The report shows how artificial intelligence is moving from early testing to practical use in investment promotion. Agencies face growing pressure to deliver faster, more targeted services, and are using AI to automate tasks, identify investors and support decisions.
But adoption is uneven, with many agencies falling behind, especially in developing economies. The report sets out practical ways forward, based on each agency’s level of digital maturity.
The report’s main message is clear. Successful AI adoption depends on a strong strategy, careful sequencing and steady capacity building.
AI adoption is advancing – but remains highly uneven
AI use is concentrated in more advanced economies. 82% of investment promotion agencies reporting AI use are in high or upper-middle-income countries.
Only 16% are in least developed countries and small island developing states. Across 76 agencies reviewed in these economies, just 13% have deployed visible AI tools. Most of these tools are chatbots used for basic automation.
Several factors explain this gap:
- Limited digital infrastructure
- Skills gaps
- Weak data governance frameworks
Even so, agencies don’t need to be fully ready to begin. The report shows that targeted use of existing tools can deliver results. Progress can be built step by step, as infrastructure and skills improve.
Four core functions show where AI delivers value
AI use in investment promotion falls into four main functions, which differ in complexity and purpose.
Operational automation is the most accessible starting point. Chatbots and automated systems handle routine investor questions, licensing steps and workflows. This improves response times and reduces repetitive work.
Information synthesis turns data into usable insights. AI tools process large volumes of information, such as media reports and investor queries. This helps agencies understand trends and support decisions.
Predictive decision-making supports better targeting. Machine learning uses past data to estimate which investors are most likely to invest. This helps agencies focus their efforts.
Generative AI creates new content. It can draft proposals, marketing materials and analytical notes. These outputs still require human review.
These functions allow agencies to start small and move gradually to more advanced uses.
Early results show measurable efficiency and targeting gains
Examples from early adopters show clear results.
In the Republic of Korea, an AI system identifies companies likely to make additional investments. Between 2023 and 2025, 38.5% of these companies went on to invest. This is more than double the 17.7% baseline.
The system also saved over 1,000 staff hours each year, showing how AI can improve efficiency in daily work.
In the Democratic Republic of the Congo, an AI chatbot improved performance across several areas:
- 30% more qualified contacts
- 50% faster response times
- 20% higher conversion rates
These cases show that AI can improve efficiency and investor targeting when applied to clear tasks.
A phased approach helps agencies move forward
The report outlines a step-by-step approach to AI adoption.
- Foundation: build digital systems and improve data
- Initialisation: test low-risk AI tools
- Scaling: expand tools that work well
- Maturity: strengthen governance and oversight
Starting with low-risk applications is important. Common entry points include chatbots for investor inquiries, generative tools for content creation and AI features already built into customer relationship management (CRM) systems
Estimated costs range from $300 to $6,000 per year, depending on the tool and how it is used.
Data, capacity and governance remain key challenges
AI adoption carries some risks. And many agencies face important constraints.
Data quality is a major issue. AI systems need reliable and well-structured data. Weak data can lead to poor results and damage investor trust.
Other challenges include:
- Limited technical expertise
- Ongoing maintenance needs
- Data protection and cybersecurity risks
- Resistance to change within organisations
Human oversight remains essential, especially for high-stakes decisions.
AI isn’t a one-off solution. It’s an ongoing capability that requires investment, learning and adaptation.