There is a fundamental disconnect plaguing AI initiatives across manufacturing. AI capabilities are advancing at breakneck speed, yet 93% of manufacturing professionals cite disconnected and siloed production systems as a major pain point in their operations today.

Or, as a manufacturing leader at Pfizer recently told me: “If I hear one more person mention agentic AI and then realize they’re using paper batch records, I’m going to scream.”

The problem also continues to grow as companies chase AI headlines while the foundation crumbles beneath them. For example, AI development in life sciences is spiking: the Food and Drug Administration (FDA) has now cleared more than 950 AI and machine learning medical devices, up from fewer than 400 in 2020.

The result of this next-gen, potentially life-saving development? Cool proofs of concept that never deliver scalable ROI because companies aren’t ready for production. It’s time for businesses to take a step back and fix that fundamental disconnect first.

Tackling the Integration Paradox

In a recent survey, 59% of manufacturing professionals identified integrated systems as the number one prerequisite for effectively deploying AI. Yet 24% cited integration issues as their top barrier to deployment.

In other words: we know what we need. Companies just haven’t done the work to get there.

More than half of life sciences organizations operate with disconnected digital systems. They have ERP systems, quality management platforms, and manufacturing execution systems, but these tools are essentially islands. These companies create moments of brilliance, but the disconnect prevents sustained forward movement.

At my current company, we began building data infrastructure to support analytics years ago. We consolidated data sources, established semantic consistency, and built integration pipelines. As AI capabilities accelerated, we realized we had accidentally built exactly what AI requires, meaning we can now run AI workflows at pace.

However, in most companies, executives will be dazzled by a demo, then someone spoils the party by asking: “How do we get this into the workflow of 500 employees?” The inevitable answer is they can’t. Their current systems are unable to access the data or integrate with existing workflows. It’s a fireworks show — impressive in the moment, but with zero lasting value.

The Four-Step Framework For Real AI Readiness

Here is the basic framework for companies that want AI to deliver actual ROI instead of just impressive demos:

Step 1: Build Your Digital Foundation

Start by replacing paper processes with digital systems, implementing real-time data capture, and establishing data integrity standards. If your critical data lives in banker boxes or binders, AI can’t touch it. These are the table stakes, and are not optional for any kind of AI readiness.

Step 2: Achieve Data Maturity

Consolidate data sources across systems. Tackle semantic consistency: Does “SKU” in your ERP mean the same thing as “item ID” in your CRM and “part number” in your quality management system? AI must understand the data with which it is working or results will be unpredictable and trust in the systems will evaporate before they can even gain traction.

Build integrations so systems actually communicate. Establish clear data governance, including who and what can access what data and the rules they have to follow. Without proper governance, you risk proprietary information leaking into public AI models that competitors can query.

This is the hard, time-consuming work, and where most companies fail. Nobody wants to do it because it’s not glamorous and it requires investment. But skip it, and you’re permanently stuck in proof-of-concept purgatory

Step 3: Establish AI Readiness

Only after your foundation and data maturity are solid should you move here. Identify specific use cases with measurable ROI, and assess whether you have sufficient data to support them, as many companies end up discovering they don’t. Define success metrics and plan pilot implementations using a risk-based approach, starting with lower-risk applications

Step 4: Deploy And Scale

Deploy AI solutions with human-in-the-loop controls. In regulated environments, AI recommends and humans decide; a binary effort that is non-negotiable for quality and compliance. Monitor performance continuously. Scale based on proven ROI, not executive enthusiasm.

Companies that execute all four steps achieve significant return on investment, including reductions in cost of maintenance and unexpected equipment downtime. But those returns only materialize when the foundation is solid.

Acknowledging the Competitive Reality

The AI race isn’t won by whoever has access to the latest foundation models. Those are open to everyone. It’s won by whoever did the unsexy foundational work that lets AI actually scale across operations.

Companies that invested in data infrastructure years ago are running at an incredible pace today. Companies still using paper batch records while talking about agentic AI? They’re already behind, and the gap widens daily.

You can chase AI headlines, or you can build the infrastructure that enables real ROI. Choose wisely, because once your competitors reach that inflection point where AI delivers compounding returns, catching up becomes exponentially harder.

The data work to become AI-ready isn’t glamorous, but it’s the only path to sustainable AI advantage.