The hype around artificial intelligence (AI) in finance has been building for years, with promises of predictive analytics, automated processes, and unprecedented insights. However, the reality of AI adoption in finance teams is more nuanced. While some companies have made significant strides in leveraging AI to drive business value, others are still struggling to get started.

The pace of AI adoption in finance is indeed accelerating. A recent report by Gartner found that close to 60% of finance teams are piloting or fully implementing AI projects. However, only 7% of CFOs are reporting a strong impact from that investment. This discrepancy highlights the challenges that many finance teams face in effectively implementing AI.

Overcoming the Challenges of AI Adoption

One of the primary challenges facing finance teams is the complexity of integrating AI into their existing processes. This is particularly true for companies with large, decentralized systems and multiple stakeholders. According to Mohit Sharma, ACMA, CGMA, who has developed AI-powered solutions for finance teams, "the biggest challenge is not the technology itself, but rather the ability to integrate it with existing systems and processes."

To overcome this challenge, finance teams must first identify the specific pain points they want to address with AI. This could be anything from automating manual processes to improving predictive analytics. Once the goals are clear, teams can begin to explore the various AI tools and technologies available, from natural language processing to machine learning.

Practical Applications of AI in Finance

So, what are companies actually doing with AI in finance? One example is Pinaka AI, a startup founded by Sharma to address the common problem of late payments. The company's software uses AI to predict which customers will be late making a payment and the specific reason they appear likely to miss it. The algorithm can make predictions with 96% accuracy and provides actionable insights to help finance teams take proactive steps to resolve the issue.

Another example is CREW Network, a trade association connecting women in commercial real estate. According to Janice Stucke, CPA, CGMA candidate, who took over the finance department at CREW Network, "we needed to completely automate the entire infrastructure." The team used generative AI to transform the company's fragmented data into a unified chart of accounts, enabling them to automate systems and improve payment processing.

The Role of Data in AI Adoption

Data is at the heart of AI adoption in finance. Without high-quality, relevant data, AI models cannot learn and improve. However, many finance teams struggle to provide the necessary data, either due to technical limitations or data quality issues. To overcome this challenge, teams must first identify the data they need to support their AI initiatives. This could include customer data, transaction data, or market data.

Once the data is identified, teams can begin to explore the various data sources available, from internal systems to external data providers. According to Lawrence Amadi, ACMA, CGMA, who has worked with clients to transform their Subscriber Identity Module (SIM) systems, "the key is to create a unified and accessible source of data that can be leveraged by AI models."

Portfolio Implications of AI Adoption

So, what does AI adoption mean for portfolios? In theory, AI-powered predictive analytics and automated processes could help finance teams make more informed investment decisions. However, the reality is more complex. According to a recent report by KPMG, only 12% of CFOs believe that AI will significantly improve investment decisions over the next two years.

Despite the challenges, there are opportunities for finance teams to leverage AI to drive business value. One example is the use of AI-powered natural language processing to analyze large volumes of text data, such as company filings and news articles. This could help finance teams identify trends and patterns that might otherwise go unnoticed.

Practical Implementation of AI in Finance

So, how should finance teams actually implement AI in their portfolios? One approach is to start small, by automating manual processes or improving predictive analytics. Another approach is to explore the use of AI-powered tools and technologies, such as natural language processing or machine learning.

According to Sharma, "the key is to think in terms of finance, not technology. What is the return to my business? When does the solution break even?" By focusing on the business value of AI, finance teams can begin to make more informed decisions about how to leverage this technology to drive business results.

Actionable Steps for Finance Teams

So, what are the actionable steps that finance teams can take to start leveraging AI in their portfolios? One step is to identify the specific pain points they want to address with AI. Another step is to explore the various AI tools and technologies available, from natural language processing to machine learning.

Finally, teams should focus on the business value of AI, rather than the technology itself. By doing so, they can begin to make more informed decisions about how to leverage this technology to drive business results.