AI has been making technology headlines for some time now. Today, it has established itself as a powerful tool for reducing tedious and repetitive digital tasks, as well as helping to reduce human error, for example in relevant sectors such as emergency equipment development.
In the first newkinco’s white paper -“Use Bots, Remain Human: How AI Can Unleash Your Creativity”- the experienced digital strategist Arne Leichsenring breaks down what these technologies are, their potential applications, why you should use them and how they are already part of newkinco.
Jumping right in
Companies are facing ongoing competitive pressure and are increasingly confronted with becoming more efficient while meeting increased quality requirements. New process automation technologies, such as Robotic Process Automation (RPA) and Artificial Intelligence (AI), can make a decisive contribution to boost efficiency as well as to significantly improve process quality.
The use of these technologies — often referred to as Intelligent Automation (IA), Intelligent Process Automation (IPA) or Hyperautomation — improves operational efficiencies and decision-making across organizations by simplifying processes, reducing manual tasks and freeing up staff to focus on more creative areas.
It allows organizations to combine human know-how with AI in order to speed up business processes, reduce errors and minimize human input.
Technologies — What is out there?
First, we need to distinguish between AI and RPA. AI includes a wide range of technologies such as Machine Learning (ML), Optical Character Recognition (OCR), Natural Language Processing (NLP) and so on. RPA is a technology that can be easily used by anyone to automate digital tasks. It allows users to create bots by observing human digital actions. With RPA, software users create software robots or “bots” that can learn, imitate, and then execute rule-based business processes.
Where companies have previously automated processes with low-value potential (task-based automation), companies more and more link RPA with advanced analytics and AI technologies.
For example, the popular messenger and collaboration tool Discord offers to their users a wide range of existing bots as well as APIs to implement their own bots, such as your own AI moderator bot for Discord.
How Algorithms Can Have Your Back
Machine Learning relies on algorithms to predict outcomes based on data. The algorithms identify trends, commonalities, and correlations between variables, using statistical analysis to predict outcomes and future events. Then, as the program continues to run, the algorithms further improve their predictions based on subsequent datasets.
For example, ML has recently been applied to detect anomalies in manufacturing processes. Using ML, health monitoring of the equipment can be automated where the specialities of the sensor device’s data like vibrations, sound, temperature, etc. from the collected data can be learned through training. This is useful to identify early wear and tear of equipment and avoid catastrophic damage. It can catch the smallest flaw that the human eye may miss.
OCR is another technology that uses Deep Learning to recognize characters. It is of great use in manufacturing to automate processes that are subject to human errors due to fatigue or casual behaviour. These activities include verifications of lot code, batch code, expiry date etc.
The Chatbot Case
NLP is a type of AI technology that describes the capacity of software to read and understand natural human language — written as well as spoken. Typical Use Case for NLP is the application of chatbots: a chatbot is a conversational tool used to automate communications. Chatbots can complete a wide variety of services, ranging from fun to functional tasks. Currently, rule-based chatbots are a popular e-commerce tool for routine customer service requests. The reason is that they are easy to build, and though simplistic, can get basic tasks done. But as AI has advanced significantly, and continues to improve, it’s highly likely that we’ll see a rise of the more complex machine learning chatbots.
By using NLP, developers can organize and structure the mass of unstructured data to perform tasks such as Named Entity Recognition (NER) which is used to locate and classify named entities in unstructured natural languages into predefined categories such as the organizations, names, locations etc. Further microservices allow users to extract existing metadata or to (semi-) automatically create tags for databases, archives, or content management systems. They can also automate the processing of scanned documents, 3D models, images or videos and thereby significantly reduce the manual effort for tagging, improve consistency in indexing and increase the findability of files for search engines.
Benefits — What justifies all the fuzz (Why?)
The transformative potential of IA is that it creates the opportunity to reshape the seamless integration of technology, work processes and people in organizations.
To remain competitive in today’s environment, organizations must be able to quickly shift to new business models, rapidly develop new services and products, and scale new tools and technologies on short notice.
IA platforms provide many benefits across industries as a result of the use of large amounts of data, precision of calculations, analysis and resulting business implementation. From our point of view, there are 5 key benefits:
1. Data accuracy
Especially for a wide range of information systems, archives and large databases, IA helps to reduce the risk of transactional errors — including incorrect data inputs, manual errors, incomplete processes, and mistakes in rule application — to improve overall data accuracy and data-driven decision making. It enables transferring unstructured data into structured information while performing data hygiene and increasing data consistency.
2. Speed and efficiency
Depending on the grade of automation, IA reduces or even eliminates manual tasks, which are often repetitive and time-intensive, giving humans the time needed to complete high-value tasks. In doing so, IA dramatically reduces process cycle time — with handling times typically falling by 50–60%. That can significantly improve outcomes, adding efficiencies and reducing cost. Not only will speed and efficiency result in improved customer satisfaction, but it can also lead to increased levels of employee satisfaction.
3. Flexibility and scalability
IA effectively decouples resource costs from process volume. This capability greatly simplifies operational scaling, enabling organizations to focus resources on other key areas of expansion, organizational change, and capacity increases. Moreover, it gives organizations more flexibility in reacting to demand fluctuations, which is particularly useful for big companies with international growth ambitions in times of volatile markets.
4. Ease of use
Since IA solutions are automated processes that feed easily into the existing IT system, there is no need for extensive changes for legacy systems. Compared to other forms of automation and transformation, IA solutions are easier to implement, configure and maintain — typically via a simple, intuitive interface.
5. Decision Making
Access to data can be significantly increased by IA and thereby create a high degree of information transparency. This is perhaps the biggest benefit of IA because it enables delineating between rules-based decisions and those requiring greater judgment or analysis. By doing so, highly trained staff can devote greater attention to cases and tasks that require more cognitive thinking or empathy, such as client interaction and account management.
Newkinco Approach — How They Are Part of Our Processes (How?)
More than ever, organizations face the challenge of navigating the complexity of numerous technologies and an ever-growing vendor landscape. In our view, an iterative approach for implementing IA solutions is absolutely critical to success. The following are simple steps that are part of our processes and can help you find out how IA can also be beneficial for you.
1. Find your unique user case
You have to develop an in-depth understanding of the business needs. This primary step is crucial to identify specific use cases by combining AI, science and human expertise in order to select the right processes for automation. It helps also to define the scope of the overall project and to define a rough implementation roadmap that fits the most to the client organization.
2. Confirm your guesswork
The second step is to conduct a feasibility study or a proof of concept to evaluate the applicability of IA solutions in those areas of the organization which have the highest potential. Before you go ahead and implement a full, enterprise-wide transformation, start by testing your plans on a smaller scale. Small proofs-of-concept will help you identify problems you may have initially overlooked and avoid making mistakes on a large scale.
3. Build upon the proof
After you determine the technical feasibility you can start to develop a business case and evaluate IA solutions according to their cost-benefit potential. This stage is about creating a base for business decisions. It is important to check the technical requirements of the specific organization and then select suitable technologies which can be integrated into existing IT architectures and fulfil compliance and IT security needs. From our experience, you can leverage a lot of existing solutions, use low code platforms or adapt algorithms from other applications.
4. Test it and improve
Implementation is an ongoing process, with IA solutions being optimized and fine-tuned over time. We recommend starting by developing a user-centric prototype or a minimum viable product (MVP), i.e. the most stripped-down version of the product that can still accomplish the task. In this way, you can quickly test it with real users, see what works and what doesn’t and make changes accordingly by integrating user feedback into the application.
Any implementation roadmap should combine quick wins with larger longer-term developments. After successful implementation of the initial business cases, organizations can generally use the same roadmap to develop and implement further IA solutions in-house, with limited or no external support.
An essential element of a truly intelligent type of future of work, however, means that we do expand the workforce where both humans and machines will be part of, but with the aim to improve humanity and well-being while also being more efficient in the execution of our jobs.
