Finance technology is crucial to streamline operations, improve decision-making and stay adaptive to business needs.
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Must-Have Technologies for the Finance Function
Ninety-two percent of CFOs plan to increase investment in finance technology this year, yet only 30% of technology projects succeed. Existing, complex and siloed legacy technology makes it difficult for finance to decide what finance technology to prioritize and which capabilities to invest in. CFOs responsible for developing a finance technology strategy, download our guide to:
- Reorganize your finance technology portfolio using a composable finance technology strategy.
- Align finance technology to ensure decisions around technology strategy, investment and deployment support the right objective.
- Champion a flexible finance technology governance.
How to select your organization’s must-have finance technologies
Consider these four issues in developing the technology roadmap and investment strategy for your finance function.
- Finance technology trends
- Finance technology roadmap
- RPA, AI and hyperautomation
- AI success in finance
Finance technology trends
The following strategic technology trends are having a profound and immediate impact on finance function 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. By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling.
Finance technology roadmap
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 No. 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 greater organization. It 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 No. 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 capabilities gaps the organization needs to fill. The tiger team also looks externally at the market for finance technology to identify emerging capabilities in which it should consider investing. With that information in hand, the tiger 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 No. 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. Use Gartner BuySmart™ to validate the prioritized list against external market parameters and best practices.
Step No. 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 No. 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.
RPA, AI and hyperautomation
Finance functions have been deploying robot process automation (RPA) for many years to automate simple, repetitive, judgment-free processes to improve speed and accuracy and free employees from mundane tasks. RPA uses software scripts with if-then rules to execute defined processes leveraging structured data. It has become ubiquitous in enterprises, particularly in the finance function, due to the relatively low cost and speed of deployment and reliable returns in the form of efficiency gains and cost savings.
Yet RPA also has limits for organizations pursuing hyperautomation. Simply put, RPA is designed for doing (task execution) and is therefore inappropriate for complex, dynamic processes that require analysis and judgment. Automating these types of processes requires artificial intelligence (AI). Also, each RPA deployment is stand-alone, designed to automate a specific process and not scalable beyond its original use case.
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. Unlike RPA, AI is not a stand-alone software or application but is embedded within newer finance function technology applications. These include intelligent business process management suites (iBPMSs), integration platform as a service (iPaaS) and decision management systems. AI’s value 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.
CFOs are commonly misled to believe that deploying new automation applications with AI will allow them to retire existing RPA programs. That is not usually true. New financial function technologies instead complement RPA. RPA solutions can also be enhanced to include AI and thereby progress toward end-to-end automation.
AI success in finance
AI adoption is still in its infancy as a finance function technology — three in four finance organizations that have deployed AI only did so in the past two years. And only 30% of finance functions using AI are considered “leading” users, meaning that their AI deployments moved as fast or faster and had as much or more impact than expected. Four actions set those AI-leading finance organizations apart.
Hiring new AI experts. AI-leading finance organizations are equally likely to hire new talent (32%), upskill current talent (36%) or borrow talent from the IT department (32%). Yet when we compare their approaches with those of non-AI-leading organizations, the latter are far more likely to try to upskill in-house talent (50%) and far less likely to look outside the organization for the talent they need (10%). Recruiting external AI-specific staff brings significant benefits, in particular an outside perspective that helps organizations think beyond entrenched processes and mindsets.
Buying new technologies with embedded AI. Eighty-four percent of leading AI users buy their AI embedded in applications, which allows for faster deployment across a broader set of use cases.
Experimenting broadly through early pilots. AI-leading finance organizations deploy twice as many pilots — a median of eight — in the first year as nonleaders. This practice establishes a culture of experimentation, as well as an understanding that some pilots will fail. It also allows the finance function to leverage the technology in more areas (e.g., for accounting processes and financial analysis).
Choosing a data-savvy AI leader. Eighty percent of AI-leading finance organizations put a data-savvy leader, such as the head of financial planning and analysis (FP&A) or the head of finance analytics, in charge of their AI deployments. In contrast, only 44% of nonleaders choose one of these roles to lead AI. Nonleaders also commonly assign the comptroller to the chief AI role.