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.”
Once a company starts seriously discussing AI, potential use cases appear everywhere.
The challenge is determining
The AI Discovery Sprint is designed to answer those questions before significant engineering investment begins.
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.
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 atUnderstand 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 examineAnd ask questions such asSeparate interesting ideas from opportunities worth pursuing.
Each opportunity is evaluated against the factors that determine whether it deserves investment.
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 defineAt 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.
A structured view of the AI opportunities identified across the processes investigated.
Opportunities evaluated against business value, feasibility, data readiness and implementation complexity.
A clear recommendation for where Margins believes the business should start.
The expected business outcome and the baseline against which success can be measured.
How the proposed AI-enabled process should work from the user’s perspective.
The models, data, integrations and infrastructure likely required.
The practical path from current state to production.
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.
You’re buying clarity.
We don’t force every opportunity into these categories. They simply give us useful lenses through which to examine the business.
What work doesn’t need to be done manually?
Document processing, administration, cross-system workflows, classification and repetitive tasks.
What would become valuable if you knew it earlier?
Customer behaviour, demand, risk, anomalies, equipment issues and operational changes.
Where could better information improve a decision?
Prioritization, planning, scheduling, recommendations, resource allocation and risk assessment.
What does your company know but struggle to access?
Documents, policies, procedures, customer history and expertise living inside people’s heads.
Where could AI make your people materially more effective?
Sales, field operations, finance, service, management and other knowledge-intensive roles.
Could we know a customer is being lost before the salesperson realizes it?
Use changes in purchasing behaviour to identify revenue deterioration early enough for commercial teams to intervene.
A predictive system using four years of invoice history, multiple detectors and seasonal analysis.
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.
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.
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.
The Sprint involves a small group of people who collectively understand the business problem and how the operation actually works.
Provides business priorities and decision-making context.
Understands the operation and is accountable for its performance.
Know the details, exceptions and realities that rarely appear in process documentation.
Provides context around systems, data, architecture and technical constraints.
Small group. Focused engagement. Concrete outcome.
Once the Sprint is complete, there are three legitimate outcomes.
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 →The opportunity is promising, but one assumption needs proving.
Run a focused technical prototype or data validation before committing to the complete implementation.
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.
Validate the opportunity before committing significant engineering investment.
Define what should change in the business and how it will be measured.
Evaluate value and technical feasibility as one problem.
Enter engineering with the process, solution, requirements and success criteria already understood.
Give leadership, operations and technology a shared understanding of what is being built and why.
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 callNo predetermined solution. No technology looking for a problem.
