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.
4. Risk, privacy and governance: As organizations embrace AI, establishing clear policies and governance frameworks becomes essential. While early concerns about data security have somewhat eased as vendors improve their offerings, maintaining proper safeguards remains vital. A fundamental principle applies here: just as organizations wouldn’t allow employees to move proprietary data into personal cloud storage accounts, they shouldn’t permit the use of unauthorized AI tools with sensitive information. Effective AI governance includes establishing clear usage policies, continuously assessing risk across deployed tools and monitoring both sanctioned and unsanctioned AI usage within the organization.
5. Adoption: Technology deployment alone doesn’t guarantee success. Actual value comes from widespread adoption. AI tools require habit formation rather than simple mandate-driven implementation. Successful adoption strategies include developing organizational competency models that facilitate knowledge sharing, deploying change enablement teams to communicate progress and engage champions and capturing and publicizing wins to build momentum. The goal is making AI tools part of daily routines through structured support and celebration of success.

Business value of AI

• Time savings and efficiency: AI revolutionizes how organizations use time, automating repetitive tasks and streamlining decision-making processes. What once took hours of manual data entry can now be completed in seconds. Tasks requiring days to analyze can now provide instant insights. Whether scheduling meetings, analyzing customer feedback or optimizing workflows, AI enables organizations to work smarter, not just faster.
• Enhanced accuracy and reliability: One of AI’s most powerful advantages is its ability to improve accuracy dramatically. From detecting anomalies in financial data to predicting customer behavior, AI systems analyze vast amounts of information with precision exceeding human capability. By minimizing human error and continuously learning from new data, AI helps organizations make smarter, faster and more reliable decisions while ensuring consistency, reducing risk and enhancing quality.
• Improved strategic initiatives: Through automation, businesses reduce operational expenses while redirecting human effort toward more strategic initiatives. AI also provides better decision support by quickly analyzing large amounts of data, highlighting trends and providing insights that enable more informed decision-making.

The IFS approach to AI: Evolution of IFS AI systems investments 

IFS recognized early that organizations operating in manufacturing, aerospace and defense (A&D), energy and utilities and other asset-intensive industries need AI that understands their specific workflows, challenges and regulatory requirements. Rather than offering generic AI as a separate tool, IFS has developed a purpose-built Industrial AI platform that embeds intelligence directly into operational processes. This approach ensures that AI recommendations are grounded in industry-specific context, compliance requirements and real-world operational constraints. IFS has organized their AI approach around four complementary pillars that together provide comprehensive coverage for organizations at any stage of their AI journey:

• Traditional AI: Building on established algorithms, sensor data ingestion and connectivity solutions that have proven valuable over time. This foundation includes asset data management and traditional machine learning applications.
• Embedded AI: Embedded AI integrates capabilities directly into everyday user workflows. This includes features like realistic payment predictions, voice-assisted work order creation and context-aware assistance that brings relevant documents, drawings and technical bulletins to users automatically.
• Agentic AI: Digital workers that perform background tasks autonomously while keeping humans informed and in control. Users can even build their own digital workers through plain English interfaces, customizing automation to address specific business challenges. With pre-built connectors to over two dozen external systems, these agents can orchestrate workflows across multiple software products, not just within a single platform.
• Nexus Black: Bespoke AI solutions that aim to pursue moonshot AI use cases, unbounded by existing products or services. This team builds solutions in weeks rather than months, combining any available market technologies to solve specific customer challenges. One example includes storm management simulations for utilities, modeling weather events to optimize spare parts staging, mutual aid positioning and resource allocation to minimize outages.

Turn AI potential into performance with Baker Tilly

Whether you're exploring initial use cases or scaling enterprise-wide AI solutions, our team is ready to guide you through every step. With deep expertise in IFS technologies and a proven framework for AI readiness, we help organizations unlock real value, mitigate risks and build future-ready operations.