AI is a part of our life and most people don’t even acknowledge it.
Everyone is familiar with driverless cars or voice assistants like Apple's Siri, Microsoft's Cortana, or Google's Alexa, but there are many lesser-known examples. Intelligent algorithms, capable of self-learning, suggest the products to buy, films or music tracks similar to our tastes, they know how to answer customer questions via chat, they can recognize a person's face to enable access, support doctors in reading X-ray images and diagnoses, filter CVs to choose the perfect candidate.
There are numerous examples of how the introduction of artificial intelligence in business processes has brought positive impact: AI is automating repetitive and low added value parts of the processes previously carried out by humans, reducing errors, allowing the development of new products and services, by its introduction bringing the positive impact on business processes.
We are at the beginning, but in the next few years, we will see a quantum leap in this direction.
The major corporations globally are taking the first steps in this direction: the first to arrive at concrete results will benefit from an enormous competitive advantage.
But how to achieve that, and apply artificial intelligence in your organization?
In this article, we want to provide the practical guide that will help to create a structured approach to apply AI in your company. Read on!
Artificial Intelligence in business processes
AI was born in the 1950s, but only today that the technological advances in the field of computing power, data availability, and the ability to analyze them for solving complex problems have allowed such applications to be developed and spread.
The underlying technologies are mature and are available through APIs and cloud services at affordable costs. However, applying AI is not that simple, as to introduce AI into processes you will need a design approach.
For applying AI in your organization, you need to prepare on a certain level, and then apply our 5 steps to implement AI in your organization which we will describe below.
The preparation: demystify AI
For many people, there is still something mystical or threatening about AI. Although intelligent technologies act invisibly in our everyday objects, the image of AI often emerges as a futuristic, dystopian phenomenon.
Therefore, the active demystification of AI comes first. Because: Trust is only created through knowledge. Fairness and transparency, as well as the assumption of responsibility, are increasingly coming into focus as values. This means taking the step away from AI as a threat - towards AI that inspires people, creates innovations, facilitates collaboration, and functions as a “complement to human ingenuity”.
Companies achieve this by creating transparency concerning planned processes, technologies, and their scope of application and by disclosing working methods.
The basis: creating trust among employees
Welcoming artificial intelligence into work means introducing employees to a “shared workplace with technology” and the new way of working. Advantages and benefits should be communicated using case studies in meetings or via the intranet so that they can be understood even without a technological background. It is crucial that advantages are not only perceived but internalized. The partial takeover by AI creates good relief and time savings for value-adding activities, especially with manual, repetitive tasks. A good AI solution is therefore flexible, scalable, and adaptable to a wide variety of specialist departments and scenarios.
In order to bring employees and AI together in a trusting manner, it is important to create an atmosphere of cooperation instead of control. In a completely new world of work with unclear tasks, employees often have to find their role anew. This also includes acquiring more skills in automation. Companies should offer training courses, workshops, and certification programs - also for HR decision-makers who need to develop new internal programs and training courses. It is just as important to communicate your specific meaning to employees.
Establishing ethical principles
A “laissez-faire” mentality is not appropriate given the profound changes brought about by AI. Above all, companies that use AI in their products should be pioneers and develop their own ethical frameworks for introduction, which are adapted with regard to branch, company, industry, and market.
In practice, such a framework is a document that contains the motivations and conditions of the AI innovation path. Reasons could be that competition demands innovation or that companies want to create new values.
Developers must therefore exchange ideas with ethics experts from various disciplines at an early stage in order to understand all the levels at which they are responsible. In order to avoid a loss of trust, interactions between humans and AI should be explicitly regulated. Human counterparts regularly check the activities for risks.
The Process: Five Pragmatic Recommendations
In order for AI to enrich companies in the long term, we have collected specific tips for use in companies.
First of all, it is important to understand the opportunities offered by AI, target pilot projects, and keep an eye on profitability. Realistic assessments of the possible opportunities and risks of trend technologies are a must.
Trained employees in the area of algorithm development are rare. Companies should develop in-house AI competencies while working with specialized third-party providers. Above all, you will need to create a link between the developers, data scientists, and management.
Data is the fuel for AI applications. Technologies such as machine learning provide a uniform data format to handle and link unstructured data such as a sensor or social media data.
Companies should combine knowledge about their own products and processes with AI applications and feed it into the systems before they start the self-learning process.
Small projects bring first experiences with AI. This is achieved with the help of cost-effective cloud-based basic interfaces, which can be easily expanded and scaled as required.
This is how companies succeed in introducing AI
- Actively demystify AI and see it as an opportunity
- Communicate planned deployment scenarios and the procedure
- Test and demonstrate the added value of the introduction
- Choose flexible, scalable, and adaptive solutions
- Bring employees together with AI creating trust to it and train them further
- Create a good database
- Combine internal skills with external skills
- Gather the first experience in small projects
If until 10 years ago the barriers to the introduction of businesses were linked to the lack of instrumentation or inadequate analytical skills, today the issue is not technological, but mainly the absence of cultural and specific skills. According to experts, today 70% of the effort related to an AI project is for the redesign of processes, 10% for writing the algorithms, and only 10% for the technological part.
Currently, the most advanced sectors in the adoption of artificial intelligence projects are banking, finance and insurance, automotive, energy, logistics, and telco.
Because of these trends, many business leaders are probably wondering how they can implement the issues in their own company.
But what exactly does the use of artificial intelligence mean for the management or the individual employees of a company? Let’s discover below.
Five steps to implementing artificial intelligence
The areas of application of artificial intelligence are diverse. So, it is often difficult to find the right entry point. For the integration to succeed and also achieve the desired success, a structured approach is necessary. There are a few steps to follow:
1. Identify and determine the use case
The first step towards a sustainable introduction is to find out which problems within the company need to be solved through the use of AI and to define which goals are to be achieved.
Often it quickly becomes apparent that there are many points of contact, but it is not very efficient to deal with all of them at the same time. It makes sense to focus on a certain use case from a specific department to get started.
2. Define and establish success criteria
Once a suitable use case has been found, it is easier to define the next steps and the criteria for success, because not every available solution is suitable for every use case.
These success criteria are:
- Business needs;
- Data sources and data quality;
- Semantic Relationships and Extraction;
- Calculation of the return on investment.
Business needs mean the deviation between the actual state (lived practice) and the target state with optimal implementation. After the specific deviations have been worked out and the requirements for implementation have been defined, those responsible for the project must determine which data is required from which data sources to achieve the desired goals.
For this, we have prepared a list by using which you can introduce the AI processes smoothly.
Brief check: Introduction of AI processes
- What is the specific problem?
- What do you want to achieve with it?
- Where can the data be found?
- What do the users say?
- Have all specifications been achieved (return on investment)?
To extract information from this existing data and make it usable as knowledge, the relationships between the various pieces of information must be analyzed, models built and other information interpreted and linked correctly. In terms of impact control, it is necessary to define meaningful key performance indicators (KPI) in advance, that is understandable for both employees and other parties involved. These serve as the basis for the ROI calculation and as hard facts for measuring success.
3. Proof of concept with company data
A proof of concept (PoC) is an important milestone in the introduction of AI solutions. It helps to find the right provider. Companies should use their data to test whether the identified requirements can be implemented with the solution. A PoC with your data also has the advantage of recognizing problems at an early stage. Besides, after a successful PoC, all findings can be seamlessly transferred into real operation.
A factor that should not be underestimated in the successful introduction of AI solutions is the quality of the existing company data. This should be checked before implementation. A bad quality or insufficient amount of information can make the use of machine learning and artificial intelligence much more difficult (garbage-in, garbage-out principle).
4. Include employees
In terms of continuous change management, it is necessary to involve employees in the process at an early stage. Specialist departments can best assess where the solution still needs to be improved or provide valuable input – especially when it comes to training the AI solution.
Only through continuous use and active feedback the artificial intelligence does learn, deliver more precise results, and subsequently support you in your daily work. The feedback from key users is essential for the later rollout and the acceptance of the employees because they act as multipliers in the company.
5. Validate the ROI calculation
If all tests are satisfactory, the transition to real operation takes place. All settings from the PoC can be adopted directly and the ROI calculation created at the beginning can be validated.
Once the project has started successfully, the AI system can be rolled out to other departments or fields of activity until it is finally used throughout the company and all corporate processes can be transformed (business process transformation).
If implemented correctly, artificial intelligence opens up incredible opportunities for companies. It is important to involve employees, create ethical foundations and build a solid base of experience and skills. This is the only way to strengthen trust, customer relationships, and the company's image in the long term.
If artificial intelligence is used carefully, it considerably increases the performance of a company. AI can become a real game-changer when it comes to competitive advantages in highly competitive markets.