Most organizations will fail to realize the full value of new financial application purchases because they are not accounting for digital capabilities that they will require in the future, according to Gartner, Inc.

Gartner experts at the Gartner CFO & Finance Executive Conference discussed key drivers of financial application buying behaviour and the associated challenges of maximizing returns on new technology investments, as predictive analytics, artificial intelligence (AI) and machine learning capabilities will increasingly shape the needs of finance departments in the future.

“The criteria on which financial applications are being selected today largely do not reflect the future needs of these departments,” said John Van Decker, vice president analyst at Gartner. “Seventy-nine percent of financial applications buyers sought to improve efficiencies with their solution, while 59% were seeking better business outcomes. This suggests buyers are putting an overemphasis on simply improving their system of record with their solution, while not accounting for differentiation and transformation opportunities. Only 36% of buyers thought improving cost management was a reason to upgrade their financial applications, and just 9% cited driving revenue growth as a factor in their buying decision.”

According to Gartner research, upgrading financial applications solutions should result in a minimum total cost of ownership savings of 10% to 20%. Leading finance departments will seek to accelerate these efficiencies by driving cost management and revenue growth opportunities within their organizations. Financial applications leaders should seek solutions that will enhance their ability to innovate, rather than simply settling for efficiency gains.

Realizing the full value of technology investments

The purchasing of financial application software, including financial planning and analysis, and financial close applications, continue to be driven by ease of use, functional capabilities, flexibility and price. This suggests that buyers largely view the market as commoditized, with the result that the full value of these solutions is not being captured by organizations.

“We continue to see many organizations fail to go beyond the basic functionality of the solutions they purchase,” said Mr. Van Decker. “Some organizations see their solution as simply an upgrade on legacy technology and overpay for advanced functionality that they never utilize. In other cases, a lack of training users or not thinking through how the solution relates to their finance transformation strategy is to blame.”

To ensure a greater share of value is achieved with their solution, financial applications leaders should first define a project plan before implementation. More focused solutions that feature predictive analytics, AI/machine learning capabilities and proven acceptance outside finance for use in integrated financial planning capabilities should help drive the purchase decision.

Buying criteria should reflect future initiatives

Gartner research shows that financial applications leaders must support a range of new technology initiatives from predictive analytics to the ability to incorporate artificial intelligence and machine learning to enhance financial analysis. Forty-nine percent of financial applications leaders surveyed said predictive analytics was a top finance technology initiative, while 36% emphasized artificial intelligence and machine learning.

“When assessing vendors, it’s crucial to understand how they will incorporate AI and machine learning into their product, as we anticipate these technologies will be mainstream within three to five years,” said Mr. Van Decker. “While blockchain technology remains a lesser concern to today’s buyers, the ability to incorporate this capability should remain on buyers’ radar screens as it will impact core financial implementations and become foundational to the financial consolidation process within five to eight years.”

Before selecting a new solution, financial applications leaders should include their digital transformation strategy as part of the evaluation process. Ensuring that a solution’s roadmap includes capabilities such as predictive analytics and AI/machine learning capabilities will significantly improve future processes.