Case Study Johnson & Johnson: Why IT Leaders Are Turning to Purpose-Built AI Solutions
by Carsten Krause, June 13, 2024
In the rapidly evolving landscape of artificial intelligence, the focus has often been on large language models (LLMs) like GPT-4. However, a growing number of IT leaders are discovering that smaller, purpose-built AI models can offer substantial benefits. These specialized models are increasingly being adopted due to their efficiency, cost-effectiveness, and lower propensity for errors.
The Shift Toward Smaller AI Models
The recent surge in AI development has seen significant interest in LLMs, which are celebrated for their ability to handle a wide array of tasks. However, companies like Microsoft and Apple are pioneering the use of small language models (SLMs), recognizing their potential for specific, niche applications. Microsoft introduced its Phi-3 SLMs in April (source), and Apple followed suit with eight small language models designed for handheld devices (source).
Dave Bullock, CTO at UJET, a contact-center-as-a-service provider, highlights the advantages of SLMs. “SLMs can be tailored to perform a particular function with a defined data set, allowing organizations to have complete control over data utilization,” says Bullock (source).
Low Barriers to Entry
One of the most compelling advantages of SLMs is their affordability and accessibility. Unlike LLMs, which often come with high licensing costs, SLMs can be implemented at a fraction of the cost. Open-source platforms like Hugging Face provide numerous free-to-use AI models that can be customized to meet specific requirements (source).
“You might already have a GPU in your video game machine, or you can just rent GPU power from a provider to train your model,” Bullock explains (source). This ease of access lowers the entry barrier for companies, enabling them to experiment with AI without significant financial investment.
Johnson & Johnson: Pioneering AI in Healthcare
Overview
Johnson & Johnson, a global leader in healthcare, has been at the forefront of leveraging AI to revolutionize its operations. By focusing on purpose-built AI models, the company has streamlined its drug discovery and development process, achieving remarkable efficiency and accuracy.
The adoption rate of AI in healthcare has seen a significant increase from 2019 to 2023. Starting at 20% in 2019, it has climbed to 75% by 2023. This trend reflects the growing confidence in AI’s ability to enhance healthcare delivery, driven by advancements in both technology and its applications in medical diagnostics, patient care, and operational efficiency.
Source: Statista – AI in Healthcare
AI in Drug Discovery
Johnson & Johnson has implemented smaller, purpose-built AI models to analyze biological data, significantly reducing the time and cost associated with bringing new drugs to market. These AI models can process vast amounts of data to identify potential drug candidates faster and more accurately than traditional methods.
Dr. Mathai Mammen, Global Head of Research and Development at Johnson & Johnson, explains, “By employing focused AI models, we’ve enhanced our ability to pinpoint promising drug candidates, accelerating our R&D efforts” (source).
Reducing Development Time
The traditional drug development process is notoriously lengthy and costly. Johnson & Johnson’s use of AI has cut down this timeline significantly. The AI models employed by the company can quickly analyze genetic data, predict molecular behavior, and simulate drug interactions, thus speeding up the initial phases of drug discovery.
AI has drastically reduced the time required for various phases of the drug discovery process. Traditional methods took up to 60 months for clinical trials, whereas AI models have reduced this time to 36 months. This acceleration is crucial for getting new, life-saving drugs to market more quickly.
Source: Nature Biotechnology – AI in Drug Discovery
Cost Efficiency
The cost of developing a new drug can reach billions of dollars, but Johnson & Johnson’s AI-driven approach has managed to reduce these expenses. By using specialized AI models, the company can identify and eliminate ineffective compounds early in the process, saving both time and resources.
The use of AI in drug development has led to a substantial reduction in costs over the years. The cost reduction has increased from 5% in 2019 to 40% in 2023. This significant cost saving is attributed to AI’s ability to streamline processes, reduce trial-and-error in drug discovery, and enhance data analysis capabilities.
Source: McKinsey & Company – The Impact of AI on Drug Development Costs
Enhancing Precision and Control
Smaller AI models allow for a high degree of precision and control, which is crucial when dealing with complex biological data. These models can be tailored to specific tasks, such as identifying biomarkers for disease or predicting patient responses to treatments. This precision not only improves the accuracy of predictions but also ensures that the data used is relevant and specific.
Predictive analytics in healthcare has seen marked improvement in accuracy due to AI. Traditional methods achieved around 70% accuracy in 2019, improving marginally to 78% by 2023. In contrast, AI-driven analytics started at 75% accuracy in 2019 and reached 90% by 2023, demonstrating AI’s superior ability to analyze and predict outcomes.
Source: Journal of Medical Internet Research – AI in Predictive Analytics
Real-World Applications
One of the standout applications of Johnson & Johnson’s AI initiative is in the field of oncology. The company has developed AI models that can analyze patient data to identify potential cancer treatments personalized to individual patients. This personalized approach increases the likelihood of treatment success and reduces adverse effects.
Collaboration and Open Innovation
Johnson & Johnson has also embraced a collaborative approach to AI innovation. By partnering with research institutions and leveraging open-source AI tools, the company has been able to stay at the cutting edge of AI technology. This collaborative model not only accelerates innovation but also ensures that the benefits of AI are widely distributed across the healthcare ecosystem.
The Broader Implications of Purpose-Built AI
Low Barriers to Entry
One of the most compelling advantages of SLMs is their affordability and accessibility. Unlike LLMs, which often come with high licensing costs, SLMs can be implemented at a fraction of the cost. Open-source platforms like Hugging Face provide numerous free-to-use AI models that can be customized to meet specific requirements (source).
Practical Applications in Other Industries
While Johnson & Johnson’s case study focuses on healthcare, the principles of purpose-built AI can be applied across various industries. For example, financial services can use SLMs to detect fraud, retail companies can optimize inventory management, and manufacturing firms can predict equipment failures.
Choosing the Right AI Tool
When deciding on the appropriate AI model, CIOs and CDOs must consider various factors such as response time, cost, data privacy, and specific needs. John Bodrozic, co-founder and CIO at HomeZada, recommends running short, rapid experiments to determine if an AI solution fits their requirements. This iterative approach prevents wasted resources on ineffective AI projects (source).
David Lloyd from UJET warns against the impulsive adoption of AI. “CIOs need to ensure that AI is the right solution for their problem. Sometimes, traditional methods might be more effective,” he advises (source).
Conclusion: The CDO TIMES Bottom Line
In the quest for AI-driven innovation, bigger is not always better. Purpose-built small language models and traditional AI tools offer tailored, cost-effective solutions for specialized tasks. By focusing on specific needs and leveraging accessible AI technologies, companies like Johnson & Johnson are achieving significant benefits without the overhead of large language models.
As the AI landscape continues to evolve, the strategic use of purpose-built AIs will be crucial for organizations aiming to maximize their data’s potential while maintaining control and efficiency. IT leaders must assess their unique requirements and experiment with various AI models to identify the most suitable and effective solutions.
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