In 2024, we’re seeing exciting changes in the IT world, with companies diving into AI integration, modernising infrastructure, and automating everyday IT tasks. While these moves promise greater efficiency and innovation, we can’t overlook potential downsides, particularly with the increasing reliance on IT automation.
According to a 2024 State of the CIO survey, companies are eagerly investing in IT automation across various areas like administration, reporting, and customer support. Gartner estimates even suggest that by 2024, businesses aim to automate at least 69% of daily management tasks. Amidst this widespread use of automation, it’s vital to know about the hidden risks it carries.
Below are some common downsides to automation strategies that IT leaders should keep in mind.
Data Silos
In many organisations, different departments maintain separate databases or systems to manage their data. One of the primary objectives of IT automation is to optimise processes and facilitate data integration across various departments. However, if automation efforts are implemented without considering integration with other data sources, they can inadvertently create data silos. These silos hinder access to crucial data, resulting in inconsistencies in data management practices and inefficiencies in operational processes.
New Security Threats
Businesses have been worried about the security risks associated with AI, and the same concern applies to IT automation. It introduces new avenues for potential attacks, whether due to poorly written scripts or misconfigurations, creating vulnerabilities that malicious actors can exploit to gain unauthorised access or disrupt operations. As multiple systems communicate and share data, the risk of cyberattacks like ransomware, insider threats, and supply chain compromises rises.
Complacency
When tasks are automated, there’s a temptation to assume that everything is running smoothly without the need for human intervention or oversight. This can lead to neglecting important aspects like monitoring for errors, updating automation scripts, or dealing with security threats. Moreover, if automation processes face unexpected errors or disruptions, teams used to depending solely on automated solutions may find it hard to respond quickly and effectively. This may lead to major downtime or operational problems.
Lack of Governance
Governance isn’t a given when it comes to IT automation. While many companies adopt automation to improve efficiency, they often overlook the crucial need for governance. Without proper governance frameworks in place, companies risk exposing sensitive data to unauthorised access, violating regulatory requirements, and compromising data integrity. Moreover, poor data quality resulting from inadequate governance can undermine the reliability and accuracy of automated processes, leading to errors, inefficiencies, and poor decision-making.
Technical Debt Accumulation
Initially, automation initiatives may prioritise speed of implementation over long-term considerations such as code quality, documentation, and scalability. This can result in the creation of scripts, workflows, or configurations that are hastily developed, poorly documented, and difficult to maintain.
As automation projects progress, the lack of attention to technical debt can lead to various challenges. For example, scripts may become bloated with redundant code or lack proper error handling, making them prone to failures and difficult to troubleshoot. Similarly, automation workflows may lack modularity and reusability, leading to duplication of effort and increased complexity. Moreover, maintenance efforts increase and scalability becomes limited as automation solutions become increasingly fragile and difficult to extend.
Mitigating Risks in IT Automation
Now that we’ve explored the potential downsides of IT automation, let’s delve into strategies to effectively mitigate these risks and ensure successful implementation.
Data Integration Strategy: Develop a comprehensive strategy that prioritises seamless communication and integration between automated systems and existing data sources. Breaking down data silos improves accessibility and consistency across departments.
Security Best Practices: Implement robust measures such as regular audits, encryption protocols, and access controls to mitigate the risk of cyberattacks and data breaches. Employee training programs can raise awareness about security threats and promote adherence to protocols.
Continuous Monitoring and Oversight: Establish protocols for ongoing monitoring to detect and address errors, anomalies, and security breaches promptly. Proactive monitoring prevents complacency and ensures the reliability of automated systems.
Governance Frameworks: Develop and enforce governance frameworks defining roles, responsibilities, and procedures for managing automated processes. Incorporate compliance requirements and data privacy regulations to minimize risks and protect sensitive information.
Technical Debt Management: Prioritize code quality, documentation, and scalability during development to avoid accruing technical debt. Regular review and refactoring maintain agility and scalability over time.
Conclusion
While automation promises increased efficiency and progress, it also introduces its own set of risks and complexities. As companies adopt automation, they must understand that success involves more than just implementing new technology. It requires a comprehensive approach that includes preparing the organisation and fostering a culture of adaptation and collaboration. This means investing in employee training and ensuring effective communication and teamwork between IT and other departments.
Moreover, organisations must stay flexible and responsive to changes in technology. The IT industry is always evolving, so being able to adapt quickly is vital for long-term success.
