The Challenge:
A software company building agentic AI for finance workflows — procure-to-pay, order-to-cash, and the accounting operations surrounding them — faced a challenge specific to its category: building for finance teams requires knowing how accounting and finance actually run day to day, not how they appear in a product demo.
Its buyers, CFOs and controllers, were evaluating the platform with a specific question in mind: where does this automation produce a genuine return, and where does it simply move work from one place to another without solving anything.
How Liv Data Helped:
Liv Data brought operating experience into the product and go-to-market process, rather than reviewing the platform from the outside.
Workflow Advisory
- Advised on how accounts payable, accounts receivable, procurement, close, and reporting actually operate inside a mid-market finance organization
- Ensured product design reflected real-world process, exception handling, and control requirements, rather than an idealized workflow
Go-to-Market Strategy
- Advised on positioning, buyer motion, and how the offering should be framed to finance leadership
- Informed this positioning with insight from conversations already taking place with that same buyer profile
ROI Discipline
- Worked on identifying where AI automation delivers genuine payback in finance workflows and where it does not
- Mapped automation to the specific processes with a measurable return, so deployments could be evaluated on results rather than novelty
Field Evidence
- Deployed and ran the platform directly inside client environments, so the advice given was grounded in operating the product in production rather than assessing it from the outside
The Outcome:
- Liv Data’s finance-operations experience and field evidence shaped product and positioning decisions directly
- The automation platform is now used inside Liv Data client engagements where the return case is clear and measurable
- The relationship functions in both directions: operating knowledge informs the product, and the product’s real-world performance is validated through direct use
Why It Worked:
Advising from direct operating experience, rather than from a purely external or theoretical vantage point, meant the guidance reflected how finance teams actually work — not how a platform assumes they work. That distinction is often what separates AI tools that deliver genuine returns from those that simply relocate existing work.
