2 Game-Changing Gartner-Recommended Frameworks to Transform Your Order to Cash

Automation

Gartner considers automation a primary enabler for an Order to Cash process transformation. Most Order to Cash processes require a high degree of human intervention. Enterprises remain manual in extracting credit data from multiple credit bureaus, tracking invoice payment details on customer portals, and closing the open invoices by matching them with the corresponding payments. Automating these low-impact manual processes is the first step to transforming the order to cash function.

Things to consider when adopting automation

Typically when shared services and GBS leaders plan to automate, they think of investing in Robotic Process Automation (RPA) technology. While RPA might be a good starting point for automating processes, an essential thing to understand is its limited capabilities to drive scalable results. Deploying RPA for business operations is not a one-time exercise and requires significant IT support to upgrade the RPA bots based on changing business requirements.

As a result, the Gartner report primarily focuses on application software with RPA capabilities available as out-of-the-box offerings – that means the internal IT teams don’t have to invest their bandwidth in managing the RPA bots. 

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Artificial Intelligence (AI)

The Gartner Magic Quadrant report claims that embedded Artificial Intelligence (AI) in integrated O2C applications will automate over 50% of business decision-making within the O2C process by 2023. AI leverages financial transaction data and analyzes the trends to share recommendations and predictions with leaders, helping them improve their decision-making quality.

Things to consider when adopting AI

Shared services and GBS leaders need to remember that deploying AI across their operations requires three broad steps.

Step 1: Having access to data libraries

The availability of financial transaction data from leading big data companies like – HP Enterprise, IBM, and Teradata makes it easy for business analysts to build and enhance Machine Learning algorithms.

Step 2: Training ML algorithms to act on financial transaction datasets

For ML capabilities to help improve decision-making quality, data analysts need to develop algorithms trained on these large financial transaction datasets. This enables the machine to understand data trends and develop AI-powered intuitive insights critical to making informed decisions.

Step 3: Pairing AI Skill Sets with Subject-Matter Experts in the Organization

Implementing AI generally starts with data scientists looking at variables across datasets critical to making significant business decisions. Most organizations believe that onboarding the right AI skills will help them successfully execute AI capabilities. To drive real-world AI use cases across global order to cash operations, it is essential to leverage insights from internal subject matter experts and guide the data analysts and data scientists to understand the complex business requirements. The Gartner report lists down technology vendors that help enterprises with the above prerequisites like – access to extensive data libraries, training the machine on trillions of dollars of financial transaction data, and collaborating data engineering and domain expertise to make accurate business decisions and improve cash flow.

Analytics 

Gartner says predictive analytics capabilities form a robust backbone to execute daily A/R operations. From extracting key insights across high volumes of customer data to presenting the data in a more consumable manner via pre-built dashboards – advanced analytics tools in accounts receivables significantly improve executive visibility over global A/R health.

Things to consider when adopting advanced analytics

Advanced analytics should allow O2C teams to measure the impact of digital transformation initiatives periodically. The analytics dashboards must provide global business leaders with a 360-degree view of the critical leading and lagging indicators across the core A/R process to strategically optimize process performance.

Assistants (Digital Assistants)

Gartner expects digital assistants to be a standard feature across all Order to Cash processes over the next two years. Alexa and Siri – Amazon and Apple’s voice-activated digital assistants are convenient tools in the B2C space powered by a large volume of data. Digital assistants based on Natural Language Processing (NLP) capabilities in the B2B enterprise world are slowly evolving to provide similar insightful experiences for O2C teams by analyzing the end user’s intent.

Things to consider when adopting digital assistants

Digital assistants could add value to critical O2C processes such as credit and collections by automating low-impact activities like transcribing live calls, drafting dunning correspondences, and recommending action items for later. They will enable teams to allocate their bandwidth towards more strategic decision-making tasks like credit limit upgrades.

Application Programming Interface (APIs)

Gartner believes prebuilt Application Programming Interface (API) is an integral component of modern O2C solutions. APIs act as the building blocks of modern cloud-based infrastructures enabling interaction across multiple business applications that need to work together.

API capabilities enable data to be exchanged between an organization’s ERP and CRM tools, accounts payable (A/P) portals, and trade promotion management (TPM) systems to provide an integrated experience of different processes interacting in tandem with each other.

Things to consider when adopting APIs

Enterprise O2C applications with prebuilt APIs enable seamless integration and flow of A/R-related customer data across ERPs like Oracle Netsuite and TPM systems like SAP TPM.

Gartner’s Framework For Better Business Decision Making

Given the challenges and potential adverse outcomes and impact of challenges posed by market volatilities, one might think that undertaking a new digital transformation project would be out of the question. Digital transformation seems impossible with limited resources, budget cuts, furloughs, etc. But some thinking on this, and you would realize that digital transformation in this environment is not only possible but also very likely to be successful if planned in the right way. In an uncertain economy like the one we are in today, you can’t afford to make wrong decisions, and it becomes imperative that you consider a strategic approach to vet your decisions before finalizing them. To build this strategic approach to decision making, Gartner has built a framework.

Under rapidly evolving market conditions, you can’t really say what is right and what is not just by looking at one or two factors. Gartner’s Cost Optimization Decision Framework allows A/R teams to make the right choice in this tough economy, thus, helping them Sustain, Recover, and Thrive. Use this framework to evaluate how every decision you’d make would fare against what should be your top consideration right now. The parameters against which you need to evaluate any new decision include the potential financial benefits of the decision, overall business impact, time requirement, degree of technical and operational risk, and investment requirement. There are multiple levels in which a decision would impact each one of these criteria, but the most ideal and optimum choice would be the one making way for large potential benefits, positive overall impact, has minimum time requirement, low technical risk, and operational risk and minimum investment requirement.

How to Use Gartner’s Decision-Making Framework in A/R Digital Transformation 

Let’s take the example of credit management automation. Whether it’s onboarding new customers or setting up periodic reviews or gathering data from credit agencies to assess credit risk, credit management if done manually is tedious and prone to errors. Automating credit management not only speeds up credit application processing, but it also provides real-time credit risk monitoring with simplified credit scoring, and segment the customers to automatically assign credit limits for low-risk, high-volume customers. In order to better understand this, let us fare against this decision framework and see how it can improve the efficiency of your credit team. Automating the credit management process has medium finance benefits, as it helps reduce the costs of paper applications. The overall business impact is positive, and the time taken to deploy is minimal. There is little to no operational and technical risk. Also, with HighRadius, you would be working with subscription-based modules, so there is no heavy licensing fee, but only an annually recurring subscription fee.

Conclusion

Several B2B enterprises run essential finance functions like accounts receivable, accounts payable, and inventory management in silos. Most of these occur due to disparate ERP systems and a lack of cross-team collaboration. The Gartner-recommended 5A Technology Framework will allow order to cash teams to eliminate low-impact repetitive work like document retrieval from customer portals, improve the quality of decision making with AI performing tasks like prioritizing which customers to follow up with for dunning, and providing real-time visibility to GBS leaders over global O2C KPIs for proactive course-correction.

HighRadius equips global A/R teams with an autonomous finance solution that addresses the limitations of traditional O2C software by leveraging data and advanced technologies, which has led to its recognition as a ‘Leader’ for the second year in a row in 2023 Gartner® Magic Quadrant™ for Integrated Invoice to Cash Applications. The report placed HighRadius in the top-most quadrant, with the highest score across ‘Ability to Execute’ and ‘Completeness of Vision.’