Tag: Product data

Inside many manufacturers, two areas of investment are moving in different directions.

AI investment is growing. Sustainability and product-data work is facing greater pressure to demonstrate its value. These may look like separate conversations. In practice, they depend on much of the same data.

The product, supplier, material and lifecycle information collected for LCA, product carbon footprints and compliance is also part of the foundation AI needs to support credible product decisions.

When that data is fragmented, AI can produce answers quickly without the full product context. When it is connected and traceable, the same foundation can support decisions across product development, compliance, cost, sustainability and supply-chain risk.

What has your sustainability team already built?

Producing an LCA, product carbon footprint, compliance assessment or carbon report requires more than running a calculation.

Sustainability and product stewardship teams may already:

• Map products to components and materials
• Collect information from suppliers
• Bring together data from PLM, ERP and procurement systems
• Reconcile records that do not agree
• Identify gaps in product and supplier information
• Document calculation methods and assumptions
• Check whether results can be traced and defended
• Repeat analyses across multiple products

These capabilities are not the whole of AI readiness. AI programs also need appropriate technology, governance, security and ownership. But they are part of the data work AI depends on.

This does not mean relabelling every sustainability project as an AI initiative. It means examining the data foundation already being built before treating AI readiness as a separate program.

Why does AI need connected product data?

AI works with the information it can access. In manufacturing, the full product picture rarely sits in one place.

Product structures may sit in PLM. Supplier information may be held in procurement tools and spreadsheets. Cost data may sit in ERP. Material and lifecycle information may be managed by sustainability or product stewardship teams.

Each source contains part of the answer. None necessarily contains the whole.When those sources remain disconnected, AI cannot see a complete and consistent product view.

Common problems include:

• Different systems describing the same product in different ways
• Incomplete supplier and purchased-part information
• Materials recorded at inconsistent levels of detail
• Teams working from different assumptions or baselines
• Data being reconciled manually for each analysis
• Outputs that cannot be traced back to their sources

AI can analyse an individual source quickly and still produce an incomplete answer.

Speed cuts both ways. Better data allows AI to analyse more information across more products. Weak data allows it to reproduce incomplete or inconsistent results at the same scale.

Much of the work that makes AI useful happens underneath the interface. It involves connecting systems, resolving conflicting records, filling gaps and creating product models that can be used more than once.

How do you know if your product data is ready to support AI?

These five questions offer a useful starting point:

1. Does sustainability data sit across several systems without a consistent product view?
2. Is it difficult to obtain consistent, verifiable information from suppliers?
3. Does the team spend more time preparing data than analysing it?
4. Can LCA and product carbon footprint results be traced and defended?
5. Does every reporting cycle require much of the same data work to be repeated?

If several sound familiar, the organisation may need to strengthen its product-data foundation before scaling AI-supported analysis.

This is not a formal maturity score. It is a way to identify where the immediate limitation may sit. The problem may not be whether the AI model can perform the task. It may be whether the information provided to the model is complete and reliable enough to support the result.

What can connected product data make possible?

Supplier data provides one practical example. When Microsoft developed a new LCA methodology with Makersite, the share of its total product carbon footprint calculated using suppliers’ primary data rose from an average of 20% to over 70%.

The approach also reduced modelling time and inconsistencies associated with practitioner decisions.

The value was not simply a faster final calculation. Microsoft created a more representative product model based on deeper supplier and material information.

Connecting PLM, ERP, supplier and sustainability data can also bring environmental and compliance information into the design stage, before key product decisions are fixed.

A shared product-data foundation can help teams explore questions such as:

• Which material options could reduce environmental impact
• Which substances create compliance exposure?
• Where are the main product-cost drivers?
• Which suppliers or materials introduce supply-chain risk?
• How could a material substitution affect cost, compliance and environmental impact?
• Which products in the portfolio should be reviewed first?

These questions span product development, procurement, compliance, sustainability and finance. The same underlying information can support several teams, even when each team is asking a different question.

Why does reuse matter?

Product and supplier data becomes more valuable when it can support more than one analysis. A product model containing component, material and supplier information may contribute to:

• Lifecycle assessment
• Product carbon footprints
• Compliance analysis
• Material substitution
• Product costing
• Supply-chain risk analysis
• Portfolio screening

The value is not limited to the first calculation the data was collected to complete. A reusable foundation allows different teams to examine different questions without rebuilding the underlying product picture each time.

It can also reduce the risk of teams reaching different conclusions because they are working from different product records, supplier information or assumptions.

How can sustainability teams broaden the business case?

Adding the word “AI” to an existing sustainability project is unlikely to make the case stronger on its own. A more credible approach is to show how the underlying data work supports decisions across the organisation:

Identify who needs the same data
Sustainability, product development, procurement, compliance and finance may depend on overlapping product and supplier records. Making that overlap visible can help position the work as shared product-data infrastructure rather than an isolated sustainability project.

Connect the data to specific decisions
The value becomes clearer when the business case explains what teams will be able to assess or decide.

For example:
-Compare material alternatives
-Identify compliance exposure earlier
-Understand product-cost drivers
-Review supplier and material risks
-Evaluate environmental impact during design
-Prioritise products for further analysis

This is more specific than saying that a project will simply “enable AI.”

Show the work underneath the output
The report, dashboard or AI response is the visible result. Producing a trustworthy result may require teams to:

-Connect data across systems
-Resolve inconsistent records
-Fill product, supplier and material gaps
-Document assumptions
-Build reusable product models
-Apply consistent calculation methods

Making this work visible helps explain why credible AI requires more than selecting a model or adding a new interface.

Show what can be reused
A product model developed for sustainability analysis may also support compliance, costing or supply-chain risk. Showing that reuse gives the investment a broader organisational case.

It does not guarantee access to AI funding. Budgets and ownership will differ between companies. But it can create a more relevant conversation with the teams responsible for data, product development and AI priorities.

Does regulatory uncertainty change the case?

Regulation has given many manufacturers a clear reason to invest in sustainability and product data. When requirements or timelines become uncertain, the immediate compliance argument can become harder to defend.

That does not resolve the underlying data problem. Manufacturers still need reliable product, material and supplier information to assess sustainability, compliance, cost and supply-chain risk.

The same foundation is also needed if AI is expected to support those decisions. The reason for investing may shift. The underlying data need does not.

Two questions AI pilots need to answer

AI pilots often combine two separate questions:

• Can the technology perform the task?
• Is the organisation’s data ready to support the task?

A polished demonstration may answer the first question, but it does not show whether the underlying data is complete, traceable and reusable enough to answer the second.

A useful pilot can also test whether the data is:

• Complete enough for the intended decision
• Traceable to a source
• Consistent across products
• Repeatable without extensive manual work
• Suitable for use beyond a small demonstration

This helps distinguish between a promising interface and a process the business can trust and use at scale.

The business case is broader than one function

Sustainability data and AI investment should not be treated as unrelated conversations.

Product structures, material intelligence, supplier data and lifecycle insights are already part of the information foundation AI needs to support manufacturing decisions.

The question is not only whether AI can perform a task. It is whether the underlying product and supply-chain data is connected, traceable and reusable enough to support decisions the business can trust. That foundation is what makes both sustainability and AI investment worthwhile.

The questions come up fast now. Can AI speed up LCA? Help with PCFs, Scope 3, compliance, costing, or supplier data?

Those are fair questions. But a better first question is simpler: which product data can already support an answer your business can trust?

No manufacturer starts with perfect data. Product structures may be incomplete. Supplier inputs may be missing. Material data may sit in another system. Methodology may live in spreadsheets or expert memory.

That does not mean AI has no role to play. It means teams need to know where the data is strong enough to use, where it needs review, and where the gaps need a governed way to be filled.

In short: product data is ready for AI when it is structured enough to support decisions that can be repeated, traced, reviewed, and defended.

AI readiness is not a prompt problem

A better prompt will not repair scattered PLM and ERP data. It will not explain why one emissions factor should be used over another. It will not connect a material to the right component if that relationship is missing. And it will not create an audit trail where none exists. That is why AI readiness is really a product data problem.

In many manufacturer conversations we’ve had recently, the pattern is consistent. Teams know what they need to do — calculate emissions, build PCFs, check compliance, answer customer requests. The harder part is making those processes repeatable.

Too much of the work still depends on manual collection, expert judgment, disconnected systems, and repeated cleanup. That can work for one product, one report, or one deadline. It does not become a reusable operating model.

AI becomes more useful when the same product data, assumptions, and review history can be reused rather than rebuilt for every request.

Four things to look at first

These are not signs of failure, they are merely starting points. Use them to understand where your data is strong enough to begin, where it needs review, and where the process needs work first:

1. Look at where your product data lives

Product data rarely lives in one place. For example, PLM may hold the product structure. ERP may hold cost or sourcing data. Procurement may hold supplier records. Sustainability teams may keep separate calculation files. Compliance teams may work from certificates, substance declarations, and supplier documents.

The first step is not to fix every system at once. It is to map which systems hold the data needed for the workflow you care about.

For example, a PCF workflow needs product structure, material data, supplier input, process assumptions, datasets, and methodology. If those relationships are unclear, AI is working from fragments. If they are mapped, even partially, teams can see where to start.

2. Look at which supplier data can be trusted

Supplier data is often the first visible blocker. Manufacturers may be missing weight data, material composition, process details, supplier-specific emissions factors, or evidence behind supplier claims. When data does arrive, it may come in different formats, follow different assumptions, or lack enough context to trust.

That does not mean supplier data needs to be perfect before teams can start. It means teams need to know which inputs are primary, which are estimated, which need expert review, and which assumptions must stay attached to the result.

AI can help teams move faster through structured information. It cannot turn weak supplier data into reliable input by itself. The useful work is separating what can be used confidently from what needs review or enrichment.

3. Look at which steps are still rebuilt by hand

Spreadsheets are usually the symptom, not the problem. Teams use them because data does not move cleanly between systems. They collect files, clean tables, compare versions, fill gaps, and rebuild calculations every time a new request comes in.That is manageable for an isolated project. It breaks down at portfolio scale.

The useful question is: which parts of the workflow are repeated often enough to standardize?

If teams keep rebuilding the same product structure, assumptions, review steps, or data mappings, that is a good place to start. Capturing those pieces makes the next cycle easier and gives AI-supported workflows something stable to build on.

4. Look at which outputs need to be defended

Manufacturers do not only need answers – they need answers they can explain.

A PCF result needs source data, assumptions, allocation logic, methodology, and traceability. A compliance result needs evidence behind the material, substance, supplier, regulation, and product variant. A Scope 3 calculation needs a clear link between activity data, emissions factors, and method.

For AI-supported workflows, the method matters as much as the answer. The practical question is not only “can AI produce this?” It is: what would someone need to review before they trusted it?

That review path should be visible from the start.

What AI-ready product data looks like

AI does not just need more data. It needs the right relationships between data: which material belongs to which component, which supplier provided which input, which dataset and methodology apply, and which assumptions were made.

Without that context, a PCF is just a carbon number with no lineage. A compliance result becomes a pass/fail answer no one can substantiate. A costing decision becomes a price estimate detached from its sourcing assumptions.

With that context, each becomes a result the business can trace and repeat.

AI-ready data does not mean perfect data. It means data structured well enough to support repeatable, traceable, defensible decisions. In practice, that means it is:

-connected to product structure
-traceable to source
-current enough to use
-enriched where internal data is missing
-governed by clear methodology
-reusable across LCA, PCF, Scope 3, compliance, costing, and product development
– reviewable by experts

The last point matters: AI should not replace expert review. It should make review faster and more consistent by giving teams structured input, surfacing gaps, and keeping the method attached to the result. AI should not replace expert review. For example, Makersite’s Chem AI can help make chemical data reviews faster and more consistent by turning incomplete inputs into structured, traceable models, surfacing gaps for expert review, and keeping the methodology attached to the result.

Internal company data is essential but rarely enough on its own. Manufacturers also need external datasets and governed gap-filling where internal data falls short, with assumptions recorded and dataset lineage kept visible.

That is what separates a one-off answer from a system the business can use again.

How to start with the data you have

Getting product data ready for AI does not mean waiting for a perfect data estate. A better starting point is to choose one workflow where the business already feels the pain and where enough data exists to make progress.

That could be a recurring customer PCF request. A product family with repeated LCA work. A compliance workflow where supplier evidence is hard to track.

Start there. Map the data needed for that workflow. Identify what already exists. Mark what is missing. Separate trusted inputs from estimates. Attach assumptions to the result. Decide where expert review is needed.

This gives teams a practical path from scattered data to a repeatable process. It also gives AI a clearer role: not producing unsupported answers, but helping teams work faster from structured inputs, visible gaps, and traceable methods.

The goal is trusted decisions

The companies that get value from AI will not be the ones with the most data. They will be the ones with product data structured well enough for AI, experts, and business teams to work from the same foundation.

That is where Makersite fits in, because Makersite helps manufacturers build that structured product data foundation, connecting product, supplier, environmental, cost, and compliance data so teams can evaluate decisions across the product lifecycle from the same model.

It supports expert judgment and makes well-grounded decisions easier to reach.

So before asking what AI can automate, start with one practical test: can your product data support a decision your team needs to repeat, trace, review, and explain? If it can, start there. If it cannot, identify which part of the foundation needs work first.