Automating Bureaucracy

FEA01x - edited feature imageBureaucracy was known to be the plague of the USSR, but has recently become a heavy burden for private companies. New regulations such as Sarbanes-Oxley, Dodd-Frank, and GDPR, spanning all functions from HR to accounting to product labelling to vendor certifications, are constantly put in place by various states where companies operate. These rules have the major effect of forcing companies to hire people whose job is to check that all operations are compliant with the set of rules in place at any given moment. These jobs add no value; they are pure cost, and their recent explosion is simply an increase in the global cost of doing business.

The overall effect is either a loss of productivity or price increases for the clients to cover for these additional unwanted expenses. The only benefit is trying to avoid serious problems in the form of heavy fines, jail time for executives due to non-compliance, or loss of business due to reputation damage.

Responding to bureaucratic needs used to be done in paper forms requiring many boxes to be checked. Getting the required approval stamps and signatures from the required authorized persons is now partly replaced with Information Systems accessible to many in the organization. These systems record and trigger transactions and can be equipped with pre-defined software-based verification processes before a transaction can be performed. Nevertheless, this still requires a lot of non-value-adding human intervention.

Worse, there is never any guarantee that these verification processes actually match current regulations. Additional difficulties stem from the fact that the information is usually stored in separate databases, each using different models for such elements as employees, vendors, clients, client credit, bank accounts, processes, or products. It takes skills for a business analyst to reconcile these systems, the first ones being to figure out where the necessary data is stored, how to access it, and knowing who is responsible for a certain piece of data. Developing programs to match the disparate data requires time and money, the result is never guaranteed, while obsolescence at time of delivery and rigidity to change are a sure thing.

It is legitimate in our age of Artificial Intelligence to expect many of these tasks to be automated, data to be understandable by anyone and to get better, always up-to-date compliance for a lower cost.

Automating Bureaucracy: Why it is difficult

Different organs of an organization function at different rhythms. Annual planning and setting objectives for sales and production people is done annually. Invoice processing is usually done once or twice a month. Filling vacant position takes a variable time, usually months. IT projects take months to be defined and also months to be completed. Regulations are set at random times, accidents take place without forewarning…

Different organs also have different ways of prioritizing their work and each have access to information that is limited to their specific area. An accountant will process the invoices in the order they were received and will require validation from a remote manager that may be hard to reach. A plant manager will also function in this way, perhaps with some leeway for some urgent order. A salesperson will prioritize its client visits according to the client’s potential in sales. An IT manager will organize the work of the teams according to the availability of resources and current projects.

Each of the organs require data that is located in different places. In the case of an old fashioned government bureaucracy, the data sat in paper folders that had to be searched, usually in different locations. You want a passport in France, you need to get a birth certificate from your birth town. To get it, you need to write a letter to the proper office, make sure that you include a return stamp, etc., which takes days if not weeks. You may also need a residence certificate that comes from your current residence town. You also need to write to get it. In any case, the order in which each local administration will process your request has nothing to do with how urgent it is for you to get a passport. Now, if there is any mismatch, such as a typo, in any of the required documents, then you may be facing a multi-year complex procedure to get it fixed.

In the case of a compliance officer in a company, the situation is quite similar: in order for a contract or transaction to be approved, it needs to go through the compliance officer. The latter needs to review the said transaction, in the order that is convenient to him, then find all the related pieces of information that will let him take his decision based on current business rules and regulations. That information may be found in paper folders in another building, in one or many databases, or in excel files maintained by some unknown employee. So just to collect all the information required to approve a transaction may be very lengthy. It is clearly super-inefficient and unacceptable in the information age.

Unfortunately, it is almost impossible to perfectly unify all information systems in a company. Different software are used, they have been parametrized according to different needs by different people at different times, so there is no unity. A client may be designated in system a by “CLIENT”, by “U_CLIENT” in system b and “Customer” or “Client” in system C. Similar Contracts for different types of clients may also sit in different databases and be characterized differently. So the officer needs to develop some heuristics to find the information and to match it to the tasks at hand. This implicit knowledge is quite painful to formalize. Doing so would require defining a very complex and expensive IT project involving all departments, get them to agree on a global definition of business items and terms. That could take months to complete, so companies usually function with heterogeneous IT systems and count on smart employees to compensate for the defects, which should not be their primary job.

In all companies, data does not sit in a single database and are not accessed by a single application such as SAP. Typically, there will be a system for accounting using its own database, another one for HR, another one for shop floor planning, another one for CRM, etc. It is therefore almost impossible to measure the impact of any change in one of these systems to all the others. If suddenly, there is a management decision to categorize items in one system, there is no way to automatically enforce the change in other systems related to the same items. The only possibility is for a business or IT analyst to extract the data from all concerned systems and find a way to implement the changes everywhere necessary. This is lengthy, matching is usually not possible, and there is no guarantee that the result will be what is expected. Until all mismatches have been fixed, there is a risk that some non-compliant transactions take place.

Consequently, each organ has no view on how its actions or lack thereof impacts the rest of the company. An invoice paid too late because the accountant had too much work on that day may result in a major contract being lost. Using a specific ingredient that just came in from a supplier in the production may result in having to throw away the whole production batch and lose sales in a distant place, etc. Top management certainly has no time to go through all the data from the whole company. If it insisted on being involved for all transactions, just as in the USSR, then everything would take even longer and lead to more inefficiencies.

Most actions in a company, at any level, require collaborations with other parties. And coordinating activities, identifying the right people, the right information, accessing it, bringing a team in a room for a discussion, all take time that it is hard to account for, yet consume a lot of non-value adding employee time. These delays cause friction in the system and many inefficiencies, some with a possible major impact. Even at the age of computers, people still have full agendas, IT systems may change and getting access takes longer than desired, or people can be sick.

In summary, the way work is organized cannot be better optimized with standard tools, for the following reasons:

  1. Different rhythms for different organs.
  2. Different prioritization rules for different organs. Never by global enterprise value.
  3. Partial view of the information by each organ.
  4. Lack of global visibility of the impact of each impact.
  5. Delay between stages required to complete a transaction.

Nothing can be done about that: it would make no sense for a salesperson to control the production planning, nor for top management to control every minute action. Some processes take a long time that cannot be processed. Workdays only have so many hours, etc. So, we have grown accustomed to the inefficiencies that result. And it is usually the slowest element in the chain that control the overall rhythm. The longer the slowest processes are, the less reactive to change the company is.

When are all of these organs in sync? Almost never, because of this. So, the system always works in a less-than-optimal fashion.

Validations by another party will always be necessary, as well as defining procedures, setting rules and respecting regulations. All of these tasks require human intervention. On the other hand, all that makes them possible: coordination, identification of data sources and people, access, messaging, etc. can be automated and the proper messages generated automatically. With proper and timely access to all the relevant data, an intelligent system can also be expected to classify everything by value or global impact, rather than relying on every organ’s prioritization habits.

It would also be useful to count on that system to simulate the impact on future operations of changes in regulation or management rules.

This feature was co-written by Stéphane Zrehen PhD. 

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Dr. Walid el Abed

Dr. Walid el Abed

Dr. Walid el Abed is a French linguist and computer scientist, and is known for the proposition of a new model called the Semantic Meta Model (SMM) (Meta Modèle Sémantique - MMS). He is the founder and CEO of Global Data Excellence. He is a worldwide renowned expert and visionary in AI and data-related topics, with 30 years of on-field experience across various sectors.

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