1. Slow and steady wins the race
The first way IT teams can master AI deployment is by approaching implementation gradually. Kickstarting the adoption of a new AI tool can be stressful, as it is a period full of moving parts and unknown risks. It’s critical that the business follows a structured deployment process to ensure that employees can get up to speed and IT is not overwhelmed with queries about the new system.
A phased approach may alleviate some initial pain points. With so many business tools already introducing AI-powered feature sets, some businesses may benefit by trialling a trusted AI solution before launching an organisation-wide adoption programme.
For example, exploring an AI solution that powers audit and compliance solutions before integrating a department-agnostic technology can allow businesses to measure its progress over time. This will inform how they will be able to move forward with future AI initiatives.
Rolling out AI in multiple phases can also promote steady progress by building excitement for the technology within the business and giving employees time to adjust to their new AI-enabled tools.
2. Bringing data together
AI is dependent on data, meaning that IT professionals should make sure that their organisation’s data is accessible and ready to use. If data sources are siloed, users may encounter issues automating manual processes and creating accurate outputs.
To master AI deployment, technology decision-makers within the business should define an integrated data strategy. This should address how the business will centralise data under one unified data lake or data warehouse – making it easier to drive AI models and produce novel insights.
Centralising data and transferring it over from another platform can be tedious. Therefore, taking steps such as mapping data sources, reducing overhead, and classifying data for varying levels of user access will allow for a smooth implementation process.
3. Getting leadership on board
However, IT change management shouldn’t occur in a vacuum. To ensure cross-departmental buy-in, IT teams can’t be the only ones responsible for implementation. According to research by SAP Concur, 37% of senior decision-makers believe that AI-driven travel and expense (T&E) solutions could help reduce strain on staffing and IT support teams.
IT leaders want to work with their fellow department heads. This is demonstrated by findings from the SAP Concur 2025 CFO Insights survey, which shows that over half (54%) of IT leaders want to collaborate with finance on digital transformation strategies.
These initiatives have far-reaching impacts on the business and need to complement its broader goals and initiatives. IT decision-makers need to work alongside finance, sales, and operations teams to establish a cross-departmental collaboration scheme. This will also ensure that technical training is adjusted for every team, enabling them to get the most value out of the AI solution.
4. Preparing the workforce for AI integration
AI has now entered the mainstream, but it’s arrived with a healthy dose of apprehension. Some workers may be wary of AI implementation, as evidenced by the 28% of respondents to a Deloitte survey worried about the threat of technology taking over their jobs.
Businesses and IT teams should communicate transparently about AI use with employees, emphasising the positive impact that it will have on their workload. Having employees involved from day one will facilitate the continuous improvement of their AI skills and help them feel confident in the technology. Another way to support the workforce is by providing training sessions throughout the deployment cycle.
AI implementation also provides an exciting opportunity for employees to improve their productivity and build future-proof AI skills. User champions in different areas of the business should be encouraged to evangelise adoption among their peers and influence them.
5. Defining success with clear goals
Nevertheless, AI implementation cannot be successful if it is not thought out. Rushing to adopt the shiniest new AI tool will likely lead to failure and disappointment if it isn’t adapted to the business’ needs.
IT professionals should collaborate with other teams to define the business case for any new AI tool. Considering the wider organisation’s goals, upcoming developments, and relevant use cases can help them choose the best-suited tool for the business.
IT teams can also evaluate a tool’s efficiency throughout implementation by establishing key performance indicators (KPIs) to grade the tool such as enhanced accuracy and hours saved in various tasks. Ensuring that the chosen AI tool aligns with the current strategy, meets regulatory standards, and complies with existing application security protocols will make its implementation easy and efficient.
Why AI implementation matters more than ever
AI is inescapable – and implementation is becoming a key consideration for all businesses. The BCG AI Radar 2025 survey reveals that one in three companies globally plan to spend over $25 million on AI technologies in 2025.
This AI optimism is echoed in the findings of this year’s SAP Concur CFO Insights survey, which shows that over half (58%) of finance leaders plan to invest in AI in 2025. As AI becomes non-negotiable for businesses also in travel and expense management (T&E), IT teams must get implementation right.
Ultimately, AI implementation doesn’t have to be stressful. IT leaders can take a central role in building the organisation’s AI posture through robust planning, data, and cross-departmental collaboration – helping deliver lasting results for the business.
