You don’t need more AI ideas. You need to know which opportunities are worth pursuing.
Margins works with your leadership and operational teams to identify where AI can materially improve the business, evaluate what is feasible, and define a practical path from opportunity to production.
Strategy informed by what it actually takes to build and operate AI in production.
AI can be applied almost everywhere. That doesn’t mean it should be.
The job of AI strategy is to understand where technology intersects with a valuable business problem — where there is enough impact, enough data, enough feasibility and enough organizational readiness to justify doing something.
We start with your business
Only then do we ask what AI should do.
Most companies have too many possibilities and too little clarity. By the time leadership begins seriously discussing AI, ideas are already coming from everywhere. None of that answers the most important question.
Margins helps turn a broad discussion about AI into a prioritized set of opportunities grounded in business value and implementation reality.
AI opportunities rarely reveal themselves from a boardroom alone. We work with the people who own and perform the processes — understanding how work happens today, where decisions are made, what information is used, where friction exists and where business value is being left on the table.
Understand strategic priorities, business objectives, pressure points and where leadership believes AI could matter.
Work with process owners and operational teams to map how the business actually works.
Understand what information exists, where it lives and what systems an AI solution would need to interact with.
Identify where AI could automate work, improve decisions, surface knowledge, predict outcomes or create entirely new capabilities.
We don’t workshop AI in the abstract. We look for places where changing the way work happens can change a business outcome.
A CEO can map these categories to the company immediately. Technology comes later.
What are people doing that machines could do?
What would be valuable to know earlier?
Where could better information improve a decision?
What does the company know but struggle to access?
Where could AI make people substantially more capable?
Once opportunities have been identified, we evaluate them against the dimensions that determine whether they are actually worth pursuing.
The output isn’t a 50-item AI wishlist. It’s a clear answer to: what should we do first, and why?
Before an AI initiative becomes an engineering project, we define what it is expected to change. Depending on the opportunity, that could mean:
The metric becomes part of the implementation — not something invented afterwards to justify it.
The engagement leaves you with enough clarity to make an investment decision and move into implementation.
A clear definition of where AI should be applied first.
An understanding of how the relevant process works today and where value is being lost.
The outcome we’re trying to create and how it will be measured.
What the AI-enabled future state should actually look like.
What information, infrastructure and integrations will be required.
The recommended architecture and technologies at the level required to validate feasibility.
A practical path from the current state into production.
The starting point against which the implementation can be evaluated.
The objective isn’t to tell you what AI could theoretically do. It’s to determine what your business should actually build.
There is a fundamental difference between designing an AI strategy on paper and designing one knowing your team may have to put it into production. Our recommendations have to survive questions like these.
We don’t hand you a roadmap and disappear. We can build what we recommend.
Explore AI EngineeringThe opportunity wasn’t “build a machine-learning model.” It was identifying that changes in purchasing behaviour could provide an early signal that revenue was at risk. That became a predictive system using four years of invoice history and multiple detection methods — validating 94.7% of revenue losses with a median 43-day warning window.
43 days earlier to act.
See the outcome →Employees were manually working across portals, PDFs, emails and payroll systems. The opportunity was to separate work requiring human judgment from work AI could understand, structure and process automatically — so people could increasingly focus on exceptions rather than repetitive processing.
See the outcome →Customer knowledge was fragmented across ERP, BI, Excel and people’s experience, leaving field teams working reactively. That became an AI mobile cockpit combining purchasing patterns, ML detection, customer briefings, spoken reports and an AI assistant.
See the outcome →Find something valuable. Make it work. Prove the economics. Build from there.
The first successful implementation creates more than one AI solution. One successful implementation becomes the foundation for the next.
Explore AI TransformationMargins combines strategy, engineering, training, Forward Deployed Engineering and Managed AI under one accountable partner.
From AI strategy to AI in production. One partner.
We start with the outcome, not the technology.
Recommendations are shaped by people who understand what it takes to implement them.
Success is defined before development begins.
The output is designed to move directly into engineering.
The same partner can take responsibility from opportunity discovery through production and continuous improvement.
You don’t need a defined AI project. Start with your business — the processes that consume time, the decisions that could be better, the information that’s difficult to access, and the problems you wish you could see earlier. We’ll explore where AI could make a meaningful difference and whether there’s an opportunity worth pursuing.
Explore AI opportunitiesNo predetermined solution. No technology looking for a problem.
