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Rethinking Finance: Putting AI First

Rethinking Finance Putting Ai First

Rethinking Finance: Putting AI First

 

 


In order to spur greater levels of innovation and competitive advantage, CFOs are reinventing finance.

Adding AI to current processes could increase efficiency in the near run. On the other hand, implementing the technology piecemeal results in small improvements.


What if CFOs could totally rethink financial processes from the ground up, putting AI at its center and generating gains on a scale that surpasses productivity, rather than just trimming off inefficiencies at the edges? Putting artificial intelligence (AI) foremost in banking puts such an alluring prospect firmly within reach.

Take the financial close cycle time as an example. By moving to continuous closure, finance teams may make decisions more quickly since they always have access to the information they need, as opposed to waiting until the conclusion of a financial quarter. AI-first finance does exactly that it reimagines routine business activities. A strategic rethinking of the AI mentality is necessary for businesses to survive in the quickly changing digital ecosystem. Furthermore, utilizing AI in finance doesn’t mean implementing it mindlessly in every procedure.


Rather, the focus should be on applying artificial intelligence to finance operations and utilizing technology to make significant advancements when and when it can.

Making this change, nevertheless, is not without its difficulties.


Talent shortages and adoption barriers


CFOs may face the following obstacles if they choose to use an AI-first approach:


The difficulty managing the change required to achieve complete alignment; the weight of technological debt and antiquated infrastructure; the absence of people with training in artificial intelligence; the restricted access to high-quality, clear, and accessible data; and the uncertainty around security, compliance, and regulatory requirements
In fact, respondents from finance functions state that “difficulty in finding, training and retaining AI talent” (34%) and “lack of data quality or strategy” (34%) are two of the top three barriers to the adoption of gen AI, according to a study done in partnership with HFS Research of 550 senior executives on their generative AI adoption.
leaders in finance are tackling these challenges head-on.

For instance, they’re enlarging their R&D departments, investing in internal staff upskilling, and forming alliances with specialized suppliers in an effort to close the talent gap.


Furthermore, according to 40% of finance executives surveyed on gen AI, they are working with tech companies that specialize in gen AI solutions to develop and improve their own technological skills.

After resolving these issues, CFOs can begin implementing their AI-first strategy by concentrating on four crucial areas.

The Four Foundational Elements of AI-First Fintech


1. Empowering data


AI is fueled by discoverable and accessible data. But in the absence of a single source of truth, data can become compartmentalized, resulting in several versions that are inaccessible to even internal business departments.

It is essential to prioritize data management because of this.


Data must be continuously parsed and sent to the appropriate systems for both structured and unstructured data. For example, freight accruals may now be accounted for on a shipment basis as soon as products leave the warehouse, rather than on a monthly basis, with adjustments for leakages on goods received vs invoices received, thanks to AI’s ability to swiftly access precise data from across the business.

In the past, in order to obtain the necessary data, the finance team would also need to execute SQL queries or other database instructions. Data accessibility has increased since users may now more readily retrieve information by typing a request in plain English.


It’s crucial to set up sound business procedures for master data governance, ethics, and compliance to support these advances. If not, businesses could run the danger of facing legal and regulatory problems, data breaches, and reputational harm.

2. Scalable technology


In AI-first finance, a strong technical infrastructure is critical.
A perfect tech stack consists of these four fundamental layers:


• A system of engagement (SOE) to facilitate automation; a system of record (SOR) for keeping routine transaction data; an insight system (SOI) for data analysis and actionable insights; and a system of orchestration (SOO), which oversees all other systems to promote their harmonious operation.

Almost every organization, when examining the four layers, will have a SOR; nevertheless, it is highly possible that the SOO has been disregarded.

An AI-first approach blurs the lines between these four layers by helping each layer to work smarter and more seamlessly together.
For instance, Copilot in Microsoft Excel can automatically handle transaction matching and financial reconciliations. It can also send follow-up emails or messages directly from the spreadsheet and manage workflows using Power Apps, a separate application a prime example of SOE, SOI and SOO all coming together.


3. Algorithm-driven operations


In AI-first finance, every finance operation boils down to an algorithm, a set of rules or instructions, that integrates into the larger tech ecosystem. Every business challenge can be turned into a predictive task that algorithms can address by forecasting solutions.

For example, warranty accruals involve estimating the number of claims a company expects to receive. By predicting the likely number of warranty claims, the company can set aside appropriate funds. These accruals are then recorded in the financial statements at the end of the month.
CFOs must embed AI-powered algorithms to improve finance operations and outcomes.


4. Human oversight for responsible AI


Results from AI are not binary. When AI offers an answer that gives a confidence value of 94%, it means it needs oversight or improvement by people with the right knowledge and context.
Having humans in the loop is as important as ever, regardless of confidence score especially in finance, where faulty or biased AI decisions can lead to severe outcomes.

As much as enterprises work to develop bias-free algorithms, humans help make sure AI-based decisions reflect the values of the company and are equitable, just and accurate. We must look at the impact of AI on the existing control environment and consider what shifts are needed, as well as the ways those controls are embedded in different processes.


5. Unlocking Exceptional Outcomes

AI-first finance is already reimagining established practices. Zero-touch processing with zero time to close, zero time to insights and zero exceptions are entirely within sight. And reconciling monthly statements can be a thing of the past for enterprises that have rethought their close cycle times with AI.


By focusing on continuous learning, experimentation and adaption companies can harness AI’s full potential by embedding it into finance operations, processes and decisions And those that do are realizing unparalleled results.

 

 


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