MindBridge Vision 2026 brought together finance leaders, accounting executives, audit professionals, AI governance experts, and technology leaders to examine the implications of AI for the future of finance and assurance.
Across the event, the focus was not simply on where AI can automate work, but on how organizations can govern it, maintain financial integrity, preserve human judgment, and adapt as financial workflows become increasingly autonomous.
As AI moves deeper into finance and audit workflows, the challenge is no longer just where it can save time. Leaders also need to decide where AI can be trusted to act, how its work should be checked, and where human judgment remains essential.
Those questions ran throughout Vision 2026. Across the sessions, five themes kept resurfacing, offering a view into how finance and assurance leaders are approaching AI today and what they need to prepare for next.
Every Vision 2026 session is now available to watch on demand.
1. AI adoption is moving faster than AI governance
AI adoption alone says very little about whether an organization is ready to trust AI with consequential work.
Mark McDonald, CEO and Founder of Finance Next, made that distinction central to his Vision session on AI governance. He identified three barriers holding finance organizations back: unclear strategy, limited trust in AI, and new security and governance vulnerabilities.
His argument was practical. Governance should not exist simply to restrict AI. Done well, it gives organizations the structure to use AI with greater confidence.
That starts with fundamentals: connect AI initiatives to the finance roadmap, classify use cases by risk, maintain an AI register, define access controls, assign process ownership, and document governance requirements.
It also means reconsidering the assumption that adding a human reviewer automatically makes an AI process reliable. As the volume of automated work grows, governance needs multiple layers of validation and independent review rather than a single human backstop.
Watch: Rethinking AI Governance: How Finance Teams Can Build Trust and Accelerate Adoption with Mark McDonald.
2. As AI gains authority, oversight has to evolve with it
Generative AI largely introduced organizations to systems that produce answers. Agentic AI raises a different governance problem because agents can pursue objectives, retrieve information, use tools, interact with systems, and take actions across multiple steps.
That means organizations need to evaluate more than the final output.
At Vision, Wenzel Reyes of MindBridge and Raj Patel of Holistic AI focused on three questions that become increasingly important as AI gains authority: What is an agent allowed to decide? When must it stop and escalate? Can a reviewer reconstruct how it reached the outcome?
Their session also challenged a simple interpretation of “human in the loop.”
Meaningful human oversight requires information, authority, competence, time, and accountability. A review step is not much of a safeguard if the reviewer cannot understand what happened, challenge the result, or devote enough time to the decision.
Testing has to evolve too. An agent can reach the correct answer while taking an unacceptable path to get there. That makes step-level evaluation, adversarial testing, traceability, and independent assurance increasingly important as organizations deploy agents into higher-impact workflows.
Watch: The Agentic Assurance Shift: How MindBridge and Holistic AI are Advancing AI Governance and Testing with Wenzel Reyes and Raj Patel.
3. Human judgment becomes more valuable as routine work changes
A consistent theme across Vision was that more automation does not make professional judgment less important.
It changes where that judgment is applied.
Calvin Harris, CEO of the New York State Society of CPAs, explored what that means for the accounting profession. For decades, early-career accountants developed expertise through repetition. They performed foundational work, made mistakes, received review comments, and gradually developed judgment.
As technology takes on more of that repetitive work, the profession has to find new ways to build the experience those tasks once provided.
Technical knowledge remains essential. But Harris argued that professional skepticism, communication, and the ability to recognize when an AI-generated answer is wrong become even more important.
Jeff Kovacs, President of Citrin Cooperman, brought a similar argument to audit.
AI and automation can change risk assessment, substantive testing, quality review, and the economics of an engagement. But risk signals are not audit conclusions. Auditors still need to interpret evidence, challenge results, apply skepticism, and determine the appropriate response.
That distinction matters.
The future of professional work is not a contest between people and AI. It is a redesign of which work technology performs and where human expertise carries the greatest value.
Watch: The Future of Finance, Accounting & Assurance with Calvin Harris and TJ Smith.
Watch: Transforming Audit: Where AI Changes the Economics, not Human Judgment with Jeff Kovacs and Sarah McGinnity.
4. Financial oversight has to keep pace with financial execution
The AI conversation can become abstract quickly. Turo brought it back to the realities of running finance at scale.
Turo’s five-person revenue accounting team manages millions of transactions each month while working toward a business-day-five close. Historically, potential issues could surface through audit testing, engineering channels, or other reviews, leaving the accounting team to investigate after the issue had already appeared.
Turo wanted a more proactive approach.
Today, month-end transaction data flows into MindBridge for analysis while the accounting team works through the close. The team reviews prioritized risk signals, investigates unusual activity, and determines whether an issue needs action from accounting, engineering, customer service, or FP&A.
That process helped Turo identify an edge-case product bug within roughly two months of implementing MindBridge. The issue was not material when identified, but finding it early gave the team time to understand the affected population and address the cause before it became more significant.
The example makes an important point about financial oversight.
Finding something unusual is only the beginning. Finance still has to understand why it matters, determine the appropriate response, and connect that response to the people responsible for acting on it.
As financial execution becomes faster and more automated, oversight needs to keep pace.
Watch: Financial Oversight at Scale: How Turo Turns Risk into Action with Fernando Cortés, Michelle Wong, and Dana Williams.
5. Independent oversight is becoming part of the AI architecture
The opening Vision keynote connected many of these ideas.
AI and automation can increasingly execute financial work, but accountability remains with finance leaders and auditors. That creates two distinct challenges.
For enterprise finance teams, MindBridge describes the Governance Gap as the widening distance between increasingly autonomous financial execution and an organization’s ability to govern financial integrity with confidence.
Autonomous Financial Oversight addresses that challenge through continuous, full-population financial oversight that is independent of the systems that execute financial work.
Audit and assurance firms face a different challenge as client environments become more automated and complex. MindBridge’s Augmented Assurance approach uses full-population financial analysis and explainable risk information to strengthen risk identification while keeping professional judgment and audit decisions with the auditor.
Both point back to one principle that appeared repeatedly throughout Vision: the system doing the work should not be the only system checking the work.
That principle is becoming more important as AI moves from answering questions to taking actions.
MindBridge’s product roadmap sessions showed how that direction is taking shape across both markets. For enterprise finance, the roadmap covers more frequent financial analysis, new machine learning control points, AI-assisted investigation, connectivity with enterprise AI assistants, and agents that support analysis design.
For audit and assurance, the roadmap extends to areas including subledger analysis, assertion-level risk assessment, agentic risk assessment, going-concern forecasting, and integration with audit workflows and approved AI systems.
Learn more about our latest release →
The workflows differ because the responsibilities differ. The common requirement is stronger visibility into what increasingly automated financial systems are doing, why something deserves attention, and where human action or judgment is required.
Watch: Governing with Confidence in an Agentic Future with Les Rechan, Mike Maziarz, and Rachel Kirkham.
What does Vision 2026 mean for finance and audit leaders?
Taken together, the Vision sessions point to a practical conclusion.
AI strategy cannot stop at adoption.
Finance organizations need to decide how AI fits into their broader roadmap, how much authority different systems should have, how that activity will be governed and how financial integrity will be independently checked.
Audit firms face their own version of that change. As client environments become more automated, firms need ways to strengthen risk identification and modernize execution while preserving professional judgment, skepticism, and accountability.
And both need to prepare people for work in which technology performs more of the mechanical activity while humans remain responsible for consequential decisions.
The organizations represented at Vision approached these questions from different perspectives. Their answers were not identical.
The common thread was accountability.
AI can take on more work. It can process more information. It can act with greater autonomy.
Responsibility for the outcome does not disappear with it.
Watch Vision 2026 on demand
All Vision 2026 sessions are now available on demand.