Private equity (PE) firms are increasingly viewing AI as a powerful asset for value creation. For portfolio companies, AI can revolutionise operations, improve customer experiences, and unlock new revenue streams.
But adopting AI effectively requires careful preparation; jumping in unprepared risks wasting investments and missing opportunities.
Introducing our AI Readiness Checklist: a practical tool to assess whether your company has the foundational elements in place to successfully adopt and scale AI initiatives. Use this checklist to gauge your AI readiness and identify areas for improvement.
Below is a checklist of the key components every portfolio company needs for AI success. If you’re checking all the boxes, you’re ready to leverage AI to its fullest potential. If not, don’t worry – each section includes actionable advice to help you close the gaps.
1. Clear Business Objectives for AI
- Have you defined specific goals for your AI initiatives?
- Are these goals aligned with broader business objectives like boosting efficiency, enhancing customer experience, or increasing revenue?
- Do you have KPIs in place to measure success?
Why It Matters: Without clear and measurable business objectives, AI projects risk becoming expensive experiments. Setting specific goals ensures that AI efforts remain focused on business priorities – so you can deliver tangible results that justify the investment.
2. Data Availability and Quality
- Are you collecting sufficient data relevant to your AI goals?
- Is your data clean, structured, and stored in a centralised system?
- Have you addressed issues like data silos or inconsistent formats?
- Are you compliant with data privacy regulations (e.g. GDPR, CCPA)?
Why It Matters: High-quality data is the backbone of successful AI. If data is incomplete, inconsistent, or scattered, the accuracy of AI models can be compromised. Ensuring your data is accessible, accurate, and compliant enables better decision-making and allows AI systems to generate meaningful insights.
3. Robust Data Infrastructure
- Do you have the necessary storage, processing, and cloud capabilities for large datasets?
- Are your systems scalable to accommodate future data growth?
- Have you invested in secure infrastructure to protect sensitive data?
Why It Matters: Strong data infrastructure supports both immediate AI needs and future growth, ensuring systems can scale with your ambitions.
If you’re looking to build scalable, durable data systems, bringing on fractional experts is an excellent option. They can drive your project forward, freeing you to focus on other strategic priorities.
4. Internal Expertise and Skills
- Does your team include professionals with AI and data science experience?
- Are there clear gaps in skills like machine learning, natural language processing, or AI implementation?
- Have you considered bringing in external fractional talent for on-demand expertise?
Why It Matters: AI expertise is critical, but building a full-time team may not be feasible for your company at this stage.
Fractional talent offers a cost-effective way to access specialised skills without long-term commitment. This flexibility allows you to scale expertise as needed, ensuring that your AI projects are guided by experienced professionals.
5. AI-Ready Tools and Technologies
- Are your existing tools and software compatible with AI integration?
- Do you have access to platforms, frameworks, and APIs suited to your use case?
- Have you budgeted for acquiring or upgrading tools?
Why It Matters: Outdated or incompatible tools can stall AI adoption. Investing in the right technologies ensures smooth implementation. Choosing scalable and adaptable tools also ensures your AI initiatives can evolve with your business needs.
6. Leadership Buy-In and Support
- Is your executive team committed to supporting AI initiatives?
- Have stakeholders been educated on AI’s value and ROI potential?
- Is there a decision-making framework for AI projects?
Why It Matters: Leadership support ensures AI projects receive the resources and attention needed to succeed. Strong leadership advocacy also helps to drive cultural acceptance of AI across the organisation, encouraging collaboration and momentum.
7. Change Management and Training
- Have you prepared your workforce for changes AI may bring?
- Do you have plans to upskill employees to work alongside AI tools?
- Are communication channels in place to address resistance or uncertainty?
Why It Matters: Effective change management minimises disruptions and fosters a workforce that embraces AI as an ally, not a threat.
This is where bringing on a fractional AI leader might prove beneficial – providing the wisdom and assurance that teams need to embrace this transition period. Plus, it’s a great opportunity to upskill in-house teams.
8. Budget and Resource Allocation
- Have you allocated sufficient funds for AI development, deployment, and maintenance?
- Have you accounted for costs like external talent, tools, and infrastructure?
- Is your financial plan flexible enough to handle unforeseen expenses?
Why It Matters: A well-planned budget ensures you can sustain AI efforts from pilot projects to full-scale implementation.
Fractional hiring offers the flexible scaling that full-time hiring simply can’t match. It’s also a cost-effective solution, allowing you to pay only for the specific projects or tasks you need, without the overhead of permanent hires.
9. Pilot Projects and Scalability
- Are you starting with small, manageable pilots to test AI applications?
- Do you have a roadmap for scaling successful projects across the organisation?
- Are your processes agile enough to adapt based on pilot outcomes?
Why It Matters: Pilot projects reduce risk while allowing you to fine-tune your approach before scaling AI across the company. It’s a smart way to test the waters before diving into the unknown.
10. Monitoring and Continuous Improvement
- Do you have systems in place to monitor AI performance and outcomes?
- Are there processes for regular updates and improvements to AI systems?
- Are you prepared to reassess AI goals as your business evolves?
Why It Matters: Continuous improvement keeps your AI systems aligned with changing business needs, ensuring sustained value creation. Regular updates help to identify performance gaps and maintain relevance in dynamic market conditions.
Next Steps: Are You AI-Ready?
Being AI-ready in 2025 isn’t just a competitive advantage – it’s a necessity for PE portfolio companies looking to boost value and stay ahead.
With this checklist as your guide, you’ll have a clear roadmap to evaluate and strengthen your AI foundations. From aligning goals to building expertise, each step ensures your company is primed for AI success.
If your company has ticked all the boxes above, congratulations – you’re ready to embark on your AI journey.
What’s next? Partner with Generative Fractional to connect with expert talent who can lead your AI strategy, fill skill gaps, or assist in scaling AI implementation. We can help you transform your readiness into tangible results.
If you’re ready to take the next step, contact us today to bridge talent gaps and accelerate your AI initiatives.
