AI projects in SAP: The real reasons for failure – and an implementation approach that works

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Why many AI projects in the SAP environment fail (and how to do it right)

Hello everyone,

Our experience in recent months shows that Most AI projects do not fail because of the technology. They fail because of false assumptions.

We are seeing a recurring dynamic in the SAP environment in particular:

  1. High expectations
  2. Rapid pilot projects
  3. Disillusionment in the company

Why is this the case – despite mature technologies?

The actual causes (a deeper look)

1. lack of process integration

Many AI solutions run “alongside the SAP system”.

Consequence:

  • No use in everyday life
  • No measurable impact

AI must be part of the transaction flow (e.g. directly in Fiori apps)

2. technical silos (BTP vs. core)

Typical pattern:

  • Model runs on BTP
  • SAP Core remains unchanged
  • Integration is minimal

Result:

  • Media breaks
  • Lack of scalability

Successful projects think architecture end-to-end

3. underestimated operating expenses

After go-live, the real work begins:

  • Model monitoring
  • Retraining
  • Data maintenance

Many business cases completely ignore these costs

4. lack of ownership

Who “owns” the model?

  • IT?
  • Department?
  • Data Team?

Without clear accountability:
→ no sustainable operation

5. unrealistic expectation of automation

AI is often seen as a substitute for decision-making.

In reality:

  • AI supported
  • Man decides

Successful projects clearly define this boundary

An approach that works in practice

Phase 1: Use case validation

  • Clear business impact
  • Check data availability
  • Quick prototype

Phase 2: Integration

  • Embedding in SAP processes
  • Consider UI/UX
  • Define roles & authorizations

Phase 3: Operation

  • Monitoring (Accuracy, Drift)
  • Governance
  • Continuous optimization

The most important insight

AI projects are not projects.
They are products that need to be operated.

Conclusion

Anyone who treats AI like a classic SAP project will fail. Success comes to those who think about technology, processes and operations together.

You don’t just want to pilot AI, you want to use it productively?
We support you in setting up a sustainable AI architecture in the SAP environment.

See you soon and good luck!

Your amotIQ solutions team

3 reale Use Cases für KI in SAP-Prozessen (Finance, Logistik, HR)

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AI in SAP: 3 real use cases from finance, logistics and HR – ready for implementation today

Hello everyone,

Many AI demos already look really impressive.
However, very few survive the transition to productive SAP processes. Because the key question is not: What is technically possible?
But rather: What is stable, scalable and economically viable?

The following three use cases are exactly that – including technical classification.

Use case 1: Finance – AI-supported invoice validation

Technical structure

  • SAP S/4HANA (FI-AP)
  • Document recognition (e.g. via SAP AI Services / OCR)
  • ML model for anomaly detection
  • Integration via BTP

What the AI actually does

  • Matching invoices with purchase orders (3-way match extended)
  • Detection of outliers (price, quantity, supplier)
  • Classification of invoices without reference

Data requirements

  • Historical invoice data (at least 12-24 months)
  • Clean vendor master data
  • Consistent booking logic

ROI driver

  • Reduction of manual checks (30-70 %)
  • Faster throughput times
  • fewer cash discount losses

Critical point: training data quality

Use Case 2: Logistics – Demand forecasting with external influencing factors

Technical structure

  • SAP IBP or S/4 embedded PP/DS
  • Extension through ML models (e.g. to BTP or external)
  • Integration of external data (weather, market, events)

Added value compared to conventional methods

  • Consideration of non-linear effects
  • Dynamic adaptation to trends
  • Better detection of outliers

Typical challenges

  • Data integration (internal + external)
  • Model maintenance
  • Explainability to the department

Without acceptance in the planning team, the use case will fail

Use Case 3: HR – Skill-based matching models

Technical structure

  • SAP SuccessFactors
  • NLP models for analyzing CVs
  • Skill ontologies / taxonomies

Concrete benefit

  • Automatic matching of candidates to jobs
  • Identification of internal talent
  • Reduction of time-to-hire

Risks

  • Bias in training data
  • Legal requirements (EU AI Act)
  • Lack of transparency

Governance is not optional here, but mandatory

What these use cases have in common

Successful implementations are characterized by:

  • Clear integration into existing SAP processes
  • Stable data pipelines
  • Continuous monitoring of the models
  • Close cooperation between business and IT

Conclusion

AI works in the SAP environment – but only under clear conditions. The difference lies not in the algorithm, but in the integration.

Do you want to know which use case really works for you?
We evaluate your data, processes and architecture – realistically and feasibly.

See you soon and good luck!

Your amotIQ solutions team

SAP Joule in use: concrete benefits instead of big promises

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SAP Joule in a practical test: What the AI copilot really brings to everyday life

Hello everyone,

“Just ask Joule.” – That’s what the new SAP world sounds like.
But what really happens when you use Copilot in productive S/4HANA environments?

With Joule, SAP is positioning a cross-system AI co-pilot that is deeply embedded in business processes for the first time. Unlike previous assistants (e.g. embedded analytics or chatbots), Joule takes up:

  • SAP Business Data Cloud / Datasphere
  • semantic business objects
  • Context from ongoing processes

to.

The crucial question is therefore not what Joule can do, but:
Where does it deliver measurable benefits in real system landscapes?

Architecture: Why Joule is different

Joule is not simply based on an LLM front end.

In practice, we see three relevant levels:

  1. Semantic layer (e.g. CDS views)
    → defines what Joule can actually understand
  2. Context integration (Fiori, processes, roles)
    → determines when and for whom Joule makes sense
  3. LLM + orchestration (SAP AI Core / Generative AI Hub)
    → generates responses and actions

In concrete terms, this means that without proper semantic modeling, Joule remains superficial.

Concrete application scenarios from projects

1. ad hoc analyses in Finance

Example:
“Why did the margin in product area X fall in the last quarter?”

What works well:

  • Access to predefined KPIs
  • Drill-down via CDS-based models

What does not work:

  • Root cause analysis without properly modeled driver logic
  • Interpretation without a business context

Joule provides data – not ready-made decisions.

2. process support in purchasing

Example:

  • Automatic summary of order histories
  • Highlighting deviations with suppliers

Added value:

  • Save time when preparing for negotiations
  • Better transparency in operational decisions

Limit:

  • No real assessment of supplier risks without external data

3. user enablement (hidden champion)

The greatest effect is often seen here:

  • Reduction of training costs
  • Faster induction of new employees
  • Less dependence on key users

A real lever, especially in complex S/4 landscapes.

The underestimated challenge: authorizations & governance

Joule accesses sensitive data. Questions quickly arise in projects such as:

  • What data can Joule aggregate across contexts?
  • How are authorizations taken into account in generative responses?
  • How do you prevent “hallucinations” in critical reports?

Without a clean IAM and governance concept, Joule quickly becomes a risk.

When Joule really pays off

From a project perspective, Joule is particularly worthwhile for:

  • High process complexity
  • many occasional users
  • already existing semantic data modeling

It does not make sense for:

  • highly fragmented data landscapes
  • poor data quality
  • Lack of process standardization

Conclusion

Joule is not a game changer at the touch of a button.
But: In well-structured SAP landscapes, it can noticeably improve usability and decision-making. The real leverage lies not in the co-pilot – but in preparing the system for it.

Would you like to integrate Joule into your SAP architecture in a meaningful way – without expensive experiments?
We will show you which requirements are really decisive.

See you soon and good luck!

Your amotIQ solutions team

Process mining meets RPA and SAP: How data-driven automation will become a success factor in 2026

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Why traditional automation alone is no longer sufficient

Hello everyone,

Many companies are already using RPA and SAP to make their business processes more efficient. In practice, however, there is often a central problem:
Automation is carried out without really understanding the processes.

The result:

  • Bots automate inefficient processes

  • SAP processes remain opaque

  • Projects deliver less ROI than expected

This is where a topic that will become increasingly important in 2026 comes into play: process mining as the basis for intelligent automation.

For IT consulting firms with a focus on RPA, SAP and project management, this opens up a decisive competitive advantage.

What is process mining – and why is it currently so relevant?

Process mining analyses real process data from systems such as SAP and visualizes how business processes actually run – not how they are documented.

Instead of making assumptions, the optimization is based on:

  • Log data from SAP

  • System events

  • real process runs

The result: complete transparency regarding throughput times, bottlenecks and manual interventions.

This is a game changer, especially in SAP-driven companies, as SAP as a central process platform provides enormous amounts of data that can be used for optimization.

The new automation strategy: first process mining, then RPA

1. create transparency about end-to-end processes

In many companies, processes have grown historically. Documentation is outdated or incomplete.
Process Mining shows:

  • Media breaks

  • Manual workarounds

  • Inefficient loops in the SAP system

2. identify automation potential in a targeted manner

Not every process is suitable for RPA.
Data-based analysis can be used to identify processes that are ideal for automation, e.g:

  • Invoice processing

  • Master data maintenance in SAP

  • Ordering processes

  • Reporting and data transfers

This prevents typical mistakes in automation projects.

3. use RPA bots strategically instead of in isolation

Without analysis, bots are often implemented selectively.
With process mining, on the other hand:

  • a scalable automation roadmap

  • a clear business case

  • measurable ROI

SAP + RPA + Process Mining: The perfect combination

Companies that use SAP benefit particularly strongly from this interaction.

Central advantages at a glance

1. data-based process optimization
SAP provides structured process data that can be used directly for process mining.

2. higher automation rate
By identifying standard processes, significantly more processes can be automated.

3. reduced error rates
RPA takes on repetitive tasks, while SAP serves as a stable system basis – a combination that significantly reduces errors.

Many automation initiatives are already demonstrating that the combination of SAP and RPA makes processes faster, more efficient and more traceable.

Effects on project management in automation projects

An often underestimated factor: the role of project management.

Modern automation projects are no longer pure IT projects, but transformation projects.
Process mining also changes the project structure:

Classic approach (outdated)

  • Workshops

  • Interviews

  • Process assumptions

  • Implementation

  • Rework

Data-driven approach (best practice 2026)

  • Process analysis via mining

  • Prioritization according to business impact

  • Agile RPA implementation

  • Continuous monitoring

This significantly reduces project risks and ensures greater acceptance in the specialist departments.

Typical challenges – and how consulting firms can solve them

1. unclear process landscapes

Solution: Systemic process analysis based on SAP data

2. lack of automation strategy

Solution: End-to-end automation roadmap instead of individual solutions

3. resistance in specialist departments

Solution: Transparent visualization of actual processes (change management)

A structured consulting approach is crucial here, as technological expertise alone is not enough – process understanding and project methodology are equally necessary.

Concrete practical examples of data-driven automation

There is particularly great potential in:

  • Finance & Controlling (e.g. invoice verification in SAP)

  • Supply Chain Management

  • Purchasing & Logistics

  • Master data management

  • HR processes

Here, the combination of SAP, RPA and process mining not only increases efficiency, but also provides strategic competitive advantages.

Why 2026 is the right time for this approach

Digitalization is evolving from pure automation to intelligent, data-based process control.
While RPA has long been considered an entry-level technology, the next evolutionary stage is now emerging: hyperautomation with a data-driven decision-making basis.

Companies that take this step early:

  • avoid bad investments in the wrong automations

  • increase your ROI sustainably

  • create scalable process structures

Conclusion: From automation to intelligent process optimization

The future of automation no longer lies in the pure use of bots, but in the intelligent combination of:

  • SAP as data and process core

  • RPA as an automation technology

  • Process mining as a strategic basis for decision-making

This represents a clear opportunity for IT consulting firms:
Those who provide strategic support for data-driven automation position themselves as long-term digitalization partners rather than pure implementation service providers.

Now is the ideal time to rethink automation projects – structured, data-based and closely interlinked with SAP and professional project management.

See you soon and good luck!
Your amotIQ solutions team

Low-code/no-code platforms vs. traditional development: the future of automation?

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Hello everyone,

When it comes to the digitalization and automation of business processes, there are many exciting technologies. But one of the topics that is getting more and more attention is low-code/no-code platforms. You may have heard of these terms before and asked yourself: “What’s the point? And what does this have to do with RPA and SAP?”

In this blog post, I want to answer exactly these questions and show how these platforms can facilitate the development of automation solutions – and what this means for your IT teams and specialist departments.

What are low-code/no-code platforms?

Quite simply, low-code/no-code platforms make it possible to create applications and automation processes without the need for in-depth programming knowledge. Instead of writing code, graphical user interfaces are used to create workflows – similar to a modular system.

The difference?

  • Low-code: A few basic programming skills are still an advantage here, but many things can still be implemented without in-depth technical knowledge.
  • No-code: Zero programming knowledge required – everything runs via drag-and-drop and ready-made modules.

How do these platforms simplify the development of RPA and SAP solutions?

  1. Speed and flexibility:
    Low-code/no-code platforms allow solutions to be developed and adapted much faster. With traditional development approaches, it often takes weeks or months to create a solution. With these platforms, you can get started straight away and have a functional product in no time at all. This saves a lot of time, especially when automating processes in RPA or SAP.
  2. Less dependence on developers:
    IT teams often have their hands full, especially with complex SAP projects. Low-code/no-code platforms enable specialist departments to create simple automation processes themselves without having to constantly rely on developers. This reduces the pressure on the IT department and encourages the specialist departments to take responsibility.
  3. Prototyping and testing:
    These platforms allow you to try out ideas quickly and test them in the form of prototypes before developing the final solution. Especially in automation, such as process automation in SAP, improvements can be identified at an early stage and incorporated into development.
  4. Scalability:
    Especially when introducing automation processes in large companies, such as the implementation of SAP modules or RPA robots, a low-code/no-code platform offers the option of quickly scaling solutions as required.

What impact does this have on IT teams and specialist departments?

Advantages for IT teams

  1. Relief for IT teams:
    IT teams no longer have to program every little thing themselves. The ease of use of low-code/no-code platforms takes a lot of pressure off developers, allowing them to focus on more complex, strategic tasks. They remain the experts for integration and scaling, while the specialist departments can take over the day-to-day work.
  2. Faster iterations:
    With low-code/no-code, IT teams can react more quickly to changes. If specialist departments have new requirements, they can adapt or extend the solution themselves without having to involve a developer every time. This leads to faster iterations and less waiting time.
  3. Better collaboration:
    These platforms promote collaboration between IT and the specialist departments. Instead of specialist departments having to formulate their wishes in long specifications and wait for implementation, they can test and iterate their requirements directly on the platform.

Advantages for specialist departments

  1. More autonomy:
    Departments that are heavily dependent on IT are given more autonomy with low-code/no-code platforms. They can quickly create their own solutions for automating business processes, such as in RPA or for adapting SAP processes, without having to rely on IT resources.
  2. Shorter time-to-market:
    If specialist departments can develop solutions more quickly, the time-to-market for new automation processes is significantly reduced. The teams are more flexible and can react more quickly to changes in the company or market.
  3. Increased innovative strength:
    As specialist departments develop the solutions themselves, they can better incorporate their specific requirements and needs into the automation solutions. This leads to more innovation and customized solutions that cover actual needs much better.

Is low-code/no-code the future?

I think the answer is clear: Yes, but in combination with traditional development.

Low-code/no-code platforms offer a fantastic opportunity to speed up automation processes and improve collaboration between IT and specialist departments. However, they are no substitute for complex, customized solutions that require in-depth technical knowledge. For many scenarios, such as large-scale SAP implementations or highly specialized RPA solutions, traditional software development remains necessary.

However, the combination of both – the flexibility of low-code/no-code and the in-depth expertise of traditional developers – creates an ideal basis for the automation of the future.

Conclusion: A step in the right direction

Low-code/no-code platforms offer enormous opportunities to simplify the development of RPA and SAP solutions and improve collaboration between specialist departments and IT teams. They accelerate processes, increase flexibility and relieve IT departments of routine tasks. If companies use this technology sensibly, they can not only increase efficiency, but also boost their innovative strength.

Do you have any questions or want to find out how you can use low-code/no-code for your automation solutions? Then get in touch with us – we’ll help you take the next step towards the future!

See you soon and good luck!

Your amotIQ solutions team

The role of project management in digitalization: success factors and pitfalls to be aware of

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Hello everyone,

When you think of digital transformation, the latest technology usually immediately springs to mind – be it artificial intelligence, cloud solutions or automated processes. But what if I told you that the real key to success is often not the technology, but the project management behind it?

As a consultant in the field of IT and digitalization, I see time and again that good project management is the key success factor for successfully implementing digital transformation. In this article, I would like to talk to you about why this is the case, what skills project managers really need in the digital age and what you should look out for to avoid pitfalls.

Why is project management the key to digital transformation?

Digital transformation is not just another IT project. It affects the entire company – from employees to processes and systems. Successfully managing all these changes requires more than just a clear vision and the right technology. It needs good project management.

Project management ensures that transformations not only work technically, but are also successful from an organizational and cultural perspective. It is about coordinating the various aspects of digitalization, using resources sensibly and maintaining an overview.

Why is this so important?

  1. Managing complexity: Digital transformation projects are often extensive and spread across several departments. A clear project plan helps to maintain focus and keep everyone involved pulling in the same direction.
  2. Accompanying change: Digitalization often also means changing the way we work. A good project manager ensures that everyone is on board and can go along with the change – from IT to the specialist department.
  3. Minimize risks: There are many risks lurking in the digital transformation, whether it’s the implementation of new technologies or the adaptation of existing processes. An experienced project manager can identify potential stumbling blocks at an early stage and act accordingly.

What skills does a project manager need in the digital age?

Project management has evolved considerably in recent years, especially in the context of digitalization. Traditional skills are still important, but in the digital world, new skills have been added that make a project manager a real guarantee of success.

  1. Agility and flexibility:
    The classic waterfall approach to project management used to be widespread. Today, more and more companies are turning to agile methods such as Scrum or Kanban. Digitization projects require flexibility, as requirements can change quickly. A project manager must be able to react quickly to new circumstances and adapt the course if necessary.
  2. Technological understanding:
    Nobody expects a project manager to be an IT specialist, but a certain basic technical understanding is essential today. Anyone managing a digital transformation project should know how modern technologies such as cloud, RPA or AI work and what challenges are associated with their implementation.
  3. Strong communication skills:
    Digitalization often affects several departments and stakeholders – from management to IT to end users. A project manager must be able to communicate with all groups on an equal footing, understand their needs and provide the right information at the right time.
  4. Change management:
    Change is never easy, especially not in large organizations. A good project manager should be able to actively manage change processes, overcome resistance and get everyone involved on board. This is where soft skills such as empathy and conflict management come into play.
  5. Data-oriented thinking:
    Data is at the heart of many digital projects. A project manager must be able to understand and use data and make data-driven decisions. This requires not only technical knowledge, but also a good understanding of business intelligence tools and KPIs.

Success factors for the digital transformation

For digital transformation to be truly successful, a few things have to work together. Here are some success factors that, in my experience, are crucial:

  1. Clear objectives: Before the project even starts, it should be clear what exactly is to be achieved. What problems are to be solved? What goals is the company pursuing? A precise definition of objectives helps to maintain focus and set the right priorities.
  2. Using resources correctly: Digital projects often require more time and resources than originally anticipated. An experienced project manager ensures that the right people with the right skills are deployed at the right time and that the project budget is kept in view.
  3. Customer-centric approach: Digitalization must serve the needs of customers – be it through faster processes, better products or an improved user experience. A project manager should therefore always keep the customer in mind and ensure that the project delivers the desired added value for the end user.
  4. Willingness to learn and continuous improvement: Digital transformation is a long-term process that is never “finished”. A project manager should promote a culture of continuous improvement in which feedback is obtained regularly and projects are adapted iteratively.

Pitfalls that you should definitely avoid

Of course, there are also a number of pitfalls that can quickly occur during digital transformation. Here are the most common ones:

  1. Unclear requirements: Without a clear understanding of the requirements at the beginning, the project can quickly get out of hand. A project manager should ensure that all stakeholders are on the same page right from the start.
  2. Lack of communication: Many different teams and departments work together in the digital transformation. Poor communication between these groups can lead to misunderstandings and delays.
  3. Ignoring the corporate culture: Technology alone does not make a successful transformation. The corporate culture has to go along with it. A project manager should therefore also keep an eye on the culture of the company and ensure that the change is accepted.

Conclusion: Project management as a success factor for digital transformation

To summarize: Project management is the linchpin when it comes to the successful implementation of digital transformation projects. Without clear structures, flexibility and the right skills, even the best technology can fail to make an impact. Project managers must not only have the technical aspects under control, but also keep an eye on the team, processes and culture.

If you are a company embarking on a digital transformation, remember that success often depends less on the technology and more on the way the project is managed. If you focus on the right skills here, nothing stands in the way of a successful transformation process!

We are happy to assist you if you need support to successfully manage digital projects. Let’s drive digital change forward together!

Your amotIQ solutions team

SAP operating models in check: cloud, on-premise or hybrid – which solution is right for your company?

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Hello everyone,

In the IT world, there is rarely a “one-size-fits-all” solution – especially when it comes to the cloud. With SAP in particular, one of the world’s leading ERP systems, there are various options for using it: On-Premise, Cloud or Hybrid. Each of these deployment options has its own advantages and disadvantages. But which is the right choice for you? Let’s go through the whole thing and highlight the differences in a relaxed but practical style.

On-premise: The classic solution – but is it still up to date?

On-premise” means that you run SAP on your own servers in the company. The infrastructure is therefore located directly on site and you have complete control over everything to do with the system. Sounds good at first, doesn’t it?

Advantages:

  • Full control: You have control over your data, security measures and the entire infrastructure.
  • Adaptability: On-premise solutions can generally be better adapted to the specific requirements and individual circumstances of a company.
  • Data protection: Especially in certain industries or with sensitive data, it can be a plus that everything remains internal.

Disadvantages:

  • High costs: Purchasing and maintaining the hardware and operating the servers is expensive. Updates and upgrades also often have to be carried out manually.
  • Scalability: As the company grows, it becomes expensive and time-consuming to adapt the infrastructure quickly.
  • Maintenance effort: IT teams have to constantly monitor and maintain the systems – which ties up resources and causes additional costs.

Cloud: Flexible, scalable and usually more cost-effective – but not without challenges

The cloud is about running SAP in a virtual environment provided by a cloud provider such as AWS, Microsoft Azure or Google Cloud. The cloud therefore offers you the opportunity to access high-performance IT infrastructure without having to buy and maintain servers yourself.

Advantages:

  • Costs: No expensive investments in hardware. You only pay for the resources you actually use.
  • Scalability: You can easily add more storage or computing power as your company grows – without any major effort.
  • Flexibility: Cloud solutions are generally quicker to set up and offer high availability, giving you round-the-clock access to the systems.
  • Innovation: Cloud providers are constantly offering new features and services that the company can use quickly and easily.

Disadvantages:

  • Dependence on the provider: You are dependent on the availability and performance of the cloud provider. If there are outages, this can affect your business processes.
  • Security concerns: Even if cloud providers offer very high security standards, the question remains as to how secure your sensitive data really is in an external infrastructure.
  • Data sovereignty: Depending on the provider’s location, there may be legal and regulatory challenges regarding data sovereignty.

Hybrid: The golden mean?

The hybrid solution combines on-premise and cloud. Part of the SAP landscape continues to run internally, while other areas are outsourced to the cloud. This option combines the advantages of both worlds and can be particularly interesting for companies that want to keep certain data or applications in-house for security reasons, but at the same time want to use the flexibility of the cloud.

Advantages:

  • Flexibility: You can decide which parts of your IT infrastructure should run in the cloud and which internally. This gives you control where you need it, while at the same time taking advantage of the benefits of the cloud.
  • Scalability and security: Critical parts of the system can remain on-premise, while less critical applications can be easily and cost-effectively outsourced to the cloud.
  • Optimization of resources: You can benefit from cost-effective cloud services without giving up complete control.

Disadvantages:

  • Complexity: Managing a hybrid solution can be technically demanding. You need experts who can handle both on-premise and cloud systems.
  • Integration: It can be challenging to seamlessly integrate on-premise and cloud solutions, especially when systems use different architectures.
  • Costs: Even if hybrid solutions offer a good balance in many cases, they can sometimes turn out to be more expensive due to their complexity and higher administrative costs.

What suits you best? – A comparison of the three options

Option costs Scalability security Flexibility
On-Premise High (hardware, maintenance) Limited scalability High control, but internal Less flexible
Cloud Low to medium (pay-as-you-go) Very high Dependent on provider, but high standards Very flexible
Hybrid Medium to high (depending on structure) Flexible, but more complex Partially high control, depending on structure Very flexible, but complex

Conclusion – Which is the right choice?

Ultimately, the choice between on-premise, cloud and hybrid depends heavily on the individual requirements of your company. If you need maximum control and customized solutions, on-premise is still a good choice. The cloud scores with flexibility, cost efficiency and scalability – especially if you want to grow quickly. The hybrid option offers the best of both worlds, but can be technically demanding and more cost-intensive.

If you are unsure which solution suits you best, we will be happy to help you make the right decision and optimize your SAP landscape. Just write to us if you have any questions or need support!

See you soon and good luck with your decision!

Your amotIQ solutions team

 

Embedded analytics in SAP: data-driven decisions in real time

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Hello everyone,

If you work with SAP in your company, you’ve probably heard how important data is for making better decisions. But hand on heart: how often does an analysis end up in your inbox as an Excel file that is somehow already out of date before you even look at it? This is exactly where embedded analytics comes into play.

As a consultant, I see time and again how companies can use real-time analytics in SAP to not only act faster, but also smarter. That’s why today we’re taking a look at what embedded analytics actually is, what functions SAP offers here – and how you can use them to get that decisive edge.

What is embedded analytics?

Imagine no longer having to switch back and forth between different tools to access your analyses. With embedded analytics, analysis functions are integrated directly into SAP. So you work in real time with data that comes directly from your system, without detours or delays.

The great thing about it is that you can use reports and dashboards directly in your workflows, whether in financial accounting, purchasing or sales. No additional exports, no extra software – everything runs exactly where you already work.

Why is this a game changer?

  1. Real-time data instead of gut feeling:
    Making decisions while the data is still warm – that is the biggest advantage of embedded analytics. You can see what’s going on immediately and can react straight away.
  2. Seamless integration:
    The analyses are not just nice to look at, they actively help you with your work. Whether in SAP S/4HANA or SAP Fiori, the data is exactly where you need it – without any additional effort.
  3. Individual insights:
    Standard reports are good, but Embedded Analytics makes it possible to design dashboards and analyses according to your own requirements. Each area gets exactly the information that is really important.

New functions you should know about

SAP has worked hard in recent years to make its analytics tools even smarter. Here are a few highlights:

  1. Smart Insights and Smart Predict:
    These AI-supported functions analyze your data and automatically provide you with insights that you may not even have had on your radar.
  2. Fiori Analytical Apps:
    With SAP Fiori, you not only get a modern user interface, but also customized analytical applications for your processes.
  3. SAP Analytics Cloud (SAC) integration:
    Embedded Analytics can be seamlessly combined with the SAP Analytics Cloud, giving you additional advanced planning and simulation options.

A few examples from practice

Here are a few use cases that show how embedded analytics can bring real benefits:

  • Finance:
    You can track how your cash flows are developing in real time and act immediately in the event of bottlenecks.
  • Purchasing:
    Automatic analyses help you to identify potential supply bottlenecks at an early stage and plan alternatives.
  • Sales:
    A dashboard shows you exactly which products are currently performing well – and where there is a need to catch up. You can therefore adapt your sales strategy at lightning speed.

How you can get started

Now you might be asking yourself: “Sounds cool, but how do we get this rolling?” Don’t worry, it’s easier than you think:

  1. Check existing tools: First look at what you already have. Many embedded analytics functions are included in SAP S/4HANA as standard.
  2. Set up dashboards: Start with a few important KPIs that really make a difference. Better to start small and then expand.
  3. Offer training: Your teams need to know how to use the tools – because this is the only way they can develop their full potential.
  4. Continuous optimization: Analyses are not a “set it and forget it” project. Check regularly whether your dashboards still fit and adapt them to new goals.

Conclusion: get more out of your data

Embedded analytics in SAP is not just a gimmick for data fans, but a real game changer for companies that want to make data-driven decisions. Real-time insights, seamless integration and smart functions ensure that you are always one step ahead.

Do you want to discover embedded analytics in your company? Or do you need support to get started? Get in touch – we’re here for you!

See you soon and good luck with your analyses!

Your amotIQ solutions team

Hyperautomation: The next evolutionary step in process automation

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Hello everyone,

If you’re asking yourself: “Hyperautomation? Sounds important, but what’s really behind it?” – then you’ve come to the right place. As an IT consultant, I help companies to make their processes more efficient and smarter. Hyperautomation is a topic that many people are excited about right now. Why? Because it’s more than just a buzzword – and offers real added value. So, let’s get to the heart of the matter.

Hyperautomation – what is it anyway?

Imagine taking classic process automation – e.g. with RPA (Robotic Process Automation) – and combining it with intelligent technologies such as artificial intelligence (AI), machine learning (ML) or process mining. The aim is not just to automate processes, but to optimize them so that they practically run themselves – and become better and better in the process.

So it’s not just about getting rid of routine tasks. Hyperautomation makes it possible to analyze entire business processes, make them more efficient and even make data-based decisions in real time.

Why is everyone talking about it?

In recent years, many companies have taken their first steps towards automation. However, they often end up with isolated solutions that save time here and there, but the big picture remains untouched. This is where hyperautomation comes in, because it not only optimizes individual tasks, but complete processes.

Why is this important? Three good reasons:

  1. More efficiency: Hyperautomation ensures that tedious routine tasks such as data synchronization, form processing or invoice receipt run automatically. This saves time and reduces the error rate.
  2. Better decisions: With AI and machine learning, processes are not only executed, but also intelligently controlled. Data flows together and decisions become more informed – in real time.
  3. Flexibility: Especially in times when requirements are constantly changing, companies need agile systems. Hyperautomation helps to react quickly and adapt new processes easily.

An example from everyday life

To make this more tangible, here is a typical scenario:

Imagine you work in purchasing. Every day, orders come in that need to be checked and approved. These are often simple routine checks – but they keep you from more important tasks.

With hyperautomation, it could look like this:

  1. Record data: Orders end up in a system, AI automatically checks whether they are complete and correct.
  2. Rule check: An RPA bot checks whether the order complies with internal guidelines.
  3. Approval: Everything OK? Then the order is approved automatically – without anyone having to intervene manually.

The result: you save time that you can use for strategic tasks – and the process runs smoothly.

How you can get started

“Okay, sounds exciting – but how do we get started?” you might be asking yourself. Here are a few practical tips:

  1. Start small: Choose a process that is clearly structured and easy to automate. This will allow you to achieve your first successes without taking any major risks.
  2. Combining technologies: RPA alone is a good start, but when combined with AI or process mining, hyperautomation becomes really powerful.
  3. Involve the team: Change only works if everyone is on board. Take your colleagues along on the journey, show them the benefits – and openly dispel any concerns.
  4. Learn and optimize: Don’t see hyperautomation as a one-off project. It is a continuous process in which you improve step by step.

Why you should start now

Hyperautomation is not a dream of the future – it is happening now. Companies that start early will secure a real competitive advantage. Not only will you increase efficiency and quality, but you will also relieve your teams and create space for innovation.

Would you like to take a closer look at the topic? Or do you need support with the first steps? Get in touch – we’ll be happy to help and advise you!

See you soon and good luck on your automation journey!

Your amotIQ solutions team

Smart Factories: How SAP and RPA are driving Industry 4.0 forward

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Hello everyone,

Industry 4.0 – a term that everyone is familiar with by now, but what exactly does it mean? It refers to the digitization of production, networked machines, automated processes, and above all, greater efficiency. Today, I want to show you how SAP and RPA (Robotic Process Automation) play a key role in making the factories of the future smarter, faster, and more flexible. Sounds exciting? Then stay tuned!

What is a “smart factory”?

Imagine a factory that controls itself. Machines that communicate with each other, exchange data in real time, and adapt themselves based on that data. A “smart factory” uses these technologies to optimize the production process, minimize errors, and take efficiency to a whole new level.

This is where SAP and RPA come into play. SAP helps to bundle all important data in a central system, thus maintaining an overview. RPA automates recurring tasks so that employees can concentrate on more strategic activities. Together, they form the backbone of a smart factory.

How SAP and RPA improve production processes

The integration of automation technologies into production processes has far-reaching advantages. SAP and RPA enable companies to not only simplify their processes, but also make them more intelligent.

  1. Real-time data and predictive maintenance:
    SAP provides a central database that enables all machines and production steps to be monitored. In combination with RPA, maintenance notifications can be triggered automatically before machine problems occur. This means that production downtime due to unexpected machine malfunctions remains an exception.
  2. Automated material procurement:
    In a smart factory, raw materials and supplies must always be available. SAP ensures that inventories are monitored in real time and orders are triggered automatically when stock levels are low. RPA can handle the entire ordering process – from creating the order to confirmation and communication with the supplier.
  3. Optimization of production orders:
    When production orders are processed manually, errors and delays can occur. With SAP and RPA, orders are automatically forwarded to the right machines and workstations, ensuring that production processes run smoothly and without delays.

Examples from the manufacturing industry

Let’s look at a few specific examples of how SAP and RPA are used in practice to make the smart factory a reality:

Automated production planning at an automotive manufacturer:
A large automotive manufacturer uses SAP to control all manufacturing processes – from ordering parts to delivering the vehicle. RPA is used to create production orders and automatically integrate them into the system. This avoids delays caused by manual entries and makes the entire production flow more efficient.

Efficient warehousing in electronics production:
An electronics manufacturer uses SAP to manage its global inventory. In combination with RPA, inventories are automatically monitored and reorders are triggered as needed. This ensures that there is never too much or too little material in the warehouse, while minimizing delivery delays.

Optimization of maintenance processes in the pharmaceutical industry:
In a large pharmaceutical factory, RPA is used to automate the maintenance of production equipment. As soon as SAP detects that a machine needs maintenance, a maintenance order is automatically created and forwarded to the maintenance team. This saves time and ensures that machines are always in perfect condition.

Why is it worth switching to a smart factory now?

The advantages of a smart factory are obvious: fewer downtimes, shorter production times, and overall higher efficiency. SAP and RPA help achieve these goals by automating processes while improving communication between different systems. The result?

Faster time to market: Automated processes ensure that products roll off the production line faster and supply chains are optimized.
Cost reduction: Operating costs are reduced by minimizing errors and manual intervention.
Greater flexibility: Smart factories can respond more quickly to market changes, whether by replacing machines or introducing new products.

Getting started with the smart factory: How do you begin?

It doesn’t always have to be a complete restructuring to benefit from a smart factory. Here are a few practical tips to get you started:

  1. Take small steps: First, focus on individual processes that are easy to automate, such as ordering materials or maintaining machines.
  2. Integrate SAP correctly: Use the SAP solutions that are already in use in your company to identify automation potential. It doesn’t always have to be a complete system change.
  3. Use RPA in existing processes: Identify manual tasks that you can automate with RPA. This saves you time and resources without having to completely overhaul your entire production structure.

Conclusion: The smart factory is the way forward

The combination of SAP and RPA is a real game changer for the manufacturing industry. Not only does it create more efficient production, it also enables companies to respond quickly and flexibly to changes. If you want to take the step towards a smart factory, now is the right time.

Get in touch with us if you want to learn more about how SAP and RPA can take your production to the next level – we’re happy to help!

See you soon and good luck on your journey to Industry 4.0!

Your amotIQ solutions team