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The importance of a holistic mandate planning in auditing

Updated: Mar 12

"It's time to overcome the paradigm of sequential planning and adopt AI-driven holistic solutions to achieve the full potential of our talents and thus our company."

"The goal of audit planning is to create an audit program that enables an audit opinion with sufficient assurance (effectiveness) and with reasonable effort (cost-effectiveness and efficiency) by the agreed deadline." EXPERT FOCUS, 2020 | 9, ARTIFICIAL INTELLIGENCE IN AUDIT PRACTICE


In the context of audit planning, auditors are required to plan the nature, extent, timing, and personnel deployment of the audit (ISA 300). Due to the fact that audit firms handle multiple mandates with overlapping timelines, this requires increased planning and coordination efforts. In particular, the availability of technical and human resources poses a challenge. Machine learning techniques can already be used in this early phase of the audit to support audit firms in the effective allocation of resources.



Einsatzmöglichkeiten von maschinellen Lernverfahren im Prüfungsprozess, nach EXPERT FOCUS, 2020 | 9, KÜNSTLICHE INTELLIGENZ IN DER PRÜFUNGSPRAXIS, Eine Bestandsaufnahme aktueller Einsatzmöglichkeiten und Herausforderungen, ANITA GIERBL, MARCO SCHREYER, PETER LEIBFRIED, DAMIAN BORTH und Expertsuisse, 2015.

Fig. 1: Applications of machine learning techniques in the audit process, according to EXPERT FOCUS, 2020 | 9, ARTIFICIAL INTELLIGENCE IN AUDIT PRACTICE, A survey of current applications and challenges, ANITA GIERBL, MARCO SCHREYER, PETER LEIBFRIED, DAMIAN BORTH, and Expertsuisse, 2015.



Creating an audit plan is challenging


Creating an audit plan involves developing an audit program that enables the audit opinion to be formed effectively and with reasonable effort (efficiency) by a specified deadline. Especially for modern large audit firms, this is not to be underestimated due to the complexity of modern audit engagements. It requires planning the nature, scope, timing, and effective staffing according to the audit standard ISA 300. Since large audit firms handle multiple engagements with overlapping timelines, usually falling within the same time frame, this results in high planning efforts. Not only professional qualifications (skills, formal qualifications, certificates, etc.) must be considered, but also personnel resources (such as availabilities, preferences, continuities, individual employees' soft skills, committee compatibility, etc.).



Requirement according to ISQC

According to ISQC 1.30, audit firms are required to appoint a responsible engagement partner for each engagement, who possesses the necessary competencies, skills, and authority to carry out their role properly. Pursuant to ISQC 1.31, this engagement partner must only assign suitable personnel with the required competencies and skills in accordance with professional standards and applicable legal and regulatory requirements according to A31. ISQC A30 requires systems to monitor the workload and availability of engagement partners to allow these individuals sufficient time to adequately fulfil their responsibilities. All these requirements make the use of modern technology more important than ever. However, the major challenge lies in this planning, particularly for large audit firms. Here, the use of modern technology such as artificial intelligence can provide crucial competitive advantages.


What procedures are used in audit planning?


Sequential planning versus holistic planning - an issue that is becoming increasingly crucial in the modern work environment. While many companies and providers of workforce planning software focus on sequential planning and identifying the best matches between each talent and work assignment pair, it is important to recognize that this alone is insufficient to achieve a globally optimal outcome for a company. The metrics show that companies relying solely on sequential planning often lag behind the global optimum.


To understand this, we need to look closely at the interactions between talents and assignments. Suppose a company plans only one talent and one assignment at a time (sequential workforce planning method). As soon as a new talent-assignment pair is formed, it has an immediate impact on many other (potential) pairs in the company that can no longer be formed. This new pair may be suboptimal and lead to poor planning elsewhere in the company due to interactions. Thus, the overall result for the company deteriorates without a human understanding of why. There is therefore an urgent need for a holistic perspective that considers all talents and assignments equally. Moreover, biases will inevitably arise due to the sequencing of planning, as pairs planned early have more degrees of freedom that can influence the planning than pairs planned later when the pool is already exhausted.


Why is holistic planning superior to sequential planning?


A crucial aspect of holistic workforce planning is the integration of Artificial Intelligence (AI). The complexity of such planning is enormous. Just to plan 100 talents, around 10^160 possible combinations would need to be considered. This number is greater than the estimated number of atoms in the universe, estimated at 10^84. In this complexity, various factors such as skills, qualifications, availabilities, and even softer factors such as efficiency and past collaboration must be considered. Only through the use of AI and intelligent algorithms is it possible to process this immense amount of data and achieve the best possible outcome.



Eine Mandatsplanung zu finden, die die Präferenzen von Talenten, dem Unternehmen und den Kunden in Einklang bringt, ist für menschliche Planer fast unmöglich wie aus einem Rechenbeispiel hervorgeht: Um nur 100 Talente zu planen, müssen rund 10^160 mögliche Kombinationen berücksichtigt werden (Zum Vgl. beträgt die geschätzte Anzahl Atome im Universum 10^84).

Fig. 2: Finding an engagement plan that aligns the preferences of talents, the company, and the clients is nearly impossible for human planners, as illustrated by a computational example: To plan just 100 talents, approximately 10^160 possible combinations must be considered (For comparison, the estimated number of atoms in the universe is 10^84).


Often, a single algorithm is not sufficient, and one must rely on various classes and families of algorithms. Depending on the complexity and requirements of the planning process, the most suitable algorithm must be identified and utilized from an algorithm portfolio. Our experience at aspaara AG has shown that with a pool of 500 talents, up to 17 different algorithm classes may need to be employed to achieve a truly accurate representation of the company, as different areas have unique planning requirements (e.g., individual offices, cost centers, or departments plan differently and have specific rules). Algorithm classes are classified based on complexity (usually space complexity and time complexity), machine capability (usually deterministic, non-deterministic, quantum mechanical, or randomized), as well as the procedure used and the problem statement (decision-making vs. optimization). Furthermore, there are numerous domain-specific algorithm classes.



Many solution providers have focused primarily on sequential planning, mainly on finding the best matches between talents and tasks without considering the underlying complexity of interactions. While this approach is useful for identifying suitable talents for specific tasks, it does not achieve the global optimum. There remains a significant gap between the capabilities of sequential planning and the holistic consideration of all talents and mandates.


We must be aware that the consequences of suboptimal workforce planning can be significant. Companies that use sequential planning as their sole strategy may face declining effectiveness, inefficient workload distribution, and untapped potential of their talents. To address these challenges and achieve the global optimum, holistic planning with AI integration is essential.


What results can be expected from holistic planning?

Our work with clients has shown that, typically, 3-9% more utilization and between 1-14% more gross margin are possible through holistic planning. At the same time, over half of conflict hours can be reduced. Additionally, employee satisfaction can be optimized through holistic planning, with up to 95% (but at least 80%) of talents receiving their preferred mandates.


In the modern workplace, innovation and progress are inseparable from utilizing the full potential of our talents. With holistic personnel deployment planning, audit firms can ensure that they optimize all resources and improve corporate performance metrics.


It is time to overcome the paradigm of sequential planning and implement AI-supported holistic solutions to achieve the full performance of our talents and thus our company. Companies that limit themselves exclusively to sequential planning will inevitably lag behind the market environment. It is time to focus on holistic personnel deployment planning and leverage the benefits that AI can provide in this regard.


The article was originally published on LinkedIn and can be accessed at the following link:



Background: What is sequential and holistic personnel deployment planning?

What is sequential personnel deployment planning? This is the common planning method that is still widely used today due to the lack of computing power and commercially available applications. In this approach, the overall problem is divided into individual talent-work assignment pairs. The goal is then to find the best possible work assignment for each talent or vice versa. In sequential planning, one proceeds from pair to pair to reduce the complexity space, without considering the entire workforce at once to find the global optimum.


What is holistic personnel deployment planning? This is a method where, unlike sequential personnel deployment planning, the overall problem is not divided into individual planning steps, each of which is then attempted to be solved. Instead, the entire problem, which includes the entire population of talents and work assignments, is kept in "working memory." This method of personnel deployment planning has only become possible through the use of artificial intelligence and today's computing capacities. Classical algorithms fail here due to the sheer complexity of the challenge.


Sources

  1. EXPERT FOCUS, 2020 | 9, ARTIFICIAL INTELLIGENCE IN AUDIT PRACTICE, A review of current applications and challenges, Dr. Anita Gierbl, Marco Schreyer, PETER LEIBFRIED, DAMIAN BORTH.



  2. Expertsuisse, 2015.


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