What are the most common mistakes made when implementing AI agents?

The most serious errors lie in incomplete, poor quality data, and the introduction of AI agents where a different method would be more effective. Another problem is that many people want an “all-rounder”. Due to the hype surrounding AI, expectations are high. And the required investment in time and money is often underestimated. It makes sense to start with small pilot projects and then draw up an implementation plan for the further use of AI agents. This leads to the first efficiency levers and success stories and companies can gradually move closer to their vision. In this way, organizations remain dynamic and can adapt quickly if new developments in this rapidly growing field shake things up again in the future.

What role does the individual employee play?

The entire staff is very important because they have to work with the AI agents in the end. A change management plan with a focus on training and empowering the workforce is crucial here. And in the best-case scenario, the employees themselves become developers. As a first step, the workforce should learn how to use artificial intelligence sensibly: What do you need to look out for to keep company data secure? What kind of questions can AI help with? How do I formulate the best prompts? The next step is to train motivated employees to become developers of AI agents themselves. They learn to recognize the potential for AI agents in their own processes and working methods and then take the initiative to implement meaningful agents on their own and share them with other colleagues.

In what range – expressed as a percentage or in other metrics – are the expected gains in efficiency of this future technology?

In the white-collar sector, we are at around 30 percent, depending on the industry and process. But I’m not a fan of such rough figures, as the increase in efficiency always depends on the process in question. For example, we have already implemented AI agents where the respective department estimated the increase in efficiency to be 100 percent – in this case, it was about validating requests for quotations in the engineering area.

What needs to be considered to ensure that AI agents and the investments made in them can have an impact over long periods of time? For example, do systems have to be set up to be future-proof and expandable in order to make further developments implementable?

Expandable is a good keyword: To avoid long specifications, broad agents should always be implemented in phases. You start with what is termed a “minimum viable product” and then gradually add new functionalities as required. To achieve this, it is of course important that the broad agent is understood as a modular construct from the outset. Not only to enable future expansions. But also so that the agent itself can evolve with the rapidly developing AI environment. For example, by making it easy to replace the language model at the core of the agent when a newer, better language model comes onto the market.

Are there industries and business areas in which AI agents promise particularly high potential for efficiency gains – or are all sectors of the economy eligible?

They are relevant for all industries, but especially for the white-collar sectors. In other words, where people sit at computers a lot and work with data and documents: Legal departments, IT development, marketing, HR, and sales can benefit greatly from this. But special solutions in the engineering area or on the shop floor can also have an impact. For one client, for example, we used a language model to cluster frequently occurring problems on the machines in production, prioritize them and then automatically make them available to the shift supervisors. This creates transparency and helps them to train employees on the most common types of errors using best practices.

What role do the experts at Porsche Consulting play when clients introduce AI agents?

In short: the transfer from the process to the agent. We look at the processes and identify potential. And use this as the basis for evaluating which AI solution would be useful. The solution is then closely scrutinized with the client’s respective experts: How should the tool be used? What data is required? Which criteria are particularly important during processing? What should the results look like so that employees experience the greatest added value? As soon as the user story and the technical and non-technical requirements are clear, you can get started. The AI agent is brought to life in short sub-projects: either first as a proof of concept, or – in the case of smaller AI agents – directly as a minimum viable product. It is important for the AI experts at Porsche Consulting to involve the workforce. As part of dedicated training courses, employees learn how to use the new tools – or are empowered to implement their own small AI agents in the future.

In which sectors does Porsche Consulting already have practical experience?

In the industrial goods, consumer goods, software as a service, aerospace, and automotive sectors – also in an international context.

In conclusion: What would be the consequences if companies dispensed with AI agents?

As with all disruptive developments, such as the internet, social media, and now artificial intelligence: If you don’t move with the times, the times will pass you by. And with a technology as rapidly developing as AI, it is important to understand it and use it effectively in the right cases. This has to be done proactively, because AI agents are not a surefire success. This keeps you competitive. It is no longer just about the profitability or competitiveness of individual companies – it is about the future viability of industry and the economy.