Revolutionising Result-Based Management: The Impact of AI

Introduction

Artificial intelligence (AI) is rapidly changing how we live and work. In the management field, AI is being used to improve decision-making, automate tasks, and gain insights from data at a scale that was previously impossible (Davenport & Ronanki, 2018). One area where AI is having a significant and growing impact is Results-Based Management (RBM).

RBM is a management approach that focuses on setting clear goals, measuring progress, and taking corrective action as needed (Mayne, 2007; United Nations Development Group [UNDG], 2011). It is a data-driven approach that helps organisations improve their performance, and AI is strengthening its effectiveness across sectors.

How AI Supports RBM

1. Set goals

AI helps analyse data, identify trends, and set realistic and evidence-informed goals. For example, AI can analyse sales or programme data to determine what is working and what is not, then help set targeted improvement goals grounded in evidence rather than assumption (Mouli & Alam, 2024).

2. Measure progress

AI can facilitate the real-time monitoring and tracking of performance data, such as sales, programme outputs and citizen satisfaction, to measure progress towards goals and identify areas needing adjustment before they become significant problems (Wang et al., 2025).

3. Take corrective action

AI aids in analysing data to identify which strategies or campaigns are underperforming, enabling organisations to pivot quickly, launch more effective approaches, and make evidence-based corrections in real time (Adepoju & Chinonyerem, 2025).

4. Identify and predict risk

AI helps analyse financial and operational data to identify potential risks, such as fraud, resource gaps, or service delivery failures, before they materialise, giving organisations time to mitigate them proactively rather than reactively (Otim et al., 2025).

5. Optimise processes

AI can analyse operational data to identify inefficiencies and suggest or implement changes that reduce costs and improve delivery across manufacturing, service provision, and programme implementation contexts (Davenport & Ronanki, 2018).

6. Generate insights

AI can analyse large volumes of data to surface insights that support better decisions, such as identifying patterns in community feedback to design programmes that meet actual needs rather than assumed ones (Wang et al., 2025).

Using AI Responsibly in RBM

AI also brings risks. Biased data can produce biased targets, opaque models can weaken accountability, and sensitive programme data must be protected (Vatamanu & Tofan, 2025). Organisations should keep people in charge of decisions, test models for bias, and follow recognised principles such as the OECD AI Principles and UNESCO’s Recommendation on the Ethics of Artificial Intelligence (Organisation for Economic Co-operation and Development [OECD], 2019; United Nations Educational, Scientific and Cultural Organization [UNESCO], 2021).

Conclusion

Overall, AI is a powerful tool that enhances Results-Based Management. Using AI, organisations can set clear goals, measure progress, take corrective action, predict risk, optimise processes, and generate actionable insights. The result is organisations that achieve their goals with greater speed, accuracy, and accountability, in both the private sector and in development programming, provided AI is used responsibly.

References