Preparing Your Finance Organization for the New Era of AI

by Robert Half and Protiviti, a Special Edition Newsletter contributor

Future-forward finance and accounting organizations were quick to embrace robotic process automation (RPA) years ago to manage mundane, repetitive back-office tasks like data entry and routine financial reporting. Many have since advanced to intelligent process automation (IPA) — RPA amplified with artificial intelligence (AI) — to streamline and improve more complex work, from tax and compliance reporting to financial statement reconciliation.

Even though AI capabilities have become a critical tool for many finance and accounting teams in their day-to-day operations, senior finance executives may still find it challenging to envision how AI will fundamentally change their decision-making processes. While finance organizations are increasingly turning to AI to enhance their operations and streamline processes, leveraging AI capabilities to improve decision-making is in the early stages.

That said, leading finance organizations are already experimenting with and, in some cases, using AI to drive more informed decision-making. Some ways they are applying AI in this manner include:

  • Automated data analysis using AI algorithms to quickly process large data sets, detect patterns and identify anomalies to enhance the accuracy and efficiency of financial analysis
  • Predictive modeling to analyze historical data and other various factors to provide more accurate forecasts
  • Cost optimization using AI to identify cost-saving opportunities, including analyzing supplier relationships, market conditions and historical expenditure patterns
  • Enhanced reporting using AI-powered dashboards and reporting tools that provide real-time insights into financial performance

As companies continue to evolve, so can their AI-powered solutions, further driving agility by instituting advanced predictive analytics and incorporating more external data sources, such economic indicators, to improve forecast accuracy. Scenario analysis may also leverage AI to model various scenarios to better understand potential consequences of different decisions and market changes.

Generative AI can provide the “why” behind financial analyses

AI’s integration into financial planning is reshaping how financial professionals can extract valuable insights from data, make more accurate projections, and optimize planning processes alongside business leaders. AI is a tool and not a replacement for finance professionals. And now, with generative AI entering workplaces, finance and accounting teams have even more opportunity to increase efficiency and accelerate their delivery of value-added work.

Generative AI a subset of AI that uses learning patterns and structures from existing data to generate original content, including text and images. McKinsey asserts that generative AI will “unleash the next wave of productivity” for businesses and their teams. And research from Goldman Sachs forecasts that productivity improvements related to generative AI could deliver a 7% boost in global gross domestic product, while increasing productivity growth by 1.5% over at least the next decade.

Recent advancements in generative AI have led to the innovation of powerful but accessible business tools like Salesforce’s Einstein GPT, Microsoft 365 Copilot[1] and BloombergGPT. The latter, for example, is an offering that Bloomberg says can “bring the full potential of AI to the financial domain” and create entirely new workflows, economic analyses and financial benchmarks for its customers. The large language model (LLM) is trained on the financial documents, terminology, trends and data that Bloomberg has collected for nearly a half-century.

Some finance organizations are already experimenting with generative AI in areas such as cash flow management, financial planning and analysis (FP&A), and anti-money laundering and fraud detection. But one way that generative AI is likely to be particularly transformative for finance functions is the speed and accuracy with which it can deliver the “why” behind automated financial analyses.

Generative AI can articulate the rationale and implications of these analyses by producing meaningful narratives about predictions and their implications. Financial analysts and others can use the technology to assemble insights in a range of formats — from bulleted lists to slide decks to summary reports — for various audiences to use for more accurate and timely business decision-making.

These are game-changing capabilities for finance organizations, which are under constant pressure to deliver better, smarter insights to the business faster. Being an early adopter of generative AI, machine learning, natural language processing and other rapidly evolving areas of AI can help your finance organization become more agile, as well as more effective.

However, even if your finance and accounting teams are well-versed at working with AI-powered solutions, your organization may struggle to derive value from more advanced AI tools if you don’t take appropriate steps to prepare. You could also end up overlooking or introducing critical risks.

Steps to achieve AI readiness include:

  1. Defining how and where AI will be applied

It sounds simple enough, but identifying where AI can provide the most value to the finance and accounting organization, and the broader business, is a critical step toward AI readiness. Remember when you had to identify initial use cases for RPA and IPA that you could try out and build on? That same thinking applies before deploying generative AI or any other new tool in your finance function.

Think about what you want to do and how you can improve the work of your teams, and then evaluate solutions designed to meet those needs. Moving too fast to grab the first shiny object you see in the AI landscape could easily result in a poor investment — and more technical debt the business doesn’t need.

Additionally, while eliminating tedious work and increasing efficiency are worthy goals, using AI to help your employees focus on more rewarding work and make the most of their best skill sets should be a top objective too. The employees in your organization who know your business well and see the opportunity to shape and adopt the technology will be powerful change agents as you navigate AI technologies and evolve your business.

  1. Preparing a strong data foundation

Everyone has heard of the “garbage in, garbage out” risk with AI — and there is no question that data quality and integrity are must-haves when working with the technology. However, there are several questions you’ll want to address to confirm you have the right data to build and evolve your models:

  • What type of data do we need?
  • Where is that data located?
  • Is it ready to use now, and is it scalable?
  • What data governance, privacy and security procedures do we have in place or need?
  1. Assessing the supply of available AI skills

What AI skills, knowledge and experience are currently present within your team or your organization? Are your employees’ skills relevant to the objectives you want to achieve with AI in the finance function?

Also, what are your plans for training and developing your teams to work with AI? In a recent Robert Half survey, 38% of U.S. workers in the finance and accounting field said they believe generative AI will have a positive impact on their career. So, there’s a strong chance many of your team members are ready to embrace upskilling opportunities around AI — they’re just waiting for the opportunity.

While your employees are learning new skills or you are searching for full-time hires with AI skills, you may want to consider engaging skilled consultants. These resources can provide critical technology expertise you need in the near term, help to implement and optimize AI tools, and assistance in making your finance and accounting teams more future-fit overall.

  1. Deciding whether to build, buy or partner

How you bring AI technologies like generative AI into your finance and accounting operations depends on what you want to do — and what you can do. For instance, building your own infrastructure for generative AI can provide a competitive advantage and give you more control. But it’s a heavy lift and it requires skill sets you may not have in-house.

Buying generative AI technology off the shelf can be an appropriate strategy if your aim is to experiment with the technology on a limited basis or if you have a specific use case that a proven product available in the marketplace can easily address.

Partnering with an AI company like Microsoft, meanwhile, can be the best route when you want to scale your use of AI quickly and want to leverage the best practices and expertise of a provider established in the space and committed to helping you succeed.

  1. Laying the groundwork for experimentation

Depending on your AI goals, you may need to set up a technology environment where you can experiment with AI and learn quickly from your successes and failures.

If your aim is to use AI tools to create new products and services for internal or external customers, you will likely need to assemble or engage a team of engineers, software developers and other specialists who can assist with rapid prototyping and iteration.

  1. Managing change

Introducing a new and transformative technology like AI into your workplace can be very disruptive. If change management is lacking, you risk creating confusion, alienating (and potentially losing) valued staff, undermining employee morale and, ultimately, underutilizing your technology investment.

Instituting a stable workflow environment around the technology can make the transition easier for everyone. That includes investing in people and business processes that can help your AI initiative be successful. Create and communicate a well-defined plan for how you intend to bring advanced AI tools into your organization and use them for transformation. Share details about when and how new tools will be implemented, what those tools are designed to do, and how those new capabilities will benefit employees and provide the business with a competitive advantage.

Most employees want to see that their employers are committed to continuous improvement and prioritizing investments that can help make the best use of everyone’s talents and reduce wasted time and work. If they understand what they need to learn, how they will learn it, and how the organization intends to measure success and collect and act on feedback, they are more likely to have a positive experience with the AI-driven change you seek to institute.

Be proactive in mitigating risks and seek cross-functional input

The new era of AI presents myriad opportunities and challenges for businesses and their finance and accounting teams. That’s why chief financial officers and other finance executives should be AI-curious, but also risk-aware. Taking a wait-and-see approach to AI means falling behind, while barreling ahead without a thoughtful foundation and strategy could lead to mistakes that set the organization back even further.

It’s also vital to recognize that for all the advancements we have seen with AI to date, it is technology that is still very new and untested in many ways. Consider, for example, that generative AI applications can produce “hallucinations” that result from factual mistakes in the source materials they use to develop answers. That’s why any content generated with AI must be evaluated to determine that it is factual. And if there are issues, they need to be investigated and corrected.

A final tip for AI success for finance leaders is to seek input and perspective from other groups in the organization when evaluating AI solutions. Your company’s technology specialists are an obvious choice, but also consider consulting with leaders from functions like internal audit and compliance.

Cross-functional input can help surface potential data security, data privacy, risk management, legal or compliance risks before you even bring advanced AI technologies like generative AI into your team’s workflows. This exercise will also help ensure that you can provide detailed answers to questions the CEO and board may have about AI and how your function is managing the risks and change related to working with the technology.

[1] Robert Half and Protiviti are members of the Microsoft AI Cloud Partner Program


Jason Flanders, global executive director, management resources practice, Robert Half

Christine Livingston, managing director, AI and Innovation practice leader, Protiviti

Andrea Vardaro Thomas, managing director, Protiviti

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