Newsletter for November 2023

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Featured Article

Risks and Benefits of Integrating AI Into Your Business

By Colin Wenngatz, MBA, Jason J Lee and Jeremy Beltgens, CPA, CA

In an increasingly digital and automated world, you’re facing more pressure than ever to reduce costs and improve efficiency with the digital tools at your fingertips. Every day, new players are entering the AI market, forcing organizations and finance departments to navigate through an ever-expanding array of technologies and vendors to choose and/or implement the tools that suit their needs.

Recent advancements in artificial intelligence through generative AI programs such as ChatGPT and Azure OpenAI have brought AI to the mainstream, further increasing its visibility, and highlighting a significant number of use-cases that have broad applicability.

AI is very good at specific activities such as pattern recognition, anomaly detection, bulk output, context-sensitive predictions, summarization and translation. Combining these strengths enables AI to generate novel outputs that seem to hold limitless potential. It’s imperative, however, to acknowledge the risks and limitations of AI programs and the impact they can have on your business. Understanding both the potential benefits and risks is key to implementing a successful AI strategy.

A primary area of concern revolves around the growing threat of data breaches and cyber-attacks. As businesses and organizations increasingly rely on cloud-based storage solutions, the integration of AI-powered systems opens the door to vulnerabilities susceptible to hacking and manipulation, thereby amplifying security risks.

There are no quick fixes, and all decisions should be made with a focus on specific business requirements. First and foremost, it's crucial to fully understand the problem and then align it with the appropriate technology.

It’s imperative to acknowledge the risks and limitations of AI programs and the impact they can have on your business.

Defining AI

Traditional artificial intelligence focuses on utilizing data to automate processes and make predictions. Although these systems can learn and make decisions, that ability does not extend to generating anything new. On the other hand, Generative AI takes AI systems one step further by layering on the ability to create something altogether new – for example, what users now see with ChatGPT. This ability to generate something new has led to much broader applicability and an unprecedented adoption rate by the general public.

AI Benefits

When leveraged appropriately and with a rigorous governance program, AI has immense potential to improve quality, streamline workflows and help businesses run more effectively. It also enables existing team members to spend less time on mundane repetitive tasks and refocus that effort into value-added and challenging tasks.

AI, particularly generative AI, can significantly enhance the efficiency of finance teams and professionals. When integrating AI into a financial setting, it's often more appropriate to view many AI applications as tools for augmentation rather than full automation. If AI can handle 70 to 80 percent of the workload or provide valuable insights, it typically places you in a better position than if the task were performed entirely manually. In today's environment, most AI systems still require finance professionals to interpret the analysis or finalize the results. This judgment and expertise remain crucial steps in the process.

Opportunities for implementing AI include:

·????? Time savings: AI can increase efficiency by taking on time-consuming tasks that would typically be done by a human. Generative AI can summarize large bodies of text into easy-to-understand language in seconds, can explain complicated topics in a simple way, can help with quick brainstorming, or complete financial workflows.?

·????? Cost savings: By enabling AI technology to help with these time-consuming tasks, businesses can allocate resources more efficiently. This includes using AI tools while performing financial analysis, identifying anomalies, journal entry testing and other more repetitive tasks. While AI systems can have material costs for implementation, that often pales in comparison to the associated benefits.

·????? Enhance creativity and innovation: Generative AI helps users think outside the box through idea generation, divergent thinking, problem-solving, and collaborative opportunities.

·????? Digital Assistants: The use of digital assistants continues to gain popularity, and can be effectively applied to specific situations within your business. Examples include responding to more common questions around month-end tasks or completing simpler AR or AP functions.

·????? Clearer communication: Generative AI can provide summarized or simplified versions of complicated information or data sets in seconds, enabling you to share information with a wider audience for increased exposure and understanding.

While AI systems can have material costs for implementation, that often pales in comparison to the associated benefits.

Risks

When implemented correctly, AI can multiply the output of finance teams and organizations; however, if not carefully considered or implemented incorrectly, AI can have the opposite effect. Within the finance or accounting industry, it’s imperative to consider these risks before jumping in:

·????? Privacy concerns: As with any application, it's vital to understand how the data you’re incorporating into the AI system is being utilized. This includes issues related to privacy, data residency, etc. We are beginning to see many European countries investigating ChatGPT’s data storage system, with Italy outright banning the program over alleged privacy violations related to ChatGPT’s collection and management of data. Canada has also introduced The Artificial Intelligence and Data Act (AIDA), which is proposing legislation on the design development and use of AI as early as 2025. Before uploading any of your organization's data into these public systems it is crucial to consider what is being shared and limit or prohibit any proprietary information from the organization being placed into the environment. Many organizations are taking strong stances on the use of systems such as ChatGPT as it can be difficult to define when it’s appropriate or not to share or query a particular topic. As a response, organizations are developing their own Large Language Models (LLMs); however, this also comes at a cost and requires domain expertise.??

·????? Plagiarism, factual inaccuracies, and copyright issues: While Generative AI is a useful tool for research and creative writing, as with taking from another person’s work, it’s important to source the information and clearly state that some or all of it came from generative AI. Text and other information provided by ChatGPT and other programs must be edited to ensure there are no incorrect statements, manufactured information, or spelling or grammatical errors. Questions remain over who owns the rights or intellectual property for AI-generated content.

·????? Lacking governance: Organizations must have a structure in place to manage the use of AI technologies to set out best practices and prevent abuse.

·????? Rushing to market: Many companies (OpenAI, Google, Microsoft, etc.) may be rushing to market before the product is safe in order to preserve market share and keep up with the competition.

·????? Legal responsibility: It’s still unclear who is legally responsible for actions taken based on information and feedback provided by AI technology. For example, if your client makes a decision based on your AI chatbot with negative outcomes, would they sue you, or the AI manufacturer?

·????? Blackbox systems and false positives: Many off-the-shelf AI systems fall into the category of blackbox, with a level of mystery that can create additional work for finance professionals as they navigate their understanding of outputs and/or false positives. However, many of the leading-edge vendors are demystifying their systems and adopting an explainable AI approach. Demystifying these systems will only enhance adoption rates and lead to better implementations.

Other Considerations

Hallucinations: Hallucinations can occur when a Generative AI model becomes convinced of incorrect facts and responds to questions with made-up answers. Such hallucinations are more likely to occur when prompts are overly vague, which highlights the importance of asking clear and defined questions. The process of structuring text that can be easily interpreted and understood by a generative AI model is referred to as prompt engineering.

Generative AI programs use “temperature” parameters to determine the level of confidence in their predictions. Lower temperatures mean more creative responses, while higher temperatures equate with greater confidence in the results. But higher temperatures don’t necessarily mean the results are more precise.

Generative AI models absorb information from across the internet and from myriad sources, some of which are reliable and trustworthy, and others that are not. It’s the sourcing of information, and input from humans, that can cause these programs to present incorrect, misleading and biased information, i.e., hallucinate.

False information can have significant real-world consequences and, while these hallucinations can be addressed, privacy and security concerns must be considered when fine-tuning a model against sensitive or confidential data. [JS1]?

AI-Generated Content, And How to Spot It:

From a risk perspective, knowing how to spot when content was authored, either partly or entirely by AI, will dictate how much you can trust the results.

Look for sentences that lack complexity or contain words that are frequently repeated. When editing content, keep an eye out for scientific facts or citations that don’t match up with manual calculations or sources, seemingly correct code that looks out of date or place, and inaccurate or stale data.

Here are a few online tools that can be used to determine if copy has been AI-generated:

·????? Content at Scale AI Detector – Best for casual writing samples

·????? Originality.AI – Best for professional writing samples

·????? Open AI Classifier – In progress. Best for long writing samples

Hallucinations can occur when a Generative AI model becomes convinced of incorrect facts and responds to questions with made-up answers.

Best Practices to Adopt a Risk-Based Approach to Using Generative AI

If you’re considering implementing the use of ChatGPT or other chatbot programs into your business, there are a few best practices to take when building a plan.

·????? Complete a risk assessment – Identify potential hazards associated with using generative AI, assess those risks and consider steps to control them. Completing tests of its use will be helpful to record how it might work for you and how the proposed controls could mitigate or decrease risk.

·????? Define the use case – Determine how, where and when you would use generative AI and the implications of its use across all elements of your business.

·????? Build a governance committee – Creating a diverse group to provide input will enable you to better characterize what ChatGPT operations will look like and prevent biases.

·????? Create policies and procedures – Make sure to have a set of rules guiding the use and misuse of AI programs. Be proactive in mitigating unwanted or harmful behaviour and document weaknesses or vulnerabilities within the program.

·?????? Provide training and resources – In line with developing policies and procedures, training employees on how and when to use chatbots and use parameters is key.

·?????? Communicate with customers and employees – Be open and transparent with employees about how introducing generative AI may impact their roles and your expectations of its use. Sharing your intentions with customers and stakeholders is vital to ensuring everyone is on the same page about what role generative AI will play in your business moving forward.

Colin Wenngatz, Partner, Data amp; Analytics, leads MNP’s Enterprise Analytics program – supporting MNP’s clients across the Country through the development of innovative data-driven solutions. Colin draws on nearly 20 years of experience in corporate strategy, analytics, and program management across varying industries. His specialized focus is on implementing and utilizing analytical platforms and tools to gather data-driven insights in support of optimizing business models, and assessing opportunities for growth and performance improvement.

Jason Lee is a Partner with MNP’s Digital Services team in Toronto. Drawing on more than two decades of experience in technology, project and account management, and innovation labs, Jason solves complex technology and business challenges to help his clients thrive.?He is experienced in 3D integration, cloud, gaming, the Internet of Things, artificial intelligence/machine learning, and data. He has been the delivery lead for large enterprise agile transformations and engagements.

Jeremy Beltgens, CPA, CA, Senior Manager, Assurance Innovation, performs a key role in the evaluation, development and deployment of technology and innovative methodologies in the firm.nbsp; His work with the Assurance Innovation team has focused on transforming MNP’s assurance practice using leading edge innovative technology.nbsp; He has a lead role in vendor relations, providing functionality guidance to the vendor and collaborating on team member education to maximize value on deployment of products.

Jeremy has led teams with deep skills in data engineering and science, audit execution, technology due diligence, and technology to adopt enterprise scale assurance solutions that are transforming the practice and stimulating a more data driven approach.

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