Table of Contents
- Why AI Maturity Benchmarking Matters for PE
- The Five Dimensions of AI Maturity
- Building Your AI Maturity Scorecard
- Assessing Data Foundations
- Evaluating AI Capability and Deployment
- Governance, Security and Compliance
- Organisational Readiness and Skills
- Prioritising Your Portfolio and Setting Roadmaps
- Tracking Progress and Value Creation
- Next Steps: Building Your Benchmarking Programme
Why AI Maturity Benchmarking Matters for PE
Private equity returns depend on identifying unrealised value and converting it into measurable returns. For the past three years, AI has been the frontier of value creation across industrial, financial services, healthcare, and technology portfolios. Yet most PE firms still treat AI as a binary outcome—either a company “has AI” or it doesn’t—rather than as a measurable maturity continuum.
The reality is messier. One portfolio company may have invested £2 million in a machine learning platform that generates zero revenue. Another may have deployed a simple rule-based automation that cut operational costs by 30%. A third may have built the data foundations needed to scale AI but lacks the technical talent to execute. Without a structured way to assess where each company actually sits, you cannot prioritise capital allocation, identify the highest-ROI transformation opportunities, or benchmark progress across your hold period.
AI maturity benchmarking is the tool that closes this gap. It lets you rank your entire portfolio by AI readiness, identify which holdings will yield the fastest returns from focused investment, and track whether your operational value-creation efforts are actually moving the needle.
Research from McKinsey on AI adoption and value creation shows that companies that move from AI pilots to scaled value creation follow a predictable maturity pathway. BCG’s research on scaling AI successfully confirms that organisational and operating-model factors—not just technology—determine whether AI investments yield returns. And Deloitte’s work on AI in business demonstrates that governance and strategy alignment are as critical as technical capability.
This guide gives you a practical framework to benchmark AI maturity across your portfolio, prioritise where to deploy capital and operational resources, and track value creation in real time.
The Five Dimensions of AI Maturity
AI maturity is not one-dimensional. A company can have strong data infrastructure but weak governance. Another might have talented engineers but no clear AI strategy. Effective benchmarking requires you to assess maturity across five distinct dimensions, each of which drives value creation differently.
Dimension 1: Data Foundations
AI only works at scale if data is reliable, accessible, and governed. Many portfolio companies have fragmented data across legacy systems, inconsistent definitions, poor data quality, and no central catalogue. This is the biggest blocker to AI ROI.
Data maturity spans: data infrastructure (cloud platforms, data warehouses, pipelines); data quality and governance (catalogues, lineage, ownership); and accessibility (who can query what, how fast, at what cost).
Dimension 2: AI Capability and Deployment
This is the most visible dimension—the models, agents, and automations a company has built or deployed. It includes both generative AI (large language models, AI agents, orchestration) and classical ML (forecasting, classification, recommendation). Maturity here means moving from pilots and proofs-of-concept to production systems with measurable ROI.
Dimension 3: Governance, Security and Compliance
AI governance is the fastest-growing concern across regulated industries. This dimension covers risk management, audit trails, bias monitoring, security controls, and regulatory compliance (SOC 2, ISO 27001, APRA, ASIC, HIPAA, etc.). Companies lacking mature governance face regulatory risk and cannot scale AI in regulated verticals.
Dimension 4: Organisational Readiness and Skills
Even strong technology can fail if the organisation isn’t ready. This dimension includes: executive alignment on AI strategy, technical hiring and retention, training and capability building, and cross-functional collaboration. Many portfolio companies have great engineers but no product-market fit for their AI efforts, or no executive mandate to prioritise them.
Dimension 5: AI Strategy and ROI Clarity
The final dimension is strategic: does the company have a clear AI strategy aligned to business outcomes? Can leadership articulate which use cases will drive revenue, cut costs, or unlock new products? Are AI investments tracked to measurable KPIs? Many companies have scattered AI projects with no connection to P&L.
Building Your AI Maturity Scorecard
A practical scorecard translates these five dimensions into a quantifiable ranking that you can apply consistently across your portfolio. Here’s how to build one.
Define Maturity Levels
Each dimension should span five maturity levels. Here’s a standard framework:
Level 1 (Nascent): No formal capability. Ad-hoc or no AI activity. Manual processes dominate.
Level 2 (Emerging): Initial pilots or proofs-of-concept. Limited scale. Siloed efforts. No governance framework.
Level 3 (Developing): Multiple production systems in use. Governance and standards beginning to emerge. Dedicated team in place. Some measurement of ROI.
Level 4 (Mature): Scaled AI systems across multiple business units. Governance and security controls in place. Measurable ROI tracked to business outcomes. Talent pipeline and training established.
Level 5 (Leading): AI is core to competitive advantage. Continuous innovation and optimisation. Governance embedded in operating model. Talent attraction and retention strong. AI drives measurable revenue or cost advantage vs. competitors.
Apply these levels consistently across each of the five dimensions.
Create a Scoring Matrix
For each portfolio company, score it 1–5 on each dimension. Then weight the dimensions based on your thesis and industry context. For example:
- Technology-heavy portfolio (SaaS, fintech): Weight data foundations and AI capability equally (30% each), governance 20%, strategy 20%.
- Industrial or logistics portfolio: Weight AI capability (35%), data foundations (25%), organisational readiness (20%), governance (15%), strategy (5%).
- Regulated portfolio (financial services, healthcare, insurance): Weight governance (35%), data foundations (30%), AI capability (20%), strategy (10%), organisational readiness (5%).
This weighting ensures your scorecard reflects what actually drives value in your portfolio’s context.
Example Scorecard
Here’s a simple template you can adapt:
| Dimension | Company A | Company B | Company C | Weight |
|---|---|---|---|---|
| Data Foundations | 2 | 4 | 3 | 25% |
| AI Capability & Deployment | 3 | 3 | 2 | 30% |
| Governance & Security | 1 | 4 | 3 | 20% |
| Organisational Readiness | 2 | 3 | 4 | 15% |
| AI Strategy & ROI Clarity | 2 | 4 | 2 | 10% |
| Weighted Score | 2.15 | 3.65 | 2.85 | 100% |
This scorecard immediately tells you that Company B is your highest-ROI target for AI-focused operational support, while Company A needs foundational work on governance and data before major AI investment will pay off.
Assessing Data Foundations
Data is the foundation of all AI value. Without reliable, accessible, governed data, AI projects fail or deliver poor ROI. Assessing data maturity is therefore the logical starting point for your benchmarking.
Infrastructure and Architecture
Begin by mapping the current data landscape. Where does data live? Is it in legacy on-premise databases, cloud data warehouses, data lakes, or a fragmented mix? How are pipelines built—ETL tools, custom code, no-code platforms?
Maturity here means:
- Level 1: Data scattered across systems with no central repository. Manual exports and ad-hoc queries. High latency and unreliability.
- Level 2: Cloud data warehouse in place (Snowflake, BigQuery, Redshift) but limited integration. Some pipelines automated, others manual.
- Level 3: Cloud warehouse with automated pipelines for core data sources. Emerging data catalogue. Some real-time capabilities.
- Level 4: Fully automated pipelines across all major systems. Data warehouse optimised for analytics. Real-time ingestion for critical use cases. Cost controls in place.
- Level 5: Multi-region, multi-cloud architecture. Real-time streaming and batch pipelines co-existing. Advanced cost optimisation (e.g., Superset for analytics, ClickHouse for time-series). Data federated across business units with clear SLAs.
Data Quality and Governance
Once you understand infrastructure, assess data quality and governance. This is often the biggest gap in portfolio companies.
Key questions:
- Do you have a data catalogue that documents what data exists, where it lives, and who owns it?
- Are data definitions consistent across systems (e.g., does “customer” mean the same thing in sales and finance)?
- Is data quality measured? Are there SLAs for freshness, completeness, and accuracy?
- Who can access what data? Are there audit trails?
- Are there documented policies for data retention, deletion, and privacy?
Maturity progression:
- Level 1: No data catalogue. Inconsistent definitions. No quality metrics. Unrestricted access.
- Level 2: Informal documentation of key datasets. Some data quality checks. Access controls beginning to emerge.
- Level 3: Data catalogue in place covering major systems. Data quality monitored for critical datasets. Role-based access controls implemented.
- Level 4: Comprehensive data catalogue with lineage and ownership. Data quality SLAs defined and tracked. Access controls granular. Regular audit reviews.
- Level 5: Automated data governance platform. Real-time quality monitoring and alerting. Predictive data quality. Privacy and compliance controls embedded.
Accessibility and Cost Control
Finally, assess how easily teams can access data and whether the cost is controlled.
A mature data organisation allows data engineers, analysts, and data scientists to query data without friction, but with appropriate guardrails. Immature organisations either lock data down (slowing value creation) or leave it wide open (creating security and cost risks).
Key metrics:
- Time from data request to query execution (hours vs. weeks indicates maturity).
- Cost per query and total data infrastructure cost as percentage of revenue.
- Number of self-serve dashboards and reports vs. ad-hoc requests.
- Adoption of data tools by non-technical users.
Evaluating AI Capability and Deployment
Once you’ve assessed data foundations, move to AI capability and deployment. This is where most portfolio companies have the most visible activity—and often the most disappointing ROI.
Generative AI and AI Agents
Generative AI is now the dominant form of new AI investment. Assess what your portfolio companies have deployed or are planning:
- Large language models (LLMs): Are they using commercial APIs (OpenAI, Anthropic, Google), open-source models (Llama, Mistral), or fine-tuned proprietary models?
- AI agents and orchestration: Are they building agentic systems that can plan, execute tasks, and iterate? Or are they using LLMs as one-shot tools?
- Retrieval-augmented generation (RAG): Are they connecting LLMs to proprietary data via RAG, enabling domain-specific knowledge?
- Production readiness: Are these systems in production with real users and measurable ROI? Or are they still in pilots or internal use?
Maturity progression for generative AI:
- Level 1: No generative AI. Awareness only.
- Level 2: Experimentation with commercial LLM APIs (ChatGPT, Claude). Internal pilots, no production use.
- Level 3: One or two generative AI applications in production (e.g., customer support chatbot, internal knowledge assistant). ROI tracked but modest.
- Level 4: Multiple generative AI applications across customer-facing and internal workflows. Agentic systems handling multi-step tasks. Measurable ROI (cost savings, revenue uplift, time savings).
- Level 5: Generative AI is embedded across the business. Custom models or fine-tuned versions deployed. Continuous optimisation of prompts, agents, and workflows. AI is driving competitive advantage.
Classical Machine Learning
Most mature portfolio companies also have classical ML systems in production—forecasting, classification, recommendation, anomaly detection. Assess the maturity of these separately.
- Scope: How many ML models are in production? What do they do?
- ROI: Can you quantify the business impact (revenue, cost savings, customer satisfaction)?
- Maintenance: Are models actively monitored and retrained? Or are they static and degrading?
- Governance: Is there a model registry? Are there policies for model validation and approval?
Maturity progression:
- Level 1: No ML in production. Ad-hoc analysis only.
- Level 2: One or two models in production, often built by a single data scientist. Limited monitoring. No formal governance.
- Level 3: Multiple models across business units. Basic monitoring and retraining. Model registry emerging.
- Level 4: Comprehensive ML platform. Models monitored for performance drift. Formal approval process for model deployment. Clear ownership and SLAs.
- Level 5: Continuous model experimentation and optimisation. Automated retraining pipelines. Advanced monitoring (fairness, bias, explainability). Models drive measurable business outcomes.
Automation and Workflow Optimization
Beyond AI, assess whether portfolio companies are using AI to automate workflows and cut operational costs.
- Robotic process automation (RPA): Are they automating repetitive manual tasks?
- Workflow automation: Are they using AI-powered tools to streamline business processes?
- Integration: Are these automations integrated with core systems or isolated point solutions?
Many portfolio companies have high-margin opportunities to cut costs through automation but haven’t prioritised it. This is often a quick win.
Governance, Security and Compliance
Governance is the fastest-growing concern in AI, and it’s often the biggest gap in portfolio companies. Regulators are moving quickly, and companies lacking governance frameworks face both regulatory risk and operational risk.
AI Risk Management and Governance Framework
Start by assessing whether the company has a formal AI governance framework. This should include:
- AI steering committee: Executive oversight of AI strategy, investment, and risk.
- AI risk policy: Documented policies for responsible AI, bias mitigation, explainability, and human oversight.
- Model governance: Processes for approving, validating, deploying, and retiring AI models.
- Audit and monitoring: Continuous monitoring of AI systems for performance, bias, and drift.
Maturity progression:
- Level 1: No formal governance. AI decisions made ad-hoc by technical teams.
- Level 2: Awareness of governance needs. Informal processes emerging. No executive steering.
- Level 3: Documented AI governance framework. AI steering committee in place. Basic risk assessment for new models.
- Level 4: Comprehensive governance framework embedded in operating model. Risk assessment and approval processes for all models. Continuous monitoring and audit.
- Level 5: AI governance is core to competitive advantage. Proactive risk management. Industry-leading transparency and accountability. Thought leadership on responsible AI.
Security and Data Protection
AI systems often process sensitive data. Assess whether the company has security controls in place.
- Data encryption: Is data encrypted in transit and at rest?
- Access controls: Are there granular controls over who can access models and data?
- Audit trails: Are there logs of all access and model decisions?
- Incident response: Is there a plan for responding to AI-related security incidents?
For regulated industries, also assess:
- Compliance frameworks: Is the company meeting APRA, ASIC, AUSTRAC (Australia), HIPAA (healthcare), GDPR (EU), or other relevant regulations?
- Data residency: Is data stored in compliant jurisdictions?
- Vendor management: Are third-party AI vendors (OpenAI, etc.) vetted for security and compliance?
Maturity progression:
- Level 1: Basic security controls only. No specific AI security considerations.
- Level 2: Security team aware of AI risks. Beginning to assess third-party vendors.
- Level 3: AI-specific security controls in place. Compliance assessment underway. Vendor SLAs negotiated.
- Level 4: Comprehensive security and compliance controls. Regular audits and assessments. Compliance certifications in place (SOC 2, ISO 27001).
- Level 5: Security and compliance are competitive advantages. Proactive threat monitoring. Industry-leading controls. Thought leadership on AI security.
Regulatory and Compliance Readiness
For regulated industries, assess readiness for upcoming AI regulations and compliance requirements.
- EU AI Act: If operating in EU, assess readiness for the EU AI Act (high-risk systems require specific governance).
- APRA CPS 234 (Australia): If operating in financial services, assess readiness for APRA’s AI governance expectations.
- ASIC RG 271 (Australia): If operating in financial services, assess readiness for ASIC’s guidance on responsible AI.
- HIPAA (healthcare): If operating in healthcare, assess readiness for HIPAA compliance.
- AUSTRAC (Australia): If operating in financial services, assess readiness for AUSTRAC’s AML/CFT requirements.
Companies that are proactive on compliance gain a competitive advantage. Those that are reactive face regulatory risk and remediation costs.
Organisational Readiness and Skills
Technology alone doesn’t drive AI value. Organisational readiness is equally critical. Assess whether the company has the skills, culture, and incentives to execute AI effectively.
Executive Alignment and Strategy
Start at the top. Do the CEO and executive team have a clear, shared understanding of the AI strategy?
Key questions:
- Is there a documented AI strategy aligned to business outcomes?
- Does the board understand the AI strategy and its ROI expectations?
- Is there executive sponsorship for major AI initiatives?
- Are AI investments tracked to business metrics (revenue, cost, customer satisfaction)?
Maturity progression:
- Level 1: No AI strategy. AI investments ad-hoc and disconnected from business strategy.
- Level 2: Emerging AI strategy. Some executive awareness but limited alignment.
- Level 3: Documented AI strategy aligned to business outcomes. Executive sponsorship for major initiatives. ROI tracked.
- Level 4: AI strategy integrated into business strategy. Board-level governance. Clear ROI expectations and tracking.
- Level 5: AI is core to business strategy and competitive positioning. Continuous strategy refinement based on market and technology changes. Thought leadership.
Technical Talent and Hiring
AI execution depends on having the right technical talent. Assess the company’s ability to attract, hire, and retain AI talent.
- Current team: How many data engineers, ML engineers, data scientists, and AI specialists do you have? What’s their seniority?
- Hiring: Are you actively hiring in these roles? How long does it take to fill positions?
- Retention: What’s your turnover rate? Are key people staying?
- Capability building: Are you investing in training and upskilling existing teams?
In Australia and across most markets, AI talent is scarce and expensive. Companies that can’t compete on salary often lose people to larger tech companies. This is a critical constraint.
Maturity progression:
- Level 1: No dedicated AI team. Data and analytics work scattered across functions.
- Level 2: Small dedicated team (1–3 people). Difficulty hiring and retaining talent. No formal training.
- Level 3: Growing team (5–10 people) with clear roles and seniority levels. Active hiring. Some training programmes.
- Level 4: Mature team (10+ people) with strong hiring and retention. Formal training and career progression. Leadership bench strength.
- Level 5: World-class team that attracts top talent. Continuous learning and development. Internal thought leadership.
Cross-Functional Collaboration
AI projects fail when they’re siloed in a data team. Assess whether the company has strong cross-functional collaboration.
- Product: Are product managers involved in defining AI use cases?
- Engineering: Are software engineers collaborating with data teams to build production systems?
- Business: Are business leaders engaged in identifying and prioritising AI opportunities?
- Governance: Are legal, compliance, and risk teams involved in AI governance?
Maturity progression:
- Level 1: AI work siloed in analytics or data team. Limited cross-functional engagement.
- Level 2: Beginning to engage product and engineering. Ad-hoc collaboration.
- Level 3: Regular cross-functional collaboration on major AI initiatives. Clear roles and responsibilities.
- Level 4: Embedded cross-functional teams for major AI projects. Strong collaboration across product, engineering, and business.
- Level 5: Collaboration is embedded in operating model. AI is a shared responsibility across the organisation.
Prioritising Your Portfolio and Setting Roadmaps
Once you’ve scored your entire portfolio on AI maturity, the next step is to prioritise where to deploy capital and operational resources. Not all portfolio companies are equally ready for AI investment, and not all dimensions of maturity are equally important for value creation.
Identify Your Highest-ROI Targets
Use your scorecard to segment your portfolio into three categories:
Segment 1: Quick Wins (Score 3.5–5.0)
These companies are already mature on most dimensions. They have strong data foundations, some AI capability, and organisational readiness. They’re ready for targeted investment to scale AI and unlock additional value.
Focus here on:
- Scaling existing AI systems to new use cases or geographies.
- Building agentic AI systems to automate complex workflows.
- Optimising cost and performance of existing ML systems.
- Expanding the AI team to handle more projects.
Expected ROI: High. Payback period: 6–12 months.
Segment 2: High-Potential Builds (Score 2.5–3.5)
These companies have some AI capability but are missing one or two critical dimensions. For example, they might have a great AI team but weak data foundations, or strong data but no clear strategy.
Focus here on:
- Fixing the constraint (e.g., building a cloud data warehouse, clarifying AI strategy).
- Building foundational capabilities before scaling.
- Bringing in external expertise (fractional CTO, consulting) to accelerate.
Expected ROI: Medium-to-high. Payback period: 12–18 months. This is where a fractional CTO and strategic leadership partner can add significant value.
Segment 3: Foundation Builders (Score 1.5–2.5)
These companies are nascent on AI maturity. They may have no data strategy, no AI capability, and weak organisational alignment. They require foundational work before major AI investment will pay off.
Focus here on:
- Assessing AI readiness and identifying the highest-ROI use cases.
- Building data foundations (cloud data warehouse, pipelines, governance).
- Hiring or bringing in technical leadership (fractional CTO).
- Clarifying AI strategy and getting executive alignment.
Expected ROI: Lower in the short term, but can be significant over 18–24 months. This is where a venture studio and co-build partnership can help you move from idea to MVP to scale.
Set Realistic Roadmaps
For each segment, define a realistic roadmap for the next 12–24 months.
Quick Wins Roadmap Example (12 months)
- Q1: Identify top 3 AI opportunities aligned to business strategy. Assign executive sponsor and cross-functional team.
- Q2: Pilot first opportunity. Build MVP and measure ROI.
- Q3: Scale first opportunity. Launch second opportunity.
- Q4: Scale second opportunity. Plan third opportunity for next year.
High-Potential Builds Roadmap Example (18 months)
- Q1: Diagnose constraint (e.g., data maturity assessment). Define roadmap to fix it.
- Q2–Q3: Build foundational capability (e.g., cloud data warehouse, data governance).
- Q4: Hire or bring in technical leadership. Define AI strategy.
- Q5–Q6: Pilot first AI opportunity. Build cross-functional team.
- Q7–Q8: Scale first opportunity. Plan next opportunities.
Foundation Builders Roadmap Example (24 months)
- Q1: Conduct AI readiness assessment. Identify highest-ROI use cases. Get executive alignment.
- Q2–Q3: Hire or bring in fractional CTO. Build data strategy and roadmap.
- Q4–Q5: Build cloud data warehouse. Establish data governance.
- Q6–Q7: Hire data engineering and ML team. Build AI strategy.
- Q8: Pilot first AI opportunity.
- Q9–Q10: Scale first opportunity. Plan next opportunities.
Allocate Resources and Budget
Once you’ve prioritised and set roadmaps, allocate resources accordingly.
- Quick Wins: Allocate 50% of AI budget. These generate ROI fastest.
- High-Potential Builds: Allocate 35% of AI budget. These require foundational work but have high upside.
- Foundation Builders: Allocate 15% of AI budget. These are longer-term bets.
Within each segment, also decide whether to:
- Build in-house: Hire permanent staff and build capability within the portfolio company.
- Partner with external expertise: Bring in fractional CTOs, consulting, or venture studio partners to accelerate.
- Hybrid approach: Combination of in-house hiring and external expertise.
For many portfolio companies, a hybrid approach is optimal. External expertise accelerates early progress while you’re building internal capability. PADISO’s AI advisory and fractional CTO services are designed for exactly this—providing senior technical leadership and hands-on delivery while building your team’s capability.
Tracking Progress and Value Creation
Benchmarking AI maturity is only valuable if you track progress over time and connect it to value creation. Set up a tracking system that measures both maturity progression and financial ROI.
Establish Baseline and Targets
At the start of your engagement with each portfolio company, establish a baseline maturity score and set targets for 12 and 24 months.
Example:
| Company | Current | 12-Month Target | 24-Month Target |
|---|---|---|---|
| Company A | 2.15 | 3.0 | 3.8 |
| Company B | 3.65 | 4.2 | 4.6 |
| Company C | 2.85 | 3.5 | 4.2 |
Targets should be realistic but ambitious. Moving from Level 2 to Level 3 typically takes 12 months with focused effort. Moving from Level 3 to Level 4 takes another 12–18 months.
Track Dimension-Level Progress
Also track progress on each dimension separately. This helps you identify where you’re making progress and where you’re stuck.
Example dashboard:
| Company | Data | AI Capability | Governance | Org Readiness | Strategy | Overall |
|---|---|---|---|---|---|---|
| Company A | 2→2.5 | 3→3.2 | 1→2 | 2→2.5 | 2→2.5 | 2.15→2.34 |
| Company B | 4→4.2 | 3→3.5 | 4→4.3 | 3→3.2 | 4→4.2 | 3.65→3.88 |
| Company C | 3→3.5 | 2→2.5 | 3→3.3 | 4→4.2 | 2→2.5 | 2.85→3.2 |
This tells you that Company A is making progress on data and governance but lagging on AI capability. Company B is progressing across the board but organisational readiness is flat. Company C is strong on organisational readiness but weak on AI capability and strategy.
Connect Maturity to Financial Outcomes
The ultimate test of AI maturity benchmarking is whether it correlates with value creation. Track the financial impact of AI initiatives alongside maturity progress.
Key metrics:
- Revenue uplift: New revenue generated by AI-powered products or features.
- Cost savings: Operational cost reductions from automation and optimisation.
- Time savings: Hours saved by employees through AI-powered tools (multiply by loaded cost to get financial impact).
- Customer impact: Net Promoter Score (NPS) improvements, customer retention, customer acquisition cost (CAC) reduction.
- Operational efficiency: Faster time-to-market, higher product quality, reduced defects.
Example:
| Company | AI Maturity | Revenue Uplift | Cost Savings | Total Impact | ROI |
|---|---|---|---|---|---|
| Company A | 2.34 | £0 | £150K | £150K | 15% on £1M AI investment |
| Company B | 3.88 | £500K | £200K | £700K | 70% on £1M AI investment |
| Company C | 3.2 | £100K | £100K | £200K | 20% on £1M AI investment |
This analysis reveals that Company B is generating the highest ROI despite a similar AI investment. This suggests that maturity in certain dimensions (governance, strategy, organisational readiness) is more predictive of ROI than raw AI capability.
Quarterly Reviews and Adjustments
Conduct quarterly reviews of AI maturity and financial progress. Use these reviews to:
- Celebrate wins and recognise teams that are progressing.
- Identify bottlenecks and constraints (e.g., “we’re stuck on data governance”).
- Adjust roadmaps and resource allocation based on progress and market changes.
- Escalate issues to executive sponsors.
- Share best practices across the portfolio.
For example, if multiple companies are struggling with data governance, consider bringing in a shared resource (e.g., a fractional Chief Data Officer) to help across the portfolio.
Next Steps: Building Your Benchmarking Programme
AI maturity benchmarking is not a one-time exercise. It’s an ongoing programme that evolves with your portfolio and the AI landscape.
Phase 1: Design and Baseline (Weeks 1–4)
- Define your scorecard: Adapt the five-dimension framework to your portfolio context. Decide on weighting.
- Conduct baseline assessment: Score all portfolio companies. This typically requires 2–4 hours per company (interviews with CEO, CTO, data lead, product lead).
- Segment your portfolio: Categorise companies into Quick Wins, High-Potential Builds, and Foundation Builders.
- Set targets: Define 12- and 24-month maturity targets for each company.
Phase 2: Planning and Roadmapping (Weeks 5–8)
- Set roadmaps: For each company, define a realistic roadmap to reach targets.
- Allocate resources: Decide on budget and personnel allocation across the portfolio.
- Identify external partners: For companies that need external expertise, identify and engage partners (fractional CTOs, consulting, venture studio).
- Communicate to portfolio: Share results and roadmaps with company leadership. Get buy-in.
Phase 3: Execution and Tracking (Ongoing)
- Monthly tracking: Track progress on key initiatives and milestones.
- Quarterly reviews: Conduct formal reviews of maturity progress and financial outcomes. Adjust roadmaps as needed.
- Knowledge sharing: Share best practices across the portfolio. For example, if one company has built a great data governance framework, help others adopt it.
- Annual reassessment: Conduct a full reassessment of maturity every 12 months. Update scores, targets, and roadmaps.
Building a Shared AI Platform
As you progress through your benchmarking programme, consider building shared capabilities across the portfolio:
- Shared data platform: A centralised cloud data warehouse (Snowflake, BigQuery) that portfolio companies can use to build data products.
- Shared AI infrastructure: A shared MLOps platform (e.g., Databricks, SageMaker) for training and deploying models.
- Shared governance framework: Standardised policies and processes for AI governance, security, and compliance across the portfolio.
- Shared talent: A central pool of AI specialists (data engineers, ML engineers, data scientists) who support multiple portfolio companies.
This approach accelerates time-to-value and reduces cost vs. each company building independently.
Engaging External Partners
Most PE firms don’t have the internal expertise to execute AI maturity benchmarking and roadmapping at scale. Consider engaging external partners to:
- Design your benchmarking framework: Work with a consulting partner to adapt the five-dimension framework to your portfolio context.
- Conduct baseline assessments: Have external experts interview company leadership and assess maturity.
- Develop roadmaps: Work with partners to develop realistic, phased roadmaps for each company.
- Provide fractional CTO leadership: For companies that need technical leadership, engage fractional CTOs who can advise on strategy, architecture, hiring, and vendor selection.
- Deliver operational support: For companies executing major transformations, engage partners to provide hands-on delivery support.
The best partners combine strategic thinking with hands-on delivery experience. They understand both PE value-creation frameworks and the operational realities of building AI systems. PADISO’s experience working with PE firms and portfolio companies across Australia and internationally means we understand what drives real value creation in this context.
Measuring Programme Success
Finally, measure the success of your benchmarking programme itself:
- Maturity progression: Are companies moving from one maturity level to the next on schedule?
- Financial returns: Are AI investments generating measurable revenue, cost savings, or operational improvements?
- Time-to-value: How long from initial assessment to first AI project in production?
- Team quality: Are you attracting and retaining top AI talent across the portfolio?
- Competitive advantage: Are portfolio companies gaining competitive advantage from AI vs. their peers?
These metrics will tell you whether your benchmarking programme is actually driving value creation.
Summary
AI maturity benchmarking is a practical tool for PE firms to:
- Rank portfolio companies by AI readiness using a five-dimension framework (data, AI capability, governance, organisational readiness, strategy).
- Identify high-ROI transformation opportunities by segmenting companies into Quick Wins, High-Potential Builds, and Foundation Builders.
- Set realistic roadmaps that account for each company’s starting point and constraints.
- Track progress over time and connect maturity improvements to financial outcomes.
- Allocate capital and operational resources strategically across the portfolio.
The framework is flexible and can be adapted to your portfolio’s industry, geography, and investment thesis. The key is to be consistent in your assessment methodology and rigorous in tracking progress and outcomes.
AI is no longer a “nice to have” for portfolio companies. It’s a core driver of value creation. Companies that move from AI pilots to scaled value creation will outperform those that don’t. By benchmarking AI maturity across your portfolio, you can identify which companies are ready to scale, which need foundational work, and where to deploy capital and operational resources for maximum ROI.
Start with a baseline assessment of your portfolio. Segment companies into three categories. Set realistic 12- and 24-month targets. Engage external partners where you lack internal expertise. Track progress quarterly. Connect maturity improvements to financial outcomes. And over 18–24 months, you’ll have transformed your portfolio’s AI capability and competitive positioning.
The time to start is now. The PE firms that move fastest on AI benchmarking and operational support will capture disproportionate value from their portfolios over the next 3–5 years.