An Introduction to AI Readiness
The use cases and vendor landscape are constantly evolving and will continue to change as the industry matures. With this in mind, it can be difficult to know where to leverage AI and which leading vendors to partner with.
Looking at industry trends, some of the most prominent AI use cases currently being leveraged by leading GPs include:
Relationship sentiment analysis and identifying trends to reveal hidden opportunities.
Deal sourcing and due diligence automation to gain quicker insights into the complete picture of a target company’s prospects.
Extracting data from unstructured sources, such as Capital Call notices, and automatically matching it to structured data from the Fund Administrator.
When considering the potential of AI, as with any technology, there are important pre-requisites. A centralised data source will be a requirement for success. With the emergence of Data Lakehouse technology, GPs can now have a centralised data platform containing both their structured and unstructured data, enabling real time insights, analytics, and implementation of AI use cases. However, for most GPs, significant improvements in data management, quality and operating models must occur before embarking on this journey.
Challenges Posed by the Adoption of AI in Private Markets
Before beginning any transformational journey, understanding your current state, and identifying limiting factors is crucial. The most common challenges that are encountered by GPs are:
1. Technology Infrastructure & Data Strategy
Despite the availability of front-to-back solutions, many GPs prefer market-leading options for each functional area, as end-to-end solutions often lack comprehensive functionality. However, using multiple best-of-breed systems – many of which are not integrated – leads to data silos. This not only makes decision-making and report generation time-consuming and less accurate but also limits a GP’s ability to leverage AI technology effectively.
To address data silos, systems must be integrated into a centralised data platform, using technologies such as Snowflake or Databricks, with a robust data architecture. Data platforms should incorporate automated data pipelines and a unified data model to ensure data is properly managed at the central source. By doing so, businesses gain a single, reliable source of truth, enabling business intelligence tools and AI to deliver streamlined reporting and data-driven decision-making.
2. Data Quality and Governance
The success of AI in private markets ultimately depends on the quality of the data it uses. However, maintaining high data quality requires continuous monitoring and effective controls, making robust data governance essential.
To achieve this, GPs must establish clear roles and responsibilities for data management, along with policies that employees must follow when using AI, ensuring data is not accessed or used beyond their control.
Data governors should implement automated data validation checks and conduct regular quality assessments within source systems at predefined intervals.
3. Lacking Technology and Transformation Culture
Employees may not always recognise the benefits of new technologies and may resist change when AI is introduced. A limited understanding of technological advancements or a reluctance to adapt can result in operational inefficiencies and, all too often, the prolonged use of outdated legacy systems, leading to a range of challenges.
For successful transformation, senior leadership must champion change, while employees should be encouraged to adopt a mindset that empowers them to seek and rely on data-driven decision-making. Across the industry, various approaches have been used to foster this mindset, including linking system adoption to compensation, appointing data champions, and investing in employee education.
Final Thoughts on AI Readiness
AI presents a transformative opportunity for GPs in Private Markets to scale operations, enhance efficiency, and drive data-driven decision-making. However, realising AI’s full potential requires overcoming key challenges, including fragmented technology infrastructure, poor data quality, and cultural resistance to change.
By integrating systems into a centralised data platform, strengthening data governance, and fostering a culture of innovation, GPs can position themselves for long-term success. Those who take proactive steps to address these foundational elements will not only be well-prepared to leverage AI today but will also be ready to adopt future advancements, ensuring they remain competitive in an increasingly data-driven industry.
Next Steps Towards AI Readiness in Private Markets
Navigating the complexities of AI adoption and data transformation can be challenging, but with the right expertise and approach, GPs can unlock significant value. Novatus Global has extensive experience in helping financial services organisations develop data strategies, execute data transformation and outsourcing projects, implement robust data management policies, and successfully integrate AI into their operations.
Whether you are looking to improve data quality, break down silos, or build a future-proof AI strategy, Novatus Global can provide the guidance and support needed to overcome these challenges.
Contact us today or email Nick Ibbotson to learn more about Novatus’ AI Readiness offering.
