How Private Equity Firms Can Prepare Their Portfolio Companies for AI

As PE firms seek to enhance the value of their portfolio companies, the integration of AI is no longer optional—it’s imperative. But it's not just about adopting AI for the sake of following a trend; it's about aligning AI capabilities with the unique goals and challenges of each portfolio company. A well-structured approach is crucial to ensure AI blends with the current workflow and output expectations of a firm.

The challenges are manifold. From data silos that hinder AI implementation to the reluctance of traditional industries to embrace technological change, private equity firms and their portfolio companies are grappling with a fundamental shift in how they operate. Legacy systems, a lack of AI talent, and concerns about the ethical use of AI further compound the problem. It has become difficult to determine just how much AI is right for business, and what kind of AI to implement.

In this blog post, we will outline a systematic five-step process that PE firms can follow to ensure their portfolio companies are AI-ready. This framework will not only assess current AI readiness but also lay the groundwork for a successful AI journey.

Step 1: Assessment and Strategy Development

For PE firms to use AI to its fullest potential, it's critical to assess the level of AI readiness across all departments. Begin by evaluating the portfolio company's current technological capabilities, data assets, and AI readiness. This includes familiarity with AI and overall data literacy, down to the individual team member level. Identify areas where AI can add value and determine the maturity of existing technology infrastructure.

For example, if a portfolio company already has a robust data collection system in place, it may be easier to integrate AI. On the other hand, if the company lacks proper data governance and management practices, it may require more groundwork before AI implementation can take place.

After determining the PE firm or portfolio company's AI readiness, it's time to develop an AI strategy. For that, it's important to work with the portfolio company's management team to create a clear strategy that aligns with the company's business objectives. Specific goals and key performance indicators (KPIs) for AI implementation should be defined before any AI is implemented into existing processes or used to create new ones. 

Key Questions to Ask: 

  • What is your current digital strategy, and how does AI align with it?
  • What are the specific business problems or processes that you believe AI can optimize? 
  • Do you have a workforce development plan that includes data and AI training? 
  • Is the AI model being considered scalable? 
  • What is the long-term roadmap for AI adoption within the organization?

Step 2: Talent Acquisition and Development

Acquiring and developing AI talent is a critical step in ensuring portfolio companies are ready for AI implementation. PE firms need to attract and develop skilled professionals who can drive the AI initiatives and provide ongoing support.

While it can be tempting to focus exclusively on talent acquisition, AI skills gaps and talent shortages mean that PE firms cannot solely rely on hiring new talent; they must also prioritize upskilling their current workforce.

Start by assessing the current skill sets of your portfolio company's employees to determine areas that need improvement. This can be done through surveys, interviews, or assessments. Once the skill gaps are identified, you can provide training programs, workshops, or online courses to enhance the AI capabilities of employees.

Key Questions to Ask: 

  • How does your employee training plan in AI readiness align with the company's overall business strategy and objectives?
  • What is the scale of the planned training program? Does it cover all relevant departments and job roles, or is it targeted at specific teams?
  • What training methodologies and platforms will you use to educate employees? Will you rely on in-house resources, or partner with specialized educational platforms?
  • How will you identify the current AI-related skills among employees and the gaps that need to be addressed? Is there a pre-assessment and post-assessment metric to measure the effectiveness of the training?
  • How will the training program adapt to the evolving landscape of AI technologies? Is there a plan for continuous learning and updates?
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Step 3: Technology Infrastructure and Integration

Another key step when determining how and when to implement AI is selecting the right technology infrastructure and planning for its integration. To start, firms must select AI tools and platforms that work with their companies' goals and plans for AI use. The portfolio company must choose the best options for their specific needs and budget constraints.

Once the appropriate AI tools and platforms have been selected, the next step is to integrate them into the existing technology infrastructure. This may involve connecting AI systems to other data sources and systems, ensuring compatibility and seamless data flow. It is important to consider factors such as scalability, security, and data privacy when integrating AI technology.

For example, a company may want to implement AI to collect data and analyze it for monthly record keeping. A review of the company's current technology will determine whether the AI program is compatible with its current software or technology stack. If it is not, AI implementation could cause problems rather than providing helpful solutions or time-saving support. 

Key Questions to Ask: 

  • What does your current technology stack look like, and how compatible is it with the AI technologies you plan to implement? Will you need to adopt new systems or can the AI solutions be integrated into the existing infrastructure?
  • How is your data architecture designed to support AI applications? Do you have a centralized data warehouse, and how do you ensure data quality and governance?
  • What security protocols will be put in place to protect the data and algorithms used in your AI implementations? How do you plan to ensure compliance with regulations like GDPR or CCPA?
  • How scalable is your planned AI infrastructure? Can it handle increased data loads and more complex algorithms as your AI initiatives grow?
  • What are the key milestones for AI integration, including testing and deployment phases? How will success be measured at each stage, and what are your contingency plans for any setbacks?

Step 4: Governance and Ethical Implementation

Once the technology infrastructure is in place, governance and ethical implementation are crucial for responsible and effective AI deployment. PE firms must work closely with their portfolio companies to establish clear guidelines and processes to ensure the ethical use of AI and prevent any potential harm or misuse.

Key Questions to Ask: 

  • Do you have a comprehensive ethical framework or guidelines that govern the design, development, and deployment of AI systems?
  • How are you ensuring transparency in your AI algorithms and accountability in AI-driven decisions?
  • What data governance policies are in place to protect the data used in training and operating your AI systems?
  • How are you identifying and mitigating biases in your AI models that could lead to unfair or discriminatory outcomes?
  • How are you involving various stakeholders—including employees, customers, and potentially affected communities—in the AI governance process?

Step 5: Performance Monitoring and Optimization

PE firms and their portfolio companies should look into performance monitoring and optimization strategies to make the most of AI use. It's critical to establish metrics and focus on continuous improvement post-implementation.

To establish metrics, PE firms can define and implement key performance numbers to track the impact of AI initiatives on the portfolio company's performance, such as revenue growth, cost reduction, and customer satisfaction. Every company may have different metrics they'd like to track, so sitting down and working through those will give the firm a good place to start with the portfolio company's performance monitoring setup. 

Once the metrics are established, business leaders must encourage a culture of continuous improvement in AI adoption, regularly review and optimize AI models and strategies, and adapt to changing market conditions and business objectives. That could mean implementing new AI models as they come out, sticking to AI models that currently work well, and optimizing their use within different aspects of the business to achieve key objectives, or other uses of AI based on the company's specific needs. 

Key Questions to Ask: 

  • What Key Performance Indicators (KPIs) have you identified to evaluate the success of your AI initiatives, and how do these align with your overall business objectives?
  • What tools and platforms are you using to continuously monitor the performance, health, and utilization of your AI models?
  • How have you implemented feedback mechanisms to continually refine and optimize your AI algorithms?
  • How are you tracking the costs associated with running AI models in terms of computational resources, data storage, and maintenance?
  • What measures are in place to regularly audit the AI systems for ethical considerations like bias, and for compliance with regulations such as GDPR?
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Invest in Human Potential

As PE firms navigate the ever-evolving landscape of technology and strive to position themselves and their portfolio companies for success in an AI-driven future, it’s clear that the human element is just as critical as technology itself. Without human intervention, AI simply cannot exist. It cannot be implemented well, and it may lead to confusion or frustration rather than creating helpful solutions. 

By investing in their teams, bridging the AI talent gap, and fostering a culture of continuous learning and adaptation, private equity firms can unlock the full potential of AI. This makes it possible to ensure not only the growth and competitiveness of their portfolio companies but also the long-term resilience and relevance of their organizations in a rapidly changing world. 

At Correlation One, we are prepared to come alongside you to train your workforce and prepare them for the AI-powered future. Our custom-tailored training programs feature live, instructor-led curriculum for maximum relevance and impact, and are purpose-built to address real, pressing business problems. Interested in how Correlation One can get you and your portfolio companies AI-ready? Reach out to learn more