Make technical expertise usable wherever buying happens. See the three foundations.
Advanced materials are not bought in a straight line
In advanced and engineered materials, a purchase moves through application research, technical evaluation, specification, validation, procurement and commercial approval, and a different person is often responsible for each stage.
A design engineer researches candidate materials against an application's mechanical, thermal or chemical requirements. A technical buyer or specifier tests the material and writes it into a spec. Procurement then negotiates terms, and a commercial approver signs off the account.
A distributor or channel partner frequently sits in the middle of that chain, adding a hand-off before the account ever reaches a manufacturer's own sales team.
Each hand-off is a place where context can be lost.
The application detail a design engineer captured in week one doesn't automatically survive to the procurement conversation in month six. A technically superior material can be dropped from consideration, not because it performs worse, but because its application knowledge never reached the person making the next decision.
This is why specification-led buying in advanced materials rewards the supplier who keeps application knowledge attached to the buyer's actual decision, not the supplier with the longest datasheet library.
Growth and digital experience: make expertise useful before the sales conversation
The advanced-materials buying journey starts long before a sales conversation, in searches, comparisons and technical questions a buyer runs alone, and increasingly puts to an AI assistant. Digital experience and AI visibility decide whether your application knowledge is part of that early research, or whether a competitor's is.
Get found in the questions buyers actually ask
A buyer researching a material rarely searches your product name. They search the application: the temperature range, the chemical exposure, the regulatory environment, the failure mode they're trying to avoid.
Content that answers those application questions directly, rather than only listing specifications, gets found by search engines and by AI systems assembling an answer. Content structured around a buyer's actual question earns citation. A page structured only around a part number does not.
Get believed when the claim is technically consequential
A materials buyer's next decision can affect a production line, a certification or a safety outcome, so a technical claim needs verifiable evidence, not just an assertion.
A performance claim without its supporting evidence, whether that's test data, an application note or a named use case, is a claim a technical buyer will not act on. The digital journey needs to carry that evidence next to the claim, not behind a separate request-a-datasheet form.
Get chosen through a coherent digital journey
A buyer who can research applications, compare materials and read technical evidence online still wants a sales engineer, distributor or application specialist for the questions only a person can answer.
A coherent digital journey means the buyer who's done that research arrives at the sales conversation already informed, not starting from zero.
Conversion, in this context, means giving that buyer an obvious next step, not another form between them and an answer.
Sales and modern GTM: give commercial teams context they can act on
Once a buyer engages, a sales team can only move as fast as its account information lets it. Effort doesn't fix a stale or incomplete record.
Marketing and commercial leaders in this industry describe the same underlying problem in different words: poor data quality, out-of-date information, incomplete records, and no reliable way to tell which accounts or opportunities deserve attention today.
A sales team that can see an account's site or facility, the specific application in play, who sits in the buying group, and what specification stage the opportunity has reached, can prioritise a serious specification-led opportunity over a generic enquiry.
A sales team working from a stale spreadsheet or a CRM record nobody has touched in months cannot make that call.
The hand-off problem doesn't stop at marketing and sales. Distributors, sales engineers and application or technical-service teams all touch the same account at different points, and each one can either preserve or strip the context the next person needs.
A distributor closing a deal without logging the application detail forces a technical-service team to rediscover it from scratch when the account calls with a processing question a year later.
Reliable, specification-stage-aware account data is a capability to build toward, not the common starting point in advanced materials today. Getting it right means every team touching an account, from marketing through to technical service, works from the same current picture instead of rebuilding it from memory each time.
Strategy and AI: decide what the business needs to become
Before AI can support this buying journey reliably, an advanced and engineered materials business has to answer questions no AI tool answers on its own.
Which product, application and customer knowledge actually needs to be findable and kept current, rather than living in one engineer's inbox?
Which decisions can AI reasonably support, such as surfacing a relevant application note or drafting a first-pass response, and which decisions need to stay with a qualified person who signs their name to it, such as a material's suitability for a safety-critical application?
Who owns the quality and permissions on that knowledge once AI systems start drawing on it? And what outcome would actually prove a pilot was worth running, rather than just worth demonstrating?
These are governance and knowledge questions before they're tooling questions. A business that hasn't answered them is not ready to let AI touch a customer-facing decision, whatever the underlying model can do.
The foundation for agent-ready buying
Growth, Sales and Strategy and AI aren't three separate initiatives. They depend on the same underlying foundation, and a gap in one weakens what the other two can do.
| Foundation | What breaks without it | What it enables |
|---|---|---|
| Knowledge | Application expertise stays locked in individual experts' heads or unstructured documents | Technical knowledge that content, sales and any permitted AI system can draw on consistently |
| Digital experience | Buyers can't self-serve the research they'd rather do alone, so they disengage before a conversation starts | A journey that earns findability and belief before the sales conversation |
| Commercial data | Sales can't distinguish a serious specification-led opportunity from a generic enquiry | Account, application, buying-group, specification-stage and freshness context that supports prioritisation |
| Motions | Context gets stripped at every hand-off between marketing, sales, distributors and technical service | Feedback and context that survive the hand-off instead of resetting it |
| AI operating model | AI gets added on top of fragmented knowledge and weak data, and amplifies the ambiguity already there | Bounded decisions, human attestation and clear ownership that make AI trustworthy where it's used |
Each foundation pays off on its own.
Better knowledge and digital experience improve buyer self-service even for a company running no customer-facing AI at all. Better commercial data improves ordinary sales prioritisation whether or not an AI system ever touches the account.
Treat this as an operating foundation to work toward, not a maturity score or a certification. That's what makes buying agent-ready: not a single AI feature, but knowledge, experience, data and motions that hold together well enough for an AI operating model to sit on top of them.
There's no single number that tells a leadership team it has arrived. There's only which of these five is currently the weakest, and what that weakness is costing the business.
What this looked like for an advanced-materials manufacturer
Working with a FTSE-250 advanced materials manufacturer, Graph's Growth and digital-experience work grew product, industry and buyer-related AI Overview appearances from 20 to 108 between October 2025 and August 2026, tracked via SEMrush.
CTA clicks on the pages Graph optimised rose 440%, measured through Google Analytics.
Site-wide organic traffic grew 27% year on year via SEMrush, from what is still a limited optimisation scope.
These are three separate results from Growth and digital-experience work; they do not establish a Sales or Strategy-and-AI outcome for this client, and those threads remain educational until their own governed proof exists.
Start with the part of the buying system that is holding the rest back
Identify the thread that's currently the constraint on the other two, then start there.
Growth and digital experience: Digital Experience
If buyers can't find or evaluate your application knowledge before they talk to a person, the digital journey is where to start. Explore Digital Experience.
Growth and digital experience: AI Visibility
If competitors appear in AI-generated answers to buyer questions and you don't, that's a gap worth closing on its own terms. AI Visibility.
Sales and modern GTM: Go to Market
If sales is working from stale or incomplete account context, commercial data and motion design are the constraint. Explore Graph's Go to Market approach.
Sales and modern GTM: Trade Show Playbook
Account and hand-off discipline matters at events too: a booth conversation either becomes a usable record afterwards or doesn't. Trade Show Playbook.
Strategy and AI
If the open question is what your business needs to become before AI can be trusted with a customer-facing decision, that's a strategy question first. Explore Graph's approach to AI.