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AI tools are increasingly being used throughout the deal cycle to achieve the following advantages:

Advantages of using AI tools

Benefits if using AI during deal cycle chart
Benefits of using AI during a deal

Less manual effort and errors

Benefits

Automating mundane tasks in a deal such as processing large, complex data sets should eliminate manual errors and improve the accuracy of the analysis.

Machines don’t suffer from cognitive fatigue that often leads to human errors.

Positive impact

Allows dealmakers to focus more on value adding areas such as interpreting the final analysis and forming overall conclusions; relationship building, negotiation, stakeholder communication and decision making which should lead to improved work satisfaction.

Enhance the attractiveness of the profession

Benefits

Automating some of the monotonous tasks and placing the focus of training on business strategy, analytics and value creation should attract a new calibre of students and enhance the profession from a career attractiveness point of view.

Positive impact

More high-calibre students applying to work in corporate finance, which increases the quality of teams. 

Improved creativity and quality of deliverables

Benefits

The tools can quickly provide a number of options for presenting data to illustrate a point (such as charts and page designs), upon which the user can select the most appropriate. 

Accessing more granular data and triangulating different data sets will improve the quality of the analysis. Sophisticated tools that can generate aesthetically pleasing data visualisations and dashboards will improve the quality of reports.

Positive impact

Allows for improved data visualisation and decisions and ultimately better client satisfaction. Additionally, the tool can often select and create charts and design pages much quicker than a human could. 

Improved client satisfaction.

Creates new revenue opportunities

Benefits

The use of AI opens up opportunities for advisory firms to offer unique and enhanced services to their buying or selling clients. One example includes assisting companies in their preparation for sale, such as providing enhanced forecasting and more effective management reporting, deeper analytics and better market positioning

Positive impact

Offering new add on services could enhance the revenue possibilities for advisory firms.   

Faster decision-making

Benefits

Automating tasks and quickly flagging anomalies improves focus and saves time.

Positive impact

Should facilitate a quicker transaction close and the execution of more deals.

Reduced risk and cost savings

Benefits

By being able to access micro-level data and form a more holistic view of a business by mining new sources of unstructured data, it can identify non-traditional and qualitative risks (such as reputational or market-centric risks), and as a result buyers and sellers have a greater depth of understanding of the risks of a target business.

Buyers and sellers have a greater depth of understanding of the risks of a target business by being able to access micro-level data and form a more holistic view of a business, which is made possible by mining new sources of unstructured data which can identify non-traditional and qualitative risks (such as reputational or market-centric risks).

Positive impact

This may lead to more successful transactions, or to deals being aborted but with less cost being incurred on both sides.  

Unlocking greater value

Benefits

During the screening process, Gen AI tools can pick up targets that would not be identified with traditional tools. 

Positive impact

These "hidden opportunities" could result in higher returns for buying organisations that merge with better suited targets with growth potential. 

Reduced staff cost (in the long run)

Benefits

Automating tasks requires fewer employees to complete the tasks undertaken by the AI tools.

Positive impact

Market factors aside, the automation of tasks should ultimately result in higher profit (although cost of software maintenance and hiring of AI skilled staff, at a premium, will need to be considered. Also consider whether the process being automated could be improved without the use of expensive technology).

Users should assess each AI tool they intend on using against these advantages and weigh these up against the risks and possible unintended negative consequences of implementing the tool in your deal processes. 

AI impact on staff

Experienced staff or deals subject matter experts will be required to review the output from the technologies and form a conclusion, however AI may result in fewer junior staff being required for the data extraction and analysis work. This cost saving benefit would need to be weighed up against the unintended consequence of junior staff not having the foundational training of the underlying analyses that would now be done by the AI tools. Research by King’s College on the effects of Auditor judgement in the context of automation found increased dissatisfaction in junior auditors and difficulty in learning. While some schools of thought believe AI will complement and enhance dealmakers’ capabilities, rather than replace them - particularly because dealmakers’ critical skills are relationships, networking, and negotiation which cannot be replaced by a machine (unlike many tasks of other accountants).

Many AI experts working in corporate finance believe that AI will have a positive effect on the workforce as new roles and new necessary skills will emerge (such as effective prompt engineering). It is thought that junior corporate finance employees will undertake fewer manual tasks and this should improve their job satisfaction. 

Automation vs augmentation of tasks

In the context of AI tools displacing humans, it is worth considering the difference between automation and augmentation of tasks. Automation is the process of completely replacing human decision-making and actions with technology. Automation can increase efficiency and reduce costs, but also displace human workers. A good use of automation is for repetitive tasks.

Augmentation is the process of supporting and improving human decision-making and actions with technology. Augmentation can enhance human skills and expertise, such as creativity and relationships, and offer a more sustainable approach to societal welfare and economic growth. A good example of this is augmenting of human insight.

Examples in the M&A context:

  • Automation: spell check of long form and due diligence reports, initial data cleansing and processing
  • Augmentation: reviewing / investigating anomalies in data

In practice 

A 2023 study conducted with researchers from OpenAI, OpenResearch and the University of Pennsylvania, found that Gen AI could enable accountants and auditors to spend at least 50% less time on current work tasks. Whether that leads to reduced headcount or freeing up time for professional accountants to focus on more strategic work and business development, is a matter for organisations to decide. However, it is worth noting that the research focused on accountants and auditors across the board, not just those working in corporate finance. For corporate financiers who spend a significant amount of their time on networking and negotiating, AI could augment less than 50% of their current work tasks , rather than automate and replace them. 

According to the research undertaken by Bain & Company (for its 2024 M&A report), reduced manual effort, accelerated timelines, improved focus and reduced cost were identified as the main benefits of using Gen AI (see figure 2). It is worth noting that many of the benefits are expected to be realised over the long run as time and money often needs to be invested first to get people and the IT infrastructure ready for the change. For example, over and above the expected upfront cost to invest in the technology (if building a custom AI tool), there is a time investment that will include, the upgrade of IT systems so they are ready for AI implementation, training staff on using the new technology and assessing the validity of the data. Employees also need to be trained on performing detailed reviews of the AI outputs and redoing work whilst retraining the tool on the lessons learnt. These hours would not ordinarily be chargeable hours and this extra cost will need to be factored into margins at the start of your AI implementation journey. 

On the other hand, if the confidence in data security of third-party tools is enhanced, it will be much quicker and cheaper to use market-based subscription models that will remove the need for upfront costs of custom building an AI tool.

Disclaimer

This AI in Corporate Finance content is being provided for information purposes only. ICAEW will not be liable for any reliance you place on the information in this material. You should seek independent advice © ICAEW 2024