It is quite obvious that success is determined by human aspects rather than technological factors. We have identified four key organisational actions that enable successful AI implementation at scale (Figure 1).
#1 Establish a Data Culture
The traditional focus for companies has been on ensuring access to good, clean data sets and the proper use of that data. Ecosystm research shows that only 28% of organisations focused on customer service, also focus on creating a data-driven organisational culture. But our experience has shown that culture is more critical than having the data. Does the organisation have a culture of using data to drive decisions? Does every level of the organisation understand and use data insights to do their day-to-day jobs? Is decision-making data-driven and decentralised, needing to be escalated only when there is ambiguity or need for strategic clarity? Do business teams push for new data sources when they are not able to get the insights they need?
Without this kind of culture, it may be possible to implement individual pieces of automation in a specific area or process, applying brute force to see it through. In order to transform the business and truly extract the power of AI, we advise organisations to build a culture of data-driven decision-making first. That organisational mindset, will make you capable implementing AI at scale. Focusing on changing the organisational culture will deliver greater returns than trying to implement piecemeal AI projects – even in the short to mid-term.
#2 Ingrain a Digital-First Mindset
Assuming a firm has passed the data culture hurdle, it needs to consider whether it has adopted a digital-first mindset. AI is one of many technologies that impact businesses, along with AR/VR, IoT, 5G, cloud and Blockchain to name a few. Today’s environment requires firms to be capable of utilising a variety of these technologies – often together – and possessing a workforce capable of using these digital tools.
A workforce with the digital-first mindset looks for a digital solution to problems wherever appropriate. They have a good understanding of digital technologies relevant to their space and understand key digital methodologies – such as Customer 360 to deliver a truly superior customer experience or Agile methodologies to successfully manage AI at scale.
AI needs business managers at the operational levels to work with IT or AI tech teams to pinpoint processes that are right for AI. They need to make an estimation based on historical data of what specific problems require an AI solution. This is enabled by the digital-first mindset.
#3 Demystify AI
The next step is to get business leaders, functional leaders, and business operational teams – not just those who work with AI – to acquire a basic understanding of AI.
They do not need to learn the intricacies of programming or how to create neural networks or anything nearly as technical in nature. However, all levels from the leadership down should have a solid understanding of what AI can do, the basics of how it works, how the process of training data results in improved outcomes and so on. They need to understand the continuous learning nature of AI solutions, getting better over time. While AI tools may recommend an answer, human insight is often needed to make a correct decision off this recommendation.
#4 Drive Implementation Bottom-Up
AI projects need alignment, objectives, strategy – and leadership and executive buy-in. But a very important aspect of an AI-driven organisation that is able to build scalable AI, is letting projects run bottom up.
As an example, a reputed Life Sciences company embarked on a multi-year AI project to improve productivity. They wanted to use NLP, Discovery, Cognitive Assist and ML to augment clinical proficiency of doctors and expected significant benefits in drug discovery and clinical trials by leveraging the immense dataset that was built over the last 20 years.
The company ran this like any other transformation project, with a central program management team taking the lead with the help of an AI Centre of Competency. These two teams developed a compelling business case, and identified initial pilots aligned with the long-term objectives of the program. However, after 18 months, they had very few tangible outcomes. Everyone including doctors, research scientists, technicians, and administrators, who participated in the program had their own interpretation of what AI was not able to do.
Discussion revealed that the doctors and researchers felt that they were training AI to replace themselves. Seeing a tool trying to mimic the same access and understanding of numerous documents baffled them at best. They were not ready to work with AI programs step-by-step to help AI tools learn and discover new insights.
At this point, we suggested approaching the project bottom-up – wherein the participating teams would decide specific projects to take up. This developed a culture where teams collaborated as well as competed with each other, to find new ways to use AI. Employees were shown a roadmap of how their jobs would be enhanced by offloading routine decisions to AI. They were shown that AI tools augment the employees’ cognitive capabilities and made them more effective.
The team working on critical trials found these tools extremely useful and were able to collaborate with other organisations specialising in similar trials. They created the metadata and used ML algorithms to discover new insights. Working bottom-up led to a very successful AI deployment.
We have seen time and again that while leadership may set the strategy and objectives, it is best to let the teams work bottom-up to come up with the projects to implement.
#5 Invest in Upskilling
The four “keys” are important to build an AI-powered, future-proof enterprise. They are all human related – and when they come together to work as a winning formula is when organisations invest in upskilling. Upskilling is the common glue and each factor requires specific kinds of upskilling (Figure 2).
Upskilling needs vary by organisational level and the key being addressed. The bottom line is that upskilling is a universal requirement for driving AI at scale, successfully. And many organisations are realising it fast – Bosch and DBS Bank are some of the notable examples.
How much is your organisation invested in upskilling for AI implementation at scale? Share your stories in the comment box below.
Continued Focus on R&D. Life sciences companies operate in an extremely competitive global market where they have to work on new products against a backdrop of competition from generics and a global concern over rising healthcare expenditure. Apart from regulatory challenges, they also face immense competition from local manufacturers as they enter each new market.
Re-thinking their Distribution Strategy. Sales and distribution for many pharma and medtech organisations have been traditional – using agents, distributors, clinicians, and healthcare providers. But now they need to change their go-to-market strategies, target patients and consumers directly and package their product offerings into value-added services. This will require them to incorporate customer experience enhancers in their R&D, going beyond drug discovery and product innovation.
Tracking Global Regulations. Governments across the world are trying to manage their healthcare budgets. They are also more focused on chronic disease management. The focus has shifted to value-based medicine in general, but pharma and medtech products are being increasingly held accountable by health outcomes. Governments are increasingly implementing drug reforms around what clinicians can prescribe. Global Life Sciences organisations have to constantly monitor the regulations in the multiple countries where they operate and sell. They are also accountable for their entire supply chain, especially ensuring a high product quality and fraud prevention.
The global Ecosystm AI study reveals the top priorities for Life Sciences organisations, focused on adopting emerging technologies (Figure 1). They appear to be investing in emerging technology especially in their R&D and clinical discovery and Manufacturing functions.
Technology as an Enabler of Life Sciences Transformation
Discovery and Development
With the evolution of technology, Life Sciences organisations are able to automate much of the mundane tasks around drug discovery and apply AI and machine learning to transform their drug discovery and development process. They are increasingly leveraging their ecosystem of smaller pharma and medtech companies, research laboratories, academic institutions, and technology providers to make the process more time and cost efficient.
Using an AI algorithm, the researchers at the Massachusetts Institute of Technology have discovered an antibiotic compound that can kill many species of antibiotic-resistant bacteria. MIT’s algorithm screens millions of chemical compounds and chooses the antibiotics which have the potential to eliminate bacteria resistant to existing drugs. Harvard’s Wyss Institute for Biologically Inspired Engineering is manufacturing 3D printed organ-on-a-chip to give insights on cell, tissue, and organ biology to help the pharma sector with drug development, disease modelling and finally in the development of personalised medicine.
Life Sciences are also engaging more with technology partners – whether emerging start-ups or established players. Pfizer and Saama are working together on AI clinical data mining. The companies are developing and deploying an AI-based analytical tool where Pfizer provides clinical data and domain knowledge to train models on the Saama Life Science Analytics Cloud (LSAC). Saama was identified as a partner at a hackathon. Sanofi and Google have established a new virtual Innovation Lab to develop scientific and commercial solutions, using multiple Google capabilities from cloud computing to AI.
Tech providers also keep evolving their capabilities in the Life Sciences industry for more efficient drug discovery and better treatment protocols. Microsoft’s Project Hanover uses machine learning to develop a personalised drug protocol to manage acute myeloid leukaemia. Similarly, Apple’s ResearchKit – an open-source framework is meant to help researchers and developers create iOS-based applications in the field of medical research.
Manufacturing and Logistics
The industry also faces the challenges faced by any Manufacturing organisation and has the need to deploy manufacturing analytics, and advanced supply chain technology for better process and optimisation and agility. There is also the need for complete visibility over their supply chain and inventory for traceability, safety, and fraud prevention. Emerging technologies such as Blockchain will become increasingly relevant for real-time track and trace capability.
The MediLedger Network was established as an open network to the entire pharma supply chain. The project brings a consortium of some of the world’s largest pharmaceutical companies, and logistics providers to improve drug supply chain management.
Since the data on the distributed ledger is encrypted, it creates a secure system without any vulnerabilities. This eliminates counterfeit products and ultimately ensures the quality of the pharma products and promotes increased patient safety. To foster security and improve the supply chain, the United States Food and Drug Administration (USFDA) successfully completed a pilot with a group including IBM, KPMG, Merck and Walmart to support U.S. Drug Supply Chain Security Act (DSCSA) to trace vaccines and prescription medicines throughout the country.
Diagnostics and Personalised Healthcare
As more devices (consumer and enterprise) and applications enter the market, people will take ownership and interest in their own health outcomes. This is seeing a continued growth in online communities and comparison sites (on physicians, hospitals, and pharmaceutical products). Increasingly, insurance providers will use data from wearable devices for a more personalised approach; promoting and rewarding good health practices.
Beyond the use of wearables and health and wellness apps, we will also see an exponential increase of home-based healthcare products and services – whether for primary care and chronic disease management, or long-term and palliative care. As patients become more engaged with their care, the life sciences industry is beginning to serve them through personalised approach, medicines, right diagnosis and through advanced medical devices and products.
An online tool developed by the University of Virginia Health Systems helps identify patients that have a high risk of getting a stroke and helps them reduce that risk. This tool calculates the patient’s probability of suffering a stroke by measuring the severity of their metabolic syndrome – taking into account a number of conditions that include high blood pressure, abnormal cholesterol levels and excess body fat. Life Sciences organisations are increasingly having to invest in customer-focused solutions such as these.
Wearables with special smart software to monitor health parameters, gauge drug compatibility and monitor complications are being implemented by Life Sciences organisations. The US FDA approved a pill called Abilify MyCite fitted with a tiny ingestible sensor that communicates with a patch worn by the patient to transmit data on a smartphone. Medtech companies continue to develop FDA approved health devices that can monitor chronic conditions. Smart continuous glucose monitoring (CGM) and insulin pens send blood glucose level data to smartphone applications allowing the wearer to easily check their information and detect trends.
What is interesting about these personalised products is that not only do they improve clinical outcomes, they also give Life Sciences companies access to rich data that can be used for further product development and improvement.
The Life Sciences industry will continue to operate in an unpredictable and competitive market. This is evident by the several mergers and acquisitions that we witness in the industry. As they continue to use cutting-edge technology for their R&D practices, they will leverage technology to transform other functions as well.
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