The workforce shift from automation to intelligence

04 April 2024 4 min. read

Today’s workforce continues to be buckled down with repetitive tasks. Robbin-Jan Haar, Consultant at IG&H, explains why automation as we know it is no longer the ideal solution.

Despite all the technological revolutions of the past centuries, our workforce is still occupied with repetitive, high-volume tasks. No matter what industry is looked at, tedious and repetitive work is an integral part of operations.

The only difference to the industrial era is that many of these tasks have shifted from a factory into an office environment. The repetition remains, albeit with cleaner hands.

The workforce shift from automation to intelligence

Such a factory environment of repetitive tasks behind our desks poses three fundamental issues to our businesses:

Humans are limited by the speed of their hands and minds, and this may vary greatly depending on the time of the day or the amount of sleep enjoyed last night.

The simple truth is that humans make mistakes. Repetitive tasks often lead to less focus and drifting minds, and thus increases the chance of mistakes.

We are given creativity, intelligence and emotions. Wasting these assets on repetitive tasks is bound to result in an unhappy workforce – especially amongst millennials and Gen-Z.

The solution to repetition has always been automation, from the steam engine and factory robots to spreadsheets. But despite this automation, today's workforce is still stuck on repetitive tasks. Why? Because automation often only works combined with human intelligence. This poses an issue when we can only access this intelligence from nine to five, with limited speed, and some collateral mistakes and employee turnover as part of the deal.

From automation to intelligence

Automation as we know it needs to transform into intelligent automation. With the launch and applicability of Generative AI, intelligent automation had its debut in 2023. Much of the human intelligence required in business processes today has to do with the interpretation of input data, and formulation of the right output.

An example of input interpretation could be customer questions in emails or chat, which come in various formats and tones of voice. An example of output formulation could be the answer to that specific question, tailored exactly to the needs, concerns, and tone of voice of that specific customer. That’s where Generative AI comes in – partially freeing the workforce of the burden of structuring input and output as a repetitive process.

Generative AI alone does not yet lead to intelligent automation. But combining Generative AI with low-code automation solutions creates an intelligent automation powerhouse. This combination can help the business streamline for efficiency, often in a matter of a few weeks, and in just five easy steps:

1) Identification
Intelligent automation starts by identifying the business processes(es) where the biggest impact is likely to be made (repetitive manual tasks with high occurrence).

2) Analysis
High-impact processes are analyzed for feasibility of automation. Typical criteria to look for are the number of systems involved, the departments the process spans, how structured the input is and how many variations may occur.

3) Design
The automated process is designed, visualized and relevant business rules are determined. This step also describes the integration needs with each of the systems in place.

4) Implementation
The configuration and integration of the automated flow in relevant systems, for example low-code platforms such as Microsoft Power Platform or OutSystems and building the connection and prompts in ChatGPT. Training and communication with the workforce are part of the implementation step.

5) Optimization
Deploy new features to learn and optimize. The automated process output gives clear performance metrics that help to further fine-tune the intelligent automation and extend it to other identified processes.

When done well, intelligent automation effectively increases process efficiency, First Time Right (FTR) and employee satisfaction. And it doesn’t stop there: the applicability and possibilities of ChatGPT accelerate as we speak, also beyond streamlining for efficiency.

We’ve seen use cases of ChatGPT supporting customer support agents with better decision-making or even automated sales recommendations and inspiration tailored to the needs of the customer – where ChatGPT interacts directly with the customer in a natural dialogue as if with a real, well-trained sales agent.

Yesterday, we were automating processes, but without intelligence. Today, we are on the verge of streamlining for efficiency through intelligent automation. And tomorrow… who knows? What we do know for certain is that a further acceleration of tech evolution will happen in the years to come, where we will continue to streamline for efficiency.