AI trends to watch in 2024

2023 saw the meteoric rise of generative artificial intelligence (AI) — the novel technology used to create new content and ideas, including verbal, such as conversations and stories, but also visual, like images and videos.

Here are the top trends shaping the landscape of artificial intelligence in 2024. From groundbreaking algorithm advancements to key ethical considerations, explore the key factors expected to drive AI next year.

Increased automation in reporting

Reporting requirements have traditionally weighed heavily on businesses, and the weight of this responsibility is constantly increasing. From navigating the escalating demands of ESG reporting to managing regulatory filings and non-financial disclosure, companies find themselves investing considerable time and resources into these traditionally labor-intensive activities. Over time, the reporting demands intensified, with reports becoming more complex and expanding both in scope and granularity of data needed for their generation.

Reporting activities comprise tasks that, although cognitive in nature, are still routine and repetitive, which makes them an ideal candidate for automation.

This is where AI steps in as a transformative force in this dynamic scenario. Businesses will more and more leverage the power of sophisticated AI systems to aggregate vast amounts of data and analyze it with remarkable speed and precision, to create comprehensive corporate reports.

For example, businesses operating across different markets are typically entangled in a complex web of cross-border financial transactions; collecting this vast amount of data from disparate sources spanning multiple markets and geographies with different accounting systems, currencies, and regulatory frameworks can be a challenge.

However, advanced machine learning algorithms designed for the automated creation of reports, spanning internal domains like sales, marketing, business intelligence, and supply chain, as well as external realms such as ESG and compliance, significantly accelerate the entire process. These algorithms not only process vast datasets but also recognise patterns, enabling quicker identification of trends and anomalies.

Changes to accounting systems

Automated preparation of financial statements

Historically, finance departments have dedicated substantial time to time-consuming, repetitive and error-prone activities such as data entry, transaction classification, and invoice processing. Financial data is typically dispersed throughout the entire organisation, residing in different ERP systems, internal databases and often even outdated spreadsheets.

The challenge is further compounded by the absence of standardized reporting practices across different jurisdictions, requiring businesses to navigate through variations in reporting requirements across each country. In such a setting, the manual preparation of accounting reports requires significant time and effort from accounting teams.

In 2024, a growing trend among companies will be the utilisation of advanced AI algorithms for the creation of financial statements to automate data collection, preprocessing, and report generation processes. These AI tools will be increasingly deployed to sift through disparate databases, collect required data, clean it and validate it to ensure that information in financial statements is not only accurate but also aligned with relevant standards and regulations.

With the complete automation of the process, modern accounting systems will be configured to provide not only periodic but real-time reporting, with embedded alerts that notify stakeholders when predefined thresholds of specific values are reached.

Automated financial statement analysis

AI will not solely serve to improve data quality and compliance; it will also drive financial analysis on an unprecedented scale and speed.

NLP models specialising in finance will be increasingly deployed to help companies not only streamline and expedite the development of financial statements but also conduct insightful analyses of these statements.

Data mining techniques will be more and more deployed to identify subtle patterns within financial data that might go unnoticed through traditional analysis methods to enable a more comprehensive understanding of the organisation's financial dynamics.

AI technology will continue to advance

While initially limited to structured data—information following predefined formats—AI capabilities are progressively expanding beyond these structured confines.

Advanced natural language processing (NLP) algorithms that can interpret human language empower companies to delve deeper and analyze unstructured data—information lacking a predefined format and organisation.

Unstructured data is a corporate treasure trove, as it accounts for as much as 90% of all enterprise data. Tapping into this vast potential can enable powerful data analytics that create more insightful and nuanced reporting, analysis, and business projections.

In 2024, companies will increasingly deploy techniques such as data scraping and text analytics to extract enterprise’s unstructured data from sources such as text files, customer feedback, emails, presentations, web pages and even audio and video files and images to automate financial and non-financial reporting and analysis.

Regulatory shifts

As AI becomes more enmeshed in business reality and our daily lives, it attracts increasing regulatory scrutiny. 2024 will see the introduction of key regulatory changes in major jurisdictions.

The EU’s AI Act with top-down prescriptive rules is in the final stages of passing. Often coined as the world’s first rules on AI, this act aims to ensure that AI systems are safe, transparent, traceable, non-discriminatory and environmentally friendly.

In contrast to Europe’s comprehensive AI legislation, the US is more likely to follow a decentralised approach comprising sectoral and state AI regulations.

Ethical AI and corporate governance

As AI continues to transform the business landscape, there’s a growing push for its use within an ethical and inclusive framework to ensure that the AI-driven future is more equitable and reliable.

In 2024, we will see continued focus on tackling ethical dilemmas and remaining vigilant for new ones that may arise.

According to a recent IBM study, 96% of surveyed business leaders deploying or planning to deploy generative AI are actively involved in shaping new ethical and governance frameworks.

Corporate governance will continue to play a key role in ensuring the ethical use of AI, emphasizing transparency and accountability in data processing and AI algorithms to prevent discrimination and unfair decisions. A distinct category of professionals known as AI ethicists will gain increased prominence as businesses tackle emerging ethical considerations associated with this transformative technology.

Risk factors in AI to watch for

AI stands apart from most other technologies by not just facilitating business decision-making but doing so within what is commonly referred to as a black box. The technology uses large datasets to identify hidden patterns that would often escape the human eye. However, fully understanding how AI arrives at decisions, let alone controlling the process, remains a significant challenge. With the increasing autonomy of AI systems in decision-making, it is essential to ensure that the mechanisms behind these decisions are fully comprehended.

Another risk factor is the potential for reinforcing existing biases. AI models learn from data, and the data is not created in a vacuum; instead, it reflects existing human decisions, which can be riddled with biases. When biased data is fed into AI models, AI systems amplify these biases, perpetuating systemic errors.

How HLB can help

In the dynamic world of AI, effectively navigating its complexities is not just a challenge but an essential prerequisite for businesses pursuing sustainable growth. HLB Global offers expertise to help businesses leverage AI effectively while mitigating risks and fostering regulatory compliance.