--- title: "AI Discovery Sprint | Margins" description: "Find the right AI opportunity and define how to make it real — a focused engagement that ends in an implementation-ready business case." url: https://margins.agency/services/ai-discovery-sprint --- AI Discovery Sprint # Find the right AI opportunity. Define how to make it real. A focused engagement designed to identify where AI can create meaningful value in your business, validate the strongest opportunities, and turn the best one into an implementation-ready business case. Work directly with Margins’ AI strategists and engineers to move from “Where could we use AI?” to “This is what we should build first.” - Book a discovery call - See what you get ↓ The sprint 1. 01 **Understand** → Areas of focus 2. 02 **Discover** → AI opportunity map 3. 03 **Prioritize** → Prioritized opportunities 4. 04 **Define** → Implementation-ready opportunity **Focused engagement** Defined scope **Leadership + Operations** We work with the people who know the business **Business + Technical** Value and feasibility assessed together **Implementation-ready** Designed to move directly into engineering The right first move ## Most companies don’t have an AI idea problem. They have a prioritization problem. Once a company starts seriously discussing AI, potential use cases appear everywhere. - Automate this process. - Build an internal assistant. - Predict this outcome. - Add an agent here. - Connect company knowledge there. The challenge is determining 1. 01 Which opportunity creates enough value to matter? 2. 02 Which can actually be implemented with the data and systems we have? 3. 03 Which will people use? 4. 04 Which can prove value quickly enough to justify further investment? 5. 05 Which creates foundations we can build on afterwards? The AI Discovery Sprint is designed to answer those questions before significant engineering investment begins. How it works ## We go where the work actually happens. We don’t begin with a list of AI technologies. We begin by understanding how your business operates today. Margins works with leadership to understand strategic priorities, then with the people closest to the relevant processes to understand where time, information, knowledge and value are being lost. From there, we identify where AI could materially change the outcome. 1. Your business 2. Leadership priorities 3. Processes · people · systems · data 4. AI opportunities 5. Value × Feasibility 6. Prioritized opportunity 7. Solution + business case ## Four connected phases. One concrete outcome. Phases 1. 01 Understand 2. 02 Discover 3. 03 Prioritize 4. 04 Define Book a discovery call Phase 01 ### Understand Start with what the business is trying to achieve. We meet with leadership to understand the company’s priorities, operational challenges, existing AI activity and areas where the organization believes there may be opportunity. We look at - Business objectives - Operational pain points - Strategic priorities - Existing AI initiatives - Processes worth investigating Output **Areas of focus for deeper discovery.** Phase 02 ### Discover Understand how the work actually happens. We work with process owners and subject-matter experts to understand the current operation. Not how the process is supposed to work. How it actually works. We examine - Processes - Decisions - People - Systems - Data - Documents - Knowledge - Exceptions - Manual work And ask questions such as - Where are people spending unnecessary time? - Where is information fragmented? - What problems are discovered too late? - Where does important knowledge depend on specific people? - Where are decisions made without enough context? - What repetitive work doesn’t require human judgment? Output **AI opportunity map.** Phase 03 ### Prioritize Separate interesting ideas from opportunities worth pursuing. Each opportunity is evaluated against the factors that determine whether it deserves investment. Build first High value · high feasibility Strategic bets Quick wins Deprioritize Feasibility →Business value → - **Business impact** What materially changes if this works? - **Technical feasibility** Can today’s AI reliably solve it? - **Data readiness** Do we have the information required? - **Integration complexity** What systems need to be involved? - **Adoption** Can it realistically become part of the workflow? - **Time to value** How quickly can we validate the economics? - **Strategic leverage** Does it create foundations for future AI initiatives? Output **Prioritized AI opportunities with a recommended first move.** Phase 04 ### Define Turn the strongest opportunity into something you can actually build. Once we’ve identified where to start, Margins defines the proposed solution at enough depth to make an informed implementation decision. We define 1. The business problem 2. The future-state workflow 3. The AI capability required 4. Data requirements 5. System integrations 6. Technical approach 7. User interaction 8. Implementation scope 9. Success metrics 10. Risks and assumptions 11. Path to production Output **An implementation-ready AI opportunity.** Deliverables ## Not another AI strategy deck. At the end of the Sprint, you should know what to build, why it’s worth building, what it will take, and how you’ll know whether it worked. 01 ### AI Opportunity Map A structured view of the AI opportunities identified across the processes investigated. 02 ### Prioritization Matrix Opportunities evaluated against business value, feasibility, data readiness and implementation complexity. 03 ### Recommended First Use Case A clear recommendation for where Margins believes the business should start. 04 ### Business Case The expected business outcome and the baseline against which success can be measured. 05 ### Solution Concept How the proposed AI-enabled process should work from the user’s perspective. 06 ### Technical Direction The models, data, integrations and infrastructure likely required. 07 ### Implementation Roadmap The practical path from current state to production. 08 ### Executive Readout A clear management-level presentation of the findings, recommendation and next decision. Enough strategy to make the right decision. Enough engineering to know the decision can be implemented. Before / after ## What changes during the Sprint? You’re buying clarity. Start “We know we need to do something with AI.” After leadership discovery “These are the areas where AI could materially affect our business.” After process discovery “These are the specific opportunities inside those operations.” After prioritization “These are the opportunities worth pursuing.” After solutioning “This is what we should build first.” After business case “This is what success should look like.” After technical definition “This is how we get it into production.” What we look for ## Five places we typically look for value. We don’t force every opportunity into these categories. They simply give us useful lenses through which to examine the business. **Automate** What work doesn’t need to be done manually? Document processing, administration, cross-system workflows, classification and repetitive tasks. **Predict** What would become valuable if you knew it earlier? Customer behaviour, demand, risk, anomalies, equipment issues and operational changes. **Decide** Where could better information improve a decision? Prioritization, planning, scheduling, recommendations, resource allocation and risk assessment. **Know** What does your company know but struggle to access? Documents, policies, procedures, customer history and expertise living inside people’s heads. **Assist** Where could AI make your people materially more effective? Sales, field operations, finance, service, management and other knowledge-intensive roles. What a discovery can uncover ## The business question matters more than the technology. [All client outcomes →](https://margins.agency/client-outcomes) Marikomerc The business question Could we know a customer is being lost before the salesperson realizes it? The opportunity Use changes in purchasing behaviour to identify revenue deterioration early enough for commercial teams to intervene. What it became A predictive system using four years of invoice history, multiple detectors and seasonal analysis. **43 days** median early warning **94.7%** validated revenue losses detected **€3.7M** annualized exposure identified [See the outcome →](https://margins.agency/client-outcomes/marikomerc) Unija How much of this process actually requires a person? Separate repetitive payroll administration from work requiring human judgment. An AI operations layer processing documents and connecting portal, CRM and ERP workflows while people increasingly handle exceptions. [See the outcome →](https://margins.agency/client-outcomes/unija) CIAK What should a salesperson know before walking into the next customer? Turn fragmented ERP, BI, Excel and organizational knowledge into actionable intelligence for field teams. An AI mobile cockpit combining purchasing patterns, ML detection, customer briefings, spoken reporting and an AI assistant. [See the outcome →](https://margins.agency/client-outcomes/ciak) Why Margins ## We don’t discover opportunities we wouldn’t know how to build. The fastest way to produce an impressive but useless AI roadmap is to separate the people recommending AI from the people responsible for implementing it. Our strategy work is informed by engineering from the beginning — so feasibility questions happen during discovery. The result is not just an attractive opportunity. It’s an opportunity we understand how to implement. - Can the required data actually be accessed? - Is its quality sufficient? - Can AI reliably perform the task? - Which systems need to be integrated? - Where does human judgment remain necessary? - What happens when AI is wrong? - What does the production architecture require? - How will performance be measured? - What will it cost to operate? Your team ## The right people in the room matter. The Sprint involves a small group of people who collectively understand the business problem and how the operation actually works. 01 ### Executive Sponsor Provides business priorities and decision-making context. 02 ### Process Owner Understands the operation and is accountable for its performance. 03 ### Subject-Matter Experts Know the details, exceptions and realities that rarely appear in process documentation. 04 ### IT / Data Provides context around systems, data, architecture and technical constraints. From Margins, depending on the opportunity AI Strategy AI Engineering Product / Business Analysis Technical Architecture Small group. Focused engagement. Concrete outcome. From discovery to production ## Discovery should create momentum, not another pause. Once the Sprint is complete, there are three legitimate outcomes. ### Build The opportunity is strong. The business case is compelling and the implementation is feasible. Move directly into solution design and AI Engineering. [Explore AI Engineering →](https://margins.agency/services/ai-engineering) ### Validate The opportunity is promising, but one assumption needs proving. Run a focused technical prototype or data validation before committing to the complete implementation. ### Don’t build The economics or feasibility aren’t strong enough. Stop before spending significant engineering budget. Finding out that an AI idea isn’t worth building is also a successful Discovery. Where the sprint fits ## The Sprint is where a broad AI ambition becomes a specific implementation decision. 1. 01 **AI Opportunity Consultation** 2. 02 **Discovery Sprint** *You are here* 3. 03 **Solution & Business Case** 4. 04 **AI Engineering** 5. 05 **Deploy** 6. 06 **Train + Adopt** 7. 07 **Managed AI** 8. 08 **Improve** Why start with a Discovery Sprint ## Decide with evidence, then build with confidence. 01 ### Reduce the risk of building the wrong thing. Validate the opportunity before committing significant engineering investment. 02 ### Connect technology to economics. Define what should change in the business and how it will be measured. 03 ### Bring business and technology together. Evaluate value and technical feasibility as one problem. 04 ### Move faster afterwards. Enter engineering with the process, solution, requirements and success criteria already understood. 05 ### Create organizational alignment. Give leadership, operations and technology a shared understanding of what is being built and why. Start here ## You don’t need to know what AI to build. Tell us about your business. We’ll help identify where AI could make a meaningful difference, determine whether the opportunity is worth pursuing, and define the path to make it real. [Book a discovery call](https://margins.agency/contact/discovery-sprint) No predetermined solution. No technology looking for a problem.