AI at ZILLION
Put AI to work
We take AI out of the pilot and into sales, planning, production and purchasing, together with the people who run them. Vendor neutral, hands-on and built to last
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Ways we put AI to work
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Cases from our projects
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AI and data partners
The problem
AI works Scaling it is hard
Most companies have tried AI. Far fewer use it every day where the money is made. In our AI transformations, the same three things decide whether AI stays a pilot or becomes a result
How we do it
From idea to daily use
- 01
Find the value
An AI opportunity scan across your value chain. Every idea gets a business case, and we start where impact is high and data is good enough
Prioritised AI portfolio - 02
Get the data ready
Data quality, master data and integrations fixed where it matters, with governance, security and the AI Act built in from the start
Data and governance in place - 03
Build it into the process
AI designed into S&OP, maintenance, purchasing and customer service, and into the ERP, together with the people who will use it
AI in daily operations - 04
Make it stick
Training, new routines and KPIs on the front line, so AI is used every day and the next AI solution is easier than the last
Adoption that scales
AI with people, not around them
The tools change. The people who run the process make it work
Safe by design
Your data stays yours
The first question from the board is rarely what AI can do. It is where the data goes. So we set AI up in a protected, GDPR-compliant environment before anyone types a single prompt, and we are honest about what it should not do
Company accounts, not private ones
One company workspace with central admin, single sign-on and offboarding, on business terms where your data is not used to train the models
GDPR documented from day one
Data processing agreement, EU data storage where the platform offers it, records of processing and an impact assessment where the use case needs one
Access follows your rights
When AI is connected to SharePoint, ERP or CRM, it only sees what the user is already allowed to see. No shortcuts around your permissions
AI Act risk classification
Every AI solution is classified, with transparency and human oversight built in where the regulation asks for it
Policy and training before go-live
A short, clear AI policy on which tools, which data and who decides, and every user trained before the first prompt
Logged, owned and auditable
Usage logs, retention rules and a named owner for every AI solution, so IT, legal and the auditor can follow what happens
Know the limits
AI drafts, people decide
Output is checked by a person before it reaches a customer, a supplier or a decision
Not everything belongs in a prompt
Personal, health and other sensitive data stay out unless there is a legal basis and a protected set-up
Answers are only as good as the sources
Without clean data and documents, AI is confidently wrong. That is why data comes before the model
Free tools are the real risk
Shadow AI on private accounts is where data leaks. A safe company alternative is how you stop it
Where AI fits
Where AI pays off
Across the whole value chain, from the market to the supplier. Pick an offering to filter
- Commercial ExcellenceConversation analyticsAI that listens across customer calls and chats, finds the root causes of repeat contacts and turns them into training and better service
- Commercial ExcellenceFaster bids and quotesAI-drafted first versions of bids and offers, so sales teams spend their time on the customer, not the document
- Commercial ExcellenceCustomer and price insightPatterns in sales and service data that show where to grow, where to adjust prices and which customers to develop
- Supply ChainAI in supply chain planningOne of our two supply chain spearheads: forecasts, planning parameters and inventory targets supported by AI, built into the S&OP and the ERP
- Supply ChainDemand sensingEarlier and sharper demand signals, so production and purchasing react before backlog or excess stock builds up
- Supply ChainInventory that releases cashAI-supported segmentation and safety stock levels that cut working capital without hurting delivery performance
- OperationsPredictive maintenanceMachine and sensor data that predicts failures before they stop the line, planned into maintenance and the bottleneck
- OperationsRoot causes in production dataAI that finds the patterns behind quality defects and losses, feeding structured problem solving on the shop floor
- OperationsDigital standardsWork instructions and processes that people and AI agents can follow, kept alive in the operating system
- SourcingSpend analyticsAI that classifies and cleans spend data fast, showing where the savings are by category and supplier
- SourcingShould-cost and negotiation prepCost breakdowns and negotiation material built faster, so every round starts from facts
- SourcingContracts under controlAI-supported review of supplier and IT contracts that flags risk, renewal dates and savings potential
- AI & DigitalizationAI strategy and adoptionFrom pilots to production: AI opportunities, business cases, data readiness and adoption on the front line
- AI & DigitalizationAI governance and the AI ActPolicies, risk classification and compliance built into IT and business processes from day one
- AI & DigitalizationAI in ERP and core systemsAI and automation designed into ERP, CRM and data platforms, so it scales beyond a single solution
- ESGESG data without the spreadsheetsAI that collects and checks ESG data across sites and suppliers, so CSRD reporting is traceable
- ESGDecisions on impactEmissions and resource data linked to operations and sourcing, so ESG becomes part of monthly performance reviews
In practice
AI data and automation at work
Cases from our projects. Open one to see what we did and what changed
Customer service lifted with AI-driven coaching
The customer service organisation needed to be modernised and made more efficient across branches and online banking
Higher customer satisfaction with one virtual customer centre
A very large volume of annual customer enquiries was handled in silos
+50% order volume with no additional capacity
- +50%
- order volume, no extra capacity
- 4 weeks → 4 days
- quotation lead time
- 30 h → 30 s
- documentation reporting
From low to high and stable delivery performance
- High & stable
- delivery performance
One live truth across the external supply chain
- Significantly shorter
- planning cycle
+25% back-office productivity with standard work and RPA
- +25%
- productivity
Watch
From experiments to business value
AI works, but many struggle to scale it. ZILLION shares experience from a series of larger AI transformations
In Danish
Practise what we preach
We use AI ourselves
01
AI is how we work
Our consultants have been through an all-hands AI boot camp, and AI is part of our daily work, from analysis and workshops to reports
02
Our own AI knowledge base
Our cases, methods and expertise live in one AI-ready knowledge base, so the experience of the whole team is available on every project
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We bring it to your teams
The same tools, habits and governance we use ourselves, set up for your organisation, so your people can work with AI from day one
Solution partners
AI and data partners we bring in
Questions
What leaders ask us
Where should we start with AI
Where the value is highest and the data is good enough. We run a short opportunity scan across sales, supply chain, operations and procurement, put a business case on every idea and pick the first two or three to build
Do we need perfect data first
No. Most companies have enough data to start. We fix the data that matters for the chosen AI solutions, and set up ownership and governance so quality keeps improving
How do you handle the AI Act, GDPR and security
From day one. Company accounts on business terms, a data processing agreement, access that follows your existing rights, AI Act risk classification and a clear AI policy, all in place before the first prompt
Which AI tools and platforms do you use
We are vendor neutral. We work with the leading AI platforms, the AI built into your ERP and CRM, and specialist partners, and recommend what fits your case and your IT landscape
How do you make sure AI is actually used
We build AI into the processes people already run, involve them in the design, train them and track usage and impact. That is the difference between a pilot and a result
Ready to put AI to work
Start with an AI opportunity scan across your value chain. We will show you where AI pays off first