Magazine Article | May 1, 2019

How AI Can Accelerate Life Sciences

Source: Life Science Leader

By Roy Nicholson & Nate Regimbal

Roy Nicholson
There’s no doubt that artificial intelligence holds great promise for the life sciences industry. A recent report on AI trends through 2023 by Research and Markets states “the tremendous demand for AI in life sciences applications, such as drug discovery and patient monitoring, is opening up new opportunities,” but it added that organizations may be hesitant about solutions that reduce jobs or come with high initial costs.

Indeed, many life sciences organizations are wary about the escalating buzz around AI. So, as you consider your next, or first, venture into AI, how can you ensure that this technology effectively accelerates and streamlines your work?

Nate Regimbal


It can be hard to clarify exactly where AI has succeeded and where it's likely to succeed for your organization. One common thread among companies that have successfully implemented AI is that they began by looking for optimization. Rather than asking, "How can we use this new technology?" they asked, "What processes within our organization provide the biggest opportunity for optimization?"

To help identify these opportunities, some organizations created teams within their finance divisions. These teams look holistically across the organization to find processes where optimization could provide business benefits.

Even if an emerging technology solution is sold as not requiring process changes, a process evaluation and an organizational technology evaluation can help create a strong business case for a technology implementation. Some examples of how automation, machine learning, and other AI capabilities can be used in life sciences include:

  • Automated lab testing in R&D that can drive cost reductions or the reallocation of resources for other development initiatives
  • Predictive solutions that identify, for instance, which chronic disease patients a physician is likely to see soon in order to help field reps present any relevant treatment alternatives
  • Call-center transcription analysis that helps identify trending key words and questions for training call center and medical staff
  • Sales forecasting, especially for chronic disease treatments, analyzing a combination of data from patients, prescriptions, and physicians.

When an organization has zeroed in on some opportunities for optimization, the next question is, "How?" With a host of AI vendors and consultant agencies, life sciences organizations don't need to go it alone . But they do need to balance costs and develop key in-house AI expertise.


Given the dynamic and complex nature of AI, most organizations can't afford to wait while their in-house expertise matures. But organizations miss out on important value and create a dependency if they use only external AI agencies.

With the AI models available now, organizations can design use cases and formulate solutions on their own. So, what types of expertise matter for in-house teams? How can organizations establish the right balance of external expertise with internal insight on proprietary information?

  • IN-HOUSE LEADERSHIP: AI efforts should be led by internal staff who can capture and integrate the lessons learned and proprietary information that needs to be passed on to future projects.
  • EXTERNAL EXPERTISE: External consultants, teams, and tools can help with implementing new solutions, ERP systems, and an enterprise platform or data warehouse across the organization.

It’s important for internal resources to understand how the organization feeds data to its new solutions, how the master data is managed, and other structural aspects for a data platform. A central enterprise data platform helps provide a standard for the tools and solutions that ultimately deliver business value.

As an organization considers how much AI and machine- learning expertise it should develop internally, it can consider three general models:

  1. PROPRIETARY-PLATFORM MODEL: An organization can build its own team of technologists and data scientists who are entirely responsible to build solutions on the organization’s unique data in order to achieve unique AI goals.
  2. DATA-SCIENCE MODEL: An organization can acquire an AI platform, hiring a supporting team that includes one or more technologists and data scientists who help the organization use the platform to achieve its goals.
  3. MANAGED-SERVICE MODEL: An organization can call upon a cloud machine-learning capability or other service-based AI capabilities, providing data for external data scientists to process and report upon. This is common in pilot projects.

Once an organization aligns itself with an AI approach, it needs to choose the tools that will best integrate with users and processes.


Behind any successful AI solution is a source (or sources) of data and a set of tools that deliver the key capabilities. To deliver those capabilities, you need the tools to perform three types of work:

  1. MINING: The data mining, or the plumbing, extracts data from various sources and puts it into an environment ready for analytic processing.
  2. ANALYTICS: The analysis applies the logic, calculations, hierarchies, and algorithms that are specific for analytics (as opposed to transaction processing).
  3. PRESENTATION: The presentation tells a story or presents a visualization that informs business decision making, transformative action, or even compliance reporting.

Most organizations already have access to a range of mining, analytics, and presentation solutions. For life sciences organizations, some tools that have proven particularly valuable include:

  • BIRST - business intelligence software that can help mine volumes of financial, prescription, and clinical data
  • BLACKLINE - a cloud-based accounting tool that can automate reconciliation processes
  • MICROSOFT EXCEL - most AI platforms offer connection to Excel
  • NETSUITE - cloud-based ERP solutions useful for processing financial data
  • POWER BI - creates interactive dashboards to analyze data for decision making
  • QLIK - helps analyze physician data and tracks in-market performance and more
  • SALESFORCE - helps track sales opportunities and forecast business or inventory
  • TABLEAU - financial and marketing analytics for identifying when to combine different data sets
  • WER.AI - provides use case-specific AI solutions that are supported via an AI-as-a-Service subscription agreement

It's critical to remember that your processes for data generation are the foundation of your AI solution's quality. It's important to have tight controls and process discipline to help your solution provide accurate real-time data to the field.


An enterprise AI strategy can help teams ensure that they work together to build value in a central data standard that will sustain business value for present and future solutions. The enterprise strategy needs to clearly articulate the processes and procedures that govern data . And it falls squarely within the realm of an organization's internal expertise. Process optimization, data management, and governance may not grab many headlines, but they are the foundation of AI solutions that accelerate the search for tomorrow's critical treatments and cures.

NATE REGIMBAL is a digital transformation consultant for Grant Thornton LLP, helping companies define and implement digital and organizational strategies and solutions that enable business and growth objectives.

ROY NICHOLSON is a principal and leader within Grant Thornton LLP’s Technology Strategy and Management practice, with a focus on digital transformation.