AI Applied: Rethinking ERP in the age of AI 

Artificial intelligence (AI) is no longer something organizations need to search for or layer onto existing systems. Increasingly, AI is already embedded into the enterprise platforms businesses use every day. 

As ERP providers continue introducing AI capabilities directly into their solutions, organizations are encountering a new challenge. The question is no longer whether AI should be adopted; it is how businesses should rethink the way work gets done in an AI-enabled environment. 

The shift from software selection to process transformation 

For years, organizations focused heavily on selecting the right technology platforms. Decisions centered around evaluating functionality, identifying fit-to-requirements, and choosing systems that aligned with operational needs. 

Today, those conversations are evolving. 

Many leading ERP providers already include AI capabilities within their platforms. As a result, organizations are spending less time asking where AI fits and more time considering how AI changes business processes and decision-making. 

Questions are becoming more operational: 

• How should workflows change in an AI-enabled environment?  
• How can organizations trust AI-generated outputs?  
• What role should employees play in reviewing or approving AI actions?  
• How can teams prepare for a different way of working?  

The conversation is moving beyond technology implementation and toward broader business transformation. 

Understanding agentic AI 

One of the concepts gaining traction across the enterprise technology landscape is agentic AI. 

Traditional AI capabilities often focused on analyzing information, identifying trends and providing recommendations. Agentic AI takes that one step further by helping systems move from insight to action. 

Importantly, organizations are increasingly approaching these capabilities through a "human-in-the-loop" model, where people remain responsible for oversight, governance and approvals while AI supports execution. This approach helps ensure AI acts as an enabler for employees rather than a replacement for them. 

Rather than simply surfacing information, AI-enabled workflows can complete actions on behalf of users. 

For example, instead of navigating multiple screens to create purchase requests, process employee changes or complete repetitive administrative tasks, employees may eventually define parameters, review recommendations and approve actions while AI handles much of the execution. 

This shift changes how employees spend their time. 

Instead of focusing on repetitive transactions and manual processes, teams can dedicate more effort to strategic initiatives, business planning, and delivering value to the organization. 

This evolution is not about removing people from the process entirely. It is about shifting employees toward higher-value work while AI supports efficiency and execution. 

A practical framework: Crawl, walk, run 

Although AI capabilities are advancing quickly, successful adoption rarely happens all at once.  

One of the most effective approaches organizations can take is a phased strategy that balances innovation with change management. 

Crawl: Introduce passive AI capabilities 
Begin with lower-risk features such as content suggestions, chat assistants, or embedded recommendations that help users become familiar with AI in their daily work. 

Walk: Expand AI-driven insights 
Introduce AI recommendations that guide decisions and analyze information before actions are taken. 

Run: Enable intelligent execution 
Deploy more advanced capabilities that automate workflows and complete larger actions with appropriate oversight, allowing employees to spend more time focusing on business outcomes. 

Moving through these phases gradually can help organizations increase adoption while reducing resistance to change. 

Technology alone is not enough 

AI adoption challenges are often less about technology and more about people. 

Many organizations encounter resistance simply because employees are comfortable with familiar processes and hesitant to adopt new ways of working. 

Successful adoption requires helping employees understand that AI is designed to reduce repetitive work and create more time for higher-value activities — not simply introduce change for the sake of change. 

Just as important, organizations need a strong foundation of clean, reliable data. AI systems are only as effective as the information supporting them. Understanding what data exists, why it is collected, and whether it can be trusted should be considered a critical first step in any AI initiative.  

How we can help 

Navigating AI adoption requires more than implementing new technology. Organizations need a strategy that aligns technology investments with business goals, workforce readiness and long-term operational outcomes. 

Baker Tilly helps organizations evaluate emerging AI capabilities, assess the impact on existing ERP environments and develop practical adoption road maps. Whether organizations are preparing for cloud transformation, exploring embedded AI features, or building a broader AI strategy, our teams can help identify opportunities, manage change and create a path toward measurable value. 

As embedded and agentic AI continue to evolve, organizations that establish strong foundations and take a thoughtful approach to adoption will be better positioned to improve efficiency and rethink how work gets done.