Enterprise Transformation Through Data, AI, and Automation - Course Monster Blog

Effective data architecture can help organizations strike a balance between cost and simplicity, reducing data storage expenses and making it easy for data scientists and business users to access reliable data. It can also help to integrate various enterprise systems and applications, breaking down silos and allowing the organization to take advantage of current and future investments. To maximize the return on AI and automation investments, organizations should consider implementing automated processes, methodologies, and tools that manage and govern the use of AI within the organization.

Taking advantage of automation for LOB and IT activities

Data can be used to fully digitize an organization through the use of automation and AI, but the challenge lies in integrating and implementing these technologies across different departments and IT systems.

Here are five capabilities to consider for line-of-business functions:

1. Using process mining to identify opportunities for automation and evaluating the potential impact of automation initiatives before investing in them can help organizations scale their automation efforts effectively.
2. Implementing robotic process automation (RPA) can help organizations automate repetitive, manual tasks, saving time and improving efficiency.
3. A workflow engine to automate digital workflows
4. Operational decision management involves using data analysis, automation, and governance to make rules-based business decisions in real time.
5. Effective content management is crucial for handling the increasing volume of content needed to support business operations and decision-making.
6. Document processing involves using technology to read and extract data from documents, and then organizing and storing the data for use.

When considering the digitization of IT, there are three key areas to evaluate:

1. Enterprise observability involves using data and monitoring tools to improve the performance of applications and accelerate continuous integration and delivery (CI/CD) pipelines.
2. Application resource management involves using data and tools to optimize the allocation of computing, storage, and network resources to applications.
3. Using AI to proactively monitor IT environments for potential risks or warning signs of outages can help organizations prevent disruptions and improve the overall reliability of their IT systems.

Help increase ROI on data, AI, and automation investments by making data and AI ethics a part of your culture

For AI to have a meaningful impact on an organization, it is important to properly integrate it into key processes, such as supply chain procurement, marketing, sales, and finance. It is also essential to ensure that the people who run these processes have the necessary data literacy skills to take advantage of and challenge the insights provided by AI systems. If data users do not understand or agree with the options presented by an AI system, they may not follow established processes, which can pose risks to data and AI ethics and compliance with data privacy standards. Therefore, it is important to cultivate a culture of data and AI ethics within the organization and ensure that all data users have the necessary skills and understanding to use data responsibly.

Creating a data-driven organization requires addressing a range of issues across IT, leadership, and line-of-business functions. However, the benefits of doing so are significant. It lays the foundation for enterprise-wide automation and IT and gives organizations a competitive advantage by allowing them to quickly identify opportunities for cost savings, growth, and even the development of new business models.