01 · Why are we transforming?
AI transformation is a deliberate change in how Agrinesia works, decides, and operates—proven by business value.
WHAT SUCCESS COULD MEAN
02 · What might the scope be?
A hypothesis about the conditions that may help—offered for discussion.
See how value and constraints move across functions.
Start with real problems, not places to put AI.
Architecture, security, integration, and reliable delivery.
Clear boundaries and resolve cross-functional trade-offs when needed.
HOW AGRINESIA CREATES VALUE
Digitise selected workflows and build trusted data around real business needs. Readiness grows alongside useful applications.
How broad should the ambition be—and where would it be sensible to start?
03 · Where are the problems?
Look across functions and the value chain. The most useful starting point may sit between teams.
04 · Problem → solution fit
“Stores run out of the right SKU.” Check adherence, reorder rules, demand uncertainty, and system coordination. Integration can use ordinary software; agents fit tasks requiring adaptive decisions.
05 · What could this look like?
Illustrative opportunities. Confirm the problem, delivery scope, and expected improvement with each function.
06 · How could a pilot become transformation?
For employee initiatives and dedicated projects, test a bounded scope, observe adoption, and review improvement before expanding.
Agree the owner, scope, baseline, target, and guardrails.
Prototype and test the critical assumptions.
Integrate data, controls, roles, and daily work.
Drive adoption, confidence, ownership, and learning.
Compare with the baseline and target; stop, adapt, or expand.
Progress means sustained changes in work and measurable improvement. Learning and adaptation continue after launch.
07 · How could I contribute?
AI Transformation · applying principal product management experience
08 · Why me?
Operations, Finance, Marketing, CS, Commercial, Engineering.
Building the system was only half the product. Dozens of teams had to move.
LLMs, agents, evaluations, model choices, code, production AI.
09 · Where the work starts
A proposed way of working for Agrinesia. The situations below are illustrative; actual problems and priorities need to be confirmed with the teams.
The employee shows the work and its bottleneck. Their manager helps choose a useful improvement; approved AI tools, practice time, and support help them try it.
Start with recurring work, low variability, and clear bottlenecks.Operations brings the shared need. Business, product, IT, data, and engineering scope a tool that gathers fresh demand signals and uses them to inform forecasts.
A dedicated project can start here from day one.To agree: where employees raise problems, who reviews them, and which executive sponsor resolves priorities across functions.
10 · Employees improve daily work
Example: an employee copies outlet data into a daily report, fixes formatting, and checks totals. Start by observing those steps and where the time goes.
Provide approved AI tools, rules for company data, and time to practise. Teach people to describe the problem, check results, and recognise when they need help.
Use AI to help with a formula or Apps Script. The employee tests it on known reports, checks totals and exceptions, and compares time and corrections with the old method.
A named support person helps when it fails. Managers review whether the task improved. Employees share useful examples so colleagues can learn and adapt them.
That can be fine for individual work with agreed quality and data boundaries. If the methods produce conflicting numbers or affect a shared handoff, the team needs a common definition, output, and check.
To agree: tool access, learning and support capacity, and manager time. Training should match the tools provided and the work employees need to improve.
11 · When colleagues depend on a tool
Example: one employee’s reporting script becomes useful to the whole team. Colleagues now rely on its output to do their jobs.
| What changes? | What the team agrees | Who takes responsibility? |
|---|---|---|
| A common workflow | Which data, definitions, output, and checks everyone uses; which exceptions still need review. | The function’s owner agrees the working method with its users. |
| A supported tool | Who can access it, where it runs, how changes are tested, and how to continue if it fails. | A named maintainer supports the tool, with IT involved where needed. |
| A team habit | Which old steps are retired, how colleagues learn the new method, and where they report issues. | The manager supports adoption; users report failures and verify outputs. |
A shared spreadsheet script may already be part of the team’s working system. A hosted app can improve access or speed, but still needs ownership, support, and reliable results.
To agree: who reviews shared tools and provides maintenance. Sensitive access, integration, or operational consequences may require dedicated project delivery.
12 · A shared business need becomes a project
Illustrative project: gather and process relevant information as close to real time as sources allow, so the demand model can reflect changing conditions across outlets and regions.
Candidate inputs: recent sales and orders, stock availability, promotions, prices, holidays, weather, and local events. Agree source access, refresh frequency, and ownership.
Align each signal by time, outlet, and product. Check freshness and missing data; extract relevant details from text where useful. Test which variables improve the forecast.
Show updated forecasts, uncertainty, and stale or missing inputs. Planners review changes and record adjustments. Start with selected outlets, products, and a defined forecast horizon.
Explore a broad set of signals, then retain those that improve forecasts on later, unseen periods. New inputs can refresh predictions; model retraining follows a separate validation schedule.
To agree: forecast horizon, initial outlets and products, available signals, freshness targets, delivery capacity, and who maintains the data and model.
13 · How Agrinesia knows it is working
Before starting, the business owner and users record the current measure, data source, and period. Agree a target, quality or risk limits, and a review date.
| Example | Baseline: what happens today? | What good looks like |
|---|---|---|
| Daily reporting | Preparation time per report, corrections, and time spent checking totals. | Employees use and verify the new method independently. Preparation time falls without more errors. |
| A shared team tool | Duplicate versions, mismatched outputs, support issues, and handoff delays. | The team uses agreed outputs and checks. Handoffs improve; another person can support the tool. |
| Demand forecasting tool | Current forecast error and bias, input freshness, and time spent gathering information for the same products, outlets, and horizon. | Relevant signals arrive at agreed intervals. Forecasts improve against the current method on unseen periods; planners use updates and review exceptions. |
Numeric targets follow discovery. Compare equivalent periods or groups, accounting for seasonality and other changes. Revenue and cost effects can follow improvements in quality, speed, capacity, and risk.
Choose a bounded problem with an owner and participating users. For forecasting, test access to selected signals and whether they improve a baseline model. Pilot where feasible, then review the evidence and resources needed for the next step.
The initial engagement also helps both sides assess working fit. WMS and TMS integrations are separate projects, outside this forecasting scope.