We talk a lot about AI in R&D - but the truth is, most pharma and chemical companies can’t even access their data effectively. Here’s why:

We talk a lot about AI in R&D - but the truth is, most pharma and chemical companies can’t even access their data effectively. Here’s why:

Every year, R&D teams across pharmaceutical companies and CDMOs run tens of thousands of experiments.

These are high-value data points, containing information about process conditions, yields, impurities, and other critical metadata.

But most of that data ends up locked away:

• Buried in disconnected ELNs
• Trapped in inconsistent spreadsheets
• Saved locally on machines (like PCs for HPLCs)
• Or worse: remembered only by the person who ran the campaign.

When a new project begins, chemists might have no idea that similar work has already been done - by a colleague in the same company, maybe just two doors down.

So they start from scratch, turning to literature search or discussions with colleagues, not because they want to, but because the systems around them offer no better option.

This is where AI and advanced modeling fall short - not because the algorithms aren’t good enough, but because machine learning models can’t learn from data they can’t see.

To make matters worse, there’s a deeper challenge that few people talk about: site-to-site fragmentation.

Different locations in the same company often use different data systems, naming conventions, or documentation styles - making collaboration and reuse incredibly difficult.

So we end up in a world where companies are sitting on a goldmine of data, but still making decisions like they’re data-poor.

At ReactWise, we’re tackling this head-on.

We help pharma and CDMO teams turn internal experimental data into a strategic asset - across teams, across sites, across time.

Our platform integrates with increasing electronic lab notebooks, supports Excel uploads, and makes it easy to structure, track, and reuse data from single experiments to entire campaigns.

This makes your data ready for AI - enabling smarter predictions, fewer experiments, and faster R&D cycles.

What’s the biggest internal barrier to collaboration you’ve seen in your organization?

I’d love to hear how you're tackling it - or how we might be able to support you.

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