivate self-efficacy to build career resilience. Attendees will then learn how to adapt the ITIL 4 Continual Improvement Model to establish a structured, repeatable framework for ongoing growth. By merging AI capabilities with critical soft skills—emotional intelligence, strategic vision, and non-authoritative leadership—participants will transform from transactional requirements gatherers into indispensable strategic partners. Attendees leave with an actionable Personal Power Plan to thrive alongside AI and lead their careers with confidence.

Learning Objectives: 

• Assess career susceptibility to market volatility and AI disruption using the Career Health Belief Model.
• Adapt the ITIL 4 Continual Improvement Model to structure an actionable professional growth plan.
• Combine AI tools with emotional intelligence to elevate strategic business analysis leadership skills.

Most journey maps get drawn once, then thrown in a drawer. I’ll show you how to build one that earns its keep, and how to diagnose it well enough that you can actually act on it.

I’ll share the workflow I use to create journey maps for my clients and use them to identify growth opportunities.

First, gather the evidence via interviews, questionnaires, and document review. Don’t rely on AI to generate this input. Get it from the source.

Next, hand it all to AI and let it draft the current-state journey. The draft won’t be right, so I’ll show you how to apply judgment to refine it until you’re proud to show it to stakeholders.

Finally, diagnose the journey map to identify the gaps and inconsistencies that point to improvements in your customer experience.

For the companies I work with, that diagnosis points to the actions that move their business to its next stage.

Learning Objectives:

• Prepare the interviews, questionnaires, and documents that ground a journey map in evidence.
• Refine an AI-drafted journey map by testing it against your evidence.
• Distinguish genuine gaps from normal variation to focus your customer-experience fixes.

Organizations are built to manage change as something with a beginning, an end and a reasonably definable destination. But that assumption is breaking down. AI, geopolitics, technological acceleration and shifting institutional conditions increasingly interact in ways that make the operating environment less predictable. The challenge is no longer simply how to manage change faster. It is how to build the strategic capacity to act when leaders cannot know in advance exactly what the change will be.This session explores how organizations can move from episodic change management towards continuous strategic capacity: sensing what is changing, distinguishing more certain developments from genuine uncertainty, understanding systemic consequences, developing plausible futures and creating options before committing to a single course of action. Examples will be provided from both public and private sector.

Learning Objectives:

• Distinguish between managing change and building organizational capacity to act under uncertainty.
• Identify where traditional planning and transformation approaches break down when multiple uncertainties interact.
• Apply a practical sequence, sensing, systemic understanding, plausible futures, consequences and optionality,  to improve strategic action before certainty is available.

AI won’t just change your job description. It will change what your organization values in you.

For professionals working at the intersection of business, technology, decisions, and change, that creates both risk and opportunity. As AI takes on more of the work that once differentiated people, some professionals will see their influence and value grow. Others may find that what once made them relevant and in demand no longer does. So what will separate the two? Technical fluency will matter, but it won’t be enough. The differentiator will increasingly be how well you apply judgment, shape decisions, and stay trusted as AI changes the work around you. In this closing keynote, Dora challenges attendees to rethink what it means to future-proof a career in the age of AI. Rather than chasing every new tool or reacting to each wave of change, the real work is knowing where to evolve, where to deepen your impact inside the organization, and how to position yourself for the opportunities ahead.

Learning Objectives: 

• A clearer view of how AI is reshaping career value and expectations inside organizations
• A practical lens for where to focus their growth as roles and work evolve
• Clarity on where human judgment matters most, and how to turn that into impact and influence

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