PE Value Creation —
Maximizing Return on Investment
In ROI optimization, data is the overlooked
game-changer for portfolio companies
Private equity firms seeking to maximize the return on investment of their portfolio companies often face a key challenge: the underutilization of data and AI as value levers. Despite the transformative potential of these resources, many portfolio companies overlook their value, failing to integrate data and AI strategies into their growth plans. This can lead to missed opportunities for operational efficiency, strategic decision-making, and ultimately, increased profitability upon exit. Navigating this landscape and unlocking the hidden value of data and AI requires a shift in perspective and strategic guidance.
At HYGHT, we harness the untapped potential of data and AI to maximize portfolio company ROI. Using our structured approach, we build robust data foundations, automate costly processes, and enhance business models. Our strategy not only unlocks immediate value but also positions companies for increased profitability upon exit. With HYGHT, data and AI transform from overlooked assets to powerful growth levers.
"Companies that choose not to use data to create value risk hastening their own obsolescence or, at the very least, losing competitive advantage."
Boston Consulting Group
Unleashing the power of data and AI for
private equity performance improvement
From pain points and costly processes to optimisation potentials
01
Understanding the business & strategy
- Analyze the organization’s current business model and strategy to comprehend its operational dynamics
- Understand the role of data within the business model and how it contributes to strategic objectives
- Identify areas where data and AI can enhance the existing business model and strategy
02
Identifying pain points & costly processes
- Identify key pain points in the organization’s operations, particularly those that are data-related or could be improved with better data management
- Highlight costly processes that could be streamlined or automated, reducing expenses and increasing efficiency
- Prioritize these areas based on factors such as the potential for improvement, cost savings, and strategic alignment
03
Defining a light value case
- Define the potential for optimization in each of the identified pain points and costly processes
- Consider how data management improvements or AI technologies could address these issues
- Quantify these potentials where possible, providing a clear view of the potential benefits of optimization
Translating into a strategy with an implementation roadmap
01
Designing the target state
- Design a desired future state where the identified optimization potentials have been realized
- Conduct a fit-gap analysis to identify the differences between the current state and this desired future state
- This analysis should highlight the changes needed in terms of data management practices, technology, skills, and culture
02
Detail an implementation roadmap
- Based on the fit-gap analysis, design a detailed roadmap for achieving the desired future state
- This roadmap outlines the steps needed, the resources required, and the timeline for implementation incl. a light value case
- It should also define clear responsibilities and success metrics for each step, providing a guide for the implementation process
Portfolios with limited budget require
focus to maximize ROI from data & AI
01
What many companies do
- Often overlook the potential of data and AI as value levers for enhancing the performance of their portfolio companies
- May lack a structured approach to integrating data and AI strategies into their value creation plans
- Sometimes neglect the importance of a strong data foundation and effective data governance for leveraging AI
- May not identify or prioritize costly processes that could be automated or enhanced with AI technologies
- Often miss out on driving change management activities that are critical for the successful adoption of data and AI initiatives
02
What we do differently
- Follow a structured approach to integrating data and AI strategies into value creation plans, starting with understanding the business model and strategy
- Focus on building a robust data foundation and designing a light but effective data governance framework that is tailored to the organization’s unique dynamics
- Identify and prioritize costly processes that could be streamlined or automated with AI, defining clear optimization potentials
- Drive change management activities to ensure meaningful adoption of data and AI initiatives and design a detailed roadmap for effective implementation
- Support the implementation of the roadmap to ensure value creation
