Insights
Practical notes from Data Department for people who make operational decisions: how to choose an AI project, why forecast accuracy is not enough, how to plan inventory under uncertainty and where generative AI pays off.
How to choose an AI consulting company: 10 questions to ask
A 10-question checklist for comparing AI and machine learning consultancies, with a scoring table and the warning signs to look for.
Read more →AI strategyWhy AI projects stall after the first model, and how to get them used
Most AI projects stop because nobody uses the output, not because the model fails. Four owners and six checks that keep a model in daily use.
Read more →PricingWhat data should a dynamic pricing model use?
A pricing model is only as defensible as its inputs. Use data that describes the market, not data that describes the person.
Read more →AI strategyPreparing a grant-funded AI project in Türkiye: a readiness checklist
Before applying for public R&D support in Türkiye, check that the AI project is ready on decision, data, metric, team and documentation.
Read more →Generative AIHow to give an AI agent safe access to your ERP
Give an AI agent a badge, not a master key. Narrow read access, human approval for actions and an audit trail make ERP agents safe to run.
Read more →ForecastingWhy forecast accuracy is not enough
A more accurate forecast does not guarantee a better decision. Uncertainty, asymmetric costs and simulation close the gap.
Read more →AI leadershipWhat does a fractional Chief AI Officer do?
The fractional Chief AI Officer role explained, with a comparison against full-time hires and project consultants, a 90-day plan and success measures.
Read more →Inventory optimisationSpare-parts inventory optimisation with probabilistic forecasting
A practical guide to spare-parts stocking with probabilistic forecasts, intermittent demand methods, newsvendor logic and part segmentation.
Read more →AI strategyHow to choose your first AI project
A practical method for picking a first AI project that reaches production, with a scoring table and the mistakes to avoid.
Read more →Generative AIGenerative AI for document processing in manufacturing
A practical guide to generative AI document processing for manufacturers, from workflow and accuracy targets to human review and data protection.
Read more →Not sure where AI would pay off? Ask us.
Book a free 30-minute AI opportunity review. A senior consultant looks at the decision you want to improve and tells you honestly whether AI fits, what data it needs and what a first step would look like. We reply within two working days.