When I started evaluating engineering organizations—both here in Colombia, where I've built teams for US companies expanding into Medellín, and across the broader LATAM region—I quickly realized that traditional checklists don't cut it anymore. We're no longer just assessing whether a company has good code and a solid infrastructure. We're now asking: Can this engineering organization actually ship AI features? Does the team understand how to maintain production ML systems? Is the technical foundation stable enough to support the weight of AI-driven transformation?
The private equity world is waking up to this shift. In 2025-2026, PE firms increasingly recognize that AI capability isn't optional—it's fundamental to understanding whether an investment has real upside or just hype. Yet most investors still lack a coherent framework for evaluating it. They know something feels off, but they can't articulate why.
Over the last few years, I've been deeply involved in technology due diligence on teams transitioning to AI-driven development. I've seen companies with shiny ML models sitting unused on servers, and scrappy teams without a single AI specialist shipping features faster than the competition. I've learned that what matters isn't whether a company has already implemented AI—it's whether the organization can.
Let me walk you through what actually matters in technology assessment right now.
Why Traditional Tech Due Diligence Falls Short
The conventional approach to tech due diligence is roughly: Does the code look clean? Is there documentation? Are there security issues? These are important baseline questions, but they miss the modern challenge entirely.
Traditional frameworks assume a relatively stable technology landscape. The main risk is technical debt accumulation and outdated systems. And sure, that still matters. But we now have an entirely new dimension: the ability to leverage AI effectively.
Here's what makes 2025-2026 different: Companies that can't adopt AI won't just miss an optimization opportunity—they'll become structurally uncompetitive. An engineering team that can't evaluate, integrate, and maintain AI tools will watch competitors ship features in weeks that take them months.
More importantly, the organizational requirements for AI success are different from traditional software development. You need data governance in place before you can build reliable AI systems. You need clear ownership and accountability structures, because AI failures often hide in ambiguous responsibility. You need a culture of experimentation, but also rigorous testing—the balance is different from traditional feature development.
The problem I see most often is that investors ask about AI readiness as an afterthought. They've already hired a tech advisor to assess the codebase, architecture, and team. Then someone says, "Oh, should we also ask about AI?" and it becomes a add-on question rather than a central lens through which to evaluate the entire organization.
This is backwards. In 2026, AI readiness should be integrated into every dimension of your technology assessment.
The Core Pillars of Engineering Organization Assessment
Let me break down how I think about evaluating an engineering organization for PE investment purposes. I focus on four interconnected pillars, and the way they fit together matters as much as the individual pieces.
1. Engineering Fundamentals and Execution Capability
Start here. If a team can't consistently ship features without chaos, nothing else matters. I'm looking at:
- Deployment frequency: How often can they release? The 2025 DORA report found that more than 50% of engineering teams deploy less than once a week. That's alarming. High-performing teams deploy multiple times per day. When deployment is friction-filled, everything takes longer—including AI integration.
- Change failure rate: When they deploy, how often does something break? If deployment failures require more than a week to recover (15% of teams still face this), the organization is too fragile for AI experimentation.
- Development velocity and predictability: Can they estimate work accurately? Do they consistently ship what they commit to? This tells me whether the team has the discipline necessary for AI projects, which require structured experimentation and measurable outcomes.
2. Technical Architecture and Infrastructure Readiness
This is where you separate organizations with a real future from those holding on to yesterday's decisions. I evaluate:
- Monolith vs. distributed systems: Monolithic architectures with tight coupling make it harder to integrate AI components safely. Microservices and modular design enable AI integration without risk of system-wide failures.
- Data pipeline maturity: This is critical. If data isn't flowing cleanly through the system with proper governance, any AI implementation becomes a data retrieval nightmare. I ask: How long does it take to extract a specific dataset for analysis? If the answer is "months" or "we'd need to ask the person who built that system," that's a massive red flag.
- Infrastructure and scalability: Can the system handle variable loads? Are they using cloud infrastructure with auto-scaling, or are they manually managing servers? AI workloads are unpredictable—you need infrastructure that can flex.
- Multi-tenancy and isolation: Especially for SaaS companies. If the architecture wasn't designed with multi-tenancy in mind, adding AI features later becomes a security and compliance nightmare.
3. Team Capability and Culture
The best architecture in the world fails if the team can't execute. I'm evaluating:
- Technical depth in relevant areas: Do they have people who understand the cloud platform they're using? Do they understand API design? Is there at least one person who's worked with ML systems in production? (This is increasingly table-stakes.)
- Communication and alignment: Can the engineering team articulate what the company actually does? If different team members give you different answers about core technical decisions, that's a serious red flag. I've walked into organizations where the platform team's vision directly contradicts the CTO's vision, and nothing moves.
- Learning orientation: Are people experimenting with new tools and techniques? Or are they resistant to change? In the age of AI, you need people who are comfortable with controlled experimentation.
- Operational discipline: Do they have clear code review processes? Are tests mandatory? Is there monitoring and alerting in place? These practices create the discipline needed for responsible AI development.
4. Data Governance and AI-Specific Readiness
This is the dimension that separates the mature organizations from the rest. I evaluate:
- Data quality and cataloging: Do they know what data they have? Can they trace data lineage? If data quality is poor, any AI model trained on that data will fail in production.
- Governance structures: Are there formal policies about data access, retention, and use? (Only 32% of organizations have formal AI governance policies. That's shocking.) Are decisions about data made by committee or by whoever needs it that day?
- Bias and fairness considerations: For AI models, particularly those affecting customer-facing decisions, have they thought about potential biases? Have they tested models against different demographic groups?
- Model management and monitoring: If they've already deployed AI, how are they monitoring it? Are they tracking model drift (where model performance degrades over time)? Can they roll back a bad model quickly?
The integration between these four pillars is the real story. An organization with excellent team capability but poor data governance will struggle. A company with beautiful architecture but weak deployment discipline will never move fast enough. Your job is to understand how these dimensions reinforce or undermine each other.
Assessing AI Integration Maturity
Now, the specific question: How ready is this organization to leverage AI as a competitive advantage?
I use a maturity model with four levels:
Stage 1 - Experimenting: The team is aware of AI tools and is trying things out. They've prototyped with ChatGPT or Claude, maybe tried Copilot for code completion. But there's no systematic approach. It's scattered, and results vary wildly. This is fine as a starting point, but if your organization is stuck here, you're behind.
Stage 2 - Pilot Projects: The organization has committed resources to an AI project. They've hired someone with ML experience or brought in a consultant. They're building something with clear business value. But the project is isolated—it's not integrated into regular development practices. Success depends on the enthusiasm of one team or individual. Scaling is unclear.
Stage 3 - Systematic Integration: AI tools and practices are embedded into the development workflow. Code generation tools are part of the standard IDE setup. Data pipelines are designed to support ML. There's governance in place for evaluating and deploying new AI capabilities. This requires organizational change, not just technical change.
Stage 4 - AI-Driven Organization: The company's competitive advantage is built on AI. Decisions are informed by data and models. The entire product strategy is designed around AI capabilities. This is the rare, valuable place. Most organizations won't reach this in the next 2-3 years, but it's the destination you're assessing.
Most organizations I evaluate are between Stages 1 and 2. The jump to Stage 3 requires not just technology implementation, but organizational change—clearer data ownership, systematic evaluation of AI tools, integration into hiring and team structure.
Here's what concerns me: Many PE firms are looking for companies already at Stage 3 or 4. But if you can identify organizations at Stage 2 with strong fundamentals and good leadership, the upside of helping them reach Stage 3 can be enormous.
Technical Debt: Not Always the Enemy
Let me address something that often confuses investors: Technical debt doesn't automatically disqualify a company.
Yes, unmanaged technical debt is terrible. Research shows that accumulated tech debt can consume 20-40% of development capacity, pulling resources away from innovation. If debt is preventing the team from shipping features or adding new capabilities, that's a serious problem.
But some technical debt is actually a feature of young, fast-moving companies. A company that deliberately chose a monolithic architecture because it was faster to build initially, but now has the discipline and team to thoughtfully refactor it—that's different from a company with chaotic code that nobody understands.
The question I ask is: Can the team articulate the trade-offs? Do they understand why the debt exists and what the cost of retiring it would be? Or do they just shrug and say "it's a mess"?
For AI specifically, I'm looking at a new category of debt: AI implementation debt. This includes models in production without proper monitoring, AI features that require manual triggers when they should be automated, or ML pipelines that are fragile and break constantly. This debt is particularly dangerous because it's often invisible in code audits. You only discover it when you run into a production failure.
Red Flags You Can't Ignore
Based on hundreds of technology assessments I've reviewed, here are the patterns that consistently correlate with problems:
Intellectual Property Uncertainty: If the company can't easily produce a list of all the agreements assigning IP to the company, that's a problem. Have all employees and contractors signed invention assignment agreements? If the answer is "we're not sure" or "some of them," you're taking on significant legal risk. In the context of AI, this becomes even more important—if they've used open-source models or training data with unclear licensing, you inherit that risk.
Team Misalignment on Core Strategy: If the CTO believes the product architecture should be microservices and modular, but the senior engineer who's actually building things is convinced a monolith is the right approach, and they've never resolved this disagreement—that's a red flag. It means decisions are being made without consensus, and that usually means they're being reversed or reworked.
No Clear Owner for Critical Systems: If you ask "Who owns the data pipeline?" and you get "well, it depends on which part," that's bad. Especially for AI projects, you need clear ownership. Data quality issues, model drift, compliance failures—all of these need an owner who's accountable.
Poor Security and Compliance Practices: Are security assessments done regularly? Do they have a security champion on the team? Is there a regular audit or penetration test schedule? For AI specifically, are they thinking about data privacy and regulatory compliance? If they're cavalier about security, they'll be cavalier about AI risks too.
Inability to Articulate Business-Technology Alignment: If different people give you different answers about what the core business problem is that the technology solves, or if the technology strategy doesn't clearly connect to business outcomes, that's a signal that decisions are being made in isolation from business reality.
What Good Looks Like
Before you finish your assessment, ask yourself: Can I imagine this team successfully integrating a new AI capability into their product within the next 18 months?
Good teams can. They might not have deployed AI yet, but the fundamentals are in place. They have deployment discipline. They understand their data. They have clear ownership and accountability. They can articulate what they're trying to build and why.
When I assess a team as "good" for AI readiness, here's what I typically see:
- Regular deployments: At minimum, weekly. Ideally, multiple times per week.
- Clear data architecture: I can understand how data flows through the system. Governance exists, even if it's lightweight.
- Technical leadership aligned with execution: The CTO and the engineers building the product are moving in the same direction.
- Willingness to invest in infrastructure: They're not penny-pinching on cloud infrastructure. They understand that speed of iteration matters.
- Honest about limitations: The team can tell me what they're not good at. This self-awareness is actually a positive signal.
- Active monitoring and observability: They know what's happening in production. Dashboards exist. Alerts are configured.
The Framework for Your Assessment
When you're evaluating engineering organizations, I'd recommend a structured approach:
- Start with deployment patterns: How fast can they move? This is the foundation.
- Map the architecture: Can you draw a diagram of how the system works? If the team can't help you do this in an hour, something's wrong.
- Understand team structure and incentives: How are engineers compensated? What are they optimized to do? How are decisions made?
- Audit data infrastructure: Where does data flow? Who owns it? How reliable is it?
- Evaluate current AI implementation (if it exists): What have they tried? What worked and what didn't? What did they learn?
- Talk to engineers directly: Get past the executive narrative. Ask engineers what they'd change if they could. Ask about their biggest frustrations.
This isn't a one-week engagement. This is a multi-week process where you're building an increasingly sophisticated understanding of how the organization actually works, not how it describes itself.
The Investment Implication
Here's my blunt take: In 2026, technology assessment is not about finding perfect organizations. It's about finding organizations with strong fundamentals that have the capacity to improve.
If you find a company at Stage 2 AI maturity with excellent deployment discipline, clear data architecture, and aligned leadership, you've found something valuable. With the right support, you can help them reach Stage 3 and unlock significant competitive advantage.
Conversely, if you find a company that looks great on surface-level metrics but has poor team alignment, chaotic decision-making, or data governance that doesn't exist—the nicest codebase in the world won't save you.
The PE firms winning in 2026 are those treating technology assessment as a central factor in valuation and value creation strategy, not an afterthought. They're hiring technical experts who can dig deep. They're asking hard questions about AI readiness. And they're realistic about what transformation takes time and what's a clear red flag.
As you think about your next technology-focused investment, start with this question: Does this organization have the capacity to build and maintain competitive AI capabilities?
If the answer is yes, you've found something worth digging into deeper. If the answer is no, no amount of hype or polished pitching will change that.