Bioelectronics has recently hit the headlines as Google has teamed up with GlaxoSmithKline, mixing chemistry and biology to develop implantable electronic devices that can help treat chronic diseases.

The whole area of artificial intelligence (AI) is developing at a rapid pace but is still often built around auto-bot style tools, which provide an intelligent question and answer interaction.

However, what happens when you have more complex business processes that require more than a simple ‘yes’ or ‘no’ response?

Most information and content repositories, and their subsequent business process flows, are based on modelling years of experience to provide semi-automated and compliant business processes.

They enable organisations to adhere to standards and operate as efficiently as possible, or so it would seem.

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The world around us, however, is rapidly changing, and traditional ways of doing things are being superseded, such as the administration of medicine and its individual customisation to patients.

Doctors no longer simply talk about patient treatment for a particular illness, but rather the whole notion of patient lifestyle management, or patient care, which is tailored to each patient.

As new and tailored variants of medicines are developed can traditional clinical trial management solutions still meet the evolving needs of life sciences companies?

The answer is yes, but at the moment these solutions and data aren’t being used to their full potential.

For example, organisations can create ever more complex and intertwined network process flows, but the issue with this is that it still relies on understanding what those flows are and engineering them into an organisation’s business process architecture.

However, what if different types of content and workflow management solutions could dynamically alter flows based on its own perceptible AI?

This would mean that process flows built into repository-based systems would evolve based on information pulses and sensory data used as inputs to affect change.

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In simpler terms, think of this as software that lives, breathes and has the capacity to alter its state based on other influences, both internal and external to it.

This cannot happen without having clean, accurate and managed data.

As an example, flu vaccine manufacturers today often have process flows that ramp up the production of flu vaccines based on seasonal data, i.e. as winter starts to set in, more flu vaccine is produced.

However, there is an opportunity with AI for process flows to dynamically target the production of different types of flu vaccine automatically.

Based on other types of data, such as hospitals reporting particular outbreaks or even particular manufacturing lines being unavailable or unable to cope, workloads can be automatically reassigned to other production lines.

In instances like these, traditional content and workflow software adapts and evolves based on the interpretation of lots of structured and unstructured data.

It moves from providing cures to outbreaks to a level of preventative care that does not exist today.

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Life Science companies move beyond reacting to outbreaks to preventative care that also has a first mover market advantage as another positive by product.

Today, enterprise content management (ECM) processes still require someone with intelligence to understand the defined flow (for example managing clinical trials), as it is a highly regulatory process that has a lot of governance and control.

By rethinking this problem leveraging AI, this could help to predict the next approval process and automate many of the lines of approvals that humans currently sit and often become, the single point of failure in a process workflow.

With the AI market expected to be worth $5.05 billion by 2020, this is an important part of the digital transformation journey that organisations must consider and begin thinking about.

Sourced by Stéphane Barberet, vice president EMEA – enterprise content division at Dell EMC

Check out Information Age’s Innovation Spotlight series where Ken Mulvany, co-founder and director of BenevolentAI, discusses AI’s transformative effect on the medicinal industry: