Optimize and Prioritize Your Finance Technology Investments

Streamline operations and enhance decision making with a finance technology plan aligned to business needs.

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Gain insights into the cutting-edge finance technology strategy that is transforming the finance function.

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Define your finance technology strategy to drive greater agility

Ninety-two percent of CFOs plan to increase investment in finance technology this year, yet only 30% of technology projects succeed.

Download our white paper and discover how to:

  • Break down traditional technology silos and reduce portfolio complexity 

  • Ensure your technology strategy, investment and deployment decisions support the right objectives

  • Adopt a forward-looking, flexible finance technology and governance strategy that better meets the needs of the business

What CFOs need to know about finance technology

Understand the finance technologies that are transforming the finance function.

Today’s finance technology trends prioritize agility and adaptation

The following strategic technology trends are having a profound and immediate impact on finance technology investments because they enable fast adaptation to changing business conditions.

  • Cloud-native platforms. Organizations are moving to cloud platforms for all major finance applications, including core financials, financial planning and analysis, financial close, and ancillary financial value chain solutions. By 2025, cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives — up from less than 40% in 2021.

  • Composable applications. The ideal finance function technology portfolio is modular, allowing finance team users to acquire, assemble and configure different application capabilities in personalized ways that facilitate their work. Through 2024, 50% of financial application leaders will incorporate a composable financial management system approach.

  • Hyperautomation. Finance functions enable hyperautomation — an approach of rapidly identifying, vetting and automating as many business processes as possible — using an orchestrated combination of multiple technologies, tools or platforms. These include AI, ML, RPA, intelligent business performance management suites (iBPMS), low-code tools and others. By 2024, organizations will reduce operational costs by 30% by combining hyperautomation technologies with redesigned operational processes.

  • Decision intelligence. Understanding how the organization makes decisions requires breaking down the decision-making process and aligning each component with a standardized framework. These components are, in turn, aligned with a set of technologies and techniques to support modular automation and continuous improvements.

Make the most of finance technology opportunities with a proven process

A technology roadmap that includes well-defined opportunities and implementation timelines sets the finance function up to deliver new capabilities to drive efficiency and effective decision making. Follow this five-step process to create the finance function technology roadmap:

Step 1: Prepare to build the roadmap

Lay the groundwork for the roadmap by documenting the key participants from finance, IT and procurement and by creating a finance technology roadmap “tiger team.” This team documents the project’s objectives and expected outcomes and how they align with the goals of both the finance function and the broader organization. The team also inventories the existing technology capabilities and maps them to key finance processes to define the current state of finance digitalization in the organization.

Step 2: Identify opportunities

Using the technology capabilities inventory, the tiger team assesses the effectiveness of the organization’s current finance technology capabilities to identify systems in need of upgrading, as well as capability gaps the organization needs to fill. The tiger team also looks externally at the market for finance technology to identify emerging capabilities in which finance should consider investing. With that information in hand, the team engages the finance function and IT leadership to create a full technology opportunities list informed by functional needs and in-house IT priorities.

Step 3: Select technologies for investment

The full list of finance function technology opportunities will be longer than the organization can pursue at one time. In this step, the tiger team evaluates and prioritizes finance technology opportunities using a standardized set of value criteria, success factors, risks and inhibitors to calculate their relative business value. Then the team can use Gartner BuySmart™ to validate the prioritized list against external market parameters and best practices.

Step 4: Create and communicate the roadmap

Set timelines for implementing the final set of prioritized finance function technologies. Assign stakeholders who will be accountable for each technology project. Document key elements of the process and the ultimate roadmap, and share it with constituents in finance, IT and the broader organization.

Step 5: Monitor progress against the roadmap

Even with a roadmap in hand, the process is not over. Finance teams continue to monitor progress toward realizing the existing finance function technology roadmap, while also monitoring the current state of technology in the organization and the emerging technology market to identify systems ripe for retirement and adoption.

Avoid hurdles, drive faster results and achieve significant returns using AI in finance

Artificial intelligence (AI) is an umbrella term that refers to technologies that apply advanced analysis and logic-based techniques to interpret events, support and automate decisions, and take actions.

AI is not a stand-alone software or application but is embedded within newer finance function technology applications. Robotic process automation (RPA) (see tab 4) solutions can also be enhanced to include AI and thereby progress toward end-to-end automation.

The value of AI in finance comes from improving the finance function’s ability to predict, analyze and uncover important patterns from unstructured data, and thereby automate work, make informed decisions, compute large quantities of information (including unstructured data) and avoid risk.

Progressive CFOs position finance for AI success by implementing the AI-Forward Framework — a structured approach to adopting AI in finance. The framework includes the following components:

  • Skills. Developing an organizational AI competency is the only way that financial professionals can responsibly meet their obligation to validate the integrity of financial statements when using AI to automate transactions and make decisions. Leading organizations make progress faster by hiring data scientists dedicated to finance — and then training existing staff in the basics of data science to establish a citizen data science competency.

  • Data. When using AI in finance, data is a valuable asset. Large volumes of higher-accuracy data help to build highly effective and reliable AI models. Surprisingly, cleaning historical data is not the approach favored by leading companies. Instead, leading finance organizations prioritize defining business drivers and automating the collection, correction and distribution of data. 

  • Software. New types of software vendors are building intelligent applications that use AI in finance to solve common problems. Our research shows that leading AI finance organizations are 71% more likely to purchase software platforms with embedded AI features. A growing number of vendors are differentiating themselves by building intelligent finance applications that natively leverage AI in common back-office processes like accounts receivable, accounts payable and audit.

  • Organization. AI can perform tasks previously reserved for people. In the future, an autonomous finance organization will require organizational changes that reflect the reliance on machines to do work, but today AI-forward organizations require only small, but meaningful, changes to the organizational structure. AI efforts championed by CFOs are nearly twice as likely to succeed than if the CFO takes a passive role. With the right executive support, team members recognize AI as an organizational strategy and are more likely to embrace necessary changes.

Gain efficiency across the function by leveraging robotics in finance

Enterprises have been deploying robotics in finance for many years to automate simple, repetitive, judgment-free processes, improve speed and accuracy, and free employees from mundane tasks.

Robotic process automation (RPA) uses software scripts with if-then rules to execute defined processes leveraging structured data. RPA can have a huge impact on the finance function, as much of the work that finance teams do (e.g., building reports, making entries, comparing actuals with forecasts) follows clear process steps and a defined set of rules. Programming a robot, or team of robots, to undertake this work can eliminate the risk of human error and, in many cases, complete the task in half the time it takes a human.

RPA does have limits. RPA is designed for “doing” (task execution) and is therefore inappropriate for complex, dynamic processes that require analysis and judgment. Also, each RPA deployment is stand-alone, designed to automate a specific process and not scalable beyond its original use case.

However, for most organizations, the use of RPA for finance is a cost-effective way of improving accuracy and increasing productivity, which is central to finance’s long-term strategy. And unlike traditional finance IT solutions such as an ERP installation or upgrade, implementing RPA doesn’t require a “standardize then automate” approach. In fact, RPA can automate individual tasks or components within a process that make the overall process more efficient.

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FAQ on digital technology in finance

Technology is having a profound impact on the finance function. Some of the most compelling finance technologies include: 

  • Cloud-native platforms
  • Composable applications
  • Hyperautomation
  • Decision intelligence

Among the many benefits of using RPA and AI in finance is the ability to automate repetitive activities. RPA is a proven and ubiquitous tool for driving efficiencies and increasing productivity in the finance function. Complementing RPA with AI for finance takes automation to the next level in which entire processes, not just tasks, are automated.

Selecting the right AI and RPA finance solutions can be challenging — and many employees regard AI as an employment threat. Progressive CFOs work with IT to build integrated technology roadmaps, design processes and assign tasks so that people and machines perform the work that best matches their strengths.

Drive stronger performance on your mission-critical priorities.