Our Perspective on AI   AI is not a project. It is a capability. Every industry, every workflow, every function is being reshaped. The organisations that capture this opportunity will not be the ones that automate the fastest. They will be the ones that treat AI as a true organisational capability. At Plainsight, we approach AI as a shared company capability: not a side experiment or isolated initiative. It must sit close to the business while being supported by technology, with clear but simple governance rules and structure. That means thinking beyond tools and pilots: it means adapting processes, building the right skills and putting people in a position to use AI with confidence and accountability. Why AI Matters Now AI has reached a new level of usability. It can now work with language in ways that are cheap and accessible for everyday use across the organisation: •       Reading and understanding documents •       Generating text and content •       Supporting knowledge work across many business processes •       Augmenting decision-making with data-driven insights Yet many organisations struggle to move beyond pilots. According to Gartner, by 2027 30% of all GenAI projects and 40% of all agentic AI projects will be abandoned after proof of concept. Not because the technology fails, but because AI is introduced without adapting processes, data and governance. The Real Challenge: Moving Beyond Pilots The gap between AI potential and AI impact is rarely about the technology. It is about everything around it: unclear ownership, fragmented data, missing governance, and teams that have not been equipped to work with AI effectively. When AI is layered on top of unchanged processes, it creates confusion rather than value. At the same time, AI introduces a new responsibility. Every AI output still needs human evaluation. Every recommendation needs context. Every automation needs someone who understands what it is doing and whether it should be trusted. Organisations that build this accountability into their operating model will scale AI successfully. Those that skip it will not.   That is why we treat AI as a capability; one that requires process, governance, architecture, data and people to work together. If any of those elements is missing, the strategy has a blind spot. Our Approach   Understand Your Baseline. Build What Comes Next. We believe a successful AI transformation requires a comprehensive, structured approach across six interconnected workstreams. Together, these workstreams span the full arc from strategy through to operating model, build and run, and change and adoption—ensuring nothing is left to chance. The Six Workstreams

Workstream Target Outcome 1 AI Readiness Scan A fact-based view on AI maturity, capability gaps, and organisational readiness, with priority areas clearly defined and agreed 2 From AI Strategy to Value Realisation A focused AI strategy translated into a prioritised portfolio of high-impact use cases, with selected initiatives already mobilised into build and delivery 3 AI Governance & Operating Framework Clear ownership, decision rights, and governance structures established, enabling AI to be managed as a structural capability rather than ad-hoc initiatives 4 Scalable AI Architecture A defined architectural blueprint with a rationalised AI tooling landscape, efficient cost-performance setup, and reusable data foundations 5 Data Readiness Named data owners and stewards accountable for quality at the source, practical guardrails for secure and responsible AI usage 6 AI Change & Enablement Structured change management in place to drive adoption, with clear communication, leadership alignment, and engaged AI champions

Workstream 1: AI Readiness Scan   Before building anything, understand where you stand. Through a combination of qualitative and quantitative assessment, we establish a robust view of your current AI readiness. What We Assess •       Strategic Direction: Clarity of AI vision and alignment with business strategy •       Data: Quality, accessibility, and governance of data assets •       Tools & Technology: Current landscape and integration capabilities •       Governance & Compliance: Existing frameworks and regulatory readiness •       Organisation & Processes: Operating model maturity for AI •       People, Skills & Culture: AI literacy and change readiness Deliverables •       AI readiness baseline report with maturity levels across all dimensions •       Insights on AI literacy, adoption patterns, and capability gaps •       Identified AI champions and priority focus areas •       Current-state (conceptual) data model and data readiness assessment •       Overview of current AI and technology landscape with recommendations

Workstream 2: From AI Strategy to Value Realisation   Strategy without execution is just theory. We translate strategic direction into concrete, prioritised use cases that deliver measurable value. Aligning AI Strategy with Business Strategy Through collaborative workshops, we shape your approach to AI and define the guiding principles that bring focus and direction. Because if the gears do not align, AI will not drive the strategy, it will become a cost instead. •       Strategic Priorities: Where does AI fit within your value streams? •       Where to Play: Strategic positioning across geography, markets, customer segments, products, and channels •       How to Win with AI: Where do we see the biggest potential across core value streams? •       Winning Capabilities: AI capabilities your organisation must build to scale impact •       Management Systems: Structures and routines that ensure strategy is consistently executed, owned, governed, and improved Our Value Realisation Cycle With strategic direction set, we identify, prioritise, and deliver AI use cases that create the most value. This cycle operates within a governance framework that ensures speed without sacrificing oversight. Phase Description 01. Value Discovery Identify high-impact opportunities where AI can improve business processes, reduce friction, and unlock value 02. Use Case Prioritisation Decide which ideas are worth pursuing first, balancing expected value, business impact, ownership, and technical feasibility 03. Proof of Value Build a small, controlled solution to validate impact before scaling 04. Industrialise Integrate proven use cases into systems, processes, and teams for sustainable operation at scale

Workstream 3: AI Governance & Operating Framework   The AI Lab Concept We believe in setting up an AI Lab that brings business and technical expertise together. A cross-functional hub that helps the business test AI ideas, prove their value and hand over successful ones to technical teams for organisation-wide embedding. The AI Lab serves a twofold goal: 1.     Prioritise, pilot, and decide whether to scale AI use cases 2.     Enable teams to experiment with AI safely The lab provides clear guidelines, technical expertise, and tooling to create a safe environment where use case owners can get started. The Operating Framework The framework structurally enables AI experimentation, scaling and governance—without slowing down delivery. It operates across three levels: Level Focus AI Steering Circle AI ambition and value thesis, portfolio and funding decisions, guidelines and guardrails AI Execution & Scaling Cross-functional delivery, knowledge-sharing mechanisms, experimentation frameworks AI Enablement Foundations Data and models, AI tooling, lifecycle operations, knowledge assets, organisation processes, people, skills, and culture Roles and Responsibilities: Clear Accountability Across the Organisation Successful AI adoption requires more than technology. Without clear ownership, initiatives stall in ambiguity or — worse — nobody feels accountable. That's why we work with organisations to define clear accountability using a comprehensive AI capabilities framework, supported by a RACI matrix that maps every capability to the right role. Key Roles The AI Product Owner bridges business, technology and change. This role owns the prioritised use-case backlog, drives value realisation and keeps stakeholders aligned. They are measured on use-cases in production and business value delivered. The Data Owner is accountable for data quality, definitions, lineage and compliance. No reliable data, no reliable AI — this role guards the foundation. The AI Champion is the face of AI on the work floor. They actively encourage adoption, support colleagues in applying AI day-to-day and feed practical insights back into the organisation. The Process Owner ensures AI doesn't sit alongside existing ways of working but is embedded within them. They redesign processes from AS-IS to TO-BE and update standard operating procedures accordingly. The RACI as a Compass For every AI capability we map out who is Accountable (owns the outcome), Responsible (does the work), Consulted (provides input) and Informed (kept in the loop) — spanning from the Executive Committee and AI Lab Lead through to IT and the business units. This eliminates grey areas and makes it immediately clear who is on point for every decision, incident or milestone. As AI maturity grows, responsibilities naturally shift from central teams towards the business — exactly where they belong. Workstream 4: Scalable AI Architecture   Goal: Scalable, Secure, and Reusable AI solutions should not be one-offs. We ensure solutions are built to scale by starting from selected use cases and translating them into architecture decisions, creating a reusable AI delivery pattern, and mapping the current AI tooling landscape for transparency. Architecture Considerations Component Focus AI Tooling Landscape Purpose-built solutions, integrated tools, and GPTs working together Architecture Blueprint Standard patterns for scalable deployment Integration Framework How AI connects with core systems (e.g. Workday, Microsoft 365) Security & Access Role-based access principles aligned with data protection requirements Reusable Delivery Pattern Standard architecture and documented reusable components

Workstream 5: Data Readiness   If you want AI you can trust, you need data you can trust. Data Readiness means that data is reliable enough to support decision-making, ownership is clearly defined, definitions are consistent across the organisation, sensitive data is properly protected, and AI solutions do not repeatedly fail due to underlying data issues. What We Structurally Install Data Ownership & Stewardship We assign business Data Owners and Data Stewards to own definitions, resolve issues, and keep data fit for AI use. Data Quality Framework We define “fit-for-AI” quality standards and checks—accuracy, completeness, consistency, timeliness—with embedded monitoring and remediation at the source. Data Protection Framework We classify sensitive data, enforce role-based access and protection controls, and set guardrails for secure and responsible AI usage across the data lifecycle.

Workstream 6: AI Change & Enablement   Because constant change is today’s reality, this cycle builds the habits needed to sustain AI adoption over time. We pay attention to every step and take the human side of change seriously, guiding people through the transition with openness, trust, and active involvement throughout. The Change Cycle Phase Focus ALIGN Ensure leadership sponsorship, clear objectives, and consistent messaging ENGAGE Involve key stakeholders early, activate AI champions, and create feedback loops ENABLE Build role-based AI literacy and provide practical guidance for responsible use EMBED Integrate AI into workflows, governance, and performance expectations MEASURE Track adoption and impact, adjust where needed, and reinforce momentum

Building AI as a Capability   Successful AI adoption is not about slowing down. It is about being deliberate: matching the pace of deployment with the right investments in process, governance and people. Four Structural Shifts Shift How We Address It Translate AI into capability implications AI Readiness Scan maps maturity across multiple dimensions and makes organisational implications visible from day one Redesign around business value AI Strategy to Value aligns use cases with business outcomes and embeds process redesign into strategy Institutionalise governance The AI Lab and Operating Framework provide guardrails, experimentation frameworks, and speed without sacrificing oversight Measure what actually matters AI Change and Enablement tracks real adoption, business impact, and whether teams are using AI effectively and responsibly The Two Foundations Trust Trust in AI has to be earned, not assumed. When systems are transparent and data is reliable, people are far more likely to adopt AI with confidence—and to challenge outputs when something does not look right. •       Scalable AI Architecture with transparency and explainability built in •       Data Readiness so people trust the inputs enough to question the outputs People Technology without the right enablement creates friction, not value. AI only works when people know how to use it, when to trust it, and when to push back. •       AI literacy and champion communities •       Change cycle: align, engage, enable, embed, measure •       Measure capability growth, not just adoption •       Equip teams to use AI with confidence and accountability   Is your organisation ready to treat AI as a capability, not just a tool?

Deliverables Overview   Phase 1: Discovery & Assessment •       AI Readiness baseline report •       Current-state data model and data readiness assessment •       AI tools and technology landscape assessment •       Identified AI champions and priority areas Phase 2: Strategy & Design •       Clear AI Ambition and strategic focus areas (guiding principles) •       Prioritised AI use case portfolio •       Detailed use case designs for priority initiatives •       AI governance model with defined roles and decision structures •       AI experimentation framework with practical guardrails •       AI use case lifecycle framework Phase 3: Architecture & Data •       Defined AI tooling landscape with rationalisation recommendations •       Scalable AI architecture blueprint •       Integration framework and principles •       Data ownership model with assigned owners and stewards •       Data quality standards and monitoring approach •       Data classification and protection guidelines Phase 4: Enablement & Delivery •       AI change management framework •       Stakeholder engagement plan •       AI awareness (literacy) training materials •       AI usage guidelines •       AI champion community setup •       First working AI use case(s) •       AI delivery and rollout roadmap

How We Work   Our Collaboration Model We believe in co-creation at the core. We build side-by-side with your teams, blending technical experience with guidance on strategy, change, and communication so they can confidently own and grow the solution. Weekly Rhythm •       Weekly updates between project leads •       Regular working sessions with AI champions •       Technical alignment meetings as needed Monthly Governance •       Steering Committee reviews progress •       Strategic decisions and direction validation •       Risk mitigation and roadblock resolution Our Differentiators Principle What It Means Real Impact We turn ideas into working solutions fast, so teams feel the value early. Supported by clear strategic framing, not just technical build. Co-Creation We build with your teams, blending technical experience with guidance on strategy, change, and communication. Transparency You always know what we are doing, why we are doing it, and what to expect next. End-to-End Ownership We stay involved from the spark of an idea to full adoption. If something needs turning or a new direction, we step in. People Who Care You work with experts who think like teammates. Hands-on and committed to long-term success.