Artificial intelligence is redefining engineers’ roles and accelerating tasks beyond programming

New challenges in the daily lives of technology professionals

A Business Insider report highlights how artificial intelligence is transforming the daily lives of specialists who work directly with clients. The example of Justin Johnsen, a KPMG engineer who holds the position of forward-deployed engineer, illustrates the combination of technology, consulting, and proximity to the client.

When Johnsen joined KPMG, he developed software almost entirely manually. Today, much of his work involves using AI to analyze information, structure data, and create solutions for various projects.

The role of a forward-deployed engineer also helps explain this change. Instead of receiving a request, building a solution, and delivering it, this professional accompanies the problem from the identification of the need to the final implementation.

This involves communicating with teams, understanding the business context, developing the solution, and training the client’s employees to use it.

From coding to curating context

According to the KPMG engineer, the main bottleneck in his work is no longer coding. Now, it shares space with a previous stage: gathering context and defining the project’s purpose.

AI models rely on the information provided to generate useful results. The more complex the problem, the greater the need for data, rules, objectives, and business details.

In the case reported by Business Insider, Johnsen spent months collecting requirements and refining the context before applying AI to develop an application. After this phase, the solution was delivered in approximately one month.

The experience also changed the client company’s needs, which requested support for its teams to adopt the AI and prompt development practices used in the project.

AI expands work beyond programming

Another example presented involves a risk assessment tool that combines various signals to determine the danger of doing business with a client or partner.

Artificial intelligence analyzes these signals and generates a score that can be reviewed by human evaluators, integrating into the analysis process rather than replacing it.

Furthermore, a growing portion of development is shifting to activities related to data and system integration. Companies have information scattered across different applications, legacy systems, and cloud services.

In this scenario, the professional takes on the role of connecting these sources and organizing the data so that AI systems can use it effectively.

Transformations in daily work life

The KPMG case illustrates a transformation that can affect various professions: some manual tasks are accelerated, while others become more important.

Notable activities include:

• defining the problem to be solved
• organizing and validating information
• having in-depth knowledge of the business
• communicating with other teams
• evaluating the results produced by AI
• making strategic decisions

A study of around six thousand workers from various sectors showed a reduction in time spent on emails among participants who had access to a generative AI tool. The effect was stronger in individual tasks, while activities requiring coordination with others underwent fewer changes.

This explains why the impact of artificial intelligence is not limited to replacing a task. In many cases, the technology changes how professionals allocate their time and prioritize their activities.

Context and judgment are gaining importance

For the KPMG engineer, AI makes it possible to reduce the time spent on repetitive tasks and create more space for activities that require human judgment.

This does not mean that technical knowledge has lost its importance. On the contrary, professionals need to know what information to provide to the system, evaluate whether the answer makes sense, and identify limitations or errors.

For workers in different fields, the change may mean a combination of professional knowledge and AI fluency. A financial analyst, for example, can use models to organize information but remains responsible for understanding the indicators and verifying whether the interpretation is correct.

The same principle applies to marketing, human resources, engineering, law, operations, and other areas where professionals deal with large volumes of information.

In KPMG’s case, the engineer’s role now includes not only building a solution but also teaching the client how to use it. The delivery now includes the application, documentation, best practices, and knowledge transfer.