Many accountants will have access to products and services that can provide access to generative AI capabilities. Some will look to proactively embed capabilities. Whatever the approach, there are certain considerations and actions that anyone looking to adopt generative AI should take.
Develop an understanding of the fundamentals of Generative AI
Consider business objectives
Consider use cases
As with any technology, generative AI should not be adopted for the sake of it. Identify where the opportunities lie, and focus on those. The key consideration when determining tasks to be completed by generative AI is to remember that the output of generative AI is not always trustworthy. Therefore, the best areas to start with are those that require little or no judgement, are low risk, time consuming and repetitive. Whether a level of human review is included will depend on the level of risk and volume of activity.
Generative AI is very well suited to tasks that follow a specific template and well-defined rules – for example, assigning accounts to categories. However, it is different from automation as it not only performs automated tasks, but can also augment the output with additional information eg summaries and highlighting of important points. Repeatable and scalable tasks will be more worth the investment in setup time, cost and forward planning to scale if initial use cases are successful. Generative AI is also good at providing answers to questions that can be verbalised. Effective prompt engineering is crucial to getting the right output and is covered in the “Using generative AI” section.
Evaluate model options
There are some important distinctions in the types of AI models:
- Most generative AI models are ‘foundation models’, trained on a vast quantity of data and designed to be adaptable to a variety of tasks. They offer broad potential, but may be limited in terms of context.
- The alternative approach is a ‘narrower’ AI solution, which is trained on specific data for specific purposes. This would require more setup time and cost and may be limited in how users can interact with it, but can be much more contextually aware and provide more focused responses.
- A hybrid approach, where the foundational model is used to deliver interpretation and generation of text, voice, image or other mediums, in combination with a tailored model trained on business-specific information, can deliver huge benefits. This is where tools such as such as Microsoft 365 Copilot come into play. Copilot draws on OpenAI’s GPT foundation model, and overlays this with the context of organisational data, to assist with productivity, creativity, analytics and collaboration (for example, being able to take a detailed business case Word document and turn it into a PowerPoint presentation, or produce a budget from a few bullet points in an email).
Engage the right expertise
Perform a vendor/supplier evaluation
There are lots of specialist providers, resources, and experts around that can assist non-tech organisations to explore, experiment, build and deploy Generative AI applications, and benefit from insights and industry best practices. Be aware of the available options before jumping to a particular solution and know that you don’t have to go it alone. Don’t be afraid to ask probing questions of potential suppliers around the training data used (if any), model operation, accountability, bias, privacy and any third-party assessments of their solutions. Establishing an ongoing vendor oversight process is also important to ensure that vendor supplied products and services continue to have the right governance and controls.
Which is best: public or private?
Change and people management
Data governance, storage and quality
Perfect data is a noble aim but rarely achievable in most organisations. Generative AI tools are generally good at taking in unstructured information, but feeding them with poor quality, inaccurate, or badly managed data will likely end in failure. Before training a generative AI tool on your own data, consider:
- cleaning and pre-processing data to deliver standardisation and improved quality;
- implementing a data management strategy including use of metadata (i.e. cataloguing/tagging of data);
- establishing appropriate retention policies to ensure that generative AI utilises only relevant, current information;
- integrating and consolidating data into a unified format;
- reviewing data to ensure that, before it is used, it is factually correct and free from bias;
- adopting robust data governance practices, including consideration of data ethics, privacy and protection; and
- reflecting on data storage arrangements, considering that cloud-based solutions are likely to be far more effective when looking to utilise generative AI technology due to the computing resources required.
Plan for ongoing learning
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