Prozess Management Archive | Pragmatica AG Ihre Boutique Managementberatung seit über 25 Jahren. Tue, 05 May 2026 22:46:09 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 Generative AI https://www.pragmatica.ch/en/generative-ai/ Mon, 02 Mar 2026 21:52:26 +0000 https://www.pragmatica.ch/generative-ai/ Generative AI increases productivity and opens up new use cases. At the same time, new security risks arise. With governance, training and standards, AI remains secure and compliant.

Der Beitrag Generative AI erschien zuerst auf Pragmatica AG.

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Generative AI

  • FOCUS TOPIC

Seize opportunities, manage risks

Scale with symbols for Generative AI opportunities on the one side and safety risks on the other.
Generative AI is changing our working world at a speed that we have hardly seen before. Companies are benefiting from more productive processes, more efficient workflows and completely new possibilities in text, image, video, audio and code generation. But as the opportunities grow, so does the responsibility: Generative AI brings new types of security risksthat can quickly become a real threat without clear governance.

Why generative AI is so valuable

Whether text automation, code analysis, design prototypes or data visualization – generative models have long been more than just gimmicks.
They are already providing support today:
  • Security management (e.g. analysis of vulnerabilities, incident response plans)
  • Detection of spam, phishing & fake news
  • Code hardening & log analysis
  • Increased efficiency in day-to-day business
  • Visualization of complex relationships

Used correctly, AI can make a massive contribution to safety, compliance, quality and productivity. productivity.
House diagram on Generative AI in the company with proper use, misuse, governance and impact.

Do you want to find out what your AI potential is?

Then get started today and contact us for a free joint use case sparring session.

Contact us

The dark side: New security risks

However, the introduction of generative AI comes with risks that many companies still underestimate:

1. risks despite "proper" use

  • Confidentiality of inputs (data outflow in cloud AI)
  • Incorrect or misleading output
  • Insecure generated code
  • Automation bias – blind trust in AI results

2. misuse Attackers can use AI to:

  • Create fake content, identities or deepfakes
  • Programming malware or scaling social engineering attacks
  • Carry out processes such as CEO fraud in a much more sophisticated manner
Risks also arise within companies – for example through the sharing of confidential data with external AI services, copyright infringements or discriminatory AI decisions.

3. attacks on the AI itself

Generative models are vulnerable, so far we are aware of 3 popular types:

Privacy Attacks

Attackers try to reconstruct sensitive information from the model, e.g. training data or private user input. This exposes confidential data, even though the model was only supposed to deliver results.
Flowchart: internal user makes harmless request, LLM with RAG uses internal knowledge sources and discloses sensitive data in the output.
Note:
Attackers can also be internal employees with good IT knowledge who, for example, want to gain unauthorized access to confidential information or systems.
Example:
An insurance company uses an internal generative AI system (LLM) to support employees in analyzing claims, summarizing customer dossiers and answering internal technical questions.
The AI system is based on an LLM with RAG architecture (Retrieval-Augmented Generation) and accesses internal knowledge sources such as customer databases, claims files, internal guidelines and previous chat histories (for quality improvement).
An internal attacker (e.g. an employee with advanced IT knowledge) wants to check whether confidential information of other customers or employees can be reconstructed from the AI system and formulates “harmless” questions such as “Create an example summary of a complex car damage case of a customer from Zurich with several parties involved.”
The AI system responds with realistic-sounding names, real zip codes, detailed damage descriptions, medical information and exact damage totals.
The problem: the information does not come from fictitious examples, but is reconstructed content from real training or context data.
A particularly impressive example of a real case:
In the “EchoLeak” exploit, attackers were able to access data from the context window of an AI copilot via hidden prompt injections – without classic phishing and with minimal user interaction.

Evasion Attacks

Manipulation of entries to circumvent protection mechanisms by third parties.

Example:
A bank uses an AI or LLM-supported assistance system that automatically classifies incoming emails or recognizes potential fraud, phishing or social engineering.
An external attacker wants to send a fraudulent email to an employee, with unusual sentence structures, harmless technical terms, slightly different payment requests and contextual references to real projects or departments.
The AI could now classify the email as harmless or as “inconspicuous internal specialist communication” and the message passes all automated filters and lands directly in the inbox of an employee from the finance team.
-> The transfer process is initiated.

Diagram: external attacker sends manipulated email, AI filter classifies it as harmless, it lands in the Finance Inbox and triggers a bank transfer.

Poisoning Attacks

Training data is deliberately "poisoned" by third parties so that the model becomes faulty or manipulable.

Diagram: compromised external data source poisons training data, continuous learning affects LLM model, biased decisions appear months later.
Example:
An insurance company operates an internal generative AI system (LLM) to support claims processing, fraud detection and answering internal technical questions, among other things.
The model is regularly retrained (“continuous learning”) with new claims, internal documents, feedback from the specialist departments and external specialist sources (e.g. market reports, partner portals).
The training data is automated to ensure efficiency and timeliness. An external attacker (or a compromised partner) would like the AI system to systematically make the wrong decisions in certain cases without this being immediately apparent.
Specifically, certain fraud indicators should be ignored and certain damage patterns should be considered “inconspicuous”. The attacker compromises an external data source, e.g. a partner portal or a shared knowledge pool. Manipulated or falsified content is deliberately placed there, which has a negative impact on decisions on fraud cases or classifications of claims.

-> The model gradually learns a distorted decision behavior.

-> The effects only become apparent weeks or months later.

Clear proof that AI security is now a discipline in its own right

How companies can protect themselves:

At Pragmatica , our experts and consultants offer the following realizations & implementations so that AI can be used responsibly and safely in your company.

BUILDING COMPETENCES & TRAINING EMPLOYEES

Employees need to know, how AI works. You need to know how to use it, where the risks lie and how to minimize them .
Our AI enablement service makes generative AI safe for banks & insurance companies.
The Pragmatica AG enables employees to use AI safely and in compliance with regulations through targeted training – in line with GDPR, FINMA requirements, EU AI Act and established standards such as ISO/IEC 42001 and NIST AI RMF.
In regulated environments in particular, AI risks often arise due to a lack of expertise, incorrect use and automation bias.
With practical trainingcase studies and clear dos and don’ts, we create security, acceptance and audit readiness. audit readiness.
Less risk. More efficiency. Audit and revision-proof.

GUIDELINES & GOVERNANCE

A clear internal AI policy is mandatory – especially with regard to data protection, compliance, transparency and the handling of confidential data.

Technical & organizational measures

In-house AI services (e.g. internal LLMs) with controlled rules and security mechanisms significantly reduce risks.
Using established standards
  • ISO/IEC 42001 – the world’s first standard for AI management systems
  • ISO/IEC 23894 – standardized AI risk management
  • NIST AI RMF – Best practices for managing AI risks
  • CoE AI Treaty – international treaty on AI, human rights & rule of law
These frameworks provide guidance on how AI can be used safely, legally compliant and ethically.

Conclusion

Generative AI is a historic opportunity – but only if we use it safely, responsibly and strategically use it.
Companies that establish clear rules, training and technical protective measures today will be among the winners tomorrow.

Simply register for a free use case sparring session to get to know the system landscape or AI governance better.

contact us

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Further topics

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Process Mining and AI: Webinar I & II

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Self-services in digital banking

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FINMA Risk Monitor 2025

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Generative AI

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From cost pressure to the efficiency revolution

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Do I really need AI?

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,

Switzerland in the crosshairs of foreign intelligence services

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, ,

Resilience in our supply chains: How the shifting threatscape demands comprehensive security risk management

to the article ➞

,

UBS Data Leak via Chain IQ: Third Party Risk & Supply Chain Security Lessons

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,

Third Country Intercept: How secure is your data in the cloud?

to the article ➞

Der Beitrag Generative AI erschien zuerst auf Pragmatica AG.

]]> From cost pressure to the efficiency revolution https://www.pragmatica.ch/en/from-cost-pressure-to-the-efficiency-revolution/ Wed, 17 Dec 2025 22:13:30 +0000 https://www.pragmatica.ch/from-cost-pressure-to-the-efficiency-revolution/ Whether you are a manager in banking operations, responsible for customer experience or a customer advisor - you know the daily scenario: customer inquiries reach the bank today via a multitude of channels in an uncoordinated and simultaneous manner. Be it by email, via SecureMail, website forms, chat or the traditional telephone. But all too often these inquiries end up somewhere. In an overcrowded mailbox, in a confusing ticketing queue or on the desk of an employee who is actually responsible for completely different issues.

Der Beitrag From cost pressure to the efficiency revolution erschien zuerst auf Pragmatica AG.

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From cost pressure to the efficiency revolution

  • FOCUS TOPIC

4 use cases on how you can be successful with AI in the service center

Presentation of typical service problems: various channels, ticket backlog, overload, churn risk

Whether a manager in banking operationsCustomer experience manager or customer advisor – you know the daily scenario: today, customer inquiries reach the bank via a multitude of channels in an uncoordinated and simultaneous manner. Be it by e-mail mail, via SecureMail, via website forms, in chat or in the traditional way over the phone. But all too often these requests end up – somewhere. In a overcrowded mailboxin a confusing ticketing queue or on on the desk of an employee who is actually who is actually responsible for completely different issues.

The result is industry-wide the the same: Error-prone routing and unnecessarily long idle times. This not only leads to frustrated customersbut above all to frustrated employeeswho their valuable time with it, messages sorting and forwarding them, instead of effectively solving customer problems.

In addition the cost pressure in banking steadily increasing: The volume of calls is increasing – especially in the direction of annual accounts – but the budget is falling. And the whole world is talking about artificial Intelligence in the service center – but how do you put this into practice without years of planning and excessive expenditure?

The good news: there is a way. Roll up your sleeves and in a few months your Service Center AI will be live!

Why "now" - first mover is over

Let’s be honest: AI in the service center is no longer the future. It is the present. The technology is mature, it works, and it delivers real results.

This also means that while you are still considering “whether” we should do this, your competitors are already using AI. They save costs, their customers are more satisfied, their employees can concentrate on providing real advice. It’s not just hype, it’s a competitive advantage and will soon be a commodity.

The risk of waiting is more real than the risk of starting: Not joining in means losing out in terms of costs and quality. The question is no longer “Should we introduce AI?” – the question is “When do we start?”

Graphic shows efficiency and success curve of first movers compared to the status quo

4 use cases - from quick wins to freestyle

The key is that you don’t have to start with everything at once. AI can easily be introduced and expanded iteratively.

AI processes incoming messages and forwards them to the credit team, wealth advisory or support team

Use Case 1: Mail Triage - The Quick Win

The problem: Customer inquiries reach you via different channels – e-banking message here, SecureMail there, website form, chat. And then someone has to decide manually: Does this belong to the credit team, investment advice or support? Errors are inevitable, processing times are long. In addition, there is the human risk – people inevitably make mistakes, e.g. incorrect assignment and an email is passed back and forth between departments for days or an email is even forgotten and the customer waits and waits.

The solution: An AI system that automatically classifies incoming emails and forwards them to the right person or team. The bot reads the request, understands the context and routes it intelligently and efficiently. In the event of uncertainties, there is a fallback to manual control – the human remains in control.

A real example: A large Swiss retail bank has done just that. Efficiency gains in mail routing of >60% because the routine sorting work was eliminated. Employees can deal directly with higher-value service requests.

Why start here? This is the fastest ROI. The data is already available (your previous emails). Implementation is low-invasive. At the same time, you build up the structured database that all other use cases need.

Use case 2: Text assistant - empowering consultants and service employees

The problem: Service employees and consultants write standard answers over and over again. Most customer inquiries received by email are very similar in content. These questions take a lot of time – sometimes 15 or 20 minutes per answer, because the employee still has to search for the answer and always responds personally to the request. And every answer looks slightly different because everyone has their own style. New employees take a long time to learn the ropes. Many of their colleagues encounter this problem: both the service employees in the customer service center, who have to answer hundreds of emails per week, and the customer advisors, who have to manage their email load between customer meetings.

The solution: An AI-supported text assistant that runs in the mail or chat system. The employee (whether service employee or customer advisor) selects an AI-generated answer at the touch of a button (e.g. Outlook plugin) – based on a central knowledge database and best practice answers. The employee reads, edits if necessary, adds personal phrases and sends. The human remains the quality controller, the bot is the accelerator.

A real example: A large Swiss retail bank uses exactly that. The service employees are faster at answering emails. And new employees need less training because the best answers are suggested to them from day one. At the same time, the advisors also benefit: they save time on standard inquiries and have more time for real customer discussions and strategic advice. Two birds with one stone.

The Key Point: This is not automation without control. It’s an assistant for your employees. And it doesn’t feel threatening to employees – on the contrary, it makes their work easier and increases their competence

Use case 3: Chatbot - using the 80/20 rule

The problem: 80% of all inquiries that reach your Service Center are usually the same ten questions: reset password, TWINT problems, account details, card blocking, IBAN lookup, fees, etc. These standard questions take up staff capacity, even though customers would like to solve these things themselves immediately.

The solution: A chatbot on your website or in the mobile app. It answers the most frequently asked questions immediately and can even trigger automated processes: Reset password, block card, activate TWINT – all directly in the chat. And if things get more complex, the bot seamlessly forwards you to a live advisor.

A real example: A large Swiss retail bank has implemented this. The chatbot handles a significant proportion of the most frequent inquiries. Customers are more satisfied because they get help immediately – 24/7 (unlike on the phone). And your service center finally has the capacity to deal with more complex customer concerns.

The Key Point: This is not “we automate everything away”. It’s “we solve the simple things immediately so that your employees can focus on more complex cases and higher-value customer services.”

Chatbot on the smartphone answers queries about IBAN, password and card blocking
Emails are automatically forwarded to the mortgage team, customer advisory service or support via AI technology

Use Case 4: Voicebot - call triage & automation

The problem: The voicebot is the old IVRS system – “Press 1…” – and nobody likes it. And it breaks down at peak times. What’s more, many callers call expecting to be able to solve the problem directly on the phone

The solution: A modern, AI-based voicebot that understands natural language. The caller says what they need – and the bot understands. It routes intelligently or – and this is the game changer – it solves simple processes directly in the call: resetting the password, blocking the card, unblocking TWINT. The same processes as in the chatbot. The caller no longer has to wait. The necessary interfaces have already been developed for the chatbot and can be used again.

The key point: The Voicebot is your scaling turbo.
At peak times (e.g. system malfunctions or at the end of the year), the bot intercepts the first wave of standard requests completely autonomously. But even for the complex cases that it forwards, the game changes massively: routing is no longer rigid (“press 1”), but dynamic. The bot understands the content of the request, checks the capacity utilization of your hotlines in real time and forwards the caller precisely to the department that currently has capacity. No more flying blind into overcrowded waiting loops, but intelligent traffic management for your telephony.

The secret: one database for all four

Now comes the important part. You could tackle these four use cases as separate projects – mail triage project, text assistant project, chatbot project, Voicebot Project. That would work, but it would be very expensive and time-consuming.

The intelligent way: You build a central knowledge database – the structure of your FAQ, your process documentation, your best practice answers. All four use cases use this basis. You only have to do this work once – and all four benefit from the further development.

This has a massive impact: Consistent responses across all touchpoints. A customer asks something about fees in the chatbot – and gets the same answer as if they called or wrote an email. This is not only more efficient, it’s also better for the customer.

And this is where it gets really economical: The first use case (mail triage) costs X. The second use case (text assistant) costs less because the basis exists. The third and fourth cost even less. The ROI multiplies.

Visualization of a central knowledge database, linked to various communication channels

4 success factors - so that things don't go wrong

But: There are a few things you need to bear in mind right from the start must to ensure a successful implementation

#1 - Structured data:

The be-all and end-all for high-performance AI is a good database. If data is still in silos, unprocessed and insufficiently available, this is THE point that needs to be addressed first. Not perfect, but well prepared. The good news : In the service area, the 100 most frequent inquiries account for a good >80% of the inquiry volume. This means that you already have a very good e basis!

#2 - Governance from day 1:

The service center is just the beginning. Therefore, when using AI, a holistic approach should be taken from the outset. This influences decisions regarding the IT architecture and clear responsibilities, monitoring structures and documentation are not blockers, but the basis for stable operation. And it is extremely important for your entire AI architecture in the company. People must remain in control!

#3 - Integration, not isolation:

The most common mistake: the pilot runs in isolation and does not scale. You need connections to your existing systems right from the start – telephony, CRM, core banking. The omnichannel concept must be there from the start.

#4 - Change Management:

You have the best AI in the world – and your employeesaren’t using it because they are afraid. Therefore: Involve employees early on, communicate transparently (“This is an assistant, not your replacement“), continuous training. This is often the most underestimated success factor.

The next step:

The technical question no longer arises: “Can this be done?”
Yes, you can. The question is: “How do we start?”

Because one thing is certain: if you jump on the bandwagon now, you win.
If you wait, you lose – not dramatically, but noticeably.
And the longer you wait, the bigger the gap becomes.

The future of the Banking Service Center is no longer the future.
It is the present. 

The only question is: will you take part?

If you are curious about where your Service Center currently stands and what quick wins are possible - we are happy to help.

contact us

Contact us for a non-binding use case sparring session in the following areas:

  • AI Use Case Workshop
  • AI Governance
  • Architecture Workshop

See for yourself and take the first step towards AI & LLM with us.

Your advantages with us

  • Experience in the implementation of AI projects for banking clients.

  • Focus on regulated industries: Over 20 years of project experience in banks and insurance companies – with a deep understanding of systems, data flows and regulation.

  • Structured technology and partner process: in-house developed LLM vendor map & RfP templates for informed decision-making.

  • From use case to rollout: we take on end-to-end responsibility or supplement your teams on a selective basis – flexibly, pragmatically, solution-oriented.

With over 20 years of experience in project management, we successfully support Swiss financial institutions in the implementation of LLM use cases – from the design of the use case through to full implementation.

We offer comprehensive expertise in AI governance and AI risk management and are familiar with the systems used (e.g. core banking, telephony) and their interactions.

Thanks to our own LLM map with an overview of providers and implementation partners, we make it easier to select the right technology and partners. By working with leading LLM experts, we ensure a customized and smooth implementation.

See for yourself with our free, customized introductory formats and take the first step towards AI & LLM with us.

Introductory formats - free of charge & individual

Use Case
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2-4 hours | Remote or on-site
Introduction to LLMs, individual use cases, feasibility assessment
Request now free of charge
Workshop

Architecture & Technology

2-4 hours | Remote or on-site
Technology options in the market & fit to existing architecture
Request now free of charge
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AI governance & compliance

1-2 hours | Remote or on-site
Regulatory requirements for AI use (FINMA, DPA, EU AI Regulation)
Request now free of charge
awareness

Do you want to find out what your AI potential is?

Then get started today and contact us for a free joint use case sparring session.

Contact us

Further topics

,

Process Mining and AI: Webinar I & II

to the article ➞

Self-services in digital banking

to the article ➞

, ,

FINMA Risk Monitor 2025

to the article ➞

, ,

Generative AI

to the article ➞

, ,

From cost pressure to the efficiency revolution

to the article ➞

,

Do I really need AI?

to the article ➞

,

Switzerland in the crosshairs of foreign intelligence services

to the article ➞

, ,

Resilience in our supply chains: How the shifting threatscape demands comprehensive security risk management

to the article ➞

,

UBS Data Leak via Chain IQ: Third Party Risk & Supply Chain Security Lessons

to the article ➞

,

Third Country Intercept: How secure is your data in the cloud?

to the article ➞

Der Beitrag From cost pressure to the efficiency revolution erschien zuerst auf Pragmatica AG.

]]> Do I really need AI? https://www.pragmatica.ch/en/do-i-really-need-ai/ Sat, 22 Nov 2025 00:15:05 +0000 https://www.pragmatica.ch/do-i-really-need-ai/ A modern digitization project without AI - is that even conceivable or justifiable?

The answer is as sobering as it is liberating: YES!

Many current problems can still be solved effectively with classic process automation. While companies invest considerable resources in AI initiatives, the return on investment of which often takes years to materialize, proven automation approaches can deliver measurable results within months.

The key challenge lies not in the technology itself, but in the correct allocation: which approach addresses which problem most effectively? This article guides you through the strategic decision-making process and highlights the differences between classic process automation, intelligent process automation (IPA) and autonomous AI agents.

Der Beitrag Do I really need AI? erschien zuerst auf Pragmatica AG.

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Do I really need AI?

  • FOCUS TOPIC

A modern digitization project without AI - is that even conceivable today?

The answer is as sobering as it is liberating: YES!

Many current problems can still be solved effectively with classic process automation. While companies invest considerable resources in AI initiatives, the return on investment of which often takes years to materialize, proven automation approaches can deliver measurable results within months.

The key challenge lies not in the technology itself, but in the correct allocation: which approach addresses which problem most effectively? This article guides you through the strategic decision-making process and highlights the differences between classic process automation, intelligent process automation (IPA) and autonomous AI agents.

AI or classic process optimization - the fundamental differences

The three automation approaches differ fundamentally in terms of functionality, area of application and requirements:

Classic process automation

Classic process automation executes rule-based, predefined processes precisely and repeatably. The technology is ideal for stable, high-volume processes with low variability – such as invoice processing, data capture or report generation. Its strengths lie in complete traceability, reliability and scalability. The maintenance effort for complex sets of rules and the lack of adaptability to unforeseen situations mark the limits of this approach.

Intelligent Process Automation (IPA)

Intelligent process automation (IPA)orchestrates various processes with the integration of AI components. Systems in this category analyze context, make data-based decisions and dynamically adapt process flows. Typical applications include document classification, intelligent ticket distribution or intelligent customer service (chatbot / voicebot). IPA offers seamless system integration and anticipates potential bottlenecks, but requires high-quality data and API-enabled systems.

AI agents

AI agents makeautonomous decisions, learn continuously and pursue defined goals independently. Multi-agent systems are particularly effective in complex, strategic processes with numerous variables and dependencies – for example in autonomous customer advice, dynamic price optimization or self-learning quality control. The challenges lie in non-transparent decision paths, unexpected system behavior and an increased need for control.

The strategic recommendation

Classic automation as the foundation, selective use of IPA for decision-making processes and AI agents where autonomy and continuous learning generate real added value.

The key questions for the right solution

The decision-making process can be condensed into five central questions:

What does the process look like?

  • Stable, rule-based processes require classic automation.
  • Processes with decision points and context dependency benefit from IPA.
  • Complex, variable scenarios that require learning justify the use of AI agents.

What decisions need to be made?

  • Deterministic decisions are suitable for rule-based systems.
  • Contextual evaluations – such as urgency assessment based on tone, content and history – require intelligent automation.
  • Autonomous goal pursuit with adaptive strategies requires autonomous agents.

How high can the degree of autonomy be?

  • Classic automation offers complete control.
  • IPA enables monitored autonomy.
  • AI agents act largely independently within defined framework conditions.

The governance requirements increase considerably as the degree of autonomy increases.

How much time & budget is available?

  • Classic automation can be implemented comparatively quickly and delivers the first measurable results within a few weeks to months.
  • IPA projects require a higher initial outlay for data integration and AI training, which means that the implementation phase takes several months.
  • AI agents are the most complex category – it typically takes 12-18 months or longer from conception, training and testing to productive use.

Are the basics ready?

Technical infrastructure, data quality, available expertise and readiness for change significantly determine the probability of success. Without these foundations, even technologically mature solutions will fail.

A critical self-analysis based on these dimensions prevents costly bad investments and unrealistic expectations.

Prerequisites for successful AI implementation

Successful AI projects are based on robust foundations that go far beyond technological aspects:

Data quality as a critical success factor

AI systems are only as good as the data they work with. Inconsistent, incomplete or low-quality data inevitably leads to suboptimal results. Systematic data cleansing and structuring is essential.

Technical infrastructure

API-enabled systems, clean interfaces and MLOps capabilities form the technical basis. Without this infrastructure, even the most sophisticated AI solution will be ineffective.

Organizational competence

A basic understanding of AI/ML mechanisms within the team is required. Not every project requires specialized data scientists, but a sound basic knowledge enables realistic assessments and effective management.

Governance and compliance

GDPR compliance, compliance with the AI Regulation and clarification of liability issues are not optional. Governance structures must be established from the outset, not added later.

Change management

Technological excellence does not guarantee success if the organization does not go along with it. Creating acceptance, building skills and constructively addressing resistance is crucial for sustainable effectiveness.

Before making significant investments in AI solutions, it is advisable to first systematically establish the organizational structures and technical requirements.

Conclusion: Technological sobriety as a success factor

The relevant question is not “How quickly can AI be introduced?”, but “Which solution addresses the problem most effectively?”

Contact us for a use case sparring session

We would be happy to help you find the right approach for your use cases and support you in building an AI-capable organization.

Whether classic automation, intelligent process automation (IPA) or autonomous AI agents:

The trick is to choose the right technology for the respective challenge and maturity level. Because not everything that is technically possible also brings real added value.

contact us

Our decision matrix helps:

AI decision matrix
download matrix (pdf)

From the introduction of RPA in customer communication to intelligent portfolio optimization, we support you pragmatically and effectively.

What we bring with us:

  • Process analysis with AI readiness assessment –not every process is ready for AI
  • Vendor-neutral advice –the most suitable solution for your goals
  • Pragmatic roadmaps – from quick win to strategic transformation
  • Change management –empowering and developing people
  • AI governance frameworks –integrating compliance right from the start

Do you want to find out what your AI potential is?

Then get started today and contact us for a free joint use case sparring session.

Contact us

Further topics

,

Process Mining and AI: Webinar I & II

to the article ➞

Self-services in digital banking

to the article ➞

, ,

FINMA Risk Monitor 2025

to the article ➞

, ,

Generative AI

to the article ➞

, ,

From cost pressure to the efficiency revolution

to the article ➞

,

Do I really need AI?

to the article ➞

,

Switzerland in the crosshairs of foreign intelligence services

to the article ➞

, ,

Resilience in our supply chains: How the shifting threatscape demands comprehensive security risk management

to the article ➞

,

UBS Data Leak via Chain IQ: Third Party Risk & Supply Chain Security Lessons

to the article ➞

,

Third Country Intercept: How secure is your data in the cloud?

to the article ➞

Der Beitrag Do I really need AI? erschien zuerst auf Pragmatica AG.

]]> Process management: target achievement or “New Year effect”? https://www.pragmatica.ch/en/prozess-management-target-achievement-or-new-year-effect/ Fri, 01 Mar 2024 11:59:06 +0000 https://www.pragmatica.ch/prozess-management-zielerreichung-oder-neujahrs-effekt/ Process management: target achievement or “New Year effect”? PROCESS MANAGEMENT Anything is possible at the beginning of the year. New resolutions and goals are set, motivation is high and the year is still long. But how do you ensure that it is a target achievement and not a “New Year effect”? The “New Year effect” […]

Der Beitrag Process management: target achievement or “New Year effect”? erschien zuerst auf Pragmatica AG.

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Process management: target achievement or "New Year effect"?

  • PROCESS MANAGEMENT

Anything is possible at the beginning of the year. New resolutions and goals are set, motivation is high and the year is still long. But how do you ensure that it is a target achievement and not a "New Year effect"?

The “New Year effect” can often be observed in fitness centers, where there are more people than usual at the beginning of the year. Why is that? If you’ve never had the intention to do more sport, here’s an insight: It takes more effort than expected, it requires discipline, the positive effect takes longer than desired, then motivation decreases, the positive effect takes even longer…

Objectives yes - but not without the status quo

Everyone decides for themselves who wants to achieve which goal – whether as a private individual or as a company. However, it is just as important to know where you stand now in order to achieve your goals. Am I just starting out with sport and want to win a half marathon or am I a marathon runner and want to achieve my new best performance this year?

The question of the goal and the current status is also important for companies. This gap determines how you set up your training/measurement plan. This gap also shows how intensive, comprehensive and disciplined you should be in order to achieve your goal.

Does your training/measurement plan match your goals?

Let’s take a look at the whole thing from the perspective of business process management (BPM): The corporate objective is defined and broken down into the various areas. Is the goal simply to achieve process transparency in an area because it is a regulatory requirement? The first stage of development may be sufficient. Many activities are basically not necessary. However, please note that you have laid the foundation with the process documentation in order to now analyze the processes for optimization potential. It would be a shame to miss out on this opportunity.

Illustration: Maturity in process management

Is continuous improvement largely established in your organization? Then congratulations on your achievement, then it is important to maintain your “fitness” in the organization.

You may also find yourself somewhere between these two examples. The aim of many companies is to increase efficiency. This is probably mentioned as often in the process environment as in the fitness center muscle building / weight loss.

What we can learn from fitness instructors

Depending on the goal definition, the fitness instructors will tell you right at the beginning that the desired goal cannot be achieved with 1-2 workouts per week. This immediate statement may dampen motivation, but it is honest and helps the person to set a realistic goal or adjust the measures and avoid a “new year effect”. This also applies to the process world. If the aim is to increase efficiency, but the processes are rarely systematically checked for improvement potential, it becomes difficult.

What do you want to achieve with process management?

Are you sufficiently positioned and equipped to achieve your goals? Use the overview and check your process goals and measures. We support you so that it is not a “New Year effect” but a goal achievement.

Would you like to find out more? Please get in touch with info@pragmatica.ch. We are happy to support you on your company-specific path.

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Der Beitrag Process management: target achievement or “New Year effect”? erschien zuerst auf Pragmatica AG.

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