AI-Native Accounting: How Autonomous Agents, Continuous Assurance, and Real-Time Finance Will Transform Digital Asset Accounting

A conversation with Afeez Awowole, Head of Technical Accounting and Digital Assets at Ava Labs, discussing AI-native accounting systems, autonomous agents, continuous assurance, auditability, real-time close processes, digital asset reconciliations, and the future operating model of accounting and finance. Host: Patrick Camuso, CPA, Camuso CPA Guest: Afeez Awowole, Head of Technical Accounting and Digital Assets, Ava Labs

Editor’s Note

The following transcript has been lightly edited for publication. Grammar, punctuation, and transcription errors have been corrected for readability. Portions affected by automated transcription limitations have been clarified where context made the intended meaning clear. The substance of the conversation has been preserved.

Transcript

Patrick Camuso, CPA 00:03

Welcome to the latest episode of The Financial Frontier. I’m your host, Patrick Camuso. Today we’re discussing AI-native accounting for digital assets and exploring what accounting and finance may become in an era where automation, autonomous agents, and continuous assurance increasingly replace traditional workflows. To help us navigate that future, I’m joined by Afeez Awowole, Head of Technical Accounting and Digital Assets at Ava Labs. Afeez is one of the most forward-thinking accounting professionals working at the intersection of AI, digital assets, technical accounting, and finance transformation. Afeez, welcome back to the show.

Afeez Awowole 00:50

Always a pleasure, Patrick. Thank you for having me back.

Patrick Camuso, CPA 01:03

I do want to note that you’re a returning guest, so welcome back for your second appearance on The Financial Frontier. Let’s jump right into it…

Patrick Camuso, CPA 08:49

One of the themes that keeps emerging throughout this discussion is control. As accountants, we’ve spent decades building processes around controls, segregation of duties, reviews, approvals, and governance. When people hear the phrase “autonomous accounting,” they often assume that means removing controls. But in reality, it seems like controls become even more important. How do you think about escalation paths and control design inside an AI-native accounting environment?

Afeez Awowole 09:16

That’s exactly right. Controls become more important, not less important. I often tell people that we should allow AI to handle the predictable so that humans can focus on the exceptional. The challenge is determining when something moves from predictable into exceptional. That’s where escalation design becomes critical. The escalation mechanism is really the backbone of trust inside an AI-native finance environment. The more autonomy you introduce into a process, the more clarity you need around escalation. You can’t simply create autonomous systems and hope everything works correctly. You have to explicitly define how exceptions are identified, how they are routed, and who ultimately becomes accountable for the outcome.

Patrick Camuso, CPA 10:08

So the escalation framework becomes part of the accounting architecture itself.

Afeez Awowole 10:13

Exactly. When I think about AI-native accounting systems, I think in terms of agents operating within predefined governance structures. The structure I’ve found most effective consists of multiple layers. First, you have what I call the Doer Agent. This agent performs the work. It reconciles accounts. It prepares journal entries. It tags transactions. It identifies anomalies. It executes predefined workflows. Importantly, it operates entirely inside established policies and thresholds. It does not have unlimited discretion.

Patrick Camuso, CPA 10:58

So it’s functioning much like a staff accountant would.

Afeez Awowole 11:02

That’s a good way to think about it. Then you introduce the second layer: The Reviewer Agent. This agent operates independently from the first. Its job is to verify the work that was performed. It validates policy compliance. It checks exception handling. It reviews supporting evidence. It evaluates confidence levels. And it ensures that segregation of duties is preserved throughout the workflow.

Patrick Camuso, CPA 11:40

Which is remarkably similar to how traditional accounting organizations operate today.

Afeez Awowole 11:46

Exactly. The structure itself doesn’t change very much. The participants change. Instead of staff accountants and senior accountants, you have specialized agents performing those roles. Then, depending on the complexity of the workflow, you may introduce a third layer: The Oversight Agent. This agent focuses on ambiguity. It looks for low-confidence outputs. It looks for policy uncertainty. It looks for situations where judgment may be required. Its responsibility is determining whether additional review is necessary.

Patrick Camuso, CPA 12:30

And eventually something reaches a human being.

Afeez Awowole 12:33

Always. There must always be a human controller of record. That person remains accountable. The goal isn’t eliminating accountability. The goal is improving efficiency while preserving accountability. The human controller remains responsible for governance, oversight, policy decisions, and material judgments.

Patrick Camuso, CPA 13:00

One thing that stands out to me is that the architecture you’re describing still follows many of the same principles we’ve always applied in accounting. We’re simply replacing certain human functions with AI functions.

Afeez Awowole 13:13

That’s exactly right. Good accounting principles don’t disappear. They become embedded into the operating model. Segregation of duties still matters. Review still matters. Documentation still matters. Escalation still matters. Materiality still matters. The difference is that many of those controls become automated rather than manually executed.

Patrick Camuso, CPA 13:44

And because the systems operate continuously, they can potentially identify issues much faster than traditional accounting teams.

Afeez Awowole 13:52

Exactly. A human reviewer may look at something once per month. An agent can evaluate the same process continuously. That creates opportunities to identify issues earlier and resolve them before they become larger problems.

Patrick Camuso, CPA 14:15

What role does materiality play inside these escalation structures?

Afeez Awowole 14:20

Materiality remains extremely important. For example, if an exception exceeds a predefined materiality threshold, there may be little value in routing it through multiple additional agents. It should immediately escalate to the human controller of record. Materiality should influence escalation logic. Confidence scores should influence escalation logic. Policy ambiguity should influence escalation logic. All of these factors become part of the governance framework.

Patrick Camuso, CPA 14:57

So escalation isn’t simply based on whether an exception exists. It’s based on the nature of the exception.

Afeez Awowole 15:04

Exactly. Not all exceptions are equal. Some are routine. Some are administrative. Some are highly significant. The architecture needs to recognize those differences and respond appropriately.

Patrick Camuso, CPA 15:18

One concern people often raise when discussing AI is hallucination risk. How do you think about that within accounting systems?

Afeez Awowole 15:27

That’s precisely why guardrails matter. AI systems are powerful. But they are not human. They’re capable of producing outputs that appear reasonable while still being incorrect. The responsibility of the accounting organization is creating environments where those risks are controlled. That’s why I emphasize governance so heavily. Without governance, you create risk. With governance, you create accountability.

Patrick Camuso, CPA 15:58

And that’s probably one of the most important takeaways from this discussion. AI-native accounting isn’t about removing controls. It’s about embedding controls directly into the operating system itself.

Afeez Awowole 16:10

Exactly. That’s the future. The controls don’t disappear. They become part of the architecture.

Patrick Camuso, CPA 17:44

One of the challenges I hear repeatedly from finance leaders is maintaining auditability. Speed is important. Efficiency is important. But ultimately, accounting records need to withstand scrutiny from auditors, regulators, boards, and other stakeholders. How do you preserve auditability inside an AI-native accounting environment?

Afeez Awowole 17:58

That’s a critical question. In many ways, auditability is just as important as efficiency. There’s no value in creating faster accounting processes if the resulting records can’t be trusted. The integrity of the system has to be preserved. And the way you preserve integrity is through design. You design the controls. You design the governance. You design the audit trail. You design the escalation framework. Everything begins with architecture.

Patrick Camuso, CPA 18:30

So auditability isn’t something that’s added afterward. It has to be designed from the beginning.

Afeez Awowole 18:35

Exactly. One of the first things I focus on is the audit trail. Every action performed by an agent should be logged. Every decision should be traceable. Every exception should be documented. Every escalation should be recorded. The system needs to preserve a complete record of how an outcome was reached. That includes:

• Inputs
• Reasoning
• Data sources
• Thresholds
• Overrides
• Exceptions
• Final outputs

Everything should be visible.

Patrick Camuso, CPA 19:15

Almost like a blockchain for accounting decisions.

Afeez Awowole 19:19

That’s actually a useful analogy. The objective is transparency. If someone reviews the process later, they should be able to understand exactly how the system arrived at its conclusion. The more transparent the process becomes, the easier it becomes to trust the outcome.

Patrick Camuso, CPA 19:40

What other pillars support that integrity?

Afeez Awowole 19:44

The second major pillar is agent-level segregation of duties. We already discussed the Doer Agent and the Reviewer Agent. That separation is important. One agent performs the work. A separate agent validates the work. A separate workflow governs escalation. Those responsibilities should remain independent. It’s the same principle we’ve applied to accounting teams for decades. No single individual should control every stage of a process. The same principle applies to AI systems.

Patrick Camuso, CPA 20:25

So we’re essentially translating traditional accounting controls into machine-executable controls.

Afeez Awowole 20:31

Exactly. Good governance principles don’t change simply because AI is involved. They become automated. That’s a very important distinction.

Patrick Camuso, CPA 20:43

And I imagine policy controls become even more important.

Afeez Awowole 20:47

Absolutely. The third pillar is policy-based guardrails. Agents should never be allowed to operate outside predefined policies. For example:

• They shouldn’t modify accounting policies.
• They shouldn’t override materiality thresholds.
• They shouldn’t change classification rules.
• They shouldn’t alter approval hierarchies.

Those decisions belong to humans. The agents execute within established parameters. They do not create the parameters.

Patrick Camuso, CPA 21:28

That’s a powerful distinction. The agents execute policy. They don’t create policy.

Afeez Awowole 21:33

Exactly. Governance remains a human responsibility. Execution becomes increasingly automated.

Patrick Camuso, CPA 21:42

One thing you’ve mentioned several times is hallucination risk. How do you practically manage that?

Afeez Awowole 21:49

The simplest answer is that you don’t allow the system to operate without constraints. Every AI system I design receives the same instruction at its foundation: Do not hallucinate. Work only with verified facts. Do not operate outside the defined context. If information is unavailable, escalate. If confidence is low, escalate. If ambiguity exists, escalate. Those rules become part of the operating framework.

Patrick Camuso, CPA 22:25

In some ways, that’s very similar to professional skepticism.

Afeez Awowole 22:29

It is. The difference is that we’re encoding those principles into the system itself.

Patrick Camuso, CPA 22:38

And that creates a much stronger control environment.

Afeez Awowole 22:42

Exactly. The goal isn’t simply automation. The goal is trustworthy automation. There’s a difference. Anyone can automate a process. The challenge is creating a process that remains reliable, auditable, explainable, and governable. That’s where accounting professionals bring unique value to AI deployments.

Patrick Camuso, CPA 23:10

Because ultimately, if you only focus on speed and efficiency, you’re missing half the equation.

Afeez Awowole 23:15

Exactly. Speed without controls creates risk. Automation without governance creates risk. The future belongs to organizations that can combine automation, governance, transparency, and accountability into a single operating model.

Patrick Camuso, CPA 23:38

And that’s really what makes this conversation so interesting. We’re not talking about replacing accounting. We’re talking about redesigning accounting.

Afeez Awowole 23:46

That’s exactly how I think about it. Accounting isn’t disappearing. It’s evolving. And the organizations that understand that evolution earliest will have a significant advantage.

Patrick Camuso, CPA 22:44

One of the concepts that’s receiving more attention is the idea of a zero-day close or even continuous accounting. Historically, accounting has been a periodic exercise. You close the books. You reconcile accounts. You review transactions. You prepare reports. And then you repeat the process again next month. As AI becomes more deeply integrated into finance operations, how do you see that model changing?

Afeez Awowole 23:01

I think we’re moving toward a world where accounting becomes increasingly continuous. And eventually, that extends beyond accounting into what many people are beginning to call continuous assurance. The logic is fairly simple. If systems are capable of monitoring activity continuously, why should we wait until month-end to identify issues? Why should we wait until quarter-end to review controls? Why should we wait until year-end to evaluate reporting quality? Many of those activities can occur continuously.

Patrick Camuso, CPA 23:42

So the accounting function begins shifting from periodic review to ongoing monitoring.

Afeez Awowole 23:47

Exactly. But it’s important to understand that not everything should be automated. This is where judgment becomes critical. When I think about AI deployment inside finance organizations, I separate work into two categories. The first category is data-dense, low-judgment activity. The second category is judgment-heavy activity.

Patrick Camuso, CPA 24:15

And those categories require different approaches.

Afeez Awowole 24:18

Exactly. If you’re dealing with data-dense processes that involve limited judgment, then you should automate aggressively. Those are ideal use cases. Reconciliations. Data validation. Exception identification. Data matching. Workflow routing. Transaction monitoring. Those processes benefit enormously from automation. The more data involved, the greater the opportunity.

Patrick Camuso, CPA 24:52

Especially in digital assets where transaction volumes can become overwhelming.

Afeez Awowole 24:57

Absolutely. Digital assets are one of the most compelling examples. When you compare blockchain transaction records to traditional accounting records, the scale is dramatically different. The transaction volume is enormous. The data complexity is enormous. The velocity is enormous. Those characteristics make digital asset accounting particularly well suited for AI-enabled workflows.

Patrick Camuso, CPA 25:29

But there are still areas where human judgment remains essential.

Afeez Awowole 25:34

Without question. And that’s the second category. Judgment-heavy work. Significant judgments. Policy elections. Novel transactions. Material accounting conclusions. Complex business combinations. Acquisitions. Restructurings. Areas where professional judgment directly influences the outcome. Those remain human responsibilities.

Patrick Camuso, CPA 26:05

So if a company is evaluating an acquisition, you’re not handing that entire process to an AI agent.

Afeez Awowole 26:11

Exactly. Could AI assist? Absolutely. Could AI perform research? Absolutely. Could AI summarize accounting guidance? Absolutely. Could AI help organize documentation? Absolutely. But the ultimate accounting conclusions remain the responsibility of qualified professionals. Those decisions involve judgment. They involve risk assessment. They involve business context. And they often involve significant financial consequences.

Patrick Camuso, CPA 26:48

So the future isn’t AI replacing judgment. It’s AI amplifying judgment.

Afeez Awowole 26:53

That’s a great way to describe it. AI should reduce administrative burden. It should reduce repetitive effort. It should accelerate information gathering. It should improve visibility. But ultimately, human judgment remains essential.

Patrick Camuso, CPA 27:15

That distinction is important because some people tend to think in extremes. Either AI does everything or humans do everything. The reality is probably somewhere in the middle.

Afeez Awowole 27:25

Exactly. The best operating model combines both. You automate what should be automated. You retain human judgment where human judgment adds value. The objective is not automation for the sake of automation. The objective is better outcomes.

Patrick Camuso, CPA 27:47

One thing that makes digital asset accounting particularly interesting is the sheer volume of data involved. Most traditional accounting environments don’t operate at that scale.

Afeez Awowole 27:57

That’s exactly right. And that’s one of the reasons I believe digital assets may become one of the earliest large-scale demonstrations of AI-native accounting. The data density is extraordinary. Wallet reconciliations. Subledger reconciliations. Pricing validation. Transaction classification. Blockchain activity monitoring. All of those areas generate massive amounts of information. They’re highly repetitive. They’re highly data-driven. And they’re excellent candidates for automation.

Patrick Camuso, CPA 28:38

Which means the accounting organization can spend more time focusing on interpretation rather than collection.

Afeez Awowole 28:44

Exactly. The future accounting organization spends less time gathering information and more time understanding information. That’s a fundamental shift. Historically, accountants spent enormous amounts of time collecting, organizing, cleaning, and validating data. Increasingly, AI will perform those tasks. That allows accountants to move higher in the value chain.

Patrick Camuso, CPA 29:15

Toward analysis. Toward strategy. Toward governance. Toward decision support.

Afeez Awowole 29:20

Exactly. Those are the areas where human expertise becomes increasingly valuable.

Patrick Camuso, CPA 29:28

And ultimately that’s where accounting creates the most value.

Afeez Awowole 29:32

I agree. The future accountant isn’t someone who spends all day moving data between systems. The future accountant is someone who understands systems, understands controls, understands business objectives, and knows how to translate information into decisions. That’s where the profession is heading.

Patrick Camuso, CPA 29:58

And AI becomes one of the engines that enables that transition.

Afeez Awowole 30:02

Exactly. That’s why I don’t view AI as a threat to accounting. I view it as an accelerator for accounting. The profession becomes more strategic. More analytical. More impactful. And ultimately more valuable.

Patrick Camuso, CPA 30:31

One thing that’s particularly interesting about this discussion is that we’re not talking about theoretical use cases. You’re actively deploying AI into digital asset accounting workflows today. Can you share an example where AI has materially improved the accounting process?

Afeez Awowole 30:47

Absolutely. One of the areas where we’ve seen significant impact involves digital asset reconciliations. Anyone who has worked in digital asset accounting understands how challenging those reconciliations can become. Traditional accounting close processes are already complex. You have revenue. Accounts payable. Accruals. General ledger reviews. Financial reporting. But digital asset accounting introduces an entirely separate layer of complexity. You can complete the traditional close and still have an enormous amount of work remaining inside the digital asset environment.

Patrick Camuso, CPA 31:28

Especially when you’re dealing with large transaction volumes and multiple token types.

Afeez Awowole 31:34

Exactly. One of the challenges we encountered involved token pricing. As anyone working in digital assets knows, there are thousands of tokens. Many accounting subledgers provide pricing support for major assets. But no platform covers every token that exists. As a result, accounting teams frequently spend significant amounts of time searching for pricing data on long-tail assets. And in many cases, those assets may not even be material to the financial statements.

Patrick Camuso, CPA 32:09

So the accounting team ends up spending a disproportionate amount of time researching relatively insignificant items.

Afeez Awowole 32:16

Exactly. At one point, we realized that a substantial percentage of reconciliation time was being consumed by this process. Not because the accounting was difficult. Because the information retrieval was difficult. We were spending enormous amounts of effort locating pricing data. That led us to redesign the workflow.

Patrick Camuso, CPA 32:41

And that’s where AI entered the process.

Afeez Awowole 32:44

Exactly. We built a structured workflow that systematically searches multiple pricing sources according to predefined rules. The process begins with primary sources. If no pricing information exists, it moves to secondary sources. Then tertiary sources. The workflow continues until either a reliable valuation is identified or predefined thresholds are exhausted. At that point, the system can flag the asset for review, recommend a treatment, or determine that additional investigation isn’t economically justified.

Patrick Camuso, CPA 33:26

Which is essentially applying accounting materiality concepts through automation.

Afeez Awowole 33:31

Exactly. The objective isn’t simply finding information. The objective is allocating resources intelligently.

Patrick Camuso, CPA 33:41

And what impact did that have on the close process?

Afeez Awowole 33:45

The impact has been substantial. Over the last several reporting periods, we’ve seen the time required to complete certain digital asset reconciliation processes decline dramatically. The close process continues getting faster. The quality continues improving. And the amount of manual intervention continues decreasing.

Patrick Camuso, CPA 34:08

Can you quantify that?

Afeez Awowole 34:11

In some workflows we’ve observed reductions exceeding sixty percent.

Patrick Camuso, CPA 34:18

That’s extraordinary.

Afeez Awowole 34:20

It really is. And what’s important is that those savings aren’t coming from reduced quality. They’re coming from eliminating repetitive work. The accounting team isn’t spending less time thinking. The accounting team is spending less time searching. That’s a critical distinction.

Patrick Camuso, CPA 34:45

Which ultimately allows the team to focus on higher-value analysis.

Afeez Awowole 34:49

Exactly. Every hour saved from manual work is another hour available for strategic work. That’s where the real value emerges.

Patrick Camuso, CPA 35:01

And there are benefits beyond speed.

Afeez Awowole 35:05

Absolutely. Manual processes create opportunities for error. Data-entry mistakes. Transposition errors. Missed information. Inconsistent procedures. The more manual intervention required, the greater the opportunity for variance. Automation reduces those risks.

Patrick Camuso, CPA 35:28

So you’re simultaneously increasing speed and increasing consistency.

Afeez Awowole 35:33

Exactly. That’s one of the reasons I’m so optimistic about AI-native accounting. The improvements aren’t incremental. They’re significant. We’re talking about meaningful changes in operating efficiency.

Patrick Camuso, CPA 35:52

You also mentioned using AI for technical accounting analysis. That struck me as particularly interesting because many people assume AI is only useful for repetitive operational work.

Afeez Awowole 36:03

That’s another area where we’ve seen tremendous value. One workflow I’ve developed helps support technical accounting analysis for complex transactions. The system evaluates facts and circumstances. It considers alternative treatments. It reviews supporting guidance. And it helps generate confidence assessments around different conclusions.

Patrick Camuso, CPA 36:31

Almost like a decision-support framework.

Afeez Awowole 36:35

Exactly. The objective isn’t replacing professional judgment. The objective is strengthening professional judgment. The system helps organize information. It highlights considerations. It surfaces alternative perspectives. And it helps quantify confidence levels associated with different approaches.

Patrick Camuso, CPA 37:00

Which is particularly useful when dealing with novel digital asset transactions.

Afeez Awowole 37:05

Exactly. Many digital asset transactions don’t fit neatly into traditional accounting frameworks. Having systems that can assist with research, analysis, and documentation creates significant value.

Patrick Camuso, CPA 37:20

And I imagine there are implications for auditors as well.

Afeez Awowole 37:24

Absolutely. We’ve already seen research showing that finance leaders are increasingly willing to invest in audit processes that leverage advanced technologies. The benefits extend beyond accounting teams. They extend into assurance functions as well. The more transparent and structured the information becomes, the easier it becomes to review.

Patrick Camuso, CPA 37:52

So the opportunity isn’t just faster accounting. It’s potentially better accounting and better auditing.

Afeez Awowole 37:58

Exactly. That’s the bigger picture. The objective isn’t simply automation. The objective is improving the entire financial reporting ecosystem.

Patrick Camuso, CPA 35:42

One thing that becomes clear from this discussion is that AI isn’t simply changing workflows. It’s changing the skill set required to be successful in accounting and finance. If AI is handling a significant portion of the routine work, what skills become most important for accountants over the next five to ten years?

Afeez Awowole 35:58

The first thing is willingness to engage with the technology. We’re still in the early stages of AI adoption. But the pace of development is extraordinary. Every week new tools emerge. New capabilities emerge. New use cases emerge. The technology is advancing rapidly. And because it’s advancing rapidly, professionals need to make a deliberate decision to learn.

Patrick Camuso, CPA 36:29

Almost the same way previous generations had to learn spreadsheets, email, enterprise software, and cloud computing.

Afeez Awowole 36:37

Exactly. In many ways, refusing to learn AI today is similar to refusing to learn computers thirty years ago. The technology is becoming foundational. It’s becoming part of the operating environment. And eventually, every finance professional will need some level of familiarity with it.

Patrick Camuso, CPA 36:58

So the question isn’t whether accountants will use AI. It’s how effectively they’ll use it.

Afeez Awowole 37:04

That’s exactly right. Every accountant should be asking: Where can AI improve my workflow? Where can AI eliminate repetitive work? Where can AI improve accuracy? Where can AI help me deliver more value? There is almost always at least one use case.

Patrick Camuso, CPA 37:25

And the opportunities vary depending on role and seniority.

Afeez Awowole 37:29

Absolutely. A staff accountant may use AI differently than a controller. A controller may use AI differently than a CFO. A CFO may use AI differently than a CEO. The use cases evolve as responsibilities evolve. But everyone should be participating.

Patrick Camuso, CPA 37:50

One of the concerns people frequently raise is job displacement. How do you think about that?

Afeez Awowole 37:57

Job displacement is always a difficult topic. Every major technological shift changes labor markets. That’s simply reality. But I think it’s important to understand that AI doesn’t eliminate the need for accounting. It changes the nature of accounting. The work evolves. The responsibilities evolve. The value shifts.

Patrick Camuso, CPA 38:22

Toward higher-value activities.

Afeez Awowole 38:25

Exactly. The profession moves away from repetitive processing and toward analysis, governance, strategy, and oversight. That’s where human value becomes increasingly concentrated.

Patrick Camuso, CPA 38:41

You’ve also mentioned that finance teams themselves will begin looking very different.

Afeez Awowole 38:46

I believe that’s already happening. One of the most common questions inside organizations today is no longer: “Do we hire another person?” It’s: “Can technology perform this function?” That’s a fundamentally different conversation.

Patrick Camuso, CPA 39:07

And that changes organizational design.

Afeez Awowole 39:11

Exactly. Historically, accounting teams were often structured like pyramids. Large numbers of junior staff. Fewer managers. Even fewer executives. I think that structure begins changing. The teams become leaner. More specialized. More technology-enabled.

Patrick Camuso, CPA 39:37

What does the future finance team look like?

Afeez Awowole 39:41

I think there are three critical roles. The first is the Controller of Record. This individual remains responsible for governance. Policies. Escalations. Approvals. Risk management. Oversight. Ultimately, accountability remains with a human being. The second role is what I call the Agent Administrator. This becomes an extremely important function. Someone needs to manage the agents. Someone needs to maintain workflows. Someone needs to monitor performance. Someone needs to refine controls. Someone needs to ensure that the systems continue operating correctly.

Patrick Camuso, CPA 40:25

Almost like managing a team of digital employees.

Afeez Awowole 40:29

Exactly. The same way accounting managers historically supervised staff accountants, Agent Administrators will increasingly supervise fleets of accounting agents. That role becomes strategically important.

Patrick Camuso, CPA 40:47

And the third role?

Afeez Awowole 40:50

Strategic finance. Business partnering. Scenario analysis. Forecasting. Planning. Decision support. As automation handles more of the transactional workload, finance professionals become increasingly focused on helping organizations make better decisions.

Patrick Camuso, CPA 41:15

So instead of effort at scale, we’re moving toward insight at scale.

Afeez Awowole 41:20

Exactly. Or another way to say it: We’re moving from effort-based accounting toward accuracy-based accounting. The value isn’t generated by working harder. The value is generated by producing better information and better decisions.

Patrick Camuso, CPA 41:42

One thing that really stood out to me is your description of AI as infrastructure. Can you expand on that?

Afeez Awowole 41:49

I think many people still view AI as a tool. And that’s understandable. Today we use AI tools. But over time, I believe AI becomes infrastructure. Think about electricity. Think about internet access. Think about cloud computing. Think about enterprise software. At some point those technologies stopped being optional. They became foundational. I believe AI follows the same path.

Patrick Camuso, CPA 42:23

Meaning organizations won’t ask whether they should use AI. They’ll ask how they’re deploying AI.

Afeez Awowole 42:29

Exactly. The question becomes: Where does AI create value? How does AI improve outcomes? How does AI improve productivity? How does AI improve decision-making? How does AI improve service delivery? Organizations that answer those questions effectively will create significant advantages.

Patrick Camuso, CPA 42:56

And organizations that ignore those questions?

Afeez Awowole 43:00

They risk falling behind. The pace of change is simply too fast.

Patrick Camuso, CPA 43:08

That’s probably the overarching theme of this entire conversation. AI is no longer something that accountants can afford to ignore.

Afeez Awowole 43:15

I completely agree. Whether you’re a staff accountant, controller, CFO, auditor, tax professional, consultant, or firm owner, the time to begin learning is now.

Patrick Camuso, CPA 43:34

Afeez, this has been an outstanding discussion. We covered AI-native accounting architecture, autonomous agents, escalation trees, auditability, continuous assurance, digital asset reconciliations, future finance teams, and the evolving role of accountants in an AI-driven world. I appreciate you taking the time to share your perspective.

Afeez Awowole 43:57

Thank you for having me, Patrick. It’s always a pleasure. And I look forward to continuing the conversation as the technology evolves.

Patrick Camuso, CPA 44:08

For those who want to connect with Afeez, follow his work, or discuss AI, accounting, digital assets, and finance transformation, LinkedIn is the best place to reach him. I highly encourage everyone to connect with him and follow his content.

Patrick Camuso, CPA 44:25

And thank you to everyone listening. I hope this conversation helped provide a deeper framework for thinking about AI, accounting, and the future of finance. Until next time, I’m Patrick Camuso, and this has been The Financial Frontier.