8 posts tagged with "Business Context"

Build the case around one recurring workflow, measure the work honestly and ask leadership for a small, specific decision.

Every month, someone on your finance team reformats a file, chases a correction and reconciles the result. The report goes out on time, so the work stays invisible. A business case makes it visible.

If you lead forecasting, planning and analysis, you may already see the problem. Finance managers and controllers know where the work happens. A useful proposal brings that knowledge together in a form that finance leadership and IT can evaluate.

They count different events on different dates. Some differences are legitimate. The problem starts when nobody can explain how the figures relate.

At month-end, finance collects actuals from subsidiaries that run different ERPs. Forecasts add another set of spreadsheets and CRM inputs. Before anything reaches leadership, finance has to bring the figures together, explain the differences and say why actuals moved away from the forecast.

If you lead forecasting, planning and analysis, or work as a finance manager or financial controller, you need data that is consistent enough to use and a way to spot the submissions that need correcting. Data and systems colleagues can help trace the technical causes.

Someone told me recently that SaaS is dead. I disagree — strongly — but not because the argument behind it is silly. It isn't. Three things in it are true.

Building software is getting dramatically cheaper. Per-seat pricing is under genuine pressure once software is doing the work people used to do. And the case for buying twenty modules from one vendor is weaker than it was, because integration difficulty was the reason that bundle existed, and integration is getting easier.

All fair. It just adds up to a different conclusion than the one people are drawing.

You have acquired a business. It has been trading successfully for years on its own systems, with its own processes and its own way of describing what it does.

Within weeks, someone will propose moving it onto yours.

That instinct is understandable and it is usually expensive. There is another way to get what the board actually wants, and it starts by separating two problems that are almost always treated as one.

Most organisations answer "is our data AI-ready?" with a feeling rather than a fact. The platform is modern, the warehouse is populated, a governance programme exists — so it probably is.

It usually is not, and the gap only surfaces once a model is in production and its answers start contradicting things the business knows to be true.

These seven questions give you a straight answer. They are deliberately phrased so you can respond yes or no. If a question needs a qualified answer, treat that as a no.

In the previous post we argued that AI-ready data starts with master data — that until an organisation has one definition of "customer", nothing built on top of it can be trusted.

That is the foundation. It is not the whole building.

Master data tells an AI system who your customers are. It cannot tell it what they did. Answers that mean anything to a business — which accounts are growing, which are quietly churning, where the cross-sell actually is — come from transactional data. And transactional data is only useful to AI if it is linked to the master data that gives it meaning.

"AI-ready data" is now everywhere, and most definitions of it agree: clean, governed, contextualised, accessible, traceable. All true, and all hard to argue with.

The trouble is that these describe the destination. They do not explain why so few organisations arrive.

The reason is narrower and more awkward than "data quality". Most organisations cannot agree what their core business objects actually are — and no AI system will resolve that for you. It inherits the disagreement and scales it.

A practical place to begin this conversation is by asking: what does AI actually require to be successful Despite the noise around models, innovation, and tools, the reality is simple:

AI is only as good as the data that feeds it.

For AI to deliver trustworthy, scalable value, organisations need three foundational capabilities:

1. Good-quality data.
2. Data with business context.
3. Data stored in a consistent structure.

Let’s tackle these one by one.