The business landscape is rapidly evolving, and artificial intelligence projects are at the forefront, revolutionizing industries with advanced AI algorithms that drive efficiency, enhance productivity, and transform risk management. However, with AI’s growing capabilities come new responsibilities, particularly around compliance, AI governance, data privacy, and enterprise risk. At the 2024 SIM Summit in Boston, Eliassen Group executives Kolby Kappes, Bill Gienke, and Brian McDonald discussed how AI solutions are shaping corporate strategies, adjusting cost models, and creating compliance challenges.
Cloud-Based AI Consumption Models – Affordability and Efficiency at Scale
One of the most significant benefits of deploying cloud services for AI implementation is the pay-as-you-go pricing structure. This model reduces upfront costs and allows organizations to scale their AI services according to their needs. We believe that these trends will continue to decrease expenses for shared AI consumption models, making automation tools more accessible than ever before.
Major providers like Azure, AWS, and Google Cloud offer robust platforms that support a wide range of AI applications and enable integration across systems, from chatbots to complex automation tools. These services allow organizations to implement AI quickly without the heavy lifting of custom development, providing ready-made solutions that can be deployed effectively.
Data Privacy and Security
As AI adoption grows, so do concerns about data privacy. Employing enterprise licenses is crucial for preventing sensitive data from being misused for AI training. Understanding the different types of data, including foundational model training data, current events data, and specific organizational data, is essential for effective AI deployment. Data scientists and IT teams must ensure AI models are fed clean, curated data, as inconsistencies can lead to flawed outputs, impacting real-world performance.
AI models require high-quality data to make accurate inferences, and many real-world examples highlight the risks of poor data quality, such as AI hallucinations when models misinterpret queries. Such issues emphasize the importance of informed decisions in data management to ensure reliability and consistency in outputs.
AI Productivity Gains
Employees today worry that AI might replace human jobs, but we believe that AI services are designed to augment rather than replace human roles. By integrating advanced AI tools, organizations empower knowledge workers, ensuring competitiveness in today’s market. Fostering a culture that embraces AI implementation can yield significant productivity gains across departments, improving the customer experience while enhancing efficiency.
New AI tools like Microsoft Copilot can assist developers with repetitive tasks, boosting productivity and reducing time spent on low-value coding. However, to maintain code quality and align with organizational standards, ongoing training and code reviews are essential to prevent compliance risks.
How to Measure the AI ROI
Kolby Kappes, VP of emerging technologies at Eliassen Group, encouraged businesses to closely track and measure the ROI of AI projects by analyzing productivity gains. By quantifying benefits like reduced call times, improved efficiency, and fraud detection capabilities, companies can make informed decisions about future AI investments and determine the long-term impact on their operations.
The Compliance Landscape for AI in Risk Management
As AI plays a larger role in regulated industries, compliance has become a focal point. Bill Gienke, managing director of risk and regulatory compliance at Eliassen Group, discussed the need to align AI models with regulatory standards, especially in heavily monitored sectors like financial services. He emphasized the importance of transparency and explainability to bridge regulatory gaps and manage compliance risks.
Emerging AI Compliance Frameworks
The complexity of AI algorithms is advancing faster than current regulations, prompting organizations to consider cutting-edge compliance frameworks. Recent guidance from the Department of Justice underscores the need for a risk-based approach to AI governance. With agencies moving toward an enforcement-driven model, businesses will likely face stricter expectations around compliance, even without new legislation.
Explainable and Traceable AI
Compliance frameworks are now prioritizing the need for companies to document and clearly explain AI-driven decisions, especially in critical areas like risk scoring and fraud detection. By ensuring that each decision can be traced back through the AI model’s processes, companies can demonstrate accountability and transparency. This level of traceability is essential for meeting regulatory expectations and helps prevent issues related to bias or misinterpretation. Ultimately, explainable and traceable AI builds trust and ensures that AI implementation can withstand regulatory scrutiny.
Embracing Enterprise AI
The age of cloud-based AI is upon us, offering transformative potential for businesses to drive efficiency and innovation. By leveraging advanced AI technologies and focusing on data privacy, integration flexibility, and continuous employee training, organizations can position themselves as leaders, equipped to navigate future challenges with confidence. Embracing automation tools and fostering a culture of adaptability ensures a seamless AI implementation journey, unlocking valuable insights across departments and creating long-term competitive advantages.
