Workspace & AI

Inconsistent data from sources such as Excel, emails, or disparate systems slows down decision-making in companies. Numbers contradict each other, and dashboards often show only results without explaining their causes. The result: a significant amount of time spent on reconciliation at all levels. Departments invest time in clarifying issues rather than evaluating the information. Analyses take too long because data must first be checked, categorized, and consolidated. This turns data-driven management into a manual Herculean task.

The solution to this lies not in the front end, but in the data foundation. A centralized, structured, and reliable foundation makes data usable without detours. Building on this, a central data room integrates and harmonizes this data. We call this the Workplace. Integration occurs iteratively: existing data sources are connected step by step and stabilized through automated processes. AI-powered functions expand the use of this data: anomalies are automatically detected, and trends are made visible early on. Manually created, static dashboards are no longer necessary thanks to easy access to information, and users can configure data and dashboards themselves without technical barriers.
  

Companies must ensure that regulatory requirements are correctly incorporated into products and processes. In practice, the departments involved often work in isolation: Compliance defines requirements, Engineering implements them, and Logistics verifies the results, but in between lie Excel spreadsheets, PLM, and WMS systems that are not interconnected. The consequences become apparent when decisions need to be made. Checks are based on information from multiple sources that is neither complete nor up-to-date. At the same time, responsibility lies with roles that do not generate this data themselves and cannot classify it in detail from a regulatory perspective. The workload shifts: away from the actual task, toward the procurement and verification of information.

The solution starts with the data flow. A central application connects requirements, product information, and implementation steps across the entire process. Data is linked in a structured manner and made available where it is needed. In the background, a common data model ensures that information is managed in context. Existing systems remain in place and are integrated via interfaces. This eliminates the need for additional maintenance effort while providing consistent access to relevant data. And this enables the integration of AI solutions.

Here’s how this might look in practice: A compliance manager digitally captures requirements and manages their implementation. Product development takes over, adds supporting documentation, and provides structured label data. In logistics, compliant labels can be verified, generated, and printed directly without regulatory expertise. The result automatically feeds back into the process, closing the control loop.
 

Companies must ensure product compliance worldwide. The EU Battery Regulation, PPWR, EUDR, CBAM, CSDDD, and ESG requirements—regulatory demands are constantly increasing.

This increases the effort required to reliably ensure compliance. New regulations must be researched, evaluated, applied to products, and documented. At the same time, the cycles in which requirements are created or adjusted are becoming shorter. In practice, this leads to significant coordination efforts, numerous manual checks, and a heavy reliance on human resources. Product compliance is thus evolving from an administrative issue into a competitive factor.

Our solution lies in the combination of digitalization and expert guidance—and is called DPCM. To this end, ISMO combines expertise in compliance management with a deep understanding of industrial processes and an intelligent tool. Regulatory requirements are not merely processed technically, but jointly categorized and translated into concrete measures.

The digital technology REGUL.AI supports the delivery of the DPCM service and automates administrative tasks. The AI-powered services automatically analyze regulatory frameworks, link requirements to products, and continuously process regulatory changes. This creates a scalable approach in which compliance is not handled in isolation but functions as part of the value chain.
 

Transformation initiatives often lose momentum in day-to-day operations and remain unfinished. Cost pressures are high, priorities become blurred, and progress stalls. While many companies recognize the need for change, it is difficult to put this into practice effectively in their daily operations. This is precisely

where Empowered Transformation comes in. It empowers teams to develop clear visions on their own, set priorities, and effectively drive change. How? Through expert and methodological guidance: existing competencies are specifically activated, contexts are clarified, and critical decisions are prepared collaboratively so they can subsequently be translated into concrete actions. Intelligent tools support analysis, prioritization, and progress assessment, thereby increasing speed and ensuring sustainable quality.
 

In many companies, processes have evolved over the years. Departments have established their own ways of working, teams operate in isolation, and there is no shared vision. This results in manual work, data silos, long lead times, and decisions based on assumptions rather than reliable data. At the same time, artificial intelligence is fundamentally changing the rules of the game when it comes to process digitization. What was once considered too complex, too specific, or simply not cost-effective can now be analyzed using data, automated in a targeted manner, and effectively managed.

The key to this is professional Business Process Management (BPM). It creates the necessary structure—not as a one-time project, but as a continuous feedback loop. The starting point is a robust process landscape. End-to-end processes are defined, prioritized, and aligned with concrete customer benefits. Building on this, real-world workflows are measured: lead times, costs, and variations. Only this transparency makes targeted interventions possible. Optimization, digitalization, and automation then interlock.

Artificial intelligence reinforces this approach. It significantly lowers the threshold for cost-effective automation and also enables the automation of semi-structured and unstructured workflows with lower case volumes. Bottlenecks become visible earlier, and adjustments take effect faster. This results in processes that are controllable and deliver tangible results in day-to-day operations. Decisions become more informed, response times shorter, and most importantly: companies gain a sustainable foundation on which further automation and increasingly autonomous processes can be meaningfully developed.
 

For many companies, an audit is not a process but a necessary evil. Regulatory requirements are formulated in abstract terms, leaving room for interpretation and thus risk. Preparation is manual and fragmented. Documents must be reviewed, evidence verified, nonconformities identified, and risks assessed. And with every audit, the effort starts almost from scratch.

The solution to this problem begins with the digitization of data and transforms this mandatory exercise into a continuous improvement process. Technical and methodological support ensures that requirements are categorized and translated into concrete actions. Additionally, digital tools and AI support implementation. Standard structures are stored in an intelligent platform; existing evidence is automatically assigned, tasks are distributed, and progress is documented in a traceable manner. Audit preparation thus ceases to be a project and becomes a byproduct of a functioning system.
 

IT is evolving from a mere operational factor into a central pillar of value creation. Topics such as digitalization, data platforms, and AI are creating new demands: systems must not only run reliably, but also be flexibly scalable, secure, and independently deployed. The issues of data sovereignty and regulatory compliance are taking center stage. Companies need IT structures that meet the highest standards of compliance and availability.

We have taken this vision a step further and established cynefy, a specialized unit. cynefy combines operational IT with a sovereign cloud, creating an integrated managed service model—ranging from infrastructure and IT security to strategic consulting as a fractional CIO, operations, and support. But we also create room for more: With a data lakehouse, we enable data not only to be managed but also to be utilized as a consistent foundation for analytics, automation, and AI applications.

IT reimagined as a stable, sovereign foundation for digitalization and AI—this is how complexity becomes potential.
 

Many companies are facing growing pressure to reduce costs and improve efficiency—yet at the same time, fragmented, labor-intensive workflows in support and compliance processes are holding them back precisely where savings are most urgently needed. Traditional digital transformation projects promise a solution, but they tie up capital, take a long time, and carry risks that are difficult to quantify, which is why potential savings often go untapped.

This is exactly where our “Forward Deployed Engineering” model comes in. ISMO experts work directly within your processes. They identify opportunities for automation and implement them immediately. Analysis and implementation are handled by a single team. AI accelerates both steps. We assume operational responsibility and bear a portion of the project and IT risk. This way, efficiency gains are felt during ongoing operations without requiring you to make an upfront investment.

The result is a lean, automated process with reduced staffing costs and predictable expenses. At first glance, it resembles outsourcing. In fact, it strengthens the organization from within: the skills remain within the company, only more structured, automated, and cost-effective. The approach requires no high initial investment and pays for itself through the realized savings.