Artificial Intelligence + Agile Life Science Adapting to Change Spotlight Article

Life science companies are in the beginning of an evolution of technology that will advance not only the development of novel treatments but also the expediency of end to end portfolio delivery to customers.

Can Agile Lead the Quest to Integrate AI into Life Science Teams?

It’s tempting to oversimplify artificial intelligence (AI), but many industries using large amounts of data understand first hand both its complexity and its impact on saving time for research and data analysis. Being at the next frontier of machine learning has brought us to the forefront of the next evolution of AI. While we are at the beginning of applicable cognitive technology (deep learning), enhancing life science as we know it. The impact may be seen as daunting due to the loud chatter surrounding it. But regardless of the hype, AI will have the impact to provide a path forward to business efficiency, and greater access to target treatments while providing a more efficient workforce and pipeline to treatment approval. In time the result will be cost and time efficiencies allowing for a more effective focus on a customer centric initiatives that save and improve patient’s and their quality of life.

As we shift to present date, machine learning and other technologies have already been tested with the coronavirus 2019 vaccine having been developed far more quickly than anticipated with the use of AI. But despite the milestones made there continues to be a lot of noise and disruption regarding how AI can be integrated effectively into a workforce that is not technology savvy. Executives recognize however regardless of the predicted disruption that is expected with AI’s entry into the workforce. Preparing their organization for change is essential to creating organic streams of people that can work effectively while limiting disruption of AI integration to their business.

Milestones & opportunities

Fully bringing the technology into organizations will require proactively engaging all disciplines that will utilize or contribute to the success AI brings within its tools of analytics and surveillance of data. While the decisions to incorporate AI successfully will be multifaceted, executives should look to incorporate the same theoretical principals and methods used by IT teams when applying Agile to effectively introduce the benefit and risk of the technology into their expert workforce. Further improving employee engagement and acceptance of the technology, Agile can enhance stakeholder engagement. Encouraging out of box strategies while removing traditional silos that contribute to disruption. While working to keep the organization in a cycle of continuous improvement.

Executives will need to consider the creation of experts responsible for the preparedness and selection of AI tools best suited for integration of optimizing employee, external stakeholder and customer engagement. Complemented with a matrix of Agile training strategies to integrate AI into non-technical standard functions that can be partnered to create activities that will essentially help employees and the organizational product lifecycle process evolve effectively.

Blue and White Simple List Mind Map

The evolution of AI combined with Agile talent team collaborations that are not highly skilled in technology will have a significant impact in innovating the workforce and is an important opportunity for life science companies to take on. The pathway to successfully integrate AI into the workforce requires methods that allow an organization to manage risk and behavior of key stakeholder effectively, which is why an “Agile mindset” is an important strategy to consider.

Taking these steps helps to create efficiencies to proactively recruit technology into the mainstream of the organization while extending employee and external partnerships to support the success of an organizations depth in competently optimizing AI applications.The decisions executives make to select the best AI tools for their organization will shape their ability to create the most cost effective and impactful tools to achieve workforce optimization. Which will reap significant rewards if done with prodigious intent combined with Agile solution based strategies.

Investment in AI Growth Is Expected To Increase In the Technology Sector ~30%

WIth Exceptions of china and India, the Emerging Markets have received a modest share of global Investment in advanced technologies

The evolution of AI combined with Agile talent team collaborations that are not highly skilled in technology will have a significant impact in innovating the workforce and is an important opportunity for life science companies to take on. The pathway to successfully integrate AI into the workforce requires methods that allow an organization to manage risk and behavior of key stakeholder effectively, which is why an “Agile mindset” is an important strategy to consider. 

Taking these steps helps to create efficiencies to proactively recruit technology into the mainstream of the organization while extending employee and external partnerships to support the success of an organizations depth in competently optimizing AI applications.The decisions executives make to select the best AI tools for their organization will shape their ability to create the most cost effective and impactful tools to achieve workforce optimization. Which will reap significant rewards if done with prodigious intent combined with Agile solution based strategies.

AI Technologies are maturing raiding and are already being used in a number of applications

Overall a well thought out plan which includes the assessment of the organization ability to successfully manage and monitor AI vast capability to strengthen the business will be important. Along with implementing algorithms to ensure non-bias to help bridge and strengthen patient health equity as well as improve patient diversity in clinical trials. While also monitoring the technology for patterns in digital data representations to account for the impact of the predications of the software used to build and improve patient treatments are are complaint with the ethical standards for using AI in drug development. Leadership have the opportunity to revolutionize drug delivery but also need to remain diligent to ensure analytical ethics and patient safety is leveraged over the benefits of AI’s potential negative outcomes. As long as AI research is developed to lead alongside the wisdom provided, and humane goals of the organization, AI will prove useful in innovating drug development as we know it.

About The Authors

Conrad Biltly is a Senior AI Associate in the Life Science Practice and D.Orozco is a Principal at DMO Consulting Firm

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