Written by: João Carvalho as Managing Director in Southern Europe (Spain, Portugal, Greece and Turkey) the Middle East, and Africa
As businesses accelerate their adoption of artificial intelligence (AI), strategic implementation has become critical to realising its full potential. The BCG AI Radar 2025 survey reveals a significant trend: one in three companies globally plans to invest over $25 million in AI technologies this year.
This momentum is echoed in the latest SAP Concur CFO Insights survey, which found that 58% of finance leaders—particularly those overseeing travel and expense (T&E) functions—intend to invest in AI in 2025, underscoring the need for skilled and deliberate execution.
Here are five steps for IT teams to ensure successful AI deployment:
1. Set Clear Objectives from the Start
Successful AI implementation begins with purposeful planning. Rushing to adopt the latest technology without a clear plan often leads to wasted resources and unmet expectations.
IT leaders should collaborate across departments to define compelling business cases for each AI initiative. Aligning these projects with organisational goals, forecasting future developments, and identifying specific use cases ensures that selected tools serve real business needs.
Establishing key performance indicators (KPIs)—such as accuracy gains, cost reductions, or time savings—helps quantify success. The right AI solution should support existing strategies, comply with regulatory requirements, and integrate securely within current systems.
2. Adopt a Phased Implementation Approach
A phased, structured rollout reduces risk and improves outcomes. The early stages of AI deployment can be complex, so taking a measured approach allows for technical stability and employee adaptation.
Start with a focused pilot project in a high-impact area—such as audit or compliance—before scaling up. This allows teams to gather feedback, refine processes, and build internal momentum.
Gradual adoption helps avoid the pitfalls of hasty, organisation-wide deployments and ensures that employees have the time and support to adjust to new AI-enhanced workflows.
3. Centralise and Optimise Data Resources
AI thrives on quality data. Disconnected systems and fragmented data silos can severely limit the effectiveness of even the most advanced tools.
To lay a strong foundation, IT teams should implement comprehensive data integration tactics. Whether through data lakes, warehouses, or virtualisation layers, creating unified data environments enables accurate insights and reliable automation.
While centralising data requires effort, the payoff is significant. Consolidating sources, eliminating redundancies, and applying appropriate access controls create ideal conditions for scalable AI success.
4. Support and Upskill Employees
Even as AI adoption accelerates, employee concerns persist. Deloitte research shows that 28% of workers worry that technology may replace their jobs.
Clear, transparent communication is essential. Involve employees from the outset and position AI as a tool that enhances—not replaces—their work. This builds trust and helps ease uncertainty.
AI also presents opportunities for upskilling and professional growth. Organisations should invest in training and empower internal champions to model adoption and share practical benefits across teams.
5. Lead Through Cross-Functional Collaboration
AI transformation cannot be confined to the IT department. According to SAP Concur research, 37% of senior decision-makers believe AI-powered T&E solutions could ease pressure on IT support teams.
Collaboration is key. The SAP Concur 2025 CFO Insights reveals that 54% of IT leaders want to partner more closely with finance teams on digital transformation.
AI implementations must support broader organisational goals. By building collaborative frameworks with finance, sales, operations, and beyond, IT leaders can ensure alignment, accelerate adoption, and maximise the return on AI investment.
With thoughtful planning, robust data foundations, and cross-functional leadership, IT teams can drive transformative AI adoption—unlocking efficiencies, strengthening decision-making, and delivering long-term business value.
