Build vs. buy in the age of AI: BlackLine's Patrick Villanova on valuations, risk, and trust
BlackLine has spent 25 years helping finance teams automate the work behind trusted financial reporting. But as AI accelerates software development, private valuations remain disconnected from public market realities, and CFOs face pressure to move faster, the question of what to build, what to buy, and what to trust has become more complex.
In this episode of Liquid: How CFOs Outperform, Thomas Gavaghan speaks with Patrick Villanova, CFO of BlackLine, about how BlackLine has evolved from a financial close solution into a broader platform for the Office of the CFO, why connected and reliable data is becoming essential for AI agents, and how CFOs should think about build-versus-buy software decisions when the true cost extends far beyond development.
Patrick explains why buying a company means buying not only technology, but talent, perspective, speed, and strategic fit. He also discusses why private company valuations have not fully adjusted to public market multiples, why AI has introduced a new layer of uncertainty into software valuations, and why CFOs may not always have the luxury of waiting for the market to reach equilibrium. The conversation also covers BlackLine’s approach to AI adoption, including human-in-the-loop controls, rigorous QA, customer testing, token cost discipline, and the trust required when automation touches financial reporting.
What you need to know
Patrick Villanova, CFO of BlackLine, explains why finance leaders are facing a more complicated technology landscape than ever. The through-line of the conversation is that AI may change how finance work gets done, but it does not change the need for trusted data, strong controls, security, auditability, and sound financial judgment.
BlackLine has evolved from point solution to platform
Patrick acknowledges that some in the market still think of BlackLine primarily as a financial close solution. While close remains core to the company’s history, he explains that BlackLine has expanded into a broader platform for the Office of the CFO, spanning areas such as invoice-to-cash, intercompany, financial analytics, journal automation, matching automation, and Studio360.
Studio360 is especially important because it acts as connective tissue across BlackLine’s offerings. Patrick describes it as a shared data layer that helps finance teams avoid fragmented workflows, duplicate reconciliations, and disconnected systems. That matters not only for efficiency, but also for AI. If an agent operates across 20 disconnected finance systems, it may produce 20 different answers. For AI to work safely in finance, it needs trusted, connected, and consistent data.
BlackLine is not trying to replace the ERP
A key distinction in the episode is where BlackLine sits in the enterprise finance stack. Patrick is clear that BlackLine is not an ERP and does not intend to become one. Instead, BlackLine sits on top of ERPs and fills the white space around automation, accuracy, controls, audit trails, reconciliations, and workflow.
That ERP-adjacent role is central to the value proposition. ERPs process transactions and store data, but they often do not provide the level of automation, evidence, review, support, and auditability that finance teams need. Patrick argues that this is where BlackLine creates value, especially for CFOs and CIOs who need to reduce manual intervention, improve confidence in the numbers, and maintain a single source of truth across the finance ecosystem.
Build-versus-buy software decisions now carry higher stakes
Patrick explains that build-versus-buy analysis has always been part of the CFO’s role, but the stakes are higher in today’s market. Building is usually cheaper at first, and it gives companies more control over exactly what they want to create. But building also takes time, delays revenue opportunities, redirects internal resources, and creates ongoing obligations around maintenance, security, support, auditability, and business change.
Buying, by contrast, can accelerate time to market and bring in not only technology, but also talent and domain expertise. That matters in specialized areas like finance and accounting, where the product has to support regulated workflows, auditor scrutiny, and executive accountability. Patrick also notes that the valuation environment complicates the decision. Public software multiples have come down, while many private company valuations remain several years and several multiples behind that reality. CFOs have to decide whether to pay a premium now, wait for equilibrium, or risk a competitor acquiring an asset first.
AI adoption in finance has to move at the speed of trust
Patrick is direct about BlackLine’s approach to AI: the company is leaning in, but cautiously. Internally, BlackLine is using AI tools to improve engineering productivity and is seeing record levels of code output per engineer. But because BlackLine serves finance teams that expect accuracy and trust, Patrick says the company cannot simply move as fast as possible.
That same philosophy applies to AI agents in customer-facing workflows. BlackLine is keeping humans in the loop, designing agents to operate within defined data boundaries, and giving customers the ability to test agents in their own environments with internal auditors, external auditors, and real data. Patrick’s point is that 90-plus percent automation with proper controls can create meaningful ROI, even if full autonomy is not the right goal for financial reporting.
Finance professionals need fundamentals and fluency in technology
Patrick closes with advice for both CFOs and early-career finance professionals. AI will change what finance and accounting teams do every day, but it will not eliminate the need for finance and accounting judgment. Professionals still need to understand the fundamentals deeply enough to know whether the output of a system is correct.
At the same time, avoiding AI is not a viable strategy. Patrick argues that finance professionals should lean into the technology, educate themselves, test tools carefully, and understand how automation is changing the work. The future belongs to people who combine financial fluency with technological fluency, not those who treat the two as separate disciplines.
Additional topics covered in this episode
Why BlackLine’s partnership model depends on technical connectivity, co-development, and shared data confidence.
How AI is changing software valuation narratives and creating a wait-and-see mindset in parts of the M&A market.
Why token usage and AI operating costs need to be tested before automation is scaled.
How security, SOC reporting, and data infrastructure factor into build-versus-buy decisions.
Why Patrick encourages “diplomatic dissent” as part of effective finance leadership.
Patrick Villanova, CFO, BlackLine
Patrick joined BlackLine in November of 2015 after an extensive career in public accounting. Patrick served as Principal Accounting Officer during the successful IPO of BlackLine's common stock in October 2016. Additionally, he helped implement the necessary processes and systems to achieve full SOX 404(b) compliance in 2018 and spearheaded the successful execution of two convertible note offerings in 2019 and 2021 totaling $1.5 billion. During his tenure at BlackLine, Patrick has led the acquisition and financial integration efforts of three different companies, expanding BlackLine's platform to include accounts receivable automation and more robust intercompany transaction processing capabilities. Prior to joining BlackLine, Patrick served almost 16 years at PricewaterhouseCoopers in five different offices focusing on both pre-IPO readiness projects and highly acquisitive public clients. Patrick graduated from the University of Notre Dame in 1999 with a dual major in Accounting & Computer Applications.
