It is time to establish an artificial intelligence (AI) on a large scale and for smaller companies to derive clear business, benefits from the implementation. They, but also the tech sector in general, have to change their way of thinking and no longer see AI as a new trend or buzzword but as the basis of any business operation.
More than half of executives (64 per cent) in the DACH region are already convinced that AI will lead to benefits in the future. More than half of executives (64 per cent) in the DACH region are already confident that AI will lead to help in the future. This includes, among other things, cost, time and error minimization, a more sustainable use of resources, improved customer service that contributes to greater customer satisfaction, and increasing employee and company efficiency.
With the increasing use of AI, automation’sautomation’s range of use cases is also expanding. Software robots, also known as digital employees, become intelligent through the combination with AI and can therefore make decisions themselves instead of just carrying out repetitive tasks according to a fixed pattern.
AI And RPA: A Helpful Combination
So AI is the perfect complement to Robotic Process Automation (RPA). By bringing these two technologies together, unstructured data can also be analyzed, categorized and extracted – increasing functionality and improving the output of complex and intelligent RPA workflows. With the introduction of this so-called intelligent automation, the business processes in companies in different industries are greatly optimized and made more productive since the digital employees receive cognitive skills. In addition, the integration of AI functions leads to improved work efficiency and productivity while, at the same time, greater accuracy when accessing unstructured data.
Business automation is thus elevated to new strategic areas by automating tasks that previously could only be performed manually by employees. Companies can take full advantage of the two technologies and create synergy effects by using an all-in-one platform for automating end-to-end processes and exploiting the efficiency of the intelligent functions.
The digital employees can therefore take on the following tasks:
- Problem Solving – You can independently solve logic, business and system problems
- Learning – You can adapt to evolving process patterns and derive contextual meaning
- Collaboration – You work seamlessly with people and systems
- Visual Perception – You can interpret, understand and contextualize visual information
- Planning And Scheduling – You can streamline workflows and discover opportunities for better results
- Knowledge And insight – You can gain, understand and provide insights from different data sources
AI Scaling Pitfalls And How To Avoid Them
Data Science, AI And Business Processes Do Not Harmonize:
Businesses should not view AI and its processes as separate entities. AI and the Intelligent Digital Workers should be seen as full members of the teams for AI to be successfully established in an organization. In addition, data scientists and process analysts need to be embedded in functional groups and understand business strategies and goals such as revenue growth and project execution because the scaling of AI can only work if everyone pulls in the same direction.
The Conception Of The Use Of AI Is Only One Step:
AI is often separated from the rest of the company and housed in a silo. However, the models designed by data scientists must be applied in everyday business and used within the business processes to be helpful in everyday life. Ideally, data scientists work closely with the automation specialists (“process analysts”), who can bring in the business view of the company and ensure that it is highly relevant in the corporate context.
Lack Of Executive Support:
For example, a project may need access to data under the control of another department—priority access to server computing resources, upgraded hardware, or additional means to test hypotheses. All of this requires a leader with an appropriate level of authority and authority. Therefore, there should be someone in top management responsible for the successful use of automation and AI, such as a CDO, CAO or CAIO.
Inconsistency In Technology Selection And Lack Of A Centralized AI Platform:
Lack of clarity in technology selection means that the growing demand for AI in the enterprise can no longer be met after initial positive results. The AI qualitative transformation initiative requires a centralized technology roadmap. Starting with the first AI and automation projects, the company should deal with compliance, reproducibility, training of machine learning models, and standards for data storage and testing.
People As The Key To The Successful Use Of Intelligent Automation:
AI skills help to be able to automate more and faster. Therefore, it should be established as an everyday part of every company and integrated into its processes. However, that does not mean that employees will become superfluous. On the contrary: AI should support them in their tasks. Accordingly, managers’managers’ focus must be on the acquisition and further training of the necessary skills of the employees and the cooperation between technology and people.
To bring the data and the insights gained from it directly into the main business activities while avoiding scalability problems, the data team and the analysts responsible for the business processes must work closely and trustingly together.
Automation Promotes Integration:
Existing automation of business processes simplifies the integration of AI into business processes by reducing the direct interaction between humans and AI and instead allowing the employee to continue interacting with the user interfaces they are familiar with. This lowers the acceptance threshold and can thus help to break down prejudices.
Businesses have essentially just begun to implement the capabilities that AI can provide. However, AI must no longer be seen as a new trend but must be embedded in every company’s core and everyday life. This is the only way companies can increase their productivity and efficiency and thus remain competitive in the future.
ALSO READ: Why Companies Need An Active Data Strategy
 on a large scale and for smaller companies to derive clear business,){kind=link}