Mastering AI in Business: A 5-Step Playbook for IT Teams

Artificial intelligence has transitioned from a futuristic buzzword to a boardroom imperative. As businesses strive to stay competitive, AI is becoming central to strategic decision-making and daily operations. According to the BCG AI Radar 2025 survey, one in three companies globally plans to spend over $25 million on AI technologies next year. This growing focus is also reflected in the SAP Concur CFO Insights survey, which shows that 58% of finance leaders intend to invest in AI in 2025.

From streamlining operations to transforming employee workflows, AI is now a vital component in business functions, particularly in areas like travel and expense (T&E) management. For IT teams, this presents both an opportunity and a responsibility: to lead the charge in enabling smart, scalable, and secure AI adoption. Here are five key strategies to get AI implementation right.

1. Define Clear Business Goals

Rushing into AI adoption without a clear purpose often leads to underperformance and wasted investment. Instead of reacting to the latest AI trends, organizations should start by clearly defining what they hope to achieve with the technology.

IT teams must collaborate with stakeholders across departments to develop a strong business case for any AI tool under consideration. This includes identifying specific use cases, aligning with broader organizational priorities, and determining how success will be measured. Key performance indicators (KPIs)—such as increased accuracy, cost savings, or time efficiency—help gauge the impact of the implementation.

It’s also vital to ensure that AI tools are compatible with current infrastructure, comply with regulatory requirements, and meet security protocols. A focused, goal-driven approach ensures smoother adoption and better outcomes.

2. Take a Phased, Structured Approach

AI deployment is a complex process that can introduce many uncertainties. Rolling it out all at once can overwhelm IT teams and confuse users. A gradual, step-by-step deployment allows time to adjust and resolve issues before they scale.

Organizations can start with a pilot program—perhaps by integrating AI into audit and compliance functions—before extending usage to more departments. This not only provides valuable insights into system performance and user readiness but also helps identify potential roadblocks early.

A structured, phased rollout promotes organizational learning and builds internal momentum. It also allows employees to gain confidence and experience with new tools, making the broader implementation more successful.

3. Unify and Optimize Data

AI is only as powerful as the data it uses. Disconnected or siloed data systems limit AI’s ability to automate processes and generate insights. Therefore, data readiness is a critical component of any AI initiative.

IT leaders should develop a centralized data strategy—integrating data into a unified data lake or warehouse. This makes it easier for AI models to draw meaningful conclusions and power automation.

The process can be complex and time-consuming, especially when migrating data from legacy systems. But steps like mapping data sources, minimizing redundancy, and setting user-level access controls can help streamline the transition. Clean, accessible, and secure data is a must for successful AI deployment.

4. Empower and Prepare Employees

While AI brings significant productivity gains, it also raises concerns. According to a Deloitte survey, 28% of workers worry that AI might replace their jobs. Organizations must address this anxiety head-on through proactive communication and training.

It’s crucial to involve employees early in the implementation process and clearly explain how AI will enhance—rather than replace—their roles. Highlighting the ways AI can reduce repetitive tasks and increase productivity helps ease resistance.

Training programs, workshops, and ongoing support can help staff build familiarity with AI tools. Encouraging “AI champions” within departments to advocate for the technology can further improve adoption. When employees feel supported and included, they are more likely to embrace the shift.

5. Ensure Cross-Functional Collaboration

AI adoption isn’t just an IT project—it impacts the entire business. To succeed, implementation must be a collaborative effort that brings together decision-makers from across departments.

Research from SAP Concur shows that 37% of senior leaders believe AI-driven T&E tools could reduce pressure on both staff and IT support. Moreover, 54% of IT leaders are looking to partner with finance teams on digital transformation initiatives.

Cross-functional collaboration ensures that AI strategies align with company-wide goals and that each department’s unique needs are addressed. Tailored onboarding and training for different teams help maximize the value of AI tools across the organization.

AI implementation doesn’t have to be daunting. By starting with clear objectives, adopting a phased approach, unifying data, supporting employees, and fostering collaboration, IT leaders can drive successful AI integration. With the right foundations, businesses can unlock the full potential of AI—delivering smarter operations, empowered teams, and long-term growth.