Software & AI that gives your team time back

In smaller teams, time is the scarcest resource. The same people serve customers, calculate quotes, answer messages, review documents and keep internal systems up to date. Much of this work is necessary, but it repeats every day. AI can collect, sort, compare and prepare information before an employee reviews the case and decides. Manual grind becomes a controlled, much shorter workflow.

The effect becomes tangible quickly: just 30 minutes less routine work per working day equals around 10 hours gained per month for one person. If several employees handle similar processes, the relief grows accordingly. What matters is not a generic AI tool, but integration into your actual processes, data and permissions.

Your proven software does not have to be replaced. An AI assistant can connect in a controlled way to ERP, CRM, document storage, email, a database or a line-of-business application. It prepares results, documents its work and hands important decisions to people for approval. Your core system stays stable and your existing investment stays protected.

Practice confirms that integration – not replacement – is the bottleneck. In an international Cloudera survey of 1,574 enterprise IT leaders, 96% said AI is at least partly integrated into core processes; at the same time, 37% named data integration as the top technical architecture limitation, and only 9% said all company data was available and usable for AI initiatives.¹ Similarly, according to MIT Technology Review Insights, 64% of surveyed executives prioritise data integration and pipeline tools among top investments for AI rollouts; 77% see data integration or data movement as a significant challenge.² Thinking of AI alongside the core system – and choosing the right connection path – is how you bypass that bottleneck.

In Germany, too, the pressure is rising to turn experiments into productive value: Bitkom reports that around 36% of companies already use AI (previous year: 20%), while another 47% are planning or discussing adoption.³ McKinsey also observes that many organisations have introduced tools, yet use cases are still not widely anchored in workflows and platforms – scaling remains the real task.⁴ That is exactly where we start: step-by-step integration, transparent architecture decisions and a personal introductory call in which we sketch a first map of your systems – instead of waiting for the next enterprise roadmap.

• ¹ Cloudera: The Evolution of AI: The State of Enterprise AI and Data Architecture (1,574 IT leaders, US/EMEA/APAC), 2025. cloudera.com
• ² MIT Technology Review Insights: AI readiness for C-suite leaders, 2024. Report (PDF)
• ³ Bitkom Research: Artificial Intelligence 2025. bitkom-research.de
• ⁴ McKinsey & Company: The State of AI: Global Survey 2025. mckinsey.com

Many digitalisation efforts fail not because of technology, but because of the wrong start: too large, too vague, too far from day-to-day work. Step by Step reverses that. First it becomes visible which tasks really cost your team time. Only then does a solution take shape – tailored, manageable and with a clear benefit.

The analysis is carried out by our partner Zisco IT Consulting. You talk to practitioners, not an anonymous form. The result is a concrete map: which workflows are worth tackling, what effort is realistic, and where AI or custom software makes the biggest difference.

Implementation sits with EPS Software. You receive a clear offer. If you commission it, we fully credit the analysis costs. That keeps the risk small: you start with no obligation – and only pay for the analysis as a standalone step if you do not proceed. Even then you have a solid basis for a decision.