Accenture recently published a cross-industry report titled AI: Built to Scale, from Experimental to Exponential. The report examines how over 1,500 companies are scaling Artificial Intelligence (AI) – as well as the value it unlocks – while revealing strategies for successfully adopting transformation. It provided insights and pathways that are essential to biopharma companies, who are asking how they can scale AI to become industrialized for growth.
Many biopharma companies face critical challenges including rising R&D costs, diminishing pipelines, entrance of non-traditional players and competitive pressures to offer digitally enabled, personalized customer experiences.
In this environment, AI is key to survival and growth – a viewpoint shared by a large majority of the pharma and biotech executives included in our study. Nearly all executives surveyed view AI as a critical enabler for most or all their strategic priorities – and almost 80% struggle to scale AI despite our cross-industry research showing that there is, on average, a $110 million ROI gap between companies who have successfully scaled AI and those who have not.
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Say they must leverage AI to achieve their growth objectives
Agree that if they don’t scale AI in the next five years, they risk going out of business entirely
Acknowledge they know how to pilot, but struggle to scale AI across the business
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Becoming industrialized for growth
So what are the biopharma’s that have successfully scaled AI doing differently? Our research showed that “Strategic Scalers”, on average, scaled 114 AI applications over the past three years, compared to only 53 for the laggards still in the “Proof of Concept” stage.
Strategic Scalers are embedding AI across enterprise functions, supply chain & manufacturing, R&D and commercial functions. Here’s how:
1. Embedding at the core of enterprise functions & supply chain
Maintaining employee engagement & effectiveness is critical for developing and retaining key talent. In our experience, Strategic Scalers are leveraging AI technologies to perform automated skills and work activity assessments, as well as anticipate future needs. This improves the likelihood of talent success in role, promotes upskilling and ensures key talent are focused on the highest value activities.
Going forward, AI will increasingly be more critical to finding the right candidates in the recruiting process. By using models that match candidates to roles based on skills and proficiency levels, companies can save time and effort while improving candidate success rates in a time when in-person interviews are not possible.
AI-driven search technologies are also enabling a more efficient workforce through semantic search algorithms and faster information retrieval.
In Supply Chain and Manufacturing, AI at Scale provides end-to-end visibility in real-time, allowing for improved alignment between product development and manufacturing schedules. It also helps maximize equipment reliability and utilization, optimize inventory and distribution channels, and leverage digital twins across every tower.
2. Accelerating drug discovery and clinical trials
Unfortunately, many Life Sciences (LS) companies face stagnant R&D. Strategic Scalers deploy significant resources to apply AI in digital R&D and use the technology to improve the speed, focus, and quality of drug discovery and development, getting meaningful therapies to patients faster.
For Strategic Scalers, AI is a foundational enabler deeply embedded in their R&D operating models. This includes: deploying machine learning models to improve target identification and lead optimization, tuning simulation models to design compounds and understand mechanisms of action, biomarkers to identify patients for clinical trials, machine learning to predict adverse reactions to prescribed medication, and identifying target patient population and profiles. Strategic Scalers are not just driving these as one-off use cases or POCs, but rather have AI as a key enabler of their drug discovery, clinical, pharmacovigilance, and drug launch processes.
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Accenture worked closely with Illinois Institute of Technology, leveraging AI to determine the best places to reopen clinical trials for Hepatitis-C and Alzheimer’s patients during the COVID-19 pandemic. They sought to answer several questions: what areas are the most impacted by COVID-19? Who are potential Hep-C and Alzheimer’s patients in these areas? What areas are safe – and will continue to be safe – for these patients to continue trials? Which clinical trials should continue, stop or be re-designed?
To answer these questions, the team examined patient demographics, trial location availability, and COVID-19 prevalence at potential trial locations. The use of AI in this process allowed for potentially life-saving medicines to continue development while minimizing the risk of COVID-19 on study participants and health care professionals.
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3. Driving the patient and HCP experience
Biopharma’s scaling for growth are developing next generation commercial models with AI-driven, personalized experiences for healthcare providers (HCPs) and patients. For these companies, an intelligent decisioning engine (IDE) powered by AI is used to orchestrate customer engagement across all channels – with continuous learning loops optimizing these experiences over time.
For example, deep learning models can be applied to HCP and patient data to automatically alert the rep to changes in market share, HCP prescribing behavior or increasing competitor sales. These AI-driven recommendations are used to personalize the HCP and patient experience.
Battling the barriers
There are several reasons why biopharma companies may be slow to go “all in” on AI and move beyond the “Proof of Concept” stage. Respondents to our survey identified three key barriers:
- Lack of culture for change
- Lack of buy-in from employees
- Lack of a clear roadmap
The report showed that lack of culture for change was a barrier 53% of the time – 10% more than the global average in other industries. This may be due to legacy practices and a low tolerance for risk, but our 2019 Technology Vision also showed that real cultural shifts need to begin in the c-suite, where top-down leadership can help drive transformation. With the top-down leadership defining targets and purpose of change, they can inspire employees to get on board while creating a clear roadmap for transformation.
Companies achieving success scaling AI initiatives are more likely than their Proof of Concept counterparts to ensure their employees are prepared for the journey:
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Fully understand how AI applies to role
Understand and implement responsible AI
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Interestingly, LS companies ranked funding of AI as less of a barrier than the global average (29% LS vs 38% cross-industry). LS companies, on average, are still spending more than double on scaling AI with 41% spending more than $101m, compared to only 19% of the cross-industry sample set.
Success in scaling is not dependent on the amount of money invested – so the question becomes: could biopharma companies get an equal success rate in scaling AI by spending less? Moreover, what is the playbook for pharma companies to overcome these challenges and become industrialized for growth?
In our next post, we’ll discuss the practices biopharma companies need to adopt to overcome these barriers and scale AI across their business.
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