Before AI was on everyone’s radar, innovative finance teams were embracing robotic process automation (RPA) for routine tasks like data entry and financial reporting. Many have since moved on to intelligent process automation (IPA), which combines RPA with AI to improve more complex tasks such as tax and compliance reporting and financial statement reconciliation.

Finance departments are also exploring generative AI applications in areas like cash flow management, financial planning and analysis and fraud detection. However, one of the most transformative aspects of generative AI for finance is its ability to quickly and accurately explain the reasoning behind automated financial analyses.

Generative AI can produce meaningful narratives about predictions from these analyses and their implications, which financial professionals can use to compile insights in various formats — from bullet points to presentations to summary reports — tailored for different audiences.

In search of better decisions

Now that AI is becoming a fixture of many finance teams' operations, leaders are wondering how its involvement will fundamentally improve their decision-making processes. Although finance organizations are adapting AI to enhance operations and streamline processes, leveraging it for improved decision-making is still fairly new.

Leading finance organizations and teams that are experimenting with AI to drive better decisions are finding it helpful in a number of different ways:

• Using algorithms to automate data analysis, quickly handling large datasets to spot patterns and anomalies to improve the accuracy and efficiency of financial analysis
• Predictive modeling to examine historical data and other elements to yield more precise forecasts
• Using AI-driven cost optimization to identify savings opportunities by analyzing supplier relationships, market conditions and historical spending patterns
• Improved reporting with AI-driven dashboards and tools that deliver real-time insights into financial performance

As companies grow, their AI solutions can also advance, boosting agility with predictive analytics and external data like economic indicators for more precise forecasting. AI-driven scenario analysis can model different situations so users can understand the potential outcomes of various decisions and market changes.

Is your team ready for what’s next?

The current use of AI-powered solutions in finance does not guarantee smooth adoption of more advanced AI tools as they become available. To facilitate AI readiness, finance professionals and organizations may consider the following steps.

1. Define how and where AI will be applied. First take time to consider exactly what you want to accomplish with AI tools and how they can improve the work of your team. Moving too fast without evaluating solutions designed to meet your needs could result in a poor investment and unnecessary technical debt. Keep employees in the loop as you evaluate AI use cases. They will be the ones who see the opportunity to shape and adopt technology and can be powerful change agents.

2. Prepare a strong data foundation. “Garbage in, garbage out” is the reality of AI, so there is no question that data quality and integrity are vital. Before building and evolving your models, start with these critical questions:

• What type of data do we need?
• Where is that data located?
• Is it ready to use now, and can it scale?
• What data governance, privacy and security procedures do we have in place or need?

3. Assess the supply of available AI skills: Evaluate the current AI skills, knowledge and experience within your team or organization to determine if they align with your objectives for AI in the finance function. Using what you find out, plan for training and developing your teams and, in the interim, consider engaging skilled contract talent while your employees learn.

Skilled consultants can provide critical technology expertise, help implement and optimize AI tools and make your finance teams more future-fit.

4. Build, buy or partner? How to incorporate generative AI into finance operations depends on the desired outcome and available resources. Building your own infrastructure offers control and a competitive advantage but requires specific skills. Buying off-the-shelf generative AI technology is suitable for limited experimentation or addressing specific use cases. Partnering with an established AI company allows for quick scaling and access to expertise.

5. Establish an AI experimentation framework. Your AI objectives may inspire you to create a technological ecosystem conducive to AI experimentation and rapid learning. To leverage AI in developing novel products or services, consider forming a specialized team comprising engineers, software developers and other experts who can manage fast prototyping and iterative development processes.

6. Manage change. Introducing AI into the workplace can be disruptive. Lack of change management can lead to confusion, alienation of staff, low morale and underutilization of your technology investment. That's why it’s important to invest in people and processes to support the AI initiative specifically. Communicate a clear plan for implementing new AI tools, highlighting their benefits for employees and the business. Show commitment to continuous improvement and provide clear learning objectives and feedback mechanisms to ensure a positive employee experience.

Hiring for AI-powered workplaces

Generative AI advancements are revolutionizing finance teams' capabilities and productivity. However, AI remains a tool that requires skilled professionals with the finance expertise to wield it effectively. And that talent isn’t abundant right now.

Robert Half’s Demand for Skilled Talent Report found that while almost half (45%) of finance and accounting managers want to staff new and vacated positions, 85% are facing challenges finding skilled candidates in today’s competitive hiring environment.

Attracting talent and keeping in-demand professionals today means providing not only the ability to learn and work with emerging technology like AI but also a healthy work-life balance through options like flexible work. Hiring managers also report today's finance and accounting candidates are looking for competitive compensation, including compelling perks and benefits and opportunities for career development.

Employers may also want to consider engaging resources like specialized recruiters to find the finance talent they need for their teams, including high-potential candidates willing to be trained and passive job seekers — those currently employed but prepared to make a move for the right opportunity.