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EngineeringBeat: Cloud modernization for life sciences
Building a future-ready IT ecosystem with cloud migration and AI
Increasing competition and the imperative to adopt new technologies like Generative AI (GenAI) are compelling many life sciences (LS) organizations to harness the power of the cloud more effectively. However, many enterprises are hobbled by issues with modernization, shadow IT, and technical debt. Successfully resolving these challenges involves building a future-ready IT ecosystem.
Embrace true cloud-native modernization
To speed their cloud migration and leverage institutional knowledge, many life sciences organizations prioritized moving the applications to the cloud and tasked their on-premise data center teams with overseeing cloud operations, without truly modernizing those applications to run effectively in the cloud. For example, according to Deloitte research, only 65% of surveyed LS companies are currently making significant investments in cloud-native modernization efforts.1 The result has been simply to create a “data center in the cloud” rather than a true cloud-native environment that enables them to harness the cloud’s benefits, such as speed, agility, and the ability to innovate faster.
To address the issue, it’s essential to conduct a “health check” on your cloud implementation. Start by reviewing your cloud architecture and operating model to identify inefficiencies that impact your ability to take advantage of the benefits cloud has to offer such as elasticity, scalability and high availability and assess how you manage cloud assets and conduct business charge-backs. Also, determine which applications can be partially or completely modernized to incorporate cloud-native design principles and—potentially—which applications need to be sunsetted.
Hear more about building a cloud-native architecture.
Shadow IT isn’t healthy for life sciences organizations
At many LS organizations, there are “shadow IT” operations that business units have created to gain greater speed and agility. These fragmented processes and applications often lack the enterprise guardrails necessary for optimal security and compliance. Their disruption created by shadow IT can significantly increase the risk of cyberattacks and data exposure while decreasing cyber resiliency—especially with the implementation of GenAI. Yet many organizations haven’t adequately addressed shadow IT. In a Deloitte survey of IT leaders, 84% of respondents indicated that they don’t have a sufficient plan in place to track and manage their IT assets2 , an issue that is exasperated by shadow IT.
Tackling shadow IT isn’t easy, but it’s doable with a good plan. First, conduct a comprehensive audit to ferret out unsanctioned IT assets and activities and determine technology needs across the enterprise. It’s these needs that are not being met that is creating a void filled by shadow IT. Next, listen to your business units and developers when they tell you what capabilities are required. Finally, based on the results of the audit and developer conversations, develop clear policies for IT and communicate and enforce them, while giving the business the support and services they need to produce the results they want.
Discover more about cyber risk and building cyber resiliency.
Technical debt is tangible and costly
To accelerate their cloud migration and meet data center exit deadlines, IT organizations at many LS companies have often prioritized speed over modernization. For many companies, the drive for expedience has created significant technical debt, which is the accumulation of suboptimal code and architecture decisions, that is now residing on the cloud. Technical debt can increase costs and decrease efficiency and growth. According to research, up to 70% of technology leaders view technical debt as a hindrance to their organization’s ability to innovate and the number-one cause of productivity loss.3
The first step to reducing technical debt is finding it. AI can help find it and fix it. With AI, life sciences companies can root out technical debt in their tech stacks and automate and streamline tasks like bug identification and code analysis and remediation. AI can also help automate testing and manage code environments. It’s also essential to develop a systematic legacy-system modernization plan to correct—and prevent future—technical debt.
Find out more about conquering technical debt.
GenAI has trained a microscope on IT architecture
The push to leverage GenAI for life sciences is compelling many organizations to streamline and modernize their systems. Despite the potential for significant productivity gains, however, GenAI has been difficult to implement for many LS companies because it has highlighted cultural, process, and architectural shortcomings. Moreover, the problem is growing. Deloitte research indicates that 67% of surveyed LS companies are making investments in AI/ML—so it’s a critical issue. 4
To mitigate the problems that steal value from your GenAI implementation, you can start by building a culture of collaboration and continuous learning across the enterprise. Also, review your current processes and IT architecture, and develop a clear modernization roadmap with the goal of standardizing GenAI platforms and capabilities. The roadmap should also include plans for training, workflows, and scaling operations as GenAI adoption accelerates.
Learn more about how GenAI can fuel growth
Building the future
Building an IT architecture that can drive innovation and growth, as well as support the adoption of advanced technologies such as GenAI, requires more than a quick fix. It requires addressing both technical debt and shadow IT while modernizing your systems and processes. By assessing your current environment, implementing clear IT policies, and leveraging AI to automate and augment processes, you can be better positioned to build a powerful, future-ready IT ecosystem.
Endnotes
1 Tim Smith et al., “Digital value and the industry context,” Deloitte, November 17, 2023.
2 Diederik Van Der Sijpe et al., IT Asset Management (ITAM) Global Survey 2021, Deloitte, June 2021.
3 Jim DeLoach, “Technical debt demands your attention,” Forbes, June 12, 2023; Stripe, “The developer coefficient: Software engineering efficiency and its $3 trillion impact on global GDP,” September 2018.
4 Smith et al., “Digital value and the industry context.”
Get in touch
Pete Lyons
National sector leader for Deloitte’s Life Sciences practice
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