Life Sciences Guide to AI Readiness

AI may dominate industry headlines, but most companies just aren’t seeing the results.

A joint report by McKinsey and The Wall Street Journal found that only 1% of U.S. companies have successfully scaled AI, while 43% remain stuck in pilot mode.

This mirrors what we’re seeing across the midmarket pharma space. Organizations are eager to move fast, but foundational gaps keep getting in the way. Fragmented systems. Inconsistent governance. Information trapped in silos.

When these issues go unaddressed, models fail, insights misfire, and strategies stall. Until companies confront these realities head-on, AI will stay stuck in theory while competitors build on stronger foundations.

The message is clear: everyone’s investing, but very few are ready

Ask just about any IT leader what’s holding them back from scaling AI, and most won’t say “strategy.” They’ll say “systems.” But the reality is, it’s the absence of a true data spine

A data spine is more than an integration layer or a well-maintained CRM. It’s a reliable, consistent foundation that connects information across departments, platforms, and workflows.

When built intentionally, it becomes the single source of truth teams can trust to inform decisions, power automation, and support AI use cases.

At Conexus, we often work with companies that believe integration is complete because platforms are technically connected. However, the underlying data is still fragmented, poorly governed, or misaligned across teams

What happens is that business users don’t trust the outputs, and strategic insights stall before they scale.

This is especially common in mid-sized pharma environments, where the pressure to deliver visible results can lead to isolated data efforts— dashboards, data lakes, or pilots—without the foundation to support them.

Unlike large enterprises, mid-market firms don’t always have the luxury of multi-year data transformation programs, which makes the approach even more important. Leaders must prioritize building a flexible spine that supports today’s use cases while preparing for tomorrow’s.

The goal isn’t simply to connect systems. It’s to create a foundation on which people across the organization can confidently rely.

We help clients ground their data strategy in how the business runs—how teams work, how decisions are made, and where information breaks down.

Initial gains are essential. However, sustaining progress depends on how data is accessed and used, not just where it lives.

Democratizing data means creating clarity around access: who needs it, how they use it, and what support they need to make it meaningful.

We often see hesitation rooted in fear: misuse, regulatory exposure, or simply losing control. However, when managed with intention, access becomes a driver of ownership and accountability.

Gartner predicts that 60% of organizations will fail to realize the anticipated value of their AI use cases by 2027, primarily due to weak or incohesive data governance frameworks. This makes governance not just a compliance issue, but a cultural one.

We help clients build models where access doesn’t mean chaos. It means accountability. When teams trust the data, they engage with it, challenge it, and act on it.

AI readiness starts where most companies least expect it—in the questions they haven’t answered.

What data do we even have? Who owns it? What defines quality? How is information shared across functions when priorities shift?

At Conexus, this is precisely where we start. You can’t scale what you don’t understand. We help teams map what’s working, where friction lives, and what foundational choices must be made before AI use cases are viable.

The most successful AI efforts begin with clear executive direction. Before launching any initiative, companies need executive-level clarity on purpose, priorities, and guardrails.

That means defining:

• Your AI mission
  (e.g., “get products to market faster”)
• Acceptable use guidelines
• Business outcomes that matter most

Leaders often focus on the tools themselves, but foundational alignment determines whether those tools drive results. As Payne notes,

“Tools amplify alignment—they don’t create it.” That clarity has to come first.

Without top-down alignment, AI efforts risk becoming fragmented or misinterpreted. A simple, well-communicated mandate sets the tone and clears the path.

Strategic clarity only goes so far without a mechanism for action. AI initiatives require coordination, accountability, and visibility across functions, even in lean organizations.

Start by identifying who will guide and oversee your efforts. This could be a small, crossfunctional group tasked with reviewing use cases, defining risk thresholds, and aligning expectations. Assigning data ownership and setting basic SOPs creates the operational discipline needed to move forward without chaos.

Lightweight governance makes alignment repeatable and ensures every AI project stays connected to business outcomes, not just technical curiosity.

Once governance is in place, focus on reinforcing your data spine. This is where most organizations rush and later regret it.

Start by prioritizing what matters most: clean, connected, and compliant data. Identify critical sources, reduce duplication, and resolve inconsistencies that erode trust. Integration should serve the business, not just the systems.

Next, embed the proper controls. In life sciences, that means designing with compliance in mind from the beginning. Set up audit trails, define review processes, and ensure AI outputs can be monitored and explained. Keeping a human in the loop isn’t just best practice—it’s often required.

The fastest organizations don’t skip this step. They invest in it early and avoid rebuilding later.

Effective pilots begin with clearly defined goals and shared expectations. Define what you’re testing, what success looks like, and who needs to be involved from the start. When teams understand the goals and their role in the outcome, it’s easier to avoid miscommunication, slowdowns, or scope drift.

Build in transparency from the start. Users must understand how AI outputs are generated and why they’re being surfaced, especially in hightouch roles like field sales. As Payne explains,

“If a rep gets a next-best action but has no idea where it came from, they won’t trust it. Give them context. Give them a way to respond.”

Collect feedback early and often. A solid feedback loop helps refine the model, catch blind spots, and strengthen buy-in. Internal trust doesn’t come from dashboards—it comes from involvement

Once the pilot proves value, you can move quickly to scale, but don’t treat it as a finish line.

Capture what worked, document lessons learned, and apply them consistently. Keep monitoring model performance, tracking KPIs, and collecting feedback from users.

This isn’t a one-time rollout. It’s a shift in how the business operates and makes decisions. Payne says,

“If you haven’t built the launchpad, you’re not going to Mars. You’re not even getting off the ground.”

Invest in what’s next by maintaining what’s working. The most AI-ready teams don’t chase trends; they build durable systems that can evolve with the tech.