Hyperautomation:
The new benchmark for efficiency
with RPA and AI agents

What does hyperautomation mean?

Why is hyperautomation so important?

RPA and AI - an automation dream team

The role of EMMA and AIMAX^® in hyperautomation

Practical examples: Hyperautomation in use

Outlook for the future: Competitive advantages through hyperautomation

Conclusion: Hyperautomation is far more than just a buzzword

Hyperautomation refers to the approach of automating as many workflows and processes as possible using modern technology. It therefore goes one step further than the conventional automation of individual tasks. While traditional automation often involves completing individual, isolated routine tasks without manual intervention, hyperautomation pursues a holistic approach: everything in a company that can be automated should be automated. Various smart technologies are used for this purpose - in particular Robotic process automation (RPA) for rule-based processes and artificial intelligence (AI) or machine learning for more demanding, cognitive tasks. Low-code/no-code platforms, process mining and integration tools also often play a role. Hyperautomation is therefore more of a strategy than a single technology: the aim is to identify end-to-end processes across departments and functional areas and automate them as fully as possible.

The importance of this development is underlined by market analyses. The research company Gartner has declared hyperautomation to be one of the top 10 strategic technology trends. In a survey, 85% of the companies questioned stated that they intend to increase or at least maintain their investment in hyperautomation over the next 12 months. More than half of the companies are already running four or more hyperautomation initiatives in parallel - a clear indication that this is not short-term hype, but a sustainable change. According to Gartner, hyperautomation is "rapidly transforming from an option to a survival requirement" in order to survive in the market. This strong statement shows: Those who focus on comprehensive automation now will gain a competitive advantage, while companies with outdated, manual processes run the risk of being left behind.

Firstly, because automated business processes are generally faster, more efficient and less error-prone than manual processes. They can also be better monitored and analysed, which in turn enables continuous improvements. Secondly, hyperautomation relieves employees of dull routine tasks. By freeing up human resources for higher-value activities, a company can be more innovative and agile. Hyperautomation is therefore a holistic lever that increases efficiency, agility and innovative capacity. In practice, this often leads to lower costs, faster throughput times and an improved competitive position.

In other words, through the broad use of automation tools - from RPA bots to AI agents and process analyses - business processes can be optimised in such a way that companies can achieve more without using proportionally more resources.

A key driver for hyperautomation is the tight personnel situation in many industries. The shortage of skilled labour is no longer just a buzzword, but a reality: according to a study by the ifo Institute, more than half of companies looking for staff will suffer from a shortage of applicants by the end of 2023. Finding suitable employees is becoming more difficult - a trend that is being exacerbated by demographic change. For companies, every unfilled position means extra work for the existing team and often also lost business opportunities. The average IT position in Germany remains vacant for around seven months, resulting in costs of tens of thousands of euros per vacancy. This lack of qualified specialists increases the pressure on existing employees: routine tasks, overtime and work intensification lead to frustration and an increased susceptibility to errors.

This challenge is being felt by companies across all sectors - from industry to SMEs and service providers. At the same time, they cannot afford to leave important processes undone. This is where automation offers an effective solution strategy. Instead of trying to fill every gap with new employees (who are hard to find), more and more decision-makers are turning to digital software robots and AI systems to carry out routine tasks. Automation solutions can carry out processes around the clock without getting tired or making mistakes due to carelessness. By using RPA bots and AI agents, existing teams can be relieved of specific tasks. Companies report that saved working hours directly free up internal capacities that can be used for strategic projects or creative tasks with higher added value.

Another positive side effect: automation can help to retain valuable knowledge within the company. Instead of know-how only being in the heads of a few experts (keyword "head monopolies"), automated workflows document and standardise the process steps. If experienced staff leave the company, the automated processes remain as living documentation - new employees can follow the processes and take over seamlessly. This is an invaluable advantage in times of a shortage of skilled labour.

To summarise, the combination of staff shortages and high competitive pressure creates the following challenges and opportunities:

• Compensating for labour shortages: Automation replaces repetitive tasks where employees are lacking and scales existing teams without additional recruitment.
• Reduce overload: RPA bots take on monotonous tasks (e.g. data entry) while AI agents answer queries - reducing overtime and team stress.
• Secure knowledge: Automated processes document every step. Company knowledge is available centrally and is not lost when employees leave.
• Mastering demographic change: In the long term, hyperautomation helps to close the gap created by the retirement of the baby boomer generation. Tomorrow's routine jobs can be performed by software, while human labour is focused on value-adding activities.
• Increase employee retention: By relieving employees of tedious routine work, they can devote themselves to more interesting tasks. This increases satisfaction and binds talent to the company.

The bottom line is that hyperautomation is not just an option for becoming more efficient, but in many cases an answer to the shortage of skilled labour. It makes it possible to remain capable of acting and even achieve growth despite scarce human resources. Particularly in Germany, where the population is ageing and the "war for talent" is raging, intelligent automation is becoming a decisive success factor for sustainable companies.

To make hyperautomation a reality, you need the right tools. Two technologies stand out in particular: Robotic Process Automation (RPA) and artificial intelligence (AI). Their combination enables the intelligent automation of even complex processes that were previously the preserve of humans.

Robotic Process Automation (RPA) refers to software robots or "bots" that imitate human interactions with computer systems. An RPA bot can, for example, take over clicks, keystrokes, reading and transferring data or performing calculations - practically any rule-based routine that a human performs on a computer.

Important: RPA works deterministically, i.e. the bots strictly follow predefined processes. As a result, they deliver reliably reproducible results and make virtually no errors as long as the rules are correct.

Typical areas of application for RPA include Data transfers between different applications, filling out forms, reconciling inventories, creating reports or processing invoices. In short, RPA shows its strengths wherever speed, accuracy and high repetition rates are required.

Modern RPA platforms are often no-code or low-code, which means that no in-depth programming knowledge is required to create bots. This also gives specialist users in departments (citizen developers) the opportunity to set up automations themselves - quickly and flexibly, without lengthy IT projects.

Artificial intelligence (AI) complements RPA where rigid rules alone are not enough. AI enables software to learn from data, recognise patterns and even make decisions to a certain extent. In practice, AI technologies come into play when it comes to processing unstructured information (e.g. free text, language, images) or complex decision-making logic with many variables.

Examples: An AI model can automatically read documents such as invoices or contracts (OCR with content understanding), semantically analyse and classify emails or customer enquiries, or make predictions (e.g. which customers are at risk of churning). Voice assistants and chatbots are also based on AI - they understand users' queries in natural language and generate suitable answers.

AI thus introduces a form of "digital intelligence" into processes. Thanks to current developments in the field of generative AI (such as GPT models), AI systems can now formulate human-like texts, create summaries or perform creative tasks.

Only the interplay between RPA and AI unleashes the full power of hyperautomation. RPA ensures that the same steps are always carried out precisely and at high speed. AI, on the other hand, provides flexibility and the ability to learn in order to react to variations in the process or to unforeseen inputs. In this context, we also speak of intelligent automation when RPA bots are enriched with AI functions.

Hyperautomation uses several automation tools in parallel to automate complex workflows end-to-end. For example, hyperautomation in accounting could look like this: An AI reads incoming invoices and extracts the relevant data (supplier, amount, due date), then passes the structured information to an RPA bot, which makes the booking in the financial software and triggers the payment. Without AI, the first step (reading the invoice) would be difficult for RPA to manage; without RPA, a human would have to transfer the data into the system. Together, however, RPA and AI create an end-to-end automated process.

Further components are often added on the way to hyperautomation, for example Workflow orchestration to control the sequence of complex multi-step processes; process mining to identify the best automation candidates in the first place; or API integrations to link systems directly. However, RPA and AI usually form the foundation. They are the "dream team" that is taking many of the previously tedious, slow or error-prone processes into a new era - in which machines do the work for us wherever it is possible and makes sense.

To make hyperautomation tangible, it is worth taking a look at two specific solutions: EMMA and AIMAX^®. EMMA is an award-winning RPA platform from Germany, while AIMAX^® is a versatile AI agent. They have the ability to work together seamlessly.

So how do these two tools specifically contribute to increasing efficiency and optimising processes?

EMMA stands for a new generation of robotic process automation. Unlike traditional RPA software, which often requires programming and only recognises strict rules, EMMA uses cognitive AI capabilities to make automation smarter. The platform is based on technologies such as machine vision, object recognition and intelligent text recognition. In practical terms, this means that EMMA can recognise screen content (e.g. buttons, form fields), understand documents and execute processes with human-like behaviour.

Despite or perhaps because of this advanced technology, EMMA is particularly user-friendly. As a no-code platform, it also allows non-programmers to configure automations using drag-and-drop. For companies, this means that specialist departments can automate their processes independently without having to spend months on IT projects. The result is fast implementation times - on average, automating a process only takes around 2 days, which is extremely short.

Another interesting feature is the audit-proof documentation of the process flow: each individual step is recorded with screenshots. This automatically creates a documentation of the process that can be used for auditing purposes or training - a real added value in addition to the pure increase in efficiency.

The benefits of EMMA in terms of efficiency and process optimisation can be seen in the key figures: Companies that use this RPA solution report up to a 7-fold increase in productivity per employee, an almost complete elimination of errors (almost +98% increase in quality and resilience) and significantly lower throughput times. The investment also pays for itself quickly - the return on investment is often already achieved with the automation of the first one or two processes. EMMA therefore directly addresses the needs of decision-makers: Reduce costs, increase output and minimise risks. It is no coincidence that the platform is already being used in strictly regulated sectors such as banking and insurance, where reliability and data protection are top priorities.

AIMAX^® ideally complements EMMA, as this AI agent focuses on the creative and communicative aspects of automation.

AIMAX^® can be thought of as a kind of intelligent virtual employee. It is trained to understand human enquiries and process them independently - whether from employees or customers. AIMAX^® can be addressed via a wide variety of channels: e.g. via Microsoft Teams, email, chat platforms such as WhatsApp or Signal, or in customer-facing applications.

When an enquiry comes in, AIMAX^® analyses the content with the help of natural language processing and generative AI (GenAI) and recognises the context and intention.

AIMAX^® is particularly powerful when it comes to accessing knowledge databases or documentation. It can access company knowledge - for example from Confluence pages, Jira tickets, FAQs or databases - and draws on precisely the information required to answer the question. This enables the AI agent to provide fast, precise answers to even complex questions without the need for a human to carry out laborious research.

AIMAX^® also overcomes language barriers with ease: it works multilingually and can process questions in different languages and generate answers, even if the underlying company knowledge was originally only available in one language.

The highlight is how AIMAX^® and EMMA work together. Both systems have been developed to complement each other seamlessly and each bring their strengths to bear. EMMA is unbeatable in the processing of transactions and click paths, while AIMAX^® is strong in understanding, deciding and responding. The synergy results in a powerful hyperautomation duo:

• AIMAX^® serves as a front-end assistant: it receives requests, whether from the employee ("Please create a report for me on last week's sales figures") or from the customer ("Where is my order?"), and understands what needs to be done.
• AIMAX^® uses its AI intelligence to carry out a preliminary analysis if necessary - e.g. to extract relevant data points from the enquiry or prepare suitable answers.
• Where necessary, EMMA comes into its own: AIMAX^® can trigger automated workflows in EMMA to execute actions in background systems. For example, AIMAX^® could instruct EMMA to download the desired report from the ERP system, prepare data or search for an order in the system and retrieve status information.
• EMMA carries out these steps automatically, quickly and in a documented manner. Each process step - be it opening an application, entering data or reading a value - is completed by EMMA without the need for employee intervention.
• The results of the RPA action flow back to AIMAX^®, which communicates them to the requester in a comprehensible form. Ideally, the employee or customer receives their answer or completed transaction within seconds, without any manual back and forth.

This close integration means that repetitive tasks and creative/cognitive tasks can be completed in one go. EMMA takes care of the routine, AIMAX^® takes care of the flexibility - and together they deliver what can be described as hyperautomation in its purest form. The advantages are obvious: greater efficiency, shorter response times, fewer errors and enormous scalability. The latter means that this duo could theoretically process hundreds of requests in parallel without any loss of quality - something that would be unthinkable with purely manual processing. This makes the combination of AIMAX^® and EMMA a real game changer for companies. It offers an ideal setting for effectively managing both recurring routine work and complex tasks.

Theory and technology are one thing - but what does hyperautomation with RPA and AI actually look like in practice? There are use cases in almost every area of business where the combination of RPA bots and AI agents is revolutionising processes. Here are some cross-industry use cases for inspiration.

Today's customers expect fast and precise responses. An AI agent such as AIMAX^® can automatically analyse incoming customer enquiries, such as emails or chat messages. Thanks to the connection to knowledge databases and CRM systems, it immediately generates a suitable response - personalised and tailored to the case. If a backend system needs to be consulted for clarification (e.g. checking delivery status, booking returns, changing customer data), an RPA bot takes over this interaction in the background. Example: A customer asks about the status of their order via chat. The AI agent checks the enquiry, extracts the order number, triggers a check in the ERP system via RPA and replies to the customer in seconds with the expected delivery date. The benefits: drastically reduced response times, less work for the service team and greater customer satisfaction.

HR departments have many repetitive administrative tasks - perfect for RPA. For example, an RPA bot can take over the processing of holiday requests, sick notes or time sheets. It pulls the data from emails or forms and automatically updates the HR system. AI comes into play in recruiting: modern applicant management systems use AI to read CVs (CV parsing). An AI agent can answer applicants' questions in a chat or coordinate appointments. Example: A new employee is to be onboarded. The AI agent guides them through all the important information via chat, answers questions about filling out the forms, while RPA bots automatically create a user account in the background, inform IT about the required hardware and create the entry in payroll accounting. This makes onboarding faster and more consistent and saves HR employees time, which they can use for personal welcome or strategic tasks.

Hardly any other area offers as many rule-based processes as accounting - a paradise for RPA. Invoice processing is one example: an RPA bot can open incoming PDF invoices, an AI component (OCR) reads the relevant fields (supplier, amount, date) and the bot automatically posts the invoice to the ERP system. In the event of discrepancies (e.g. amount does not match the order), the AI flags this for verification by a human. Reporting and controlling also benefit: RPA can be used to regularly collate, format and send financial reports from different systems. Example: At the end of the month, management needs a financial report. Instead of controllers spending hours manually collating figures, a hyper-automation workflow starts: RPA bots extract the required data from accounting, CRM and Excel lists; an AI checks anomalies or trends in the figures and perhaps even formulates explanations; at the end, the decision-maker automatically receives a finished report in their mailbox - punctually and reliably every month. This not only saves time, but also increases accuracy (fewer typing errors, no forgotten items) and frees up the team to analyse rather than collect data.

Hyperautomation also opens up new possibilities in customer-facing areas. Take the preparation of quotations in sales: an AI-supported system can analyse customer requirements and immediately generate a suitable draft quotation, including prices and conditions. An RPA bot fills this data into the quotation template, obtains approvals if necessary and sends the quotation to the customer. What used to take days now takes minutes - an immense advantage in the race to close the deal.

Marketing, on the other hand, uses AI agents to qualify incoming leads (analysis of contact forms, social media) or create personalised newsletters, while RPA ensures data synchronisation in campaign tools, CRM and web analytics. Example: An AI agent monitors social media channels and recognises when a user signals interest in a product. It collects the information, creates a profile of the lead; an RPA bot automatically enters this lead into the CRM and informs a sales employee or sends initial information directly to the potential customer. This means that no opportunities are lost and the marketing team can concentrate on creative campaigns instead of manual data maintenance.

There are also many starting points in internal service departments - from IT helpdesk automation to public administration. An AI agent can act as a virtual IT assistant for employees: Resetting passwords, requesting access rights, querying the status of IT tickets - a well-trained AI agent can answer or execute many of these tasks directly (possibly with RPA support to make the changes in the Active Directory or ticket system). Example: An employee writes in a chat: "I've forgotten my password." The AI agent authenticates the person using defined security questions and then triggers a password reset in the system via RPA. The problem is solved in seconds without the service desk having to intervene. In public administration, citizens could submit applications online and an AI system answers common questions or requests missing information while RPA enters the data into the authority's legacy systems. The result: faster processing times, less paperwork and less work for the clerks.

These examples are just a selection - the possible applications of RPA and AI are almost unlimited. Further examples for inspiration can be found in the Applications section. Wherever standardisable processes meet intelligent decision-making, hyperautomation can create added value.

What is important for decision-makers is that it is not a question of reorganising all processes at once. Rather, you identify the best candidates (frequent, time-consuming, error-prone processes) and start there.

Even individual automation projects can deliver tangible results that prove the success of the method. Internally, word spreads quickly and promotes acceptance.

Many companies start with pilots in areas such as finance or customer service and then roll out the lessons learnt to other departments. This gradually creates an automated process landscape - true hyperautomation.

Looking ahead, it is clear that hyperautomation will permanently change the way we work. Companies that embrace this trend early on will gain tangible competitive advantages. On the one hand, they can achieve more with the same resources without having to hire more staff. On the other hand, automated processes increase a company's resilience. In times of uncertainty (think pandemic or sudden fluctuations in demand), automated processes can be adapted and ramped up more easily without immediately creating bottlenecks. The company becomes more agile overall because it is less dependent on the limited human factor, at least for routine tasks.

Another aspect of the future is the rapidly advancing development of AI. AI systems are getting better and better at performing human-like tasks - whether in customer interaction (keyword: conversational AI), in decision support or in forecasting. With future advances in areas such as generative AI, image processing and language understanding, it will be possible to automate even more processes that may be reaching their limits today. Hyperautomation is therefore a continuous process: what is still manual today could be automated tomorrow because the technology makes it possible. Decision-makers would do well to promote a culture of automation within the company that constantly examines and realises such opportunities. This also means taking employees with them - their roles will continue to change, from data collectors or clerks to controllers, strategists and creatives who monitor the automated systems and take on the really demanding tasks.

Hyper-automation offers competitive advantages in many ways:

• Cost leadership: automated companies can offer services more cheaply as time and resources are saved internally.
• Quality and compliance: RPA and AI work according to rules and log everything. This minimises errors and failures and ensures compliance with regulations - a selling point to customers (e.g. "fewer errors in order processing") and regulators.
• Speed: A fully automated process - from order entry to delivery, from customer enquiry to solution - runs significantly faster than competitors with traditional processing. Faster quotations, faster deliveries and faster response times mean a head start in the market.
• Innovation: When routine tasks are eliminated, employees have more freedom for creative activities and innovation. Companies can concentrate more on new products, services or business models. Hyperautomation is therefore also a driver of innovation because it creates capacity for development.
• Attractiveness as an employer: Younger generations in particular welcome the use of modern technologies. A company that works with AI and automation signals that it is at the forefront of digitalisation. In addition, the chances of retaining talent increase if they are freed from monotonous work and can focus on interesting projects.

Finally, hyperautomation is expected to become standard in the medium term - just as it is already taken for granted today that emails are no longer delivered by hand but sent electronically. Gartner puts it bluntly: companies with outdated processes and a high manual workload will find it difficult to survive in the future. Hyperautomation is advancing from an optional extra to an obligation in order to remain competitive.

But this also presents an opportunity: many industries are still at the beginning of this journey. Those who invest boldly now and launch pilot projects can secure a first-mover position. The learning curve that a company goes through today will become a knowledge advantage tomorrow compared to competitors who enter the market later.