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How AI-Era Pricing Is Reshaping Finance Operations
Usage-based and hybrid pricing models are changing how B2B companies generate revenue — and creating new headaches for the finance teams behind them.
Tabs co-founder Rebecca Schwartz and PwC Partner Amit Dhir sat down to unpack exactly what that means in practice: how pricing model decisions ripple into revenue recognition, forecasting, and financial ops — and what it takes to scale without piling on manual work.
Watch the on-demand recording to get practical frameworks, real-world examples, and a clear path to operationalizing usage-based revenue — including a forward-looking take on how AI will reshape financial workflows. If your team is navigating pricing complexity heading into the back half of the year, this is worth an hour.
In 2026, AI in finance has moved well beyond isolated experiments. KPMG’s global survey of 1,013 senior finance leaders across 20 countries found active AI use had more than doubled in two years. More than three quarters reported AI use in planning, reporting, and commercial analysis. Yet only 23 percent said AI was exceeding expectations.
What finance teams are doing now
Most current projects begin with repetitive work that already has structured data. Common targets include invoice coding, accounts payable checks, reconciliations, journal preparation, anomaly detection, expense review, and month end close.
McKinsey’s 2025 survey of 102 CFOs found 44 percent used generative AI across more than five finance use cases. That was up from 7 percent in the prior survey. The change shows how quickly finance teams are moving from single pilots toward broader operating use.
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Different tools solve different parts of the work. Rule based automation executes fixed steps. Predictive AI finds patterns in transactions and forecasts. Generative AI reads documents, drafts commentary, and explains variance. Agentic AI goes further by planning tasks, calling systems, checking results, and passing exceptions to people.
Where the next projects are heading
The next stage is workflow orchestration. A finance agent could collect source data, match transactions, prepare reconciliations, investigate differences, draft journal entries, and assemble reporting commentary. Human reviewers would still approve sensitive outputs.
This model is already entering finance software and pilot programs. A May 2026 KPMG survey found half of surveyed US companies were planning to develop or orchestrate multi agent systems across finance workflows. Financial close and cash management are early areas because they contain repeated handoffs, rules, exceptions, and audit requirements.
The longer term shift may be a move from periodic finance toward continuous finance. Reconciliations can run throughout the month. Cash positions can update more often. Forecasts can react to new operating data instead of waiting for a monthly cycle. The close then becomes more about confirming records than rebuilding them.
Controls become part of the system
More autonomy also creates new failure modes. KPMG’s 2026 work on agentic financial reporting highlights cascading errors, governance complexity, and segregation of duties problems. A wrong action from one agent can feed another process before a person notices.
That makes audit trails, access controls, data lineage, model testing, approval limits, and human signoff part of project design. Finance cannot treat these controls as a later compliance task.
The work changes with the software
Current deployments point toward fewer hours spent matching records and preparing routine reports. More work moves toward exception review, model supervision, control design, scenario analysis, and business decisions.
A useful AI finance project therefore starts with one measurable process. It needs trusted data, defined ownership, approval rules, and a record of every machine action. The useful question is which decisions can be delegated without weakening evidence, ownership, or control.
This newsletter offers a regular check in rather than a full operating plan. It brings new information and practical signals to your attention. The real work happens in process design, data cleanup, controls, and daily decisions. Returning to these questions over months is what turns reading into better finance habits.
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