Back to Basics Three Things to Get Right Before Investing in AI & ML Solutions

It’s no secret that AI is one of the hottest topics being discussed across all industries.

Within life sciences alone, AI-powered solutions hold the potential to transform everything from drug discovery and clinical trial design to patient access and commercial optimization.

But before we rush to implement the latest generative AI platform or predictive algorithm, there’s one important truth we can’t ignore: AI is only as effective as the data, infrastructure, and people behind it.

So, before your team panics about adopting the newest technology—or if you’re already mid-implementation and questioning the ROI—we encourage a collective exhale.

Instead of leaping toward the shiny object, let’s focus on building the right foundation.

Here are three critical steps to take before launching your next AI initiative:

1. Data, Data, Data!

We all know the saying: “garbage in, garbage out.” When it comes to AI, the quality of your data isn’t just important—it’s everything.

Before implementing any AI solution, life sciences organizations must ensure they have strong data fundamentals in place. This includes:

A comprehensive inventory of all data sources

Standardization and cleansing of both structured and unstructured data
Integrity checks and master data management across functions

A defined data governance model to drive accountability

According to IBM Watson Health, poor data quality costs the healthcare industry $314 billion annually, largely due to inefficiencies, rework, and poor decision-making¹.

Real-world example: A global biopharma company piloting an AI platform to predict patient eligibility for rare disease trials discovered that inconsistent clinical coding and unstructured physician notes reduced model accuracy by over 40%. After investing in a data harmonization effort—standardizing terminologies, cleaning records, and enriching datasets—they improved model precision by 31%, enabling faster site screening and reducing enrollment delays².

Bottom line: With all that in mind, before you launch an AI tool, ask yourself: do you trust your data enough to let it drive business decisions?

2. Cloud Infrastructure & Cybersecurity

Cloud infrastructure is no longer optional for AI success—it’s essential.

AI models, especially large language models (LLMs), require scalable computing power and seamless data access that only the cloud can efficiently support. Cloud platforms also improve collaboration, version control, and real-time data availability across global teams.

However, with great flexibility comes great responsibility—89% of healthcare and life sciences organizations experienced a data breach or cyber incident in the past two years, according to the HIMSS 2024 Cybersecurity Survey³. Given the sensitivity of life sciences data—PHI, proprietary research, IP—cybersecurity must be a core component of every AI implementation.

Real-world example: A mid-sized biotech deploying an AI-driven clinical trial matching tool migrated legacy clinical and EHR data to the cloud. A pre-launch audit exposed weaknesses in endpoint protection and access management. After bolstering encryption protocols and implementing real-time threat detection, the company successfully avoided a ransomware attack during its go-live window⁴.

Bottom line: if you’re moving fast with AI, your cybersecurity strategy needs to move even faster.

3. People & Process

Here’s the part most companies underestimate: technology is only as effective as the people using it.
AI tools promise automation and insights, but without proper training and oversight, they can quickly become misunderstood, misused, or ignored altogether.

Employees need clear policies on how to validate and interpret AI-generated content, along with training that reinforces ethical use and human accountability.

AI is not a panacea; it won’t replace the nuanced, cross-functional decision-making required in drug development, regulatory navigation, or patient engagement. But it can free teams from repetitive work, allowing them to focus on what matters most.

And yet, only 28% of life sciences employees feel prepared to responsibly use AI in their daily work, according to a 2024 Deloitte Insights survey⁵.

Real-world example: A commercial team at a specialty pharma company launched an AI-driven HCP segmentation tool. Early usage lagged because reps lacked context on how to use the insights in the field. After launching a targeted enablement program and reorienting KPIs around insight-driven actions—not just tool usage—adoption increased by 65%, and targeting precision improved by 30%⁶.

Bottom line: AI won’t be held accountable for poor business decisions—your people will. That’s why governance, training, and change management must be embedded in any AI rollout.

All in all, the promise of AI in life sciences is real—but only if your organization is ready for it.
In conclusion, before you train a model or launch a tool, ask yourself:

• Do we trust our data?

• Is our infrastructure secure and scalable?

• Are our people prepared to use this responsibly?

If not, the answer isn’t to wait on AI—it’s to build the foundation that will allow it to thrive. In life sciences, the organizations that get the basics right won’t just adopt AI. They’ll use it to lead.

References

IBM Watson Health, “The Cost of Poor Data Quality in Healthcare” (2023)
McKinsey & Company, “How AI is Reshaping Clinical Trials: The Role of High-Quality Data in Trial Acceleration” (2023)

HIMSS, “2024 Cybersecurity Survey – Healthcare and Life Sciences” (2024)
Forrester Research, “The State of Cloud Security in Life Sciences” (2023); scenario adapted from Project Outlier engagement

Deloitte Insights, “AI Readiness in Life Sciences: Bridging the Talent Gap” (2024)

IQVIA, “Real-World Impact of AI on HCP Segmentation and Field Force Productivity” (2024)