By Roy Nicholson & Nate Regimbal
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?
START WITH THE PROCESS
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:
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.
BUILD EXPERTISE WHERE IT MATTERS
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?
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:
Once an organization aligns itself with an AI approach, it needs to choose the tools that will best integrate with users and processes.
FIND A PLATFORM AND FLEXIBLE TOOLS
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:
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:
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.
ENSURE QUALITY WITH GOVERNANCE
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.