As software becomes increasingly agentic, technical due diligence readiness has evolved from an event into an operating capability. As a result, investors, PE operating partners, boards, and corporate development teams are asking a different class of questions. They’re no longer just evaluating whether your architecture scales or your codebase is maintainable. Instead, they want to understand how autonomous systems make decisions, who owns them, what they cost, and how they’re governed. Those are fundamentally different questions, and most engineering organizations, even very good ones, aren’t yet prepared to answer them.

Technical due diligence has always been about reducing uncertainty. In the past, that uncertainty mostly lived inside the technology itself: could the platform scale, was the architecture sound, how much technical debt existed. Those questions still matter. But in the agentic era they’re no longer sufficient, because software increasingly depends on external models, autonomous workflows, AI-generated code, and providers that evolve independently of your release cycle. So the engineering challenge is no longer just building reliable systems. It’s building organizations that can explain, govern, and adapt those systems over time. The technology changed. The bigger change is organizational.

“Show Me Your Agent Logs”

No diligence team actually opens a review by literally asking for agent logs. The phrase is shorthand for something bigger: can you explain what your autonomous systems are doing? Can you trace an important decision back to a responsible human? Do you know every production agent, what it’s authorized to do, and where its authority ends? And can you demonstrate governance rather than simply describe it? Those are increasingly the questions underneath every technical due diligence readiness review, regardless of whether the company is two years old or twenty.

The organizations that answer them well aren’t necessarily the ones using the newest models or shipping the most AI features. Instead, they’re the organizations that have made their engineering systems, and their decision-making, legible. Technology rarely fails diligence because it’s imperfect. It fails because nobody outside the engineering team can understand it.

The Biggest Change Isn’t AI. It’s Continuous Diligence.

Technical due diligence used to happen at predictable moments: before a funding round, during an acquisition, ahead of an IPO. Today, the same companies increasingly revisit those questions long after the deal closes. Private equity operating partners are building teams that continuously evaluate portfolio companies using the same agentic tooling they used pre-acquisition. Boards are asking for deeper visibility into AI initiatives before approving additional investment, and internal audit functions are expanding their own reviews to cover AI governance and provider dependencies.

Diligence is becoming less of a milestone and more of a management practice, and that distinction matters. If your organization only becomes diligence-ready when a transaction appears on the horizon, you’re already behind. In practice, what makes a company easy to diligence is usually what makes it easier to run day to day, whether or not a deal is on the calendar. Good governance isn’t something you create for investors. It’s something investors recognize.

Your First Diligence Reviewer May Not Be Human

There’s another change getting far less attention. Historically, engineers wrote technical documentation for expert readers who could infer intent from incomplete documentation and fill gaps through conversation. Increasingly, the first reviewer of your technology isn’t a person at all. Agentic diligence tools can ingest thousands of documents, diagrams, contracts, and repositories in hours rather than weeks. They don’t remember hallway conversations, and they don’t know that “everyone understands how this service works.” They evaluate what’s documented, and nothing more.

That’s a real shift for technical due diligence readiness. For years, incomplete documentation slowed diligence down. Today, incomplete documentation reads as incomplete governance. A human reviewer might spend an hour talking with your team to understand an undocumented AI workflow. But an autonomous reviewer just logs the gap and moves on. Machine-readable governance is becoming just as important as machine-readable code.

Governance Is Becoming the New Technical Moat

This is why the conversation has shifted. The hard problems are no longer limited to distributed systems, cloud architecture, or software delivery. Who approved this autonomous workflow? Who reviews AI-assisted code before it reaches production? What happens if a model provider changes pricing, terms, or availability overnight? Who owns an agent whose decisions span multiple business functions? How quickly can the organization explain a critical AI decision six months later? These aren’t purely technical questions anymore. They’re governance questions, and governance is increasingly what separates organizations that inspire confidence from the ones that generate uncertainty.

That doesn’t mean engineering suddenly matters less. Instead, it means the job expanded. The modern CTO isn’t just responsible for building reliable systems. They’re responsible for building organizations that can explain those systems, at any company stage, to someone who wasn’t in the room when the team made the decisions.

What Modern Technical Due Diligence Readiness Measures

That shift changes what “ready” means. A clean codebase is no longer enough. Neither is a polished architecture deck or a well-organized data room. Modern technical due diligence readiness increasingly measures whether an organization has built the governance structures needed to operate autonomous technology responsibly over time: who reviews AI-generated code, who manages model dependencies, who quantifies technical debt, who monitors autonomous systems, and, most importantly, who remains accountable as machines make more of the operational decisions. The question isn’t whether your AI works. It’s whether your organization can explain why it works, how it’s governed, and what happens when it doesn’t. That’s where the most common governance gaps start to show, and they show up the same way at a Series B startup and a fifteen-year-old enterprise.

Five Governance Gaps Modern Diligence Exposes

Every engineering organization has technical debt and legacy decisions it would make differently today. Still, that’s normal, and it isn’t what sinks a review. Instead, the companies that struggle in technical due diligence readiness reviews are the ones that can’t explain what they have, why it exists, or who’s responsible for it. The same five gaps show up again and again across startups, PE-backed companies, and mature enterprises, a pattern that comes up in Hoola Hoop’s CTO Roundtable series, under Chatham House rules.

The Pattern Behind Every Finding

At first glance, ownership, technical debt, code review, provider dependencies, and unit economics look like five unrelated problems. They’re actually symptoms of the same one: governance. Every one of these findings comes back to four questions. Can your organization explain what exists, and why? Does it know who’s accountable? And can it explain what happens when something changes? Those aren’t engineering questions. They’re organizational questions, and they’re becoming some of the most important technical questions a CTO answers, whatever stage the company is at.

What Technical Due Diligence Readiness Looks Like in Practice

Closing these gaps rarely requires rewriting your platform. Most organizations already have the knowledge. What’s missing are the artifacts: the documentation, the named ownership, the operational discipline that turns what your team knows into something a stranger can verify in two weeks. So organizations that handle technical due diligence readiness well tend to share six governance artifacts, and they didn’t build them for investors. They built them to run the business better.

The Underlying Principle

Governance isn’t documentation, and it never has been. Technical due diligence readiness has always rewarded organizations that can demonstrate preparedness, whether the reviewer is a partner at a PE firm, a board member, or an agent reading your data room at two in the morning. Still, none of this replaces the fundamentals. If you haven’t documented your architecture decisions or priced your technical debt yet, my original technical due diligence guide is still the right starting point. What’s changed since I wrote it is the layer on top: agent governance, model provenance, and AI unit economics are now part of the same conversation, not a separate one.

Questions to Sit With

Whether your next diligence event is a term sheet, an annual portfolio review, or a board asking hard questions ahead of an acquisition you’re making, these are worth working through honestly now, before someone else asks:

• Can you name a single accountable owner for every autonomous system in production right now?
• Is your technical debt register priced in business terms, or still written in engineering language nobody outside the team can act on?
• If a diligence team, human or agentic, asked for your agent inventory tomorrow, does that document already exist?
• What happens to your core AI features if your primary model provider changes pricing or shuts off access next quarter, and is that answer written down anywhere?
• Has your team actually rehearsed your operational playbook for an AI failure, or does it only exist on paper?

A Final Thought

Technical due diligence readiness in 2026 isn’t a favor you do for a future buyer. It’s the same operating discipline that makes a CTO’s own job easier: knowing what your systems cost, who’s accountable for them, and what happens when something breaks. A deal or a portfolio review doesn’t create that need. Instead, it just puts a deadline on work that was already worth doing, whether you’re three weeks from a term sheet or three years past your last one.

I’ve sat on both sides of this table, as the CTO walking a company through an acquisition and as the person building the governance case from the inside. In fact, most of what a 2026 technical review flags isn’t a surprise to the engineering team. It’s a fact everyone already knew, and had never written down where anyone else could find it.

Leigh Newsome

Partner, Hoola Hoop · CTO Coach

Leigh Newsome is a Partner at Hoola Hoop and a CTO coach with 25 years of experience scaling product and engineering teams. He has worked with a wide range of startups and global enterprises, including Avid, Digidesign, WPP, and Kantar/Millward Brown, and successfully led TargetSpot (backed by Union Square Ventures, Bain Capital Ventures, and CBS) through its acquisition to Radionomy Group (Vivendi). When he’s not coaching CTOs, you’ll find him teaching digital audio to graduate students at NYU, building audio and signal processing applications, or flying fixed-wing aircraft, but never all three at once.

I learned to fly fixed-wing aircraft in California years before I was a CTO, and the two passions keep running into each other in unexpected ways. The clearest example right now is the conversation every CTO I work with is having about agentic AI autonomy. Much of the industry has converged on a five-level framework borrowed from self-driving cars (Swarmia, Tessl, and a half-dozen engineering blogs have all published a version). The framework is fine, but there is a more important part of the conversation that is missing.

The right conversation, the one almost no one is having, is what happens to the human in the seat as the autonomy ratchets up. In the cockpit, the answer to that question was worked out painfully over four decades of accidents, near-misses, and re-trained crews. The answer is not that autopilot replaces the pilot. The answer is that autopilot redefines what the pilot is accountable for, and almost universally raises the stakes of every remaining human decision. The same thing is happening today to the CTO and CPO role. It is worth slowing down enough to see it clearly.

The Lesson the Cockpit Already Learned

When the first generation of glass cockpits and flight management systems landed in commercial aviation in the 1980s, the assumption was the obvious one: a more capable autopilot means a less demanding job for the human. The pilots who lived through that transition will tell you the opposite happened. The hand-flying portion of the job shrank. The judgment portion grew. The number of ways a flight could go subtly wrong, before anyone noticed, expanded as the automation took on more of the routine work.

American Airlines highlighted this challenge in the now-famous 1997 training presentation “Children of the Magenta Line,” delivered by Captain Warren VanderBurgh. The presentation warned that as cockpit automation became more capable, pilots could become overly dependent on flight-management systems and less attentive to the broader operational picture. The lesson was not that automation should be avoided. Rather, pilots needed to understand the appropriate level of automation for a given situation, recognize when the system was no longer helping, and be prepared to step down a level – or fly manually – when necessary. This philosophy became an influential part of modern airline training, emphasizing that automation changes the pilot’s role from continuously controlling the aircraft to continuously understanding, monitoring, and managing it.

Air France 447, lost over the South Atlantic in 2009, remains one of the most frequently cited case studies in discussions of automation and human performance. When unreliable airspeed indications caused the autopilot to disengage, control of a still-flyable aircraft was handed back to the crew under difficult and rapidly changing conditions. The pilots struggled to diagnose the aircraft’s state, recognize an aerodynamic stall, and coordinate an effective recovery. The BEA’s final report has become essential reading for aviation and human-factors professionals because it illustrates how quickly automation failures can become understanding failures. The accident was not caused by a malfunctioning autopilot alone; it exposed the challenges of transferring control, situational awareness, and decision-making from machine to human in a high-pressure environment.

Three Forces That Raise the Leadership Tax

The dominant industry framing assumes that agentic autonomy frees leadership capacity. The opposite is closer to the truth. Three reinforcing dynamics, all of which I’m watching play out in the engineering organizations I coach, push the cost of leadership up as autonomy increases.

The cumulative effect is that the leadership tax goes up with autonomy, not down. The judgment work that used to be distributed across senior reviewers, code review forums, and the slow friction of human throughput now concentrates in fewer hands, fewer decisions, and a much shorter time window. That is not a bad thing. It is the actual job. But it is a different job than most CTOs were promoted into, and it is one nobody is teaching out loud.

Four Decisions a CTO Never Hands to Autopilot

Every cockpit checklist has a category of items that never come off the captain’s plate, no matter how capable the automation gets. Takeoff and landing briefings. The go-around decision. The diversion call. Anything involving an irreversible commitment of fuel, time, or the safety of the people in the back. These are not on the autopilot’s menu because they are not the autopilot’s job. The same logic applies to the CTO seat. Below are the four decisions I coach every technology leader to keep on a written, visible, never-delegate list.

The act of writing this list down, and reading it to your leadership team out loud, is one of the highest-leverage exercises I run with the CTOs and CPOs. It is also one of the most uncomfortable, because every line on the list is a place a leader has historically been tempted to let drift to the team, the platform, or the tooling. The drift is what produces the headlines.

Agentic AI Autonomy Demands More Leadership, Not Less

BCG’s Widening AI Value Gap report, based on a survey of 1,250 senior executives, found that only 5% of organizations are “future-built” on AI, while 60% remain laggards. The difference isn’t budget. BCG argues that leaders adopt an AI-first operating model centered on reinvention rather than incremental investment.

The organizations capturing the most value consistently do three things. First, they add new roles rather than remove them, common examples in AI-native teams include the AI Reliability Engineer, Spec Author, and Agent Orchestrator (see The AI-Native Team). Second, they spend more time on calibration, trust reviews, and retrospectives than on traditional oversight. Third, they redesign organizations around judgment rather than throughput, pairing a flatter execution layer with a denser judgment layer.

The Pre-Flight Checklist

If you are a CTO or CPO running an agentic deployment of any meaningful scale, these are the questions I would put on the page in front of you before the next leadership offsite. They are written as a checklist because a checklist is what works under load, and the next eighteen months of this role will be exactly that.

• Is your trust envelope written down, signed by the right humans, and reviewed on a cadence the team can recite from memory?
• Do the people on your team know what triggers a takeover, who calls it, and what the first three moves are once it is called?
• If an agent did something material wrong this week, can you name the human who answers for it without consulting an org chart?
• Is your never-delegate list a written artifact your direct reports can quote back to you, or is it living only in your head?
• Are you spending more leadership time on calibration and retrospectives than you were a year ago, or are you assuming the agents absorbed the work?

A Final Thought from the Left Seat

The CTOs and CPOs I work with who are getting agentic AI right are not the ones who happen to have the best models, the deepest budget, or the most aggressive rollout schedule. They are the ones who sat down on a Wednesday morning and wrote the trust envelope, the override criteria, the accountability ledger, and the never-delegate list. Then they read them out loud to the team. Then they reviewed them in 90 days. That is not exotic work. It is the work the autopilot cannot do for you.

If you found this useful and want the companion piece on how this connects to the broader board-level conversation about AI accountability, the principles in our agentic AI governance framework pair directly with the never-delegate list above. The two pieces work together: one names the leadership decisions that cannot be automated, the other names the organizational infrastructure required to enforce them.

Leigh Newsome

Partner, Hoola Hoop · CTO Coach

Leigh Newsome is a Partner at Hoola Hoop and a CTO coach with 25 years of experience scaling product and engineering teams. He has worked with a wide range of startups and global enterprises, including Avid, Digidesign, WPP, and Kantar/Millward Brown, and successfully led TargetSpot (backed by Union Square Ventures, Bain Capital Ventures, and CBS) through its acquisition to Radionomy Group (Vivendi). When he’s not coaching CTOs, you’ll find him teaching digital audio to graduate students at NYU, building audio and signal processing applications, or flying fixed-wing aircraft, but never all three at once.

The AI ROI board conversation is now one of the defining pressure points for CTOs and CPOs in 2026. According to Kyndryl’s 2025 Readiness Report, which surveyed 3,700 senior business leaders, 61% say they feel more pressure to prove AI ROI now than they did a year ago. That number does not surprise me. What surprises me is how many technology leaders are still walking into that conversation underprepared, armed with metrics that felt compelling twelve months ago and now fall flat the moment a board member asks what it means in revenue terms. To help address this, we’ve guided CTOs and CPOs across our portfolio and hosted two CTO Roundtables in March 2026, titled “CTO: Off The Record – What We’re Not Saying About AI,” providing a forum to explore the challenges and best practices for framing AI ROI.

The frustration being voiced in CTO communities right now is specific: technology leaders who made thoughtful, responsible AI investments during the 2023-2025 buildout period are now being asked to retroactively justify those decisions in financial language they were never tracking. The goalposts moved. And for many, the conversation feels unfair because the technical work was genuinely good.

But boards are not wrong to push. The AI ROI board dynamic reflects a real and legitimate shift in expectations. AI is no longer a speculative bet on future capability. It’s a significant line item in operational budgets, and boards are right to expect that spending to connect to outcomes. The question is how CTOs and CPOs build that case, credibly and on their terms, rather than having the conversation forced on them in a format they didn’t design.

Why the Old Metrics No Longer Work For AI ROI

When AI adoption was in its early stages, organizations tracked what they could measure: the percentage of engineers using Copilot, the number of AI features shipped, the volume of user interactions with AI-powered capabilities. These metrics made sense at the time. They showed momentum. They demonstrated that the organization was moving.

Boards and investors are done with momentum metrics. The AI ROI board expectation in 2026 is near-term business outcomes: revenue impact, cost reduction, and cycle time compression. CFOs, not chief AI officers, are increasingly being positioned as the accountability layer for AI returns, which means CTOs and CPOs now find themselves in conversations that require a different kind of fluency. The shift is not cosmetic. It requires a fundamentally different way of thinking about how AI investment gets measured and reported.

The Hidden Cost Problem in AI ROI

There is a specific version of the AI ROI board challenge that is hitting CTOs right now, and it deserves its own treatment. The cost side of the AI ROI equation is moving. Not incrementally, but significantly and repeatedly. Tools like GitHub Copilot have shifted toward usage-based billing in the past twelve months, and Cursor and Claude Code’s consumption-driven cost structures are creating similar budget unpredictability. This shift away from seat-based licensing has caught a large number of engineering budgets off guard.

The dynamic is this: a CTO budgets for a fixed number of AI tool seat licenses, a predictable, defensible number to put in front of a CFO. Then agentic usage scales up. An engineer running a long Claude Code session or a Cursor agent working overnight isn’t consuming a seat license. They’re running up a consumption bill. The cost structure of these tools is fundamentally different from traditional software licensing, and many organizations discovered that difference in their quarterly cloud and SaaS reconciliation rather than during budget planning.

This creates a specific AI ROI board problem. You cannot build a credible ROI case when the cost baseline keeps moving. Boards and CFOs are reasonable to question a return calculation built on a cost denominator that looked very different six months ago and may look different again in six months. The CTOs who are navigating this well are doing two things: they’re tracking AI tooling costs at the consumption level, not just the license level, and they’re building their ROI narrative with explicit assumptions about cost trajectory rather than treating current spend as a stable baseline. Acknowledging the volatility, with a clear framework for monitoring it, builds more credibility than presenting a clean number that a CFO can easily challenge.

Five AI ROI Board Patterns That Are Holding CTOs Back

Across the CTOs and CPOs I coach, I see the same patterns emerging when the AI ROI board conversation goes poorly. Recognizing yours is the starting point:

### The Common Thread

Every one of these patterns has the same root cause: AI investment was treated as a technology program when it needed to be treated as a business investment from day one. That doesn’t mean the technical work was wrong. It means the measurement and narrative infrastructure wasn’t built alongside it. The good news is that building that infrastructure now, even retroactively, is possible, and it changes the AI ROI board conversation significantly.

What Good AI ROI Board Preparation Looks Like

The CTOs I’ve seen handle the AI ROI board conversation well share a set of practices that distinguish them. None of these are complicated. All of them require doing the work before you walk into the room:

### The Underlying Principle

The AI ROI board conversation is fundamentally a trust conversation. Boards trust technology leaders who demonstrate that they understand the business implications of their investment decisions, who track what matters to the business rather than what’s easy to measure, and who can hold uncertainty honestly while still giving the board a clear enough picture to make decisions. Technical credibility gets you into the room. Business fluency keeps you there.

The CFO Partnership: Your Most Underused Asset

One pattern stands out as particularly underused among the CTOs I coach. The CFO-CTO relationship around AI has historically been adversarial: the CTO advocates for investment, the CFO scrutinizes the returns, and the conversation happens at budget time. That dynamic is shifting, and the CTOs who recognize it early are building a significant advantage in the AI ROI board conversation.

CFOs are now being positioned, internally and by their boards, as the primary accountability owners for AI returns. That’s a significant shift. It means CFOs have both the mandate and the organizational standing to be genuine allies in building the ROI narrative. A CTO who treats the CFO as a gatekeeper to be managed is missing the opportunity. A CTO who brings the CFO in as a partner in designing the measurement framework, building the ROI narrative, and presenting to the board is creating shared accountability that benefits both parties.

The practical entry point is straightforward. Before your next board preparation cycle, set up a 90-minute working session with your CFO specifically focused on the AI ROI measurement question. Agree on what the relevant business outcomes are, which metrics you can attribute directly versus indirectly, and how you want to frame the three-horizons narrative together. Do this well before the board meeting, so you’re not negotiating the framing under time pressure. The CFO who walks into a board meeting having co-built the narrative is a very different ally than the CFO who sees the slides for the first time in the briefing.

Questions to Sit With

If you’re a CTO or CPO heading into an AI ROI board conversation in the next quarter, these are the questions worth working through honestly before you walk in:

• Can you isolate your AI investment on its own cost line, clearly enough that a board member could verify the number with your CFO and get a consistent answer?
• Are your current AI metrics connected to business outcomes, such as revenue, cost reduction, or cycle time, or are they primarily adoption and usage metrics that measure input rather than impact?
• Does your CFO know your AI ROI narrative as well as you do? Could they present the financial side of it credibly without you in the room?
• Have you separated the three horizons explicitly: near-term efficiency returns, structural improvement in the next 12 months, and longer-term competitive positioning? Or are you presenting them as a single undifferentiated story?
• When a board member asks what the AI investment returned, can you answer in a way that a CFO would sign off on? If not, what would need to change in your measurement or framing to get there?

A Final Thought

The AI ROI board conversation is not going to get easier. As AI spending grows and board scrutiny increases, the expectation that technology leaders can account for their AI investments in business terms will only intensify. The CTOs who build that fluency now, before it becomes a crisis conversation, will have a significant advantage over those who continue to hope that momentum metrics will carry them through.

What I find most consistently true, coaching technology leaders through this, is that the ROI conversation is rarely the problem. The problem is the infrastructure behind it, the measurement systems, the CFO relationship, the ability to translate between technical and financial language, that was never built. Building it is not a board-meeting sprint. It’s a discipline that develops over quarters.

The same clarity that helps you navigate the AI ROI board conversation also strengthens how you manage upward across the organization. If you’re thinking about how to build stronger executive relationships alongside your financial fluency, the principles in Managing Up as a CTO or CPO are directly relevant. The skills compound. And the CTOs who develop both tend to find the board conversation stops feeling like a threat and starts feeling like an opportunity.

Leigh Newsome

Partner, Hoola Hoop · CTO Coach

Leigh Newsome is a Partner at Hoola Hoop and a CTO coach with 25 years of experience scaling product and engineering teams. He has worked with a wide range of startups and global enterprises, including Avid, Digidesign, WPP, and Kantar/Millward Brown, and successfully led TargetSpot (backed by Union Square Ventures, Bain Capital Ventures, and CBS) through its acquisition to Radionomy Group (Vivendi). When he’s not coaching CTOs, you’ll find him teaching digital audio to graduate students at NYU, building audio and signal processing applications, or flying fixed-wing aircraft, but never all three at once.