AI, Data, and Reality: What 2024 Taught Us—and What Comes Next

Introduction

In a recent conversation with RSK Life Science Media, ZONTAL’s VP of Product Management and Marketing, Dan DeAlmeida, shared a candid perspective on the state of the market, the reality of AI adoption, and where life sciences organizations are actually heading.

The discussion reflected on 2024 as a year of rapid change—and even faster expectations. But beneath the surface, a more grounded story is emerging.

One centered not on hype, but on data.

ZONTAL’s Perspective: Data as a Long-Term Asset

At its core, ZONTAL operates on a simple but powerful premise: data should remain usable over time.

As a digital preservation platform, ZONTAL ingests data from across instruments, systems, and workflows, normalizes it into standard formats, and makes it accessible for analysis, visualization, and reuse.

What makes this approach different is the philosophy behind it.

Data is not locked into systems. It remains portable, FAIR-aligned, and accessible—allowing organizations to use it across tools, platforms, and future technologies.

In a world increasingly driven by AI, that foundation is becoming critical.

2024: A Transformative—but Noisy—Year

When asked to describe 2024 in a single word, Dan’s answer was clear: transformative.

The rapid rise of generative AI pushed organizations across life sciences to rethink their roadmaps. Nearly every company began exploring how AI could fit into their strategy.

But with that excitement came noise.

Many organizations rushed to adopt AI without clearly defining what it meant for their business. In some cases, initiatives were launched without clear use cases. In others, existing data projects were deprioritized in favor of perceived quick wins.

The result was a disconnect between ambition and execution.

The Reality of AI Today

Despite the excitement, the reality of AI today is more limited—and more nuanced—than many expect.

Generative AI tools are powerful and creative, but they remain largely passive. They respond to prompts rather than actively driving decisions.

True intelligence, as Dan points out, would not wait to be asked.

This distinction matters. While current tools can accelerate tasks like summarization or content generation, they do not replace the need for structured data, defined workflows, and human expertise.

In many ways, the industry is still in the early stages—closer to experimentation than full transformation.

Why Data Still Comes First

One of the clearest themes from the discussion is that AI success depends entirely on data quality.

If data is incomplete, inconsistent, or poorly structured, AI will simply amplify those issues.

ZONTAL’s focus throughout 2024 reflected this reality. Efforts centered on ensuring that data is enriched with metadata, linked through lineage, and preserved in standardized formats.

This is what makes data AI-ready.

Not the model itself—but the structure and context behind it.

Key Achievements: Building for AI Readiness

Over the past year, ZONTAL advanced several capabilities aligned to this vision.

The introduction of chemistry-aware data enables users to search by chemical structures and substructures, bringing a new level of context to scientific data.

At the same time, the launch of a universal ELN archive allows organizations to consolidate and decommission legacy systems while preserving full experimental records—including audit trails and attachments.

These developments are not just feature enhancements. They represent a shift toward making all scientific data accessible, connected, and reusable—regardless of its origin.

The Market Challenge: AI Without a Plan

Across the industry, one of the biggest challenges remains the lack of clear use cases.

Organizations know they want to leverage AI, but often struggle to define how it will create value. This leads to fragmented initiatives, shifting priorities, and, in some cases, the defunding of foundational data projects.

There is also a growing “chicken-and-egg” dynamic.

Companies expect AI to drive efficiency, but without the right data infrastructure, those efficiencies cannot be realized. At the same time, investments in data are delayed in favor of AI experimentation.

Breaking this cycle requires a return to fundamentals—starting with data strategy.

A Shift Toward Platforms, Not Tools

Another notable trend is how organizations are rethinking technology itself.

Rather than viewing systems like ELNs, LIMS, or SDMS as standalone products, companies are beginning to treat them as components within a broader platform strategy.

The focus is shifting from individual tools to integrated ecosystems—solutions that can operate across the entire organization and evolve over time.

This reflects a more mature approach to digital transformation. One that prioritizes long-term value over short-term implementation.

Looking Ahead: From Hype to ROI

Heading into 2025, there is growing optimism—but also a sense of recalibration.

Organizations are beginning to move past the initial wave of AI hype and ask more practical questions:

What is the return on investment?
What problems are we actually solving?
How does this improve our operations?

This shift toward measurable outcomes is likely to define the next phase of digital transformation.

AI will remain central—but it will be evaluated through the lens of impact, not novelty.

The Bigger Lesson

Perhaps the most important takeaway is this:

AI is not a shortcut.

It is an outcome of getting data, systems, and workflows right.

The same pattern has played out before with other technologies—promising visions followed by the realization that execution is harder than expected.

But this time, the industry is learning faster.

And as that happens, the focus is returning to what has always mattered most:

Clean, connected, and usable data.