What It Means to Be AI-Ready – and Why Most Chemical Companies Fall Short

Artificial Intelligence (AI) is no longer a futuristic concept for the chemical industry. It’s already transforming how companies approach commercial operations: sales, marketing, and customer engagement.

Tools like AI-powered chat agents, product recommendation engines, and numerous other use cases aim to help your team work smarter, act faster, and deliver better service, even in highly technical environments.

(Watch our latest webinar, AI-Powered Sales & Marketing Enablement for Chemical Companies to learn more.)

McKinsey reports that companies empowering sales teams with technology and automation have seen consistent efficiency uplifts of 10–15%. These improvements come from freeing commercial teams to spend more time on strategic work and less on manual, back-office tasks. McKinsey’s research indicates that generative AI could increase marketing productivity by approximately 10%—equivalent to around $463 billion globally—and enhance sales productivity by about 4% (~$486 billion). Additionally, it could yield an extra 3–5% productivity gain on current global sales spending, making it one of the most significant commercial opportunities in years.

Yet, the reality remains sobering: AI projects are failing at an alarming rate. Gartner predicts 60% of AI initiatives will be abandoned by 2026 if they are not supported by AI-ready data. And while most companies plan to use AI to improve operational efficiency, nearly 80% admit they are unprepared due to weak data foundations (MIT Technology Review Insights & Snowflake). These are serious gaps that represent material risk to ROI, customer trust, and competitive edge.

For chemical companies, the challenge is even greater. Product data is complex, fragmented across systems, and often trapped in unstructured formats like PDFs or spreadsheets. Without clean, centralized, and structured data—covering specifications, certifications, safety documents, and application guidelines—AI tools struggle to consistently deliver relevant and accurate results.

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The Case for Structured Data: The Under Apreciated Foundation of AI

When it comes to AI, most conversations start with the tools, but the single most significant predictor of success isn’t the LLM itself—it’s the quality and organization of the data those tools rely on.

Think of it like building a house. Unstructured data is a chaotic pile of lumber, bricks, and pipes scattered in a field. In the chemical industry, that might mean a salesperson’s notes buried in a Word document, a PDF of an SDS, or a chemist’s email describing a product’s composition. For AI, making sense of this is a slow, error-prone process. When you leave it to AI to identify what each “piece” is, you permit it to guess how they fit together, and hope it builds something structurally sound. This is one of the fastest paths to AI “hallucinations”—outputs that sound convincing but are wrong.

Structured data, on the other hand, is like a neat stack of labeled, pre-cut materials, complete with a detailed blueprint. In practice, this could be a database with clearly defined fields for product name, chemical formula, CAS number, application data, function, and regulatory information, etc —all verified, current, and easy for AI to interpret. 

With disorganized data, your internal teams might still manage to work around it—cross-checking files, clarifying specs, and manually correcting errors. But could you confidently power customer-facing AI tools like product recommendation engines, formulation advisors, or technical support bots using that same messy foundation? Absolutely not. Without structure, AI can’t reliably distinguish between what’s relevant for each use case or the end user, and you risk giving customers inaccurate or incomplete information, or worse, information they were not supposed to have.

Structured data solves this by separating information into specific, well-defined fields, which add context and clarity. This allows you not only to make it easier for AI to interpret, but also to specify precisely which information is applicable for which tool or audience. The AI doesn’t just “find data”—it understands where and how that data should be applied.

For chemical companies, this distinction is especially critical. Product data isn’t just a list of ingredients; it’s a complex web of specifications, regulatory certifications, application guidelines, and safety considerations. Storing that information in inconsistent Excel sheets, flat PDF files, or disconnected ERP entries creates a wall between AI and the insights it’s supposed to deliver.

In technical, safety-sensitive industries, there’s no room for “close enough.” Without structured, harmonized, and centrally managed data—complete with context for both internal systems and customer-facing tools—generative AI simply can’t operate with the precision, reliability, and scale required to make a real commercial impact.
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What Does It Actually Mean to Be AI-Ready?

Being “AI-ready” isn’t about buying more AI—it’s about ensuring the foundation is solid. From our work in the chemical industry, we’ve identified three factors that enhance AI readiness.

3 Things That Add to AI Readiness:
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1. Structured, Centralized Data – Product specs, certifications, brochures, and application details stored in clearly defined, consistent fields so AI can instantly identify, retrieve, and apply them without guesswork. This removes the risk of conflicting information between departments.
   
   
2. Connected Systems – Data flowing seamlessly between ERP, CRM, regulatory, and customer-facing tools, ensuring every platform is working from the same verified source. This keeps your AI from operating in isolation or producing outdated results.
   
   
3. Engaged Teams – Teams fully committed to using AI, backed by continuous training to enhance their skills in both the technology and its practical application. This includes hands-on training, role-specific use cases, and embedding AI into daily workflows so it becomes a natural part of how work gets done. When teams possess the skills and confidence to leverage AI effectively, adoption accelerates, performance improves, and the technology delivers on its promise.

Whether it’s a technical sales rep getting precise formulation suggestions from a virtual product expert or a marketer instantly finding the right SDS for a prospect, adoption only sticks when tools consistently deliver reliable, actionable results. Without that, even the most advanced AI can become shelfware.
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How ionicPIM Bridges the AI Readiness Gap

ionicPIM serves as the single source of truth for your product information. It ingests and organizes data from across ERP, LIMS, regulatory databases, and marketing systems, then harmonizes it into structured, searchable fields. Every product attribute—from chemical formula and CAS number to safety certifications and application guidelines—is enriched and stored in a consistent format that AI tools can immediately understand and act on.

Because it integrates directly with your commercial systems, ionicPIM ensures that every AI-powered tool, like those in the Alchemist AI suite, pulls from the same, verified dataset. That means:
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• No more conflicting specs between sales and marketing.
• No more outdated safety data slipping into customer-facing channels.
• No more wasted AI potential because the inputs are unreliable.

The result is a true AI-ready environment, where data flows freely between teams, AI tools deliver accurate and actionable outputs, and commercial operations run with the speed and confidence today’s market demands.
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Take the First Step Toward AI Readiness

The difference between companies that thrive with AI and those that struggle isn’t about luck—it’s about preparation. In the chemical industry, preparation begins with structured, centralized product data and the systems to maintain its seamless flow.

If your data is scattered, inconsistent, or locked in static formats, every AI initiative you launch will work harder, cost more, and deliver less. The good news? You can fix that—starting today.

Download our AI Readiness Checklist to assess your organization's current state and identify the gaps that need to be addressed.

Learn more about ionicPIM and see how it creates the data foundation AI needs to deliver accurate, reliable, and scalable results across your commercial operations.

Your next AI project doesn’t have to be a risk. With the right foundation, it can be your competitive advantage.