Implement AI for Finance - Exploring Barriers and Benefits Part One - BARRIERS

01 Barriers to AI Solution Implementation for Finance

The implementation of AI technology in the finance department of an organization is poised to bring transformative changes. AI's impact will primarily be seen in the automation of routine and repetitive tasks, such as data entry, invoice processing, and financial report generation which would be significantly accelerated and made error-free by AI systems. This not only enhances operational efficiency but also allows finance professionals to focus on more strategic tasks, and realign workloads to take on additional projects that increase the value and impact of finance to the organization as a whole.

Moreover, AI will play a pivotal role in financial decision-making through predictive analytics and prescriptive recommendations. By analyzing historical data, AI forecasts future cash flow trends and proposes actions to take for greater outcomes, enabling better financial planning and risk management. This aspect of AI would be particularly beneficial in making informed decisions about budget allocations, financial strategies, and enabling workflows to pivot with changing conditions.

AI technology will improve the finance department's interaction with internal and external stakeholders. By automating and personalizing communication, AI enhances customer and vendor relations. Automated payment reminders, managed inboxes, personalized communications, and financial reporting are examples of how AI streamlines interactions in accounts receivable and payable.

In this blog series, we’ll explore the barriers and benefits of implementing AI solutions for the finance function in three parts, first discussing the barriers in Part One, then exploring the benefits in Part Two, and in Part Three we’ll talk about other use cases for AI in finance.

Part One

Barriers to AI Solution Implementation for Finance

TIme and Efficiency

The integration of AI within the corporate finance function presents challenges such as the need for data quality management, system integration complexities, and the requirement for skill development among finance professionals. As AI systems require high-quality data to function optimally, organizations must invest in robust data management strategies.

Additionally, the workforce will need to adapt to the changes AI will bring, requiring training and upskilling to effectively utilize AI technologies in their roles. Outlined below are several of the key challenges many organizations face when considering AI–based technology platforms.

Lackluster Data Management 

AI systems require large, diverse datasets for optimal performance, which may not be readily available in all industries. Data may also reside in disparate and siloed systems, making it difficult to aggregate and analyze. Ensuring clean, well-structured, and representative data is critical for training accurate models and high-quality, unbiased data must be in place for a successful AI implementation to be undertaken​​.

01 Lackluster Data Management_

Unreliable Results and Lack of Trust

AI may produce unreliable outcomes due to biased or incomplete datasets and algorithmic limitations. Deployment of AI systems that are inaccurate, unreliable, or poorly generalized to data and settings beyond their training creates and increases negative AI risks and reduces trustworthiness. 

Unclear Goals, ROI, and Technical Difficulties

As AI is a developing technology gaining increased attention and adoption, the goals for implementing AI and tackling technical issues such as data storage, security, and scalability remain vague and vary widely. In addition, software implementations may require significant changes to current IT infrastructure and business workflows.

Historically, finance departments often resist change, even lagging behind their counterparts in other areas of the enterprise, so unproven technology must provide convincing proof of its value and return on investment (ROI) to earn the trust of accounting and finance teams.

01 Bias in Algorithms

Bias in Algorithms

AI algorithms are susceptible to biases if the training data is not diverse or representative. This may lead to discriminatory outcomes, such as biased decisions. It is crucial to ensure that training data is unbiased and that models are regularly monitored and audited to identify and mitigate any potential biases. 

Complex Regulations and Compliance

Financial data is highly regulated, and implementing AI technology in the finance office must comply with regulatory requirements. Navigating complex and evolving regulatory landscapes is a significant challenge for businesses, especially in balancing innovation with responsible AI deployment​​.

01 Complex Regulations and Compliance

Legacy Software and Outdated Infrastructure

Adapting legacy systems to AI is cumbersome due to fundamental differences between traditional software and AI-driven models, requiring significant reengineering efforts​​. Legacy applications and platforms often rely on outdated protocols and interfaces incompatible with modern technologies, hindering efforts to create a cohesive digital infrastructure. Outdated infrastructure and the high costs associated with a new solution implementation that includes personnel and equipment upgrades could become significant barriers​​. 

01 Legacy Systems

Specialist Talent Shortage

The demand for specialized AI talent has intensified, but the supply of skilled professionals is not keeping pace, hindering the ability to effectively harness AI technologies​​. According to a McKinsey report, only 10% of the world's data scientists have the skills required for AI-related work. Because data is the foundation of every AI implementation, roles centered around data analytics skills are most in demand. 

According to 84% of CFOs continue to face significant talent shortages. The data says nearly half (47%) of CFOs believe employee burnout around hours and menial tasks, as well as accounting and finance employees changing careers, are notable factors in the quickly evaporating talent pool.

eBook - AI Governance - Transparency and Reliability

Workforce Impact

AI and automation technologies will transform jobs, necessitating workforce upskilling and retraining to adapt to new roles that require uniquely human skills​​. As AI takes over routine tasks, the demand for professionals with skills in AI management, data analysis, and strategic decision-making will increase. 

Organizations will need to invest in change management and training programs to help their workforce transition into new AI-enhanced environments and finance professionals will need to adapt and acquire new skills to remain relevant in this evolving landscape.

AI Availability in Different Countries

Variations in AI implementation across countries due to differences in resources and technological development also pose challenges​​. As an emerging technology, AI is still being tested, trained, and regulated by various governments and regulatory entities, and will take some time before a global, unified, and standardized governance is in place.

01 Unreliable Results and Lack of Trust

AI for Finance Not Prioritized

According to a recent Gartner report, finance leaders most frequently cite “other priorities” as the reason their function does not use AI. Because of their risk-adverse nature, finance professionals tend to be late to embrace emerging tech, and this is proving true for artificial intelligence-based solutions as well.

Read on:

Part Two - Benefits of AI-Driven Technology for Finance

Part Three - Additional Finance Use Cases


Source Files:

The Current State of AI Use Within Finance: 2023 Insights, 4 October 2023 - ID G00799199 - Gartner, Rajat Pandey, Marco Steecker

Quick Answer: What Are the Quick-Win Use Cases When Selling GenAI to Finance Leaders? 5 January 2024 - ID G00803754 - Gartner, Garrett Astler