Transforming Finance with Generative AI

Finance departments are under more pressure than ever. Tightening regulations, demand for faster and more accurate decision-making, increasing volumes of data, and the constant push to reduce cost are all pushing organizations to look for smarter, more efficient ways of working. Generative AI (Gen AI) is emerging as a powerful lever. It’s not just hype—it’s becoming a core part of how finance functions are evolving.

In this post, we’ll explore how finance can be transformed through Gen AI, what steps need to be taken, some example use-cases, and what organizations should watch out for to make sure the transformation is secure, measured, and delivers real value.

What Does Generative AI Mean for Finance?

Generative AI refers to a class of AI models and tools that can generate content—text, analyses, summaries, etc.—based on input data. In finance, that could mean automatically drafting narrative sections of reports, forecasting financials, detecting anomalies in transactions, helping with compliance documentation, and more.

The Hackett Group offers end-to-end Gen AI services for finance: everything from strategy creation and readiness assessments to solution design, deployment and ongoing optimization. Their approach is built for enterprise scale, meaning it takes into account real-world constraints like data governance, integration with existing systems, risk, and compliance.

Key Services & Capabilities: How to Deploy Gen AI in Finance

To get real value, adopting Gen AI in finance isn’t just about buying tools. It’s about designing a transformation, end-to-end. The Hackett Group outlines a full stack of services and capabilities that finance teams should build or bring in. Here’s what that looks like:

1. Strategy Development & Use-Case Prioritization
   • Define a Gen AI strategy that is aligned with business goals—planning, analysis, reporting, compliance.
   • Evaluate your existing capabilities to find gaps (technology, data, workforce).
   • Use frameworks/tools like AI readiness assessments or finance readiness assessments to pick the use cases likely to deliver the highest ROI.
2. Data Engineering
   • You need clean, well-governed, AI-ready data pipelines. Platforms like Snowflake, Databricks etc. are often involved.
   • Set up data governance, make sure security, privacy, access rights are all working.
3. Custom Solution & Agent Development
   • Start with a Proof of Concept (PoC) to test feasibility. Then build an MVP (minimum viable product).
   • Build AI agents to automate tasks such as budgeting, reconciliation, accounts payable/receivable, expense management etc.
4. Finance Function-Specific Solutions
   Some key areas where Gen AI can help:
   • Financial Planning & Analysis (FP&A): Generating forecasts, analyzing variances, producing narratives in reports, real-time updates.
   • Record-to-Report (R2R): Automating journal entries, reconciliation, streamlining the close process.
   • Order-to-Cash (O2C) & Procure-to-Pay (P2P): Customer onboarding, invoicing, exception handling, purchase order matching etc.
   • Treasury & Cash Flow Management: Liquidity forecasting, cash balance management, transaction pattern analysis.
   • Compliance, Audit & Internal Controls: Regulatory reporting, policy monitoring, audit documentation, raising flags for anomalies or risk.
5. Monitoring, Optimization & Scaling
   • Once deployed, Gen AI tools and agents need ongoing monitoring to ensure accuracy, security, and compliance. Model drift, changes in input data, regulatory changes—all these must be handled.
   • Optimization and maintenance, with a feedback loop, is essential. Tools and platforms also need to fit cleanly into existing ERP/EPM systems.

How to Ensure Success: Best Practices & Risks to Watch

Deploying Gen AI brings big rewards—but if done poorly, can lead to wasted resources, compliance issues, or even reputational damage. Here are what finance leaders should keep front of mind:

• AI Readiness & Gap Assessment: Before you begin, evaluate data quality, technology stack, talent, governance. If your data is messy, lacking structure, or not accessible, the AI will struggle.
• Responsible AI Practices: GDPR, internal policies, audit trails, transparency. Align AI efforts with ethics, compliance, privacy.
• Clear Use-Case Prioritization: Not every finance task needs AI. Prioritize where the rewards are biggest and where feasibility is high.
• Integration with Existing Systems: The real value comes when AI tools work with your ERP, EPM, existing workflows—not in isolation.
• Change Management & Workforce Readiness: People matter. Training, upskilling, change management are key to ensure adoption and avoid resistance. If employees don’t trust the tools or aren’t trained, even the best tech may not deliver.
• Continuous Monitoring & Governance: AI models degrade, compliance rules change, risk profiles shift. Put in place feedback loops and monitoring so you can adapt.

The Value Proposition: What Business Outcomes Can You Expect?

When finance functions get Gen AI right, the benefits are multi-dimensional:

• Significant productivity gains: Automation of repetitive and manual tasks frees up staff time for more strategic work. Hackett’s research shows potential productivity increases (e.g. 44%) when staff are supported with appropriate AI solutions.
• Faster, more accurate planning and reporting: Forecasting cycles shorten; reporting becomes more data-driven and less error-prone.
• Better decision-making: Real-time or near real-time insights, variance analysis, scenario modelling—all enable leadership to make informed decisions faster.
• Cost savings & risk reduction: Process efficiency lowers operational costs; automation reduces human errors and the risk of compliance or audit issues.
• Scalability & adaptability: As businesses grow or change, well-designed Gen AI systems can scale, adapt, and support evolving finance functions more easily than rigid legacy processes.

Structuring the AI Journey: From Ideation to Deployment

To maximize impact, The Hackett Group recommends a structured path in the AI journey:

1. Ideate & Discover
   Explore workflow bottlenecks, process inefficiencies, and areas where data is underutilized. Use tools or frameworks (e.g. AI Taxonomy, benchmarking data) to uncover potential opportunities.
2. Evaluate & Prioritize
   Assess which projects offer high value and feasibility. Table them by impact, risk, readiness.
3. Build & Prototype
   Begin with PoCs or MVPs to validate assumptions: Can the AI deliver the expected benefits? Is the data sufficient and clean? How do users react?
4. Deploy & Scale
   Integrate with existing systems, roll out to wider users, ensure security, compliance, operational stability.
5. Operate, Monitor, Improve
   Post-deployment, continuously check performance, fix issues, update models, adjust for regulatory/environmental/driving changes.

Final Thoughts

Generative AI is fast becoming a core capability in forward-looking finance organizations. But getting value from it isn’t automatic. It demands:

• A strong strategy that ties use-cases to business outcomes
• Clean, governed data and a tech foundation that can support AI workloads
• Alignment with compliance, security, and responsible AI principles
• A phased, measurable deployment with PoCs, MVPs, monitoring, and scaling
• Change management and workforce readiness so people can adopt and trust the AI tools

For organizations that get these pieces right, the payoff is large: faster planning and reporting, more accurate forecasts, lower costs, better risk compliance, and freeing up finance professionals to focus on more strategic, value-added work rather than transactional chores.

If your finance team is starting (or planning to scale) in generative AI, the journey can feel complex. But with the right roadmap, tools, partnerships, and governance in place, it becomes not just possible—but essential.