In IT, how companies use AI has become increasingly important. When implementing and scaling these technologies, the goal is to be as efficient as possible and avoid wasting potential. When approached thoughtfully and strategically, many AI applications represent the next major step forward for businesses. However, if the approach is too unstructured, overly aggressive, and lacks clear direction, they can end up costing more than they benefit.
This article focuses on best practices for using AI in IT as effectively and profitably as possible.
#1 Think More About Users and Less About Technology
Many companies place a strong focus on new technologies—and almost see them as an end in themselves. For example, there is often a strong urge to use a particular tool simply because competitors are already doing so. Others are driven by the ambition to be especially innovative, a mindset that often stems from many “AI First” strategies.
In contrast, implementations that focus heavily on the needs and preferences of users tend to deliver significantly greater value. AI solutions should never be introduced simply because they are highly visible, but because they provide meaningful support.
Employees who will use the technology should be involved as early as possible. It makes sense to specifically ask users about their expectations and preferences regarding AI, and to document the results. Combined with the IT budget, strategic objectives, technological requirements, and security considerations, these insights can be used to develop an action plan, followed by a review once implementation is complete.
This approach enables the teams involved to clearly evaluate how successful the user-centered strategy was and what lessons can be learned from it, allowing them to move into a process of continuous improvement for the implementation and use of technologies.
#2 Develop AI Maturity First
When implementing AI, many companies focus on the end result. They want measurable outcomes and meaningful improvements as quickly as possible. While understandable, this objective can be achieved much more effectively by first taking a step back and working on AI maturity.
Such maturity is achieved when factors such as process management, information management, expertise, and even company culture are ready to support successful AI implementation and usage.
#3 Offer Training
Extensive AI adoption within a company can catch some employees unprepared. This is especially true when implementation happens abruptly and rapidly, as is often the case in highly competitive environments.
Therefore, it is advisable to offer appropriate training as early as possible and give employees enough lead time to prepare for the relevant AI applications. Ideally, training should be tailored to the specific needs of individual employees. In any case, everyone involved should have the opportunity to properly prepare for the upcoming processes, expectations, and goals.
#4 Optimize First, Then Automate
This principle is worth remembering like a mantra. The logic behind it is to achieve high quality at scale – an exceptionally high level of efficiency. Processes must first be optimized before they can be automated through AI. Otherwise, automation would simply replicate existing errors and reinforce inaccuracies in insufficiently mature processes.
Not only for medium- and long-term success, but also for achieving better outcomes overall, it is worth investing a little more time upfront to produce compelling results later.
#5 Keep Routine Tasks in Focus
AI applications are often expected to create something entirely new, unlock previously unexplored opportunities, and drive transformation. In reality, the impact is often less spectacular and smaller in scale, but still highly valuable and innovative. AI primarily takes over routine tasks, resulting in significant time savings for employees, lower opportunity costs, and more room for creativity and value creation.
When companies calculate how much time many AI applications save – and the value of the resulting performance improvements and error prevention – it becomes clear how they can benefit economically, provided they approach implementation correctly.
Rather than focusing solely on the next major technological breakthrough, organizations should prioritize the practical benefits AI provides in day-to-day routine work.
#6 Target Small Use Cases
If routine work represents a frequently overlooked source of AI value, the best approach is to tackle many small steps and challenges one after another. This promises a far more successful AI implementation than large, extensive, and overly ambitious projects.
Every great journey begins with a single step, and companies should therefore address individual aspects patiently, purposefully, and with focus.
The following areas can be particularly worthwhile:
- Ticket summaries
- Documentation
- Knowledge management and self-service
- Automation of individual standard processes
- Ticket routing
#7 Optimize Knowledge Management
How companies handle knowledge, data, and information is important to AI usage in several ways:
- Existing knowledge, large volumes of data, and best-practice procedures provide an essential foundation that enables AI to operate at a high level.
- AI can make the vast amount of knowledge within an organization far more accessible. Once knowledge has been cataloged, employees can retrieve it easily using natural language.
- Machine learning generates new knowledge that companies can use, for example, to optimize their processes and workflows.
In fact, due to factors such as complexity, lack of structure, and insufficient organization, companies often utilize only a fraction of their available knowledge. Making better use of more knowledge therefore unlocks significant untapped potential.
#8 Optimize and Refine Prompts
When an AI application fails to deliver the desired results, many organizations immediately consider replacing it with another solution. However, the existing tool could often produce the desired outcome if the inputs were simply more precise and unambiguous. Prompt engineering is an important factor that is frequently underestimated or receives too little attention.
In short, the effectiveness of AI depends heavily on the quality of the inputs. Depending on the task, it is important to provide context, clearly define objectives, specify detailed requirements, and, in the case of IT issues, include complete error messages.
#9 Consider Security and Data Protection
AI security in IT is often overlooked, whether due to limited experience, pressure to implement quickly, or the assumption that nothing will go wrong.
Under no circumstances should anyone enter information such as customer data, personal information, confidential details, or internal company knowledge into an AI application without proper review.
Many security incidents and data breaches occur because there is insufficient clarity in this area. Users often simply do not realize that the information they share already falls into the category of security-relevant or strategically valuable knowledge. Therefore, the only effective solution is to establish clear AI guidelines that the company defines for its employees.
#10 Communicate Transparently and Clearly
When the implementation of new AI applications is driven primarily through a top-down approach, it generally encounters less understanding, cooperation, and engagement. Challenges such as initial resistance, fear, and uncertainty are best addressed through transparency from the very beginning.
People tend to be far less skeptical when everyone is informed about the project and its individual steps from the outset. This creates a much greater likelihood of success than leaving employees in the dark, perhaps out of concern about how they might react.
Conclusion: AI Adoption Works With Best Practices
The way companies leverage new AI technologies in IT has become critically important. The use of AI itself is rarely the question anymore. Instead, the decisive success factors are the strategies and practices used to maximize the value gained from applications, tools, and features.
For this reason, it is worthwhile to apply relevant best practices, implement the ones that fit best, and continuously monitor how AI is being used. AI maturity, a focus on routine tasks and small use cases, and effective knowledge management are particularly important factors.
What matters is that companies do not implement and use AI in a random or unstructured manner. Instead, they should act strategically and with foresight, developing an approach tailored to their specific needs and covering a variety of relevant factors.
Among these factors, choosing the right software solution plays a major role. Especially in the early stages of AI adoption, flexible usage models can be particularly advantageous.