Maximizing AI/ML Return on Investment for Portfolio Companies
Implementing AI solutions has remained a priority for private equity firms and their portfolio companies given the potential cost and productivity improvements for data quality, product innovation, DevOps and code optimization to maximize their returns.
Investing in technology and data management allows portfolio companies to gain a competitive advantage by integrating more advanced AI/ML capabilities to generate insights, predictions and recommendations that enhance their decision-making, customer satisfaction and operational efficiencies. Private equity firms and their portfolio companies have adopted a multi-pronged approach focused on assessing the return on investment for initiatives related to data quality, product innovation, development and operations and code optimization.
Data Quality for AI/ML Success
SITUATION
One of the most significant challenges in implementing AI/ML is ensuring the quality and consistency of data from disparate systems. Poor data quality across portfolio companies can lead to inaccurate predictions, unreliable insights and wasted investments. According to Gartner, each portfolio company loses an average of $12.9 million annually due to data quality issues. These inconsistencies and errors in data collection hinder portfolio companies from successfully adopting high return on investment on everything from predictive analytics to supply chain planning.
IMPACT
To avoid these pitfalls, private equity firms must invest in standardizing and improving data management across their portfolio companies. By implementing cloud-based ERP systems and automating data collection processes through data analytics like Alteryx, portfolio companies can ensure improved data accuracy, scalability and security to enable higher return on investment.
AI/ML for Product Innovation
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Many portfolio companies struggle to integrate AI/ML into their product development processes, especially new code development. Without a robust AI/ML-centered innovation strategy, portfolio companies are failing to capitalize on the full potential AI/ML to improve product innovation design, feature optimization and customer satisfaction.
IMPACT
Incorporating AI/ML into targeted aspects of product innovation, such as code quality and revised algorithms, allows portfolio companies to launch products more quickly, better meet customer needs and differentiate their offerings in the market. Research has found that GenAI solutions can increase speed to innovation by nearly 35%, automating complex tasks such as algorithm optimization and code quality improvements, enabling firms to innovate faster while maintaining high-quality standards. In fact, Gartner estimates that by as early as 2025, 80% of the product development life cycle will use GenAI to create code.
AI-Driven DevOps and Code Optimization
SITUATION
Inadequate attention to DevOps and code quality is another barrier to maximizing the benefits of AI/ML. Many portfolio companies lack the infrastructure to automate DevOps processes, leading to inefficiencies in code deployment, testing and maintenance, which in turn, results in delays, higher operational costs and suboptimal software performance.
IMPACT
Because the engineering teams of portfolio companies have historically de-prioritized code development and automation, many private equity firms are driving the adoption of DevOps and code optimization. For portfolio companies, AI-enhanced DevOps reduce manual scaling costs, accelerate time-to-market, improve product quality and minimize technical issues, strengthening their competitive position in preparation for an exit.
MorganFranklin Consulting 2025 Expectation
Portfolio companies will adopt smaller language models to address key, high-level product, data and code quality use cases. Process and data quality assessments will be critical for portfolio companies interested in adopting AL and ML, as these tools will begin to play an even more important role in exit readiness preparation. Optimizing and curating data to maximize value from technology will allow private equity firms to accelerate exits, which is essential given that the Federal Reserve will likely continue to reduce interest rates.
