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  • Fredrik the Frisian

New roles emerging in the digital era

Updated: Mar 25, 2023

Guest writer: Thomas Schibbye, CEO Glasspaper People.

As many predict, humans perform significantly less activities in digital era. Robots are slowly taking over both physical and more intelligent tasks. But, Glasspaper and Dynamic Integrations see new roles emerging while we develop and implement further steps towards artificial intelligence. When your business implements AI, a sequence of events will unfold. This article describes these events and the new roles we see emerging, as a result and a prerequisite for a successful AI implementation.

Develop smart people to initiate digitalisation

Three new expert roles making AI a success in the early adoption phase

The success of AI in early development phases is depending on reliable data. A prerequisite for this success is the acknowledgement of three new expert roles.

(1) The AI Database Expert, (2) the Information Expert and (3) the Enterprise Process Expert. These roles will be described and explained below.

(1) Starting with AI, as described in our previous article, requires the role of an AI Database Expert. AI can only function if its intelligence is connected to a database (memory) with correct answers. Especially in this early stage of AI, an operator must guarantee the data reliability for AI to function. The role is responsible for the implementation of AI, the maintenance, and the implementation of new service solution in the AI database.

Data reliability in the digital era is crucial for success. All products and service delivered are defined in a common language starting at marketing and sales, through delivery, support, but also legal and finance. Not only does the AI need correctly written solutions in the AI database, but your customers must also use this wording for products and services purchased. To enable this, a common terminology must be defined and used throughout the entire process chain internally, and during the sales and implementation process at the customer. We call this role the Information Expert.

(2) The Information Expert is responsible for defining the wording (Information Architecture link), describing the products and service delivered. Every person in the process chain is responsible for using this wording. The initial implementation of a common language is the responsibility of the unit manager. It requires an adaptation in your company’s DNA. It entails your website, the contracts defining the products or services purchased, sales presentations explaining the products and services, project management, service and support for the registration of queries in the system and the accounting system for delivering the invoices using again the same product and service definition.

After the registration of correct data in the systems, it can be transformed into reliable information using a Business Intelligence system. Reliable information leads to right business decisions and continuous enterprise transformation following changing market demands. These important steps follow Dynamic Integrations' Dynamic Business model

To embrace the digital future the definition of a common terminology is crucial. Implementing this common language referring to the core business products, services and processes is a prerequisite for a prosperous future. The development of Artificial Intelligent creates a continuous stream of opportunities for businesses and people. Using these opportunities requires a constant adaptation of your organisation. The rate and speed of these changes will only increase in the near future.

(3) There is no time for lengthy and expensive projects, risking losing peoples motivation and customer satisfaction. Speed is a key word in the digital era. Enabling speed means the implementation of decisions without a lengthy chain of command. To prevent this from happening we see the emergence of the Enterprise Process Expert. This should not be a function but a role executed by an employee with the right skills and position, ergo decision making power.

This role is overall responsible for implementing your organisational changes from a process perspective in your common application environment. The knowledge about all enterprise processes going through the common application environment is essential for your adaptation speed. If not adjusted correctly core enterprise processes will not function and you might soon be out of business. Companies should evaluate how to implement this role according to the following parameters; amount and complexity of enterprise processes, number and flexibility of your applications, cooperation with the providers and more.

We write about roles because the typical function is disappearing. People will perform various roles in the future, even for different companies or in several (networks) ecosystems. There is also an increased focus on enterprise processes entailing the entire organisation instead of the fragmented approach per traditional business function.

These two developments together form the basis for our next blog about the disappearing of organisational hierarchy and the rise of the ecosystem/process organisation.

How does AI currently influence jobs?

The last three years, the focus on AI has increased tremendously in the Norwegian market. Technologically, one of the main reasons is the commercialized computer power and growing availability of data. At the same time, several AI success stories have accelerated the insight and interest in the area. Being the largest IT-training provider and a leading IT-recruitment and consultancy agency offering AI solutions, Glasspaper is in an excellent position to catch trends in the market.

Dynamic Integrations described some of the new roles due to AI in their latest article, and Glasspaper was asked to share our experiences and view of the topic.

What we see is that the roles described, i.e. Database expert, Information expert and enterprise expert are already roles in several organisations although the names of the roles and the focus of the roles differs from organisation to organisation. The most common advertised position is Data Scientist & Machine Learning Engineer where programming in R, Python etc. is the one skill of high demand and statistics / mathematics for predictions etc. is the other area of high demand in combination with programming.

Even though the demand for expertise is increasing, we see that AI is at an early stage where most organisations are still hesitant about exploring AI solutions. Most projects are still small-scale projects to test the technology and learn about the opportunities and benefits. There are of course exceptions, but the majority of public and private organisations are still not prepared to use AI as a competitive advantage.

Many organisations still have to go through the digital transformation of automating tasks and digitalise paperwork and manually tasks. As private and public organisations manage to streamline processes digitally, we expect the focus on AI solutions to increase considerably.

During the last 3 years several consultancy companies have established their own AI teams and some, typically large enterprises have hired AI experts, but our observation is that the area is not even close to its true potential yet. That said, we can already at this stage see some of the impact on the workforce of AI being introduced.

The roles above have already been explained in the last article. However, a role partly described by Dynamic Integrations that we would like to emphasize is the domain expert. This is a role that moves business even closer to IT. With technologies like Cortana, Watson etc. someone has to teach the terminology to the computer. This is not the technologist, but a domain expert like a dentist, a recruiter, a lawyer etc. These roles might transform into becoming the Information experts later on, but currently it seems like the domain experts only conduct their new role during a project and then return to their ordinary job, probably because many of the AI solutions are still projects not being too comprehensive.

For example, to evaluate the correlation between x-rays and cancer for children, AI specialists trained IBM Watson by working closely with health personnel to transform unstructured text to detect patterns that was to comprehensive to examine through other solutions.

Another example is how some media enterprises offer marketing solutions to maximise the chances for clients to reach their target group by not only choose traditional channels, but learning what car the consumers are interested in and then again make expectations about salary levels in which is relevant for segmentation.

LinkedIn’s focus to teach the applications as to which candidates are the best match to jobs that are posted through their webpage and vice versa by defining algorithms to teach the computer what is a good match. We also read that Facebook offers targeted marketing towards obese people as Facebook has trained computers to “read” pictures showing people that are defined as obese. The information given to the computer is typically provided by Data Scientist. Consequently, as indicated earlier, currently, the biggest change in the market so far is the increased demand for skilled Data Scientists-

Related to this, the use of AI will influence amongst several product developers, sales personnel, and marketing personnel as they get more insights about their clients. This has already started, but we believe that this area is just in the early phase. AI is also in use to learn more about the whole value chain in an organisation from logistics to all client interactions. The question is whether the tasks will change for many of these people, but not their jobs.

Another job type that will arise is the maintenance and transportation of drones. This is relevant as the drones are in fact flying IoTs that is using AI to navigate and collect data. A Norwegian software company is developing the intelligence in drones to inspect grids. These drones need to be transported to areas where they will work if it is far away. As the number of drones increase they also need to be maintained. Those manually inspecting grids are likely to be of less need, which is a solid example of how AI contribute to create new jobs, but also reduce the number of others.

Guest writer: Thomas Schibbye, CEO Glasspaper People - IT-training provider and IT-recruitment agency

Writer: Fredrik the Frisian, CEO Dynamic Integrations, Software and AI development

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