Artificial intelligence (AI) is changing the way organizations do business. The organizations that use it most effectively recognize the risks of relying on computers to process huge sets of unstructured data, so they rewrite their algorithms to mimic human learning and decision-making.
Yet, according to a McKinsey & Company global survey, companies are having difficulty mitigating risks and managing cybersecurity, regulatory compliance, privacy and equity and fairness.
“When asked why companies aren’t mitigating all relevant risks, respondents most often say it’s because they lack capacity to address the full range of risks they face and have had to prioritize,” the report noted.
As a result, organizations are expanding roles of management information systems (MIS) professionals with expertise and skills to test data and analyze and document models for bias and accuracy.
What Is the Relationship Between AI and MIS?
AI finds trends, patterns and structures in new data to rewrite programs based on descriptive data models. The iterative process enables computers to do everything from play chess to understand voice commands via smart home devices to train fraud detection in credit card transactions.
“Of course, humans are still essential to set up the system and ask the right questions,” according to SAS, the analytics software and solutions provider.
Among those humans are MIS professionals who implement and manage information technology systems that support business intelligence (BI), Enterprise Resource Management (ERP) and predictive and prescriptive analytics and AI.
What Skills and Expertise Do MIS Professionals Need to Mitigate AI Risk?
IBM refers to the systems and processes that collect, organize and analyze data as “AI Model Lifecycle Management.”
That lifecycle includes stages such as data model training and implementation at scale, monitoring regulatory compliance, visualizing activity throughout the data pipeline and integrating AI throughout the organization.
At each state, MIS technology and processes are used to sample prescriptive data for skews and biases during model development and deployment, test new, unstructured data and run statistical analyses to ensure AI output aligns with regulatory compliance.
“The critical role of AI requires a well-defined and robust methodology and platform,” according to IBM. “For example, if fraud detection makes bad decisions, a business will be negatively impacted.”
How Do Business Professionals Prepare for Careers in MIS?
A Master of Science in Management Information Systems, such as the one offered online by Lamar University, equips graduates with the expertise that supports AI risk management processes through a curriculum that covers topics like:
- Information Quality Assurance. Students explore organizational security management, cryptography, physical security, infrastructure security, intrusion detection systems and network security and risk.
- Cybersecurity Management. Students gain expertise in data and systems policy and governance, asset management, framework development, data loss prevention and business continuity management.
- Data Mining and Predictive Analytics. Students use visualization and mining techniques to discover trends and structure data using SAP predictive analytics tools.
- Business Intelligence. Students study user-centric processes for exploring data relationships, trends, analytics and visualization that enable organizations to make better decisions faster.
- ERP Overview. Students learn how to integrate these business processes into an enterprise resource planning (ERP) system and gain hands-on experience with SAP solutions and simulations.
An advanced degree in information management positions graduates for management careers including those in information security and application development, as well as a path to the executive level and C-suite roles.