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Lead Generation
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Pricing Survey Form (#3)
Strategy
Team
Processes
Data
Technology
Strategy
1. How would you describe your finance team’s progress so far?
We have not yet started exploring AI.
We have identified specific use cases and are piloting AI in some test areas.
We have a clear AI strategy and are implementing it across multiple processes.
AI is fully integrated into our strategic planning and execution.
2. How involved is leadership in your finance team’s AI strategy?
Leadership is not involved at all.
They are aware of AI initiatives but are not actively supporting them.
They support AI adoption but are not providing resources.
They are actively championing AI adoption and providing resources.
They are driving both AI strategy and implementation.
3. What does your finance team’s AI adoption roadmap look like?
We don’t have a roadmap.
We have a vague idea of what we’d like to achieve, but nothing formalized.
We have a roadmap, but it’s not particularly detailed.
We have a detailed roadmap, with planned use cases tied to our strategic goals.
We have a comprehensive roadmap with use cases, timelines, success metrics, and regular review cycles.
4. What is your build vs buy policy for AI solutions?
We haven’t thought about building or buying AI capabilities.
We prefer an AI solution that’s built and maintained by a third party.
We are weighing the trade-offs between building custom AI solutions in-house versus buying external third-party solutions.
We prefer a hybrid approach, combining platform solutions with in-house development as needed.
We are developing our own custom AI solutions in-house.
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Team
1. What is the general attitude of your team toward AI?
They express significant anxiety about AI, fearing job displacement and/or inaccurate outputs.
There is some anxiety, but team members are open to learning about and working with AI.
They maintain a neutral stance, expressing uncertainty about AI’s impact on their roles.
They demonstrate comfort with AI adoption and willingness to integrate AI into their work.
They actively champion AI, proactively seeking it out as a tool that enhances strategic impact.
2. How would you describe your finance team’s AI literacy and skills?
General lack of familiarity with AI concepts and skills.
Nascent. Some team members are self-educating on AI basics.
We have widespread use of tools like ChatGPT, but limited understanding.
Widespread use of tools, with some extremely adept power-users.
Everyone on the team is trained on AI best practice.
3. Does your finance team have access to data science resources?
No
Yes
4. How does AI adoption on your finance team compare to other departments?
We’re far behind other departments in AI adoption.
We’re slightly behind other departments in AI adoption.
We’re on par with other departments in AI adoption.
We’re slightly ahead of other departments in AI adoption.
We’re leading the way in AI adoption across our organization.
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Processes
1. What does your finance team’s operating model look like for building AI solutions?
Nonexistent: We haven’t defined an operating model yet.
Decentralized: Each team builds their own AI solutions independently.
Centralized: One team handles all AI development.
Federated: Teams build AI solutions but follow shared standards.
Hybrid: We have a central AI infrastructure, but teams build out their own use cases.
2. Would you feel confident estimating the ROI your finance team has seen from AI adoption so far?
No.
I’d feel OK about it, but not confident.
Yes.
3. What does your governance framework look like?
We don’t have a governance framework.
We have some broad principles about AI governance, but nothing formalized.
We have published AI governance, but it’s not enforced.
AI governance is in place and enforced, but rarely reviewed
We have governance in place that’s enforced and regularly audited
4. Have you carried out mapping of finance processes and workflows?
Never.
We have some documentation, but it could be improved/updated.
We have a detailed process map in place.
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Data
1. How do you store your data?
We store most of our data in spreadsheets or isolated systems.
We centralize our data in a finance-owned database or planning tool.
Our data is integrated into the company’s data warehouse/data lake.
We use modern, scalable cloud architecture with APIs.
We have a real-time data platform with AI/ML capabilities and automated pipelines.
2. How clearly defined is your finance team’s data stewardship structure?
We have no formal data ownership structure.
Somewhat: Ownership is limited and mostly ad hoc.
Partially: We assign owners to select key datasets.
Mostly: All key datasets have formal stewardship with defined responsibilities.
Completely: We have enterprise-wide data governance with stewards actively managing data lifecycle and quality metrics.
3. Can you trace key financial metrics back to their sources?
No: Finance doesn’t have visibility into data lineage.
Somewhat: We have limited manual documentation tracking data sources.
Partially: Our data lineage system is on its way to being automated.
Mostly: We have implemented end-to-end lineage tracking.
Completely: We have fully automated, real-time lineage with impact analysis and version control.
4. Do your data definitions and sources follow a shared standard?
No: Definitions vary across teams.
Somewhat: We have limited alignment through shared documents or meetings.
Partially: Definitions are on their way to becoming standardized with some exceptions.
Mostly: We have standardized definitions enforced in our tooling.
Completely: We maintain a live, fully standardized data catalog with automated compliance checking and governance workflows.
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Technology
1. Does your finance team have dedicated tech resources to support them?
No: We rely entirely on central IT and compete for their time.
Somewhat: We have access to IT support, but finance is often deprioritized.
Partially: We have a few tech resources assigned to finance but there are constraints.
Dedicated team with budget: We have finance-specific tech resources.
Yes: Finance has its own tech team and resources.
2. How is your team budgeting for AI solutions?
We haven’t allocated any budget for AI solutions.
We’re using existing budgets on an ad hoc basis for AI experiments.
We have a small, dedicated budget for AI pilots and proof of concepts.
We have an established AI budget integrated into our annual planning cycle.
We have a comprehensive AI investment framework with multi-year funding, ROI tracking, and dynamic budget allocation.
3. How reliant on IT are you?
IT owns all financial data and controls access.
Finance can view data but IT manages changes and definitions.
Finance and IT jointly manage data with overlapping responsibilities.
Finance owns financial data but relies on IT for infrastructure.
Finance fully owns and governs financial data including definitions and access.
4. How interoperable is your tech stack (ERP, CRM, HRIS, etc)?
Not at all – data is manually exported and reconciled in spreadsheets.
Basic – some integrations exist but manual imports are still necessary between systems.
Core business systems are integrated with connectors/middleware, data flows regularly through batch updates.
Our full tech stack is fully connected and automated updates pull through data regularly.
All our systems are completely interoperable. We can view all our data in real-time in one source of truth.
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