Organizations are adopting AI tools at lower rates than many predicted.
Generative AI took the world by storm in late 2022 with the launch of ChatGPT from OpenAI, which attracted 100 million users in just 60 days.
But how many of them actually apply it to work instead of just for fun? It seems that most users are just exploring the new feature. Enterprises are more cautious about integrating generative AI into their workflows.
In fact, of the more than 900 financial and accounting analysts, managers, directors, chief accountants, and CFOs surveyed by Deloitte and the Institute of Management Accountants (IMA) in early 2024, only 9% said they were currently using generative AI, and 8% of those were in the testing phase.
Three-quarters of those surveyed said they are at least a year away from adoption. Three-fifths (60%) said adoption of any tool would be at least two years away, with 38% saying they have no plans to adopt generative AI.
“While interest in both traditional and generative AI is growing, the pace of adoption is slower than expected,” Deloitte said. “Finance functions appear to be waiting for more specialized solutions or advanced technologies to become available for practical use.”
The biggest challenge experts identified was integrating generative AI with existing systems. Many said they had encountered similar difficulties in previous AI projects. Concerns about security, data management, and a lack of skilled workers were also raised.
When asked about future challenges, system integration remains the top issue. However, data management and staff shortages are becoming increasingly important.
The specific integration challenges will vary from organization to organization, the survey found. However, a common issue is uniformity in system architecture, related to data management. “This is related to the noted challenges of data inconsistency between applications,” the report said.
“Inconsistencies in data management across the organization lead to difficulties in implementing integrated solutions.”
Regarding the benefits of generative AI for finance teams, the top responses from survey respondents were increased automation, reduced manual work, and more efficient data analysis from generative AI.
What makes generative AI different?
There are important differences, especially for companies that are just getting started with traditional AI before the new generative tools come along. According to Deloitte and IMA in the report “Next-Generation Financial Control: Leveraging AI and Emerging Technologies to Transform Finance and Accounting,” the differences are:
Traditional AI — Technology is typically rule-based and provides output such as metrics, labels, or classifications without the ability to generate new content.
Applications: machine learning, natural language processing, virtual assistants, deep learning, predictive analytics, robotic process automation, speech recognition, and image recognition.
AI generative — Applications that generate new content across multiple modalities (e.g., images, audio, code, voice, video) that previously required human skills and expertise to create.
Applications: text/image/video generation, synthetic data generation, automatic content management or translation, chatbot, content creation, automatic education.
Deloitte believes that businesses are missing out on the full potential of generative AI. Generative AI can enhance predictive analytics, support decision-making, and free up employees to focus on higher-value work.
The application of traditional AI and generative AI (GenAI) in accounting and finance can vary by specific function. Survey respondents indicated that advanced analytics and intelligent systems, such as data science and AI, are most commonly deployed in financial reporting and financial planning analytics (FP&A) within the financial control function.
Other areas that are also leveraging these systems include controls and compliance, treasury management, general ledger and closing, and operational accounting.
Source: CFO.vn
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