As finance departments move from experimenting with artificial intelligence to redesigning how work gets done, the quality of underlying financial data and the choice of AI architecture are becoming critical decisions. Baker Tilly will return as a Platinum Exhibitor at the Finance & Accounting Technology Expo (FATE) on November 18–19, 2026, bringing two sessions focused on data governance, ERP-native AI and emerging AI platforms.
Baker Tilly Brings AI-Ready Finance Strategy to FATE 2026
Artificial intelligence is quickly becoming a finance transformation issue rather than a technology experiment.
For CFO organizations, the difficult questions are increasingly practical: Is the underlying financial data reliable enough for AI? Should companies use AI embedded inside their ERP systems or adopt external foundation-model platforms? And who is responsible when an AI system produces an incorrect financial recommendation?
Those questions will be central to Baker Tilly’s participation in the 2026 Finance & Accounting Technology Expo, where the advisory, tax and assurance firm will return as a Platinum Exhibitor.
The third annual FATE event takes place November 18–19 at the Javits Center in New York City. Baker Tilly is scheduled to present two sessions examining the operational foundations and technology choices behind AI adoption in finance.
AI adoption starts with financial data
Baker Tilly’s first session, “Garbage In, Governed Out: Clean Up Your Financial Data Before It Cleans You Out,” will focus on a problem that can be overlooked amid enthusiasm around generative AI: models are only as useful as the information they can access.
Finance organizations typically operate across ERP systems, spreadsheets, reporting platforms, planning applications and specialized financial software. Over time, those systems can produce duplicated records, inconsistent definitions and fragmented data ownership.
That creates a problem for AI.
An AI assistant asked to summarize working capital, forecast cash flow or identify unusual transactions needs access to trustworthy and appropriately governed data. Poorly defined financial data can therefore produce outputs that look authoritative while being difficult to validate.
The issue is particularly relevant as organizations move from isolated AI pilots toward enterprise deployment.
For CFOs and finance transformation teams, data governance is becoming part of the AI business case rather than a separate IT initiative.
ERP-native AI versus external AI platforms
Baker Tilly’s second session, “Designing the AI Operating Model for Finance: Native ERP AI, Frontier AI Platforms, and the Decisions That Matter,” will address another emerging strategic choice.
Enterprise software vendors including Microsoft, Oracle, SAP and Salesforce are embedding AI capabilities directly into business applications. At the same time, companies can access increasingly capable AI models and platforms from providers such as OpenAI, Google and Anthropic.
That creates two broad approaches.
ERP-native AI can have an advantage because it operates close to the structured business data and workflows that finance teams already use. It may also simplify permissions and integration.
External AI platforms can provide greater flexibility and access to rapidly evolving frontier models, but organizations may need to build additional controls around data access, integration, security and governance.
There is no universal answer.
A finance department evaluating AI needs to consider the use case, data sensitivity, existing technology stack, integration requirements, model capabilities and the level of human oversight required.
The AI operating model is becoming a finance issue
The discussion also points to a broader change in the role of the finance function.
AI can automate tasks such as document processing, reporting preparation, variance analysis and certain forms of forecasting. But deploying those capabilities at scale requires decisions about who owns models, how outputs are reviewed and how errors are escalated.
That makes an AI operating model increasingly important.
Finance leaders may need to define which processes can be automated, which require human approval and which should remain outside AI systems altogether.
Controls around auditability and data access become particularly important for financial reporting and other high-stakes processes.
The technology therefore represents only one part of AI adoption. Governance, workflow design and organizational accountability can determine whether an implementation delivers measurable value.
Baker Tilly’s role reflects the changing finance technology market
Baker Tilly provides advisory, tax and assurance services to organizations across the United States and international markets. Its participation at FATE comes as accounting and finance technology increasingly converges with enterprise AI.
The firm’s sessions are also representative of the broader themes confronting finance technology buyers.
Historically, finance transformation discussions often centered on ERP modernization, automation, analytics and cloud migration. AI now cuts across all four.
The result is a more complicated technology landscape for CFOs.
Companies are not simply selecting an AI tool. They are deciding how AI should interact with their ERP, data warehouse, planning systems, business intelligence platforms and existing automation infrastructure.
What enterprise finance teams should watch
For finance leaders attending FATE, the most useful takeaway may be that AI readiness is less about deploying the newest model and more about building the conditions in which AI can be trusted.
That means examining data quality before expanding AI access, establishing governance around sensitive financial information and comparing embedded AI capabilities with external platforms on a use-case basis.
The competitive landscape will continue changing as Microsoft, Google, Oracle, SAP, Salesforce and specialist AI companies introduce new capabilities.
Finance teams therefore face a moving target.
Baker Tilly’s return to FATE puts those questions directly on the event agenda, offering finance professionals a framework for thinking about AI not simply as another software feature, but as a potential redesign of the finance operating model.
For enterprises, that distinction matters. The next phase of finance automation will likely be determined as much by governance and architecture as by the underlying AI model.
Market Landscape
The finance technology market is shifting from automation-first transformation toward AI-enabled operating models.
ERP vendors are embedding copilots, agents and predictive capabilities into core financial workflows, while independent AI platforms offer increasingly sophisticated reasoning and content-generation capabilities.
This creates several competing technology paths:
- ERP-native AI: tightly integrated with financial workflows and enterprise data.
- External AI platforms: greater model and application flexibility.
- AI agents: increasingly capable of executing multi-step workflows rather than simply generating text.
- Data and governance platforms: increasingly important for controlling the information AI systems consume.
- Finance automation: continuing to provide the structured workflows that AI can augment.
For CFOs, the strategic question is shifting from “Where can we use AI?” to “Which finance processes should AI operate, under what controls, and using which architecture?”
That is likely to remain a central enterprise technology debate through 2026 and beyond.
Top Insights
- Baker Tilly returns to FATE 2026 with two AI-focused sessions, addressing financial data governance and technology architecture for finance transformation leaders.
- Clean financial data is becoming an AI prerequisite, because inaccurate or inconsistent ERP and accounting information can undermine otherwise sophisticated AI applications.
- ERP-native and external AI platforms offer different trade-offs, forcing finance teams to evaluate integration, governance, flexibility, security and model capabilities.
- AI adoption increasingly requires an operating model, defining ownership, human oversight, workflow automation, data access and accountability for AI-generated outputs.
- CFO technology strategies are expanding beyond ERP modernization, with AI now influencing data governance, automation, analytics and the design of finance organizations.
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