From AI readiness to real-world impact with IFS

Artificial intelligence (AI) is no longer a futuristic concept, it’s driving innovation and creating opportunities for organizations of all sizes today. Recent data states that over 70% of the companies in the U.S. have adopted AI in some form, with UK businesses showing a 33% year-over-year increase in adoption. As organizations navigate the transformation, the question has shifted from “Should we adopt AI?” to “How do we implement AI strategically?” In a recent webinar, Baker Tilly’s Cindy Bratel, Principal, Dave DuVarney, Principal and Paul Darwin, Director, along with IFS’ Kevin Miller, Chief Technology Officer – Americas, shared practical insights, real-world examples and strategies for implementing AI initiatives. The discussion provided a comprehensive framework for organizations at every stage of their AI journey.

The five pillars of AI readiness

1. Opportunity discovery: The foundation of successful AI implementation begins with identifying proper use cases. Organizations typically approach this in two ways: organically, allowing teams to explore tools independently or through structured innovation processes that identify high-value opportunities across business functions. The most effective approach combines design thinking principles with rigorous evaluation. This means moving through discovery phases to understand organizational challenges, defining root problems, determining whether AI is the appropriate solution and then prototyping and testing before full deployment.
2. Data profiling and management: Data forms the backbone of any AI initiative, yet many organizations struggle with readiness in this area. While most companies maintain well-curated structured data in enterprise resource planning (ERP) systems, the real challenge lies with unstructured data like contracts, service documents and other information scattered across the systems. Successful AI implementation requires attention to several data quality dimensions, understanding data sources and formats, ensuring data fairness to avoid bias in machine learning models, establishing strong relationships across datasets and implementing robust governance frameworks.
3. IT environment and security: Technical infrastructure plays a critical role in AI readiness. Organizations must ensure information technology (IT) teams understand how to support AI tools, particularly those operating on consumption-based models that require monitoring. Key considerations include operational monitoring, cybersecurity measures to keep proprietary data secure, reliability and uptime management and performance tracking for both generative AI and custom predictive models. The IT environment must be prepared not just to deploy AI tools, but to maintain and optimize them over time, ensuring they deliver consistent value while meeting security and compliance requirements.