{"id":12617,"date":"2026-04-06T13:03:05","date_gmt":"2026-04-06T20:03:05","guid":{"rendered":"https:\/\/projectoutlier.com\/?p=12617"},"modified":"2026-04-17T13:09:01","modified_gmt":"2026-04-17T20:09:01","slug":"back-to-basics-three-things-to-get-right-before-investing-in-ai-ml-solutions","status":"publish","type":"post","link":"https:\/\/projectoutlier.com\/?p=12617","title":{"rendered":"Back to Basics Three Things to Get Right Before Investing in AI & ML Solutions"},"content":{"rendered":"\t\t
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Back to Basics Three Things to Get Right Before Investing in AI & ML Solutions<\/h2>

It\u2019s no secret that AI is one of the hottest topics being discussed across all industries.<\/p>

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.<\/p>

But before we rush to implement the latest generative AI platform or predictive algorithm, there\u2019s one important truth we can\u2019t ignore: AI is only as effective as the data, infrastructure, and people behind it.<\/p>

So, before your team panics about adopting the newest technology\u2014or if you’re already mid-implementation and questioning the ROI\u2014we encourage a collective exhale.<\/p>

Instead of leaping toward the shiny object, let\u2019s focus on building the right foundation.<\/p>

Here are three critical steps to take before launching your next AI initiative:<\/p>

1. Data, Data, Data!<\/h3>

We all know the saying: \u201cgarbage in, garbage out.\u201d When it comes to AI, the quality of your data isn\u2019t just important\u2014it\u2019s everything.<\/p>

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

A comprehensive inventory of all data sources<\/p>

Standardization and cleansing of both structured and unstructured data
Integrity checks and master data management across functions<\/p>

A defined data governance model to drive accountability<\/p>

According to IBM Watson Health, poor data quality costs the healthcare industry $314 billion annually, largely due to inefficiencies, rework, and poor decision-making\u00b9.<\/p>

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\u2014standardizing terminologies, cleaning records, and enriching datasets\u2014they improved model precision by 31%, enabling faster site screening and reducing enrollment delays\u00b2.<\/p>

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?<\/p>

2. Cloud Infrastructure & Cybersecurity<\/h3>

Cloud infrastructure is no longer optional for AI success\u2014it\u2019s essential.<\/p>

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.<\/p>

However, with great flexibility comes great responsibility\u201489% of healthcare and life sciences organizations experienced a data breach or cyber incident in the past two years, according to the HIMSS 2024 Cybersecurity Survey\u00b3. Given the sensitivity of life sciences data\u2014PHI, proprietary research, IP\u2014cybersecurity must be a core component of every AI implementation.<\/p>

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 window\u2074.<\/p>

Bottom line: if you\u2019re moving fast with AI, your cybersecurity strategy needs to move even faster.<\/p>

3. People & Process<\/h3>

Here\u2019s 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.<\/p>

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

AI is not a panacea; it won\u2019t 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.<\/p>

And yet, only 28% of life sciences employees feel prepared to responsibly use AI in their daily work, according to a 2024 Deloitte Insights survey\u2075.<\/p>

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\u2014not just tool usage\u2014adoption increased by 65%, and targeting precision improved by 30%\u2076.<\/p>

Bottom line: AI won\u2019t be held accountable for poor business decisions\u2014your people will. That\u2019s why governance, training, and change management must be embedded in any AI rollout.<\/p>

All in all, the promise of AI in life sciences is real\u2014but only if your organization is ready for it.
In conclusion, before you train a model or launch a tool, ask yourself:<\/p>