
AGENTIC AI IN FP&A
WITHIN PHARMACEUTICAL GBS
Welcome to the website of Group 3 from the Capstone project "Leadership & HRM". Here, you will find valuable insights into our work and project endeavors. We invite you to explore our findings and learn more about our approach to leadership and human resource management!




OUR TASK
Agentic AI in FP&A within GBS
In collaboration with Accenture, we analyzed how Agentic AI is likely to reshape FP&A within pharmaceutical Global Business Services. Mapping priority use cases and maturity horizons, identifying the key risks and governance requirements for safe deployment, and translating these implications into concrete leadership capabilities and a framework for future operating-model decisions.
STRUCTURE OF THE REPORT
1. Landscape scan
Analysis of the current GBS and Agentic AI landscape
4. Barriers & enablers
Analysis of the barriers & enablers in regards to Agentic AI adoption
2. Use case mapping
Technical deep dive into Agentic AI and where it can be applied
5. Leadership framework
Guidance for leaders on how to implement and lead with Agentic AI
3. Art of the possible
Scenario-analysis of a potential 2-, 5- and 15-year horizon
6. Case study
Case study of a pharmaceutical company adopting Agentic AI
KEY FINDINGS
1
Landscape scan
Pharma GBS consistently centralizes Finance, HR, and IT into a standardized, automation-enabled internal service backbone concentrated in global hubs. As GBS evolves, Agentic AI becomes the next step. A competitive Agentic AI landscape now provides a wide range of enabling technologies, positioning pharma GBS to scale adoption.
2
Use case mapping
Agentic AI creates the most value in data-rich, structured, cross-functional workflows, making GBS prime use cases. Scaling this requires orchestrated, synchronized agents on a single source of truth, standardized agent-to-agent communication, and tight integration with existing systems, backed by human-in-the-loop validation.
3
Art of the possible
Experts rank budgeting and forecasting as the highest-impact FP&A domains for agentic AI across 2-, 5-, and 15-year horizons because the work is cyclical, data-heavy, and rules-driven. Agentic AI can automate nearly the full chain, while shifting FP&A roles from manual preparation to judgement, interpretation, and decision support.
4
Barriers & enablers
The biggest barriers are data quality, governance, trust, and change management, with data quality the main constraint across FP&A. Scaling from pilots to enterprise impact requires the matching enablers: standardized data, clear accountability and risk controls, leadership-led trust building, and active change management to close skills gaps and reduce resistance.
5
Leadership framework
Agentic AI delivers the most value when leaders set long term direction and co own tactical planning, while managers drive day to day execution. Organizations must build a leadership skillset across technical, interpersonal and conceptual skills and anchor everything in clear accountability, strong oversight and fast communication to ensure reliable, scalable outcomes
6
Case study
Maja's firm, Accenturion, serves as a hypothetical case study demonstrating the integration of Agentic AI within their GBS. Insights are drawn from the work of Group 3 and provide a realistic depiction how Agentic AI adoption can be effectively implemented. This case study illustrates not only the strategic advantages of using AI but also practical steps taken by Accenturion.
"How is Agentic AI transforming FP&A in pharmaceutical GBS by redefining leadership practices and collaboration processes?"
Agentic AI will reshape FP&A in pharmaceutical GBS by automating budgeting and forecasting, enabling faster, more reliable real-time insights with less manual work. This shift requires leaders to orchestrate human–AI collaboration, build trust, and guide change to unlock a leaner, more adaptive FP&A model.
PROJECT RECAP
ACKNOWLEDGEMENTS
We would like to thank the Accenture-Team, consisting of Darian Manuchehr, Lara von Däniken and Teresa Spitzer, as well as our lecturer, Ms. Ursula Knorr, for the great collaboration and support throughout the project.
It was an amazing and impactful experience and we are looking forward to applying our learnings in future scenarios.





