Starting digitalization. 4. Innovative solutions for digitalization.

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The most important question that an entrepreneur and their staff ask themselves is - how will technologies help achieve the real / business goals of the company. The application of technologies should be justified by beneficial effects in the operational processes of the enterprise.

But is it worth it? What would be right: to optimize everything that the company already has, or to leap into the future through innovation?

The concept of “Innovation” is revealed in different types of activities in its own way. We will focus only on examples that work with information flows. The key features of innovation will be uniqueness (novelty) and the need for exploration. In fact, identifying a problem that is worth researching is also part of the innovative practice. However, all of this relates to the “magic in the innovation kitchen”.

For us, we need a more practical definition of innovation, so we will call it any novelty that improves the efficiency of processes and the quality of staff work. However, the innovation must be useful in real practice and not remain as just some research on paper.

How to see space for innovations in the processes at the company?

How to find those processes in our company that can be improved through innovation?

To help us efficiently and easily find starting points for exploring innovative cases, let's describe the benefits that a machine can bring for efficiency and quality:

Below are points and their descriptions gathered from our clients.

Upon completion of digitalization, they provided us with definitions of the values ​​that the machine brought to the operational processes.

All solutions related to acceleration, management, naming / meaning, transactions / data exchange, and solidarity / team collaboration, which have been identified as problems and solved later using technologies in a unique professional cultural environment of the company - can be considered innovation if there is still no typical solution through ready-made products on the market or in society.

Automation of decision-making.

By selecting from the full range of solutions, we would like to specifically talk about a whole class of innovative solutions based on collaboration between human and machine. It is about decision-making processes that previously could only take place in a person's mind.

The innovative goal in the concept of a company's digitalization could be to “clone” the thinking/cognitive processes occurring in ... the minds of highly qualified specialists, consultants, managers.

For many, this turn of events may seem unexpected.

Some skeptics may say this is too far in the future? Language models of AI still cannot think like humans. But they are not meant for these purposes. For such tasks, scientific approaches to creating strong AI should and can be used. And this process is already in full swing, and we participate in it every day.

Of course, real or strong AI is still a too scientific question, and a different computer, such as a quantum one, is needed for this. Unfortunately, today's programming languages are fundamentally designed to exclude uncertainty and are fundamentally separate from the semantics of human thought. They can only mimic human thinking in the realm of rationality.

However, nothing prevents us from using approaches to strong AI for very narrowly defined specific tasks without using neural networks and their training on vast amounts of data that are usually lacking in the enterprise. Moreover, most cognitive operations by which people teach other people have already undergone a formalization process and can thus be passed on to modern computers without waiting for hardware changes.

Therefore, we can introduce contextual meaning into the decision-making process through orchestration methods of contributions from knowledge, experience, art, practicality, etc., exploring how the best experts do it in their best versions and states, and even optimizing this process with the help of cognitive specialists.

By organizing a human-machine environment, we can either completely hand over routine decisions to the machine or significantly ease the decision-making process for the employee.

An example from our customer-case list: the head of a government committee is forced to personally review a huge stream of inquiries for legal representation of citizens from various sources because the decision to “accept for consideration” or “reject” carries legal consequences and requires a personal decision and signature of the head or their deputy. Automation is possible through “digital vision,” “highlighting,” and “naming” critical information, and then by decrypting cognitive judgment operations of a person when making decisions.

Therefore, let's carefully look at such cases and create an opportunity for the machine to make routine decisions for experts. While doing this, remember to organize the possibility for an expert to change the decision-making algorithm if circumstances change.

How such a goal of digitalization (automation) is connected to the benefits for the company when expertise can be delegated to the machine at decision points can also be seen in other specific examples:
- sorting accounting documents into categories,
- writing texts, qualified answers,
- making diagnoses,
- conducting automatic consultation for a client on mortgage lending with filling all documents and obtaining decisions from multiple banks at once...

Modeling the consequences of changes.

And if we are already charged up and into it and have discussed digitalization-automation solutions such as creating human-machine environments for decision-making support, let's also mention the possibility of machine modeling.

For instance, for evaluating the consequences of implementing changes in company processes before the actual implementation. For some companies, changes in processes happen constantly, or mistakes would have very serious consequences. In that case, creating such machine models would be very relevant.

Data economy.

Finally, in the context of innovations for digitalization, we would like to draw attention to the topic of the near future called the “data economy.” This topic is closely linked to the “personalization of working with clients” context.

The problem with big data at the moment still lies in the meaninglessness of the way the data is being collected. Ready-made software collects data and then tries to figure out how to interpret it, i.e., they try to invent scientific ways to give meaning back to the collected data, so that hypotheses can be built upon them.

Companies can organize access to their data in such a way that an answer to any question asked about their own data has a real and reliable answer, supported by real live meanings of processes in the company. Then, the use of data in the company will become not only a pass towards the technological future but also a serious competitive advantage in any macroeconomic conditions.

However, we cannot fail to note that each company contributes special solutions to all the above-mentioned tasks of digitalization. And we welcome such challenges. Because each company is a unique organism with its corporate culture, critical moments, and potentials.

It is not difficult to see that trying to summarize specific features in ready-made products for insular digitalization is still not successful. This is probably fundamentally impossible. Any organization and management specialist would likely agree with this statement.

How can one understand that their company needs to undergo digitalization?

For example, if your employees from different departments enter data for their work individually into their own software, and you need separate efforts to create a report on an order or project. Or your employees manually transfer data from one program to another. Or if you heavily rely on “star” employees. Or if you find it easier to accept losses than to organize control over them.

What can be done to stop being afraid that your key specialist will be poached by competitors?

It is known that more in-depth division of labor reduces the qualifications required of staff. Now this is also possible for cognitive actions. Digitalization will not only transfer routine cognitive actions to the machine but also be able to “clone” not just one expert, but the entire community of experts, in their best and optimized version, gathered according to the strong AI principles and handed over to the machine.


Let's talk in the next article about specific typical benefits of digitalization. We will compile another checklist, this time from a production and management perspective. In it, we will place the best digitalization practices for achieving specific company goals.