Most advisory resources on AI in finance are written for enterprise CFOs with dedicated technology teams. These are not those resources. DSG builds and deploys AI tools for the finance executives running $20M–$150M companies — where the team is lean, the data is messy, and the tools need to work in the real world.
The Four Modules
Four modules covering the most common pressure points for a lean finance team: monthly close, transaction readiness, day-to-day prompt engineering, and applied AI fluency.
A structured prompt library and workflow that compresses the monthly financial close by 30 to 40 percent. AI-assisted variance analysis, automated management commentary drafting, and exception-flagging for unusual account movements.
AI-assisted framework for preparing financials for Quality of Earnings scrutiny. Add-back identification prompts, normalization documentation templates, and a simulated PE firm diligence question set.
40+ prompts for finance professionals: variance analysis, budget narrative drafting, board presentation language, covenant monitoring, cash flow commentary. Tested across live engagements in industrial and consumer products.
A 6-week applied AI fluency program for finance executives and senior controllers. Covers AI tool selection and evaluation, financial model automation, AI-assisted reporting, and governance considerations for AI in finance.
The tools above help finance teams work more effectively. That is the productive case for AI in finance.
There is a second conversation that matters as much: what happens when AI is already in your finance function — in your AP workflow, your forecasting model, your fraud screening — and no one has established the controls around it?
That is not a technology question. It is a finance governance question. And it surfaces in a diligence process, an audit, or a fraud event if it has not been asked proactively.
AI Security, Governance & Controls
Three services for the fnance executive, audit committee, and corporate acquirer who understand that
AI adoption without a parallel controls investment creates financial risk — not just technology risk. All
three are finance-led. Deliverables are finance documents, not technology reviews.
Evaluates AI systems from the outside in: architecture, model type, data pipeline
Findings are technically accurate but operationally disconnected
Cannot assess whether AI outputs materially affect your financial statements
Delivers a report that the CFO cannot act on without translation
Does not understand how AI-generated outputs enter the general ledger
Evaluates AI from the finance function outward:what decisions do these outputs drive?
All findings expressed in control and financial terms
Directly assesses materiality to financial statements and transaction valuation
Remediation recommendations the finance team can implement without a technology intermediary
Output is a finance document — readable by aCFO, audit committee, or PE board
Three Services
Each engagement is delivered directly by Vic Datta. Output is a finance document that the CFO and audit committee can act on without a technology intermediary.
A finance-led review of whether AI use is governed well enough for diligence, audit, or regulatory scrutiny. The work centers on control readiness, accountability, policy, and board-level oversight.
Buy-side and sell-side assessment of AI systems, dependencies, and governance as part of transaction diligence. Identifies risk early, strengthens disclosure, and prevents unmanaged AI issues from affecting value.
A practical review of fraud exposure and control gaps arising from AI in finance workflows. Detects weak points in disbursements, entries, approvals, and finance process design before they become losses.
A Direct Conversation
A direct conversation is the right starting point.
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