Transforming Life Sciences Through Knowledge Work Automation

The transformation power of technology is intertwined with efficiency and progress. Despite emerging challenges, automation offers practical efficiencies and benefits the life sciences sector are starting to consider in future growth planning and mapping workflow requirements

Knowledge work automation is at the heart of this digital transformation. It redefines how tasks are executed across various sectors. Work automation is essential for modern businesses, from streamlining routine processes to driving higher productivity.

The life sciences sector stands to benefit most from process automation. Life sciences encompasses biotechnology, pharmaceuticals, and healthcare. The sector has been significantly transformed by workflow and lab automation. More than 70% of life sciences organizations use automation for research and development processes.

This statistic illustrates how much life sciences organizations have embraced advanced data analytics and automation software development. For example, they are allocating significant resources to support each step of drug development.

The intersection of automation and life sciences is more than merely a technological meeting watermark. It represents a pivotal moment in pursuing innovations that can reshape the industry. This includes both drug discovery and medical research.

The Evolution of Knowledge Work Automation

The concept of automating business tasks has evolved. Changes started with the automation of labor during the Industrial Revolution. It continued with the advent of early computing systems.

Knowledge work automation has come a long way thanks to emerging technology. Breakthroughs in artificial intelligence (AI), machine learning, and computing power have propelled automation beyond routine tasks and manual work.

Advanced technologies have empowered systems to manage high-level decision-making, data analysis, and complex problem-solving. Large to small businesses use workflow automation, from manufacturing to financial services and healthcare, to work smarter. It’s not just about reducing mistakes. It’s about gaining a competitive edge.

Applications of Work Automation in Life Sciences

Applications that improve the efficiency of scientific processes in the real world include

  • High-throughput screening involves automated techniques that rapidly analyze large samples, allowing researchers to efficiently identify potential drug candidates and conduct robust experiments.
  • Data analysis and interpretation: AI automation can process vast datasets quickly and precisely. This helps scientists uncover meaningful insights and patterns.
  • Robotic process automation (RPA):RPA is increasingly crucial in life sciences R&D. Laboratory process automation leverages robotics to process and manage samples. It also streamlines repetitive tasks, increasing productivity and enhancing flexibility and agility. Benefits include reduced manual processes, heightened accuracy, standardized workflows, and cost savings.
  • Robotics in sample handling reduces the risk of human error and increases the throughput and accuracy of laboratory workflows.
  • Automated liquid handling systems perform precise and repetitive liquid transfers. This ensures accuracy in experiments requiring careful measurement and mixing. By automating these tasks, labs can achieve higher levels of consistency.

Benefits of Knowledge Work Automation

Information automation increases efficiency and will improve productivity. How? By relieving professionals of routine and time-consuming tasks. This allows researchers to spend more time on high-value activities. The quest to increase the speed of innovation and discovery is facilitated by knowledge work automation. It contributes not only to streamlined processes, but also to knowledge workers’ professional growth and effectiveness.

Knowledge work automation also drives enhanced data accuracy. Automated systems minimize the risk of human error, ensuring precision in analyzing data and interpretation. This improves the reliability of results and elevates the overall quality of R&D.

Advancements in information technology for life sciences include handling clinical trial data and improved document management for contract research organizations. These include streamlined workflows, enhanced collaboration, and reduced risks. This drives excellence in business operations and strengthens relationships with sponsors.

Integrating technology-driven solutions ensures advantages extend beyond efficiency gains. They positively impact the life sciences industry, from project management to regulatory compliance and enhance overall customer experiences.

Addressing Challenges with AI and Automation

What are some obstacles with the automation in life sciences laboratories?

  • Financial challenges hinder the adoption of process automation systems.
  • Long-standing obstacles in practices of academic research create resistance to future automation.
  • Despite expected progress in future design of affordable, lower-level automation equipment, the market still needs further development.
  • Meeting growing demand for environmentally conscious automation poses a challenge for developers.
  • Ensuring systems remain compatible with the innovative nature of researchers, preserving the freedom to create new protocols.
  • Life sciences researchers now need working knowledge in both traditional biology “wet lab” skills and emerging “dry” automation skills.
  • Spatial constraints within laboratories and cultural challenges contribute to knowledge gaps, leading to a lag in automation software

As automation continues to evolve, a higher elevation of tools and systems will emerge. They will be used to further enhance R&D efficiency and productivity. AI-powered drug design represents a significant trend in the future of work automation.

The long term shift towards AI promises to transform the identification of potential candidates. It will accelerate research timelines and enhance the precision of therapeutic interventions.

Another emerging trend is the seamless addition of decision automation in personalized medicine. Automation technologies are expected to be crucial in customizing medical treatments to individual patient traits. This trend encompasses the automation of processes related to patient data evaluation, treatment customization, and the efficient delivery of personalized healthcare solutions. The combination of automation and personalized medicine will optimize patient outcomes.



How does knowledge work automation in life sciences differ from traditional office automation systems?

Unlike traditional office automation, knowledge work automation in life sciences is customized for complex tasks in biotechnology, pharmaceuticals, and healthcare. It involves advanced processes such as data analysis, decision-making, and problem-solving.

Why should life sciences embrace the automation of knowledge work?

Automating knowledge work management in life sciences streamlines processes and save time. This allows professionals to focus on high-value activities and contributes to maximized effectiveness. It also accelerates drug development, healthcare, and medical research innovations.

What makes the automation of knowledge work disruptive for life sciences?

The automation of knowledge work in life sciences disrupts traditional workflows by introducing advanced technologies such as AI. AI improves speed, accuracy, and overall effectiveness. This disruption transforms how tasks are executed, fostering breakthroughs in healthcare, drug discovery, and research.


Why AI and Machine Learning Will Dominate CIO Agendas in 2020

Artificial intelligence (AI) and machine learning already impact our everyday lives. These technologies mostly work so seamlessly with our daily experiences that we barely notice. For instance, machine intelligence powers the digital assistant people use on their phones, movie suggestions on streaming websites, and filters in email. In another generation, it’s possible to imagine that people will consider such revolutions as self-driving cars just as ordinary as their recommended movies on Netflix. Yet, according to CIO Magazine, AI and machine learning are just now making inroads into corporate IT departments.

AI, Machine Learning, and the Changing Roles of Chief Information Officers

In the CIO article cited above, Dave Wright serves as the CIO of Service Now. He said, in the past, his role as Chief Information Officer served to guide, build, and maintain a company’s tech infrastructure. These days, that role has evolved to focus more upon strategizing ways to use technology to benefit his organization. For instance, the CIO may not always choose to expand or even keep their own internal IT infrastructure as much as survey existing technology to see what the business can use to meet its business goals.

Sometimes, this role may involve shrinking the company’s own computing power and partnering more with providers who offer the best solutions. If companies don’t have the resources to develop their own intelligent systems, they can rely upon trusted third parties for solutions.

Facing Internal Resistance to Artificial Intelligence

Wright understands that some members of the IT or other departments may fear changes, specifically the adoption of machine learning and AI. They have concerns that they will detract from their own duties. As was the case during the first days of digital transformation”>digital transformation, these technologies seldom remove jobs but allow people performing those functions to work more productively in a way that supports their organization’s true business goals. While it’s up to CIOs to explore solutions, they also need to communicate the benefits of those solutions to their employees and other executives.

AI’s Penetration into Today’s Businesses

Right now, according to the survey CIO Magazine reported upon, almost 90% of companies do use AI and machine learning in some fashion. However, about 66% of the businesses surveyed are only researching or piloting these new smart technologies. Only about 23% responded that they either used machine intelligence either in several parts of all of their business. Wright believes that most businesses will start by using AI to help interpret and organize information. Only after they feel comfortable with that aspect, will more businesses move to using it to solve problems and later, to anticipate and remediate them.

AI vs. Machine Intelligence

As a note, sometimes people use artificial intelligence and machine learning interchangeably. Artificial intelligence describes computer systems that use their algorithms to mimic human decision-making ability. Machine learning describes a type of artificial intelligence that can use information to adapt its algorithm based upon the information that it receives. In that way, machine learning refers to a kind of artificial intelligence.

Why Cleaning Up Bad Data Matters for Effectively Using AI

As we experience the Information Age, people sometimes refer to data as the new “oil” because of its value. As companies collect more and more information, they increasingly wrestle with problems of data quality. Without proper management, information gets corrupted because its obsolete, redundant, or simply in error. As CMSWire pointed out, new compliance rules like GDPR and the California Consumer Privacy Act also can turn the problem into a regulatory hazard.

Mostly, Wright emphasized that artificial intelligence and machine learning technologies will only work as well as the information that they receive. Luckily, businesses can find an intelligent solution to help them with that task as well. Gartner says that machine-augmented data management will grow common within many organizations. In other words, AI can provide the solution to making certain that it can get the best possible information to base its processing upon.

Why Are CIOs Exploring the Benefits of AI and Machine Intelligence?

Increasingly, high-tech companies are offering smart features to consumers to help them make better and faster choices or to do things more efficiently and safely. It only makes sense that businesses can find plenty of ways to employ this technology to improve their own business processes. Even better, AI tech providers can help level the playing field, so that businesses without the resources to develop their own tech can still access it. These intelligent machines can help companies reduce threats and enjoy more value from the increasingly large amounts of information that they collect.