Embedded Data & AI Transformation.
Anyone can sell you AI.
Making it work inside a real business is the hard part.
That's the part Factor10 does. We embed with retail and consumer businesses — inside your systems, alongside your team — and build the data, analytics, and AI capability that moves revenue, margin, labour, and customer experience. And we stay until it's adopted and delivering.
How We See It
Data at the bottom. Outcomes at the top.
Most retailers have more technology than they have capability. ERP, cloud platforms, BI tools, Copilot, dashboards, vendors, data teams — and the business still asks the same questions. What's actually happening? Can we trust the number? Where does AI actually help?
Here's how we think about it. Every layer exists to serve the one above it. Data foundations exist to power analytics and AI. Analytics and AI exist to change decisions and work. And all of it exists for one reason: business results.
If a dashboard, model, or agent isn't moving revenue, margin, labour, or customer experience, it's technology sitting beside the business — not capability inside it. That's the idea in our name: trusted data, clear definitions, decision routines, AI-enabled workflows, adoption — each one a factor. And factors multiply. Factor10 builds every engagement from the top of this stack down — and does the work from the bottom up.
Why it all exists
Business Outcomes
Accelerated revenue growth · Margin improvement · Labour efficiency · Customer experience
The enablers
Analytics & AI
BI & reporting · Predictive & ML · Rules & automation · GenAI · Agentic workflows
The foundation
Trusted Data
Definitions · Semantic models · Data quality · Ownership & governance
How we're different
AI adoption is not the goal.
Business capability is.
Tool adoption is the easy part — AI transformation is operating model change. Factor10 is not a traditional strategy firm, an AI vendor, a BI shop, or a staffing bench; each of those stops where that work starts. We bring the operating model itself — the roles, ownership, and rhythms that data, analytics, and AI need to succeed inside an organization — because we've built it inside some of Canada's best-known retailers.
Strategy firms often stop at the roadmap.
We hand you a working system, run by your team.
AI vendors often start with the tool.
We start with the business problem — and build the capability for the next ten.
BI teams often stop at the report.
We stay until the decision changes.
Staffing firms provide capacity.
We take ownership of outcomes.
The AI industry has started giving this model new names — forward-deployed engineers, embedded AI teams. The labels are new. The work is familiar: sit with the business, map the workflows, understand the decisions, build the solution, prove the value, drive adoption. We've been doing it for twenty years.
The work sits in the messy middle between executive ambition and operational reality — where transformation usually gets stuck.
Sound Familiar?
The problems we get called about.
We have reports, but people still argue about the numbers.
Sales, finance, operations, and leadership each have their own version of the truth. Meetings become debates about definitions instead of decisions. We build the trusted metrics, shared definitions, and ownership that move the business from debate to action.
We have reports, but people still argue about the numbers.
Sales, finance, operations, and leadership each have their own version of the truth. Meetings become debates about definitions instead of decisions. We build the trusted metrics, shared definitions, and ownership that move the business from debate to action.
We have reports, but people still argue about the numbers.
Sales, finance, operations, and leadership each have their own version of the truth. Meetings become debates about definitions instead of decisions. We build the trusted metrics, shared definitions, and ownership that move the business from debate to action.
We have reports, but people still argue about the numbers.
Sales, finance, operations, and leadership each have their own version of the truth. Meetings become debates about definitions instead of decisions. We build the trusted metrics, shared definitions, and ownership that move the business from debate to action.
We have reports, but people still argue about the numbers.
Sales, finance, operations, and leadership each have their own version of the truth. Meetings become debates about definitions instead of decisions. We build the trusted metrics, shared definitions, and ownership that move the business from debate to action.
We have reports, but people still argue about the numbers.
Sales, finance, operations, and leadership each have their own version of the truth. Meetings become debates about definitions instead of decisions. We build the trusted metrics, shared definitions, and ownership that move the business from debate to action.
Our Framework
Every organization wants AI.
Every organization starts somewhere different.
Most of the problems above trace back to the same root: skipped stages. Plenty of firms start with an AI use case or a strategy workshop — a demo that looks great in a boardroom and rarely survives contact with the business, because the data underneath it isn't trusted, nobody owns the decision it supports, and the team using it doesn't have the habits to make it stick. Factor10 starts differently. We build the data, analytics, and operating capability required to realize AI — at the pace your business is ready to adopt it. You can't skip to AI. You build up to it.
Our Framework
Data Foundation & BI
Clean, reliable data and reporting your teams actually trust. Shared definitions, one version of the number, and the discipline to keep it that way. The least exciting part of the work — and the reason most AI projects fail, because they skip it.
Stage 02
Insights Culture
Teams that use data to make the call day to day — pricing, inventory, staffing, promotions — not just glance at a dashboard once a month. The business owns the decisions. The data team enables them.
Stage 03
AI at Work
Predictive and ML, then GenAI and agents — with something solid to stand on. It starts inside the business: forecasting, allocation, labour planning. Then it earns its way to the customer: product recommendations, personalization, better service.
Most organizations want to start at stage three. We meet you where you actually are — maturity is uneven, and that's normal. One department may be ready for agents while another is still arguing about the sales number. And not every problem needs an agent: the craft is choosing the right capability for the problem — a dashboard, an automation, a predictive model, a copilot, an agent, or a combination. The foundation is the only part you can't skip.
What we build
Four ways we build the capability.
We're not here to sell you a single AI project. Our job is to build the capability so your team can keep using data and AI to solve the next problem, and the one after that — without needing us in the room. Every service maps to a layer of the stack, and every engagement is measured at the top of it.
How we work
Six principles. Every engagement.
1
Start with the real business problem
The first ask is not always the real problem. A dashboard request may be a decision problem. A data issue may be a definition problem. An AI idea may be a workflow problem. We start with what's actually blocking value.
2
Build the context AI and analytics need
Data alone is not enough. AI needs meaning: definitions, business rules, process knowledge, decision logic. Agent failures are rarely technology problems — they're organizational-knowledge problems. The real question is whether you understand your business deeply enough to teach it to an agent.
3
Redesign the work, not just the output
The goal is not faster tasks. It's fewer handoffs, clearer decisions, and better-designed work. AI applied to a broken process just produces broken results, faster.
4
Keep the business close
Data and AI work best when the business helps shape the solution, understands the logic, trusts the output, and owns the decision. Nothing gets done to your teams.
5
Stay through adoption — and valueStart with the real business problem
Go-live is not the finish line. Neither is adoption. The work is done when people use it, trust it, and the intended value shows up in the results.
6
Measure the business, not the usage
Adoption metrics tell you whether people are using AI. Transformation metrics tell you whether decisions, work, and results are improving. Productivity is a means. Business impact is the outcome.
Representative Work
Built from real transformation work.
Factor10 is based on hands-on experience leading data, analytics, and AI transformation across retail, digital, technology, and operations-heavy businesses. Embedded with the team, doing the work, staying until it was adopted and delivering.
Enterprise Data & Analytics Transformation
Built and scaled analytics capability across complex, multi-functional organizations — modern data foundations, executive reporting, self-serve analytics, governance, team design, and business adoption.
→ Not simply more reporting: better decisions, and the muscle to keep improving.
AI-Enabled Pricing, Forecasting & Inventory Decisions
Moved pricing and operational decisions from manual analysis toward trusted recommendations, automation, and machine learning. Started with the business problem, kept merchants involved, built confidence through rules-based recommendations, and scaled only once trust was established.
→ The Factor10 pattern: don't jump to AI — build the maturity, trust, and ownership AI needs to work.
ERP Analytics & Reporting Stabilization
Turned ERP modernization into usable business reporting — definitions, validation, semantic models, operational reporting, executive visibility, business sign-off, and adoption.
→ Helped the business run from the new system, not just go live on it.
Personalization & Customer-Facing AI
Took AI to the customer: product recommendations, segmentation, personalized digital experiences, loyalty, and marketing measurement — built on the data foundations and internal trust earned first.
→ AI earns its way from the back office to the customer.
The Team Model
Built around the work, not a fixed bench.
Factor10 isn't a lone advisor, and it isn't a staffing bench. We assemble the specific mix of senior operators, architects, engineers, and analysts your engagement actually needs — and embed them with your team until the work is done, adopted, and delivering the intended value. The people who scope the work are the people who deliver it.
Leadership & Engagement
Principal / Data, Analytics, AI & Transformation Lead
Senior direction across outcomes, data strategy, AI transformation, and delivery. Keeps the work connected to value, not just activity.
Client Engagement / D&A Product Lead
Keeps the work moving across business teams, technical teams, vendors, and leadership — and translates priorities into delivery and adoption plans.
Architecture & Engineering
Solution Architect / Data Platform Lead
Designs scalable, secure, practical data and AI architecture — not a one-off.
Data / Analytics Engineer
Builds pipelines, data models, semantic layers, and trusted data assets everything else depends on.
AI Engineer
Builds AI-enabled workflows, copilots, agents, and automation that hold up in production, not just in a demo.
Leadership & Engagement
BI Developer
Builds the reporting and dashboards your teams trust and use.
Data Analyst
Connects business questions to data, validates logic, defines KPIs, and helps teams act on insight.
Power Platform / Automation Developer
Automates manual, spreadsheet-driven work using Power Automate, Dataverse, Teams, and related Microsoft technologies.
Insights
Notes from the work.
Observations on data, AI, and how businesses actually change — written from inside the work, not above it.
About the Founder
Matt St. John
Founder, Factor10 Data Consulting. A senior data, analytics, and AI transformation leader with more than 20 years across retail, digital, technology, and operations-heavy businesses — including senior leadership roles with Aritzia, Best Buy Canada, and lululemon.
Matt has led enterprise analytics teams, built modern data capabilities, scaled self-serve BI, delivered AI and machine learning use cases, supported ERP analytics transformation, and partnered with executives to turn data into measurable business value.
The problem Factor10 solves is rarely one thing. It's usually the space between business ambition, data foundations, system reality, workflow design, ownership, adoption, and measurable value. Factor10 works in that space.
"Technology has changed. The operating model hasn't. Factor10 exists to close that gap."
Why "factor10"
A factor is a multiplier — the thing that changes the outcome. The name is the ambition: not a small improvement, not another report, but a step-change in what the business is capable of with data and AI. Not 10x hype. A higher order of capability.
About the Founder
Let's talk about where data and AI are getting stuck.
If you run a retail or consumer business and you're working through any of this — trusting your reporting, stabilizing ERP analytics, moving past scattered AI pilots, or connecting technology spend to revenue, margin, and labour — Factor10 can help. Hands-on, embedded, until it's adopted and delivering the value you expected. Because real transformation means the business can do something better than it could before.