AI Implementation

Implementing AI in Finance: Strategies That Drive Results

Finance teams need AI to scale efficiently in 2025, but many implementations struggle without clear strategy. This guide shares a practical 4-phase framework that helps teams deploy AI tools, automate reporting, and achieve results while building on existing systems.

May 22, 2025

Implementing AI in Finance: Strategies That Drive Results

Finance teams are under more pressure than ever to move faster, deliver deeper insights, and do it all with leaner headcount. AI for finance teams is no longer optional–it’s the key to scaling smartly.

The business impact of successfully implementing AI in finance is becoming increasingly apparent. NVIDIA's State of AI in Financial Services survey reveals the far-reaching effects on revenue growth, cost reduction, and new business development. Nearly 70% of financial services companies report revenue increases of 5% or more from AI initiatives, while over 60% have reduced annual costs by at least 5%. The same research indicates that nearly a quarter of organizations are now planning to utilize AI to generate new business opportunities and revenue streams, shifting their focus from efficiency to driving innovation in financial services.

Here's how leading teams are getting it right from day one.

Align – Clarify the problems AI will solve and define success

Design – Map workflows and choose tools that fit your finance stack

Execute – Launch fast and prove ROI early so you can scale with confidence

Scale – Roll out successful use cases and empower the broader team

This framework enables finance teams to implement AI effectively, driving increased efficiency and improved business outcomes.

Developing an Effective AI Strategy for Finance Teams

Before diving into tools or pilots, top finance teams start by aligning leadership, identifying use cases, preparing the team, and laying out a clear plan.

AI in finance is more than a technology upgrade—it’s a shift in decision-making. That’s why it starts at the top. CFOs and the C-suite must agree on desired outcomes, whether that’s faster close cycles, improved forecast accuracy, or streamlined reporting. When the strategy is clear from leadership, it sets the tone for the rest of the team.

Yet, only 1% of executives describe their AI initiatives as "mature," according to McKinsey. Many are experimenting without a clear operational structure, which limits their impact.

A top-down vision is just the start. Success also depends on preparing your team to embrace and utilize AI confidently. Unfortunately, more than 20% of employees report receiving minimal support with AI tools. That lack of enablement leads to resistance and underuse.

Effective enablement extends beyond technical training—it involves building trust and demonstrating how AI tools simplify work. When teams feel supported and see benefits early on, adoption happens organically.

Top AI Use Cases for Finance Teams in 2025

The most effective AI implementations begin with understanding business needs, rather than focusing on technology features. Begin by evaluating where the finance function is losing time, introducing errors, or lacking timely insights. Forecasting, reconciliations, and spend analysis are often strong candidates.

Starting with focused, high-impact use cases builds early wins and organizational momentum.

Common high-ROI finance use cases include:

AI-powered financial variance analysis with automated commentary
Intelligent vendor spend analysis and cost optimization.
Automated financial reporting with real-time data visualization
Financial data storytelling for strategic decision-making

Once priorities are set, develop a clear implementation roadmap. Don’t just choose tools—define how they’ll work inside your team’s existing processes. Start with simple, practical use cases, such as variance commentary or live actuals in forecasting.

Clarify how workflows will change, who will review outputs, and how decisions will be made using AI-generated insights. Without this operational clarity, even the best tools may fall flat.

Choosing the Right AI Tools for Your Finance Team

AI should work with your existing tech stack, not force you to rebuild it. Select tools that plug into your current systems, support your workflows, and are simple enough for non-technical users to adopt.

Build on the Stack You Already Use

AI adoption shouldn’t mean abandoning your tech stack. Instead, look for tools that automate repetitive work within platforms your team already uses.

For example, Concourse integrates with Intuit, QuickBooks, Xero, NetSuite, and Sage. Setup takes less than 10 minutes, allowing teams to get started quickly without disruption.

Generic AI tools, such as ChatGPT or Claude, can be flexible but often require significant customization to meet specific needs. Traditional FP&A systems, although purpose-built, frequently involve lengthy implementation cycles and require manual upkeep. Concourse offers a middle ground: fast to deploy, purpose-built for finance, and highly usable.

Key Features of Effective Finance AI Tools

The best AI tools for finance are designed with usability and clarity in mind. Select purpose-built solutions that deliver rapid value and minimize reliance on technical resources.

Look for:

Finance-specific AI tools tailored to workflows like financial forecasting, FP&A, and financial reporting
Seamless integrations with your ERP, accounting software, and financial planning systems
Transparent, explainable AI outputs that build trust with finance stakeholders
Low-lift onboarding and intuitive interfaces designed for finance professionals
Flexibility to expand across multiple finance use cases without requiring technical expertise
Strong data security features to protect sensitive financial information

When evaluating AI tools for finance teams, prioritize solutions that understand financial data structures and can work with your chart of accounts. The most effective implementations use purpose-built financial AI that can interpret GL codes, understand financial statement relationships, and apply financial business logic without extensive configuration.

Curious how we combine flexibility with automation in reporting? Take a look at our post:

How Concourse AI Reporting Agents Transform Financial Reporting.

Once the right tools are in place, the next step is making sure clean, connected, and reliable data powers them.

Successful AI Rollout Strategies for Finance Teams

Rolling out AI doesn’t need to be a massive undertaking. Start with a targeted use case, track performance, and expand based on results.

A popular entry point is automating variance analysis, monthly reporting, or forecast prep. Tools like Concourse enable teams to launch quickly and track time savings from the very first day.

Early feedback is essential. Define what success looks like, such as time saved or accuracy improved, and check in with users regularly. Are they relying on the tool? Is it making work easier? These insights guide the next steps.

Measuring Financial and Operational Impact

Many companies struggle to quantify the impact of AI. An Emburse survey of 1,500 finance professionals found that while 66% had invested in AI, many still lacked a way to measure value.

Concourse tackles this challenge directly by tracking time saved with each interaction. Teams using Concourse have seen up to:

85% reduction in time spent generating routine reports
64% improvement in report accuracy
93% increase in stakeholder satisfaction

This creates a running log of where manual work is being eliminated and builds a clear case for internal buy-in and further investment.

Scaling AI Across Finance Workflows

Once a pilot proves successful, the next step is expansion. Focus on replicable wins and extending them across similar workflows. For example, if forecast commentary is automated in FP&A, apply that model to budget reviews or board reporting.

The path to scaling AI across your finance workflows follows three distinct phases:

Phase 1: Assessment Start by evaluating pilot performance and gathering user feedback. Your success benchmark should be for AI to achieve 90% or more of your target KPIs. This phase validates whether your initial implementation is ready for broader adoption.

Phase 2: Expansion. Focus on identifying additional workflows that share repeatable structures with your successful pilot. The key milestone here is confirming measurable cost or time savings in these new use cases, proving the model can be replicated effectively.

Phase 3: Adoption at Scale. Extend AI usage across all relevant planning, reporting, and analysis functions. Success means achieving seamless adoption across teams without disrupting existing workflows—AI becomes a natural part of how work gets done.

Supporting Team Adoption as AI Expands

As you move through each phase, successful enablement becomes critical. Offer brief onboarding sessions and integrate usage guides into existing processes. Ensure that team members understand not only how to use the tools, but also when to apply them and how to effectively interpret the outputs.

Encourage continuous feedback to refine the use of AI tools across various workflows. This iterative approach ensures that as your AI implementation scales, your team's confidence and competency scale with it.

Continuous Improvement

Adoption is just the beginning. Long-term success comes from iteration: track time savings, forecast accuracy, usage rates, and team feedback. Even minor adjustments—such as presenting outputs more clearly—can lead to lasting performance gains.

The Bottom Line

AI isn’t just changing how finance teams work—it’s redefining what’s possible. The most effective teams aren’t waiting. They’re launching now, proving value quickly, and expanding confidently with tools like Concourse.

If you’re ready to move fast, scale efficiently, and unlock more intelligent finance workflows, our AI Reporting Agents can help you get there in days, not months.